Is DDMRP Multiechelon Software?

Executive Summary

  • The Demand Driven Institute (DDI) claims that DDMRP is multi-echelon software.
  • We verify if this is true.

Introduction

The DDI has claimed that DDMRP is multi echelon software. The problem arises when one asked what is meant by this term.

How Multiechelon Methods or Software Are Normally Judged

Normally it is understood that all supply planning methods deal with supply chains as they are multi echelon by their nature. However, the ability to intelligently plan around the interactive location component is a specific subcategory of supply planning software that has unique multi echelon mathematics.

We cover this mathematics and the outcome for supply planning How to Best Understand Multi Echelon Inventory Optimization.

DDI’s Misuse of the Term Lead Time for Lead Time Reduction

One of the problems comes when the DDI uses the term decouple to mean shrink supplier lead times — which is not actually shrinking the supplier lead time. DDMRP does this by inserting more inventory. Naturally higher than expected demand has a shorter lead time from the company’s internal stocking location. This is because the company has the item in stock and can satisfy demand from stock rather than stocking out and needing to place a new order.

This is extremely confusing, and some might say deceptive, use of the term lead times. DDI also does an extremely poor job of explaining what lead time it is referring to, and leaves it up to the reader to figure it out for themselves.

Nowhere in the DDI documentation is it clear that the lead time being described as shrinking is the company to customer lead time or the stocking position to customer lead time.

To add to this unnecessary confusion, there was already a term available to be used to describe this exact thing, and this is referred to as effective lead time, which I cover in the article How to Better Understand Effective Lead Time. I am not sure who first coined the term effective lead time, but it was popularized by inventory optimization and multi-echelon software vendors. Effective lead time is the lead time that the customer perceives. It is conditional and depends upon stocking and delivery conditions given a particular scenario. If, for instance, a store is out of stock of an item, the effective lead time is the time to receive delivery from that location’s replenishment location.

And this confusion on the use of the term decoupling leads to one of the explanations of buffer stock.

Supply order generation — all relevant demand, supply and on hand information are combined at the buffer to produce an “available stock” equation for supply order generation. – DDMRP Buffer Explanation and Simulation

This stock definition is already extremely well known as either the planned stock on hand or the available stock on hand.

As this term was already established, why then was an entirely new term created called buffer to describe this stock category? The DDI could have kept the established term and simply stated that the way it calculated the planned stock on hand was distinct from other supply planning methods.

DDI makes the following claim regarding lead time reduction.

A Lead Time Reduction by 85%?

At first glance, one might ask, “How does any of this lower lead times?”

One might harken back to the unfulfilled lead time reductions from Lean proponents that never came true. However, the reason this is not the right pathway of inquiry is that the DDI is playing a word game with the term lead time reduction. In this case, DDI makes the following claim regarding lead time reduction in the document DDMRP Buffer Explanation and Simulation.

Lead time compression — decoupling supplier lead times from the consumption side of the buffer, lead times are instantly compressed.

I never recall the term lead time reduction or compression being used in this way.

First of all, what lead time is being discussed? With more inventory, the lead time for higher than expected demand is shortened. But typically this is not the correct or accepted usage of the term lead times.

The DDI receives our Golden Pinocchio Award for implying or stating that its software is multi-echelon. 

Conclusion

DDMRP is not multi echelon software. DDI is playing a word game by stating that DDMRP deals with multi echelon supply networks, but all supply planning methods “deal” with multiechelon supply networks. What makes a method multi echelon is the existence of mathematics that allows the method to intelligently manage the interdependent relationships between related stock locations.

DDI and DDMRP proponents routinely accuse DDMRP critics of not understanding DDMRP. However, the DDI must take a fair share of the blame for creating such a confusing set of terms and explanations for DDMRP, as well as seeming to provide inconsistent statements around how DDMRP works. Misdescribing DDMRP as multi echelon is another example of the many word games played by DDI.

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References

https://blogs.sap.com/2016/08/31/demand-driven-mrp-part-iv-buffers/

*https://www.slideshare.net/utkanuluay/demand-driven-mrp-buffers

How is DDMRP Buffer Stock Different from Safety Stock?

Executive Summary

  • The Demand Driven Institute (DDI) claims that buffers stocks are not related to safety stock and accuses those that make the correlation as not understanding DDMRP.
  • We evaluate if the DDI correct in this assertion.

Introduction

One of the core areas of DDMRP is how much it relies on setting buffer stocks. As we cover in the article How Accurate is DDMRP’s Explanation of Forecasting?, DDMRP does not use the forecast as an input to drive orders per se, but is only used to set the buffer stock.

DDI on Buffer Stock

The following is a video explaining the buffer stock concept by the DDI.

Decoupling Points and Decoupling

DDMRP uses the term decoupling points, which will be (and should be) confusing to many, but this is simply the buffering of demand with inventory. All supply planning systems — unless they are make to order, use decoupling points. However, be careful in assumptions in this area, as we will see, the DDI has chosen to use the term “buffer” in a very peculiar way.

The video states that ordering is preformed along with actual demand and not the forecast. This is not entirely true, as ordering is triggered by the buffer stock demand — which is based upon the forecast — among other factors.

A second thing the video says is that the entire system is managed autonomously, which is not true. Forecasting does not go away with DDMRP — as DDI states itself, and buffer stock must be set by planners. This, along with a number of other factors, should be enough to illustrate that DDMRP cannot be described as an autonomous system.

DDI’s Overuse of the Term Decoupling

As a general term, decoupling can be considered reasonably accurate, but in other areas, DDI’s use of the term decoupling is quite confusing.

While it may be acceptable to use the term to describe a stocking location as it decouples supply from demand — the problems come when the DDI uses the term decouple to mean shrink supplier lead times — which is not actually shrinking the supplier lead time. DDMRP does this by inserting more inventory. Naturally higher than expected demand has a shorter lead time from the company’s internal stocking location. This is because the company has the item in stock and can satisfy demand from stock rather than stocking out and needing to place a new order.

This is extremely confusing, and some might say deceptive use of the term lead times. DDI also does an inferior job of explaining what lead time it is referring to, and leaves it up to the reader to figure it out for themselves.

Nowhere in the DDI documentation is it clear that the lead time being described as shrinking is the company to customer lead time or the stocking position to customer lead time.

To add to this unnecessary confusion, there was already a term available to be used to describe this exact thing, and this is referred to as effective lead time, which I cover in the article How to Better Understand Effective Lead Time. I am not sure who first coined the term effective lead time, but inventory optimization and multi-echelon software vendors popularized it. Effective lead time is the lead time that the customer perceives. It is conditional and depends upon stocking and delivery conditions given a particular scenario. If, for instance, a store is out of stock of an item, the effective lead time is the time to receive delivery from that location’s replenishment location.

And this confusion on the use of the term decoupling leads to one of the explanations of buffer stock.

Supply order generation — all relevant demand, supply and on hand information are combined at the buffer to produce an “available stock” equation for supply order generation. – DDMRP Buffer Explanation and Simulation

This stock definition is already extremely well known as either the planned stock on hand or the available stock on hand.

As this term was already established, why then was an entirely new term created called buffer to describe this stock category? The DDI could have kept the established term and simply stated that the way it calculated the planned stock on hand was distinct from other supply planning methods. See the article How to Best Understand Buffer Stock, to see how the vendor PlanetTogether uses the term buffer stock accurately.

There is secondary issue here because DDMRP claims to be multiechelon software, but it is not. We cover this in the article Is DDMRP Multiechelon Software? Therefore DDMRP claims to be multiechelon but isn’t, but then it almost appears as if the developers of DDMRP do not understand multiechelon inventory optimization software, as they had a term available to them and popularized by multiechelon inventory optimization software, but did not use it.

No More Expediting?

Something else that is dubious in the video is that expediting is eliminated.

DDMRP does not eliminate expediting and variability. No supply planning system will do this.

In fact, in most cases, and depending on the forecastability of the item, it would increase expediting and variability as it cannot see future demand. Or the future demand is only accounted for in the buffer stock.

As we will see further on, this again is due to DDMRP carrying more stock. However, we will also see that the DDI then contradicts this obvious conclusion by stating that the average inventory declines.

These claims are made in the video above.

  • No Need for Accurate Forecasts?: Aren’t the forecasts being used to size the buffer stock? Less accurate forecasts mean more buffer stock than is necessary. The entire claim that the forecast is not used to drive orders, but is input to the buffer stock is extremely confusing.
  • Achieve Planned Service Levels?: How does DDMRP improve the ability to meet planned service levels? All that is occurring is the forecast is placed into the buffer stock rather than used to drive order outside of the buffer stock demand. How is this giving the company more ability to meet service levels? Given the other claims, it seems to come from simply carrying more inventory.
  • Lead Time Reduction by 85%: At first glance, one might ask, “How does any of this lower lead times?” One might harken back to the unfulfilled lead time reductions from Lean proponents that never came true. However, the reason this is not the right pathway of inquiry is that the DDI is playing a word game with the term lead time reduction. In this case, DDI makes the following claim regarding lead time reduction in the document DDMRP Buffer Explanation and Simulation.

Lead time compression — decoupling supplier lead times from the consumption side of the buffer, lead times are instantly compressed.

I never recall the term lead time reduction or compression being used in this way.

First of all, what lead time is being discussed? With more inventory, the lead time for higher than expected demand is shortened. But typically, this is not the correct or accepted usage of the term lead times.

Anyone – using DDMRP or anything other supply planning method, can accomplish the same goal by simply increasing their average stock levels.

  • 50% Less Inventory: This is getting repetitious, but why does inventory decline by 50%? That is considered an enormous amount, by the way. DDMRP has presented a different approach to planning — but there is nothing in what they present that would indicate this reduction. And, in their study, which we cover in the article Repackaged Lean as DDMRP, the inventory reduction was far less than this. Furthermore, as the primary strategy of DDMRP is to increase inventory to do things like “decouple” supplier lead times — it is odd to see a claim of reduced inventory. To achieve the other claims listed by DDI, the inventory level would have to increase, not decrease.
  • No More Firefighting: This is an extension of the no more expediting argument presented above. But it is not at all clear the expediting would be lowered. In the case of say some demand histories, such as seasonal, firefighting would most certainly be increased — but that, of course, depends upon how much the forecast is incorporated in the buffer stock. Again, DDI makes the following claim in the document DDMRP Buffer Explanation and Simulation, which leads back to the same cause that we have covered thus far from where benefits emanate from with DDMRP.

Shock absorption – dampening both supply and demand variability in order to significantly reduce or eliminate the transfer of variability which creates nervousness and the bullwhip effect.

This can be translated into carrying more inventory.

Unless DDMRP can be said to be more precise than MRP, which is difficult as it means reducing the effort put into forecasting, the only answer can come from more stock being carried.

Calculating Buffer Stock

The following is an explanation of how buffer stocks are calculated.

For each decoupled part, we’ll use a certain buffering level. The total buffer will consist of 3 zones: green, yellow and red.

The way these zones ares used will be detailed later when we discuss demand driven planning. For now a brief summary of each zone should be enough:

The green zone is used for supply order generation. It determines order size and frequency.(emphasis added)
The yellow zone is used for inventory coverage.(emphasis added)
The red zone is used for safety purposes.(emphasis added)
The size of each zone, and therefore of the total buffer, will be different for each part. This is accomplished in part by creating material groupings (buffer profiles), and in part by individual material traits.

Only Buffer Stock Stops the Bullwhip Effect?

Safety stock exists because there will be variation from what was planned versus what was actually needed. Safety stock logic launches AND expedites a new supply order when unexpected variation causes inventory to drop below a predetermined level. This can act like a grenade thrown over the supplier’s fence. This can setup a “bullwhip effect” in most supply chains. Safety stock can protect only one side of the relationship. This protection can be either inadequate or capital intensive depending upon the volatility of demand. Replenishment buffers are designed to protect both sides of the position. On the customer side it allows supplier variability to be dampened thus promoting availability. One the supplier side it allows for natural aggregation of real demand and consumption. This naturally dampens variability from the demand dize AND allows for batch performance that relates to actual demand. – Replenishment Positions Versus Safety Stock: Why Are They So Different?

It is not at all clear how safety stock naturally does this. Safety stock could do this — if the safety stock value were set too high. For example, the standard dynamic safety stock calculation will set the safety stock too high for product locations with high variability of demand, but low forecast error as we cover in the article Experiences with Dynamic or Extended Safety Stock. However, this is not a natural feature of safety stock.

Actual Demand to the Rescue?

Throughout this explanation, DDI peppers in the term “actual demand.” This is to draw the distinction between the forecast. However, there are two problems with this. The first is unless you are a make to order environment, there is a strong limit to how much you can use actual demand. This is a constant inconsistency with DDI in that they often put down the forecast, but then when questioned, they change their position to “we do use the forecast but for the buffer stock creation.” Yet in this explanation of buffer stock, they again diminish the forecast in favor of “actual demand.”

Other parts of this explanation are not at all clear.

How is DDMRP’s buffer stock calculation “naturally” aggregating or dampening? Also, which safety stock method is being castigated here because there are several different ways of calculating safety stock.

High Forecast Error Creates Burdensome Stock Levels when Safety Stock is Used But Not When Buffer Stock is Used?

DDI goes on to say about safety stock.

The definition clearly describes that safety stock is a supplementary position to guard only against variation. Consider the dramatic increase in variation across every supply chain as complexity increases worldwide. If forecast error is high then the safety stock supplementary position can become quite an extraordinary financial commitment to statistically cover that error. – Replenishment Positions Versus Safety Stock: Why Are They So Different?

The term complexity is an imprecise explanation of what has happened to demand history.

It is more accurate to say that demand histories have been eroded by a conscious decision by marketing and sales in companies, as we cover in the article How to Understand Forecasting Lumpy Demand, that has had the result of creating more intermittent demand patterns. These are not inevitable outcomes — and some companies — like In n Out Burger and Trader Joes described in the article link above, have prospered in part by not doing things that make the supply chain more difficult to manage.

On the last point, if variability is high, then safety stock is high as well. However, DDI’s buffer stock does not give one a get out of jail free card on variability. As the environment does not change (that is make to stock versus make to order), DDI’s buffer stock faces the same challenges in accounting for variability.

However, the following quotation from DDI again claims it can account for variability in an entirely new way.

Replenishment positions are not susceptible to the forecast error or the order spikes that typically overwhelm or grossly inflate statistical safety stock positions. This is because, with regard to replenishment positions, only sales orders are considered. Forecasted orders are simply not part of the supply generation question. – Replenishment Positions Versus Safety Stock: Why Are They So Different?

Here DDI states it only uses sales orders — therefore its replenishment “position” is pull — even through the safety stock includes the forecast and is both push and pull. Once again, in a make to stock environment, which is the vast majority of environments as we cover in the article The Reality of Make to Order and Forecasting, one cannot simply switch to a make to order environment if one is in a make to stock environment.

Only Replenishment Positions Decouple Lead Times?

One of the obvious differences between replenishment positions and safety stock is their relationship to lead times and where they are placed. First and foremost, replenishment positions decouple lead times whereas safety stock does not. – Replenishment Positions Versus Safety Stock: Why Are They So Different?

The term “decouple” is not a precise way of explaining the role of excess inventory that is deliberately held. However, if one wants to use this framework, there is no reason to think that the excess inventory carried from DDMRP’s buffer stock, is any more decoupling than safety stock. This is another mischaracterization of safety stock. The way that it is described by DDMRP, safety stock does not appear to do almost anything of value — while buffer stock appears to have almost magical qualities.

Safety Stock is Mostly Static?

In most cases, safety stock is relatively static. There are some very sophisticated and dynamic safety stock systems. All of these systems attempt to take forecast variability and combine that with expected changes in demand including promotions and seasonality to adjust the safety stock levels to give better protection with certain windows of time. While this is obviously superior to static positions, the dynamic portion of this approach is limited to the safety stock zone only – the rest of the on-order and on-hand inventory still corresponds directly to planned orders. – Replenishment Positions Versus Safety Stock: Why Are They So Different?

The first part of this paragraph is true. And it is a point we observe in the application we developed to calculate safety stock external to the MRP system, and which is explained in the article Brightwork Explorer and Safety Stock Calculation. And one does not need to use the dynamic safety stock calculation within the ERP system, and in fact, most company’s don’t. However, the use of static safety stock settings is primarily done because companies invest little in the maintenance of their systems. Not because this is necessarily the case. Although, ERP systems and also external planning systems also make it difficult to review safety stocks in relation to one another.

We are absolutely no fan of how safety stocks are maintained. However, there is no evidence that DDMRP buffer stocks are an improvement, and there is not just one way of calculating a safety stock. We modified the standard dynamic safety stock and created a better dynamic safety stock, but it is not the best for all companies. In fact, we have concluded that a customized dynamic safety stock calculated external to the primary supply planning system is the most desirable course to follow. However, convincing companies of this has been a difficult task.

As for the last part of the paragraph, this is another critique of using the forecast to create orders or a critique of forecast-based planning. Our previous comments about the inappropriateness of trying to apply a make to order system to a make to stock environment apply here as well.

How DDI Convolutes Terminology

What becomes quite apparent is that the term buffer stock is misnamed. The definition of a buffer is to lessen or moderate the impact of something.

Therefore, a buffer stock should, logically, only refer to safety stock. Why are order size and frequency (Green) and inventory coverage (Yellow) included in the buffer stock?

This naturally creates a lot of confusion as to what is a buffer stock, as 2/3 of the components of the buffer stock have nothing to do with lessening or moderating anything.

The description of how to setup buffer stock continues.

Buffer Profiles

Parts are grouped according to its characteristics. It is suggested that you group them according to:

  1. Part type: manufactured (m), purchased (p), or distributed (d)
  2. Decoupled lead time: short (s), medium (m), or long (l)
  3. Supply and / or demand variability: low (l), medium (m), or high (h)

This is only a suggestion, as the actual profiles created depend on the nature of the business. According to the buffer profile a part falls on, we should be able to configure some factors that will later be used for buffer sizing:

  • a lead time factor: the longer the lead time, the smaller this factor. For example, 0.2 for long lead time, 0.5 for medium, and 0.8 for short.
  • a variability factor: the more variability, the higher this factor. For example, 0.25 for low variability, 0.5 for medium, and 0.75 for high variability.

Individual Part Attributes

The following attributes, which are also used for buffer sizing, are different for each part:

  • Average daily usage (ADU) – which can be past or forward looking, or a mix of these
  • Decoupled lead time (DLT) – calculated as explained in the previous post
  • Minimum order quantity (MOQ), if applicable

Calculating Buffer Zones

To calculate the buffer zones we’ll often use the average usage over the entire decoupled lead time (ie, ADU x DLT). Though there is no standard DDMRP name for this value, I will refer to it below as Lead Time Usage (LTU).

Each zone is then calculated as:

Green zone: LTU x Lead time factor. Exception: if MOQ is higher, use MOQ instead.

Yellow zone: LTU.

Red zone: sum of “red base” (LTU x Lead time factor) and “red safety” (variability factor x red base). – SAP Blogs

I include this portion of the quote for informational purposes. I can’t think of a reason why I would want to do any of the things described in this explanation to plan an item.

 

The DDI receives our Golden Pinocchio Award for using word games to drive to its proposed benefits. 

Conclusion

The misnamed buffer stock is made up of three components, only one of which is safety stock. Therefore, while it is named as if it is only safety stock, it is, in fact, the overall inventory. Buffer could be justified as a term in that the stock level “buffers” the supply from demand. But this is an extremely imprecise usage of the term. It immediately leads to confusion and should have been caught early in the development of DDMRP when they were settling on names and descriptions.

Confusion, Most Definitely….But Confusion Started by Whom?

DDMRP proponents complain about the confusion around buffer stock — however, a major part of the confusion is due to the misnaming of the overall stock calculation in DDMRP being called “buffer stock.” It also renders some of the discussions around buffers stocks I have had with Chad Smith and other DDMRP followers extremely odd. Why did they spend so little time describing the components to buffer stock, and why did they not interchangeably use the term overall inventory?

Also, most of the explanations around DDMRP refer to it as being based on actual demand — or a consumption/reorder point method. But then if the forecast is used in the buffer stock calculation — then is this earlier assertion really true? 

If I paid a group to come up with the maximally confusing way of explaining a supply planning method, I don’t think they could have outdone the DDI.

See this definition of buffer stock in economics.

Notice that the term buffer is used accurately here, to describe a stock held for a moderating effect. 

The way DDI uses terms like lead times, buffer stocks, decoupling, and forecasts..

  1. For example, the lead time is reduced, but it isn’t
  2. The buffer stock manages variability, but it is not safety stock
  3. DDMRP does not use the forecast, but then it does and for the buffer stock

..make it appear that the DDI is using terminology in a deliberately misleading way — where part of understanding DDMRP is keeping up with what is a word game constructed by DDI.

DDI and DDMRP proponents routinely accuse DDMRP critics of not understanding DDMRP. However, the DDI must take a fair share of the blame for creating such a confusing set of terms and explanations for DDMRP, as well as seeming to provide inconsistent statements around how DDMRP works.

What We Do and Research Access

Using the Diagram

Hover over each bullet or plus sign to see more explanation. To move to a different bullet point, just “hover off” and then hover over the new bullet.

 

Research Access

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    Put our independent analysis to work for you to improve your spend.

References

https://blogs.sap.com/2016/08/31/demand-driven-mrp-part-iv-buffers/

*https://www.slideshare.net/utkanuluay/demand-driven-mrp-buffers

*https://www.slideshare.net/utkanuluay/ddmr-pvs-safetystock

How the Demand Driven Institute Tries to Make DDMRP Unfalsifiable

Executive Summary

  • The Demand Driven Institute has some rules they would like you to follow when critiquing DDMRP.
  • If you follow them, DDMRP cannot be critiqued.

Introduction

The Demand Driven Institute, led by Chad Smith and Carol Ptak, makes significant efforts to stop those with extensive experience in the MRP, forecasting, and supply planning space from critiquing their creation DDMRP. Through observing multiple interactions and debates, mostly on LinkedIn, the strategy used by the Demand Driven Institute has become apparent.

In this article, we will layout a technique that I have called “eliminated the testability of DDMRP.”

The Rules Set Out by DDI for Testability

The following was explained by Chad Smith of the DDI on the rules for critiquing DDMRP. This is the share on LinkedIn.

This is the article Can DDMRP Work in the FMCG Sector? 

This is in response to the following share by Rakesh Paras Singh.

– Please, do not just make this a DDMRP discussion. This is where so many of us are missing each other. DDMRP is simply a supply order generation and management engine. We constantly try to tell people is only a part of a bigger picture – a part that is a starting point (albeit it a rewarding one) to a larger transformation. – Chad Smith

This is the response to Chad.

ISCM Scmproknowledge.in takes an unbiased view of this debate and brings the best viewpoints for enhancing learnings and making professionals equipped with the right assessment of the approach. Enjoy learning and reading…. i have said this. You are right Chad Smith we will broaden the debate and would request a thought leadership approach to prevail here.

I have demand planning approaches in mind and not just DDMRP. The bigger picture is where focus in supply chain or even businesses is absent. I would love the debate to move beyond the question of replinshment metrics and strategy of designing and managing supply chain better. Most probably how will planning be planned post pandemic in near short, Short and long period? bringing this gap in demand planning is important? And there are many ways to do it. – Rakesh

This is the response to Rakesh.

If you want the debate to be about that broader topic then perhaps you should start with an article about that broader subject. The article you posted was about a particular supply order generation mechanism (DDMRP) in a particular industry (FMCG). That is pretty far off of the broader topic that you hope to foster your discussion around. Why start there? Quite perplexing to me.

We actually have a webinar that we just recorded that talks about two simple conclusions moving forward for companies and their leadership. It is available for free on our website including the deck (Where do Supply Chains go From Here?). https://www.demanddriveninstitute.com/videos We are also about to publish a joint POV with Accenture that will echo these thoughts. – Chad Smith

And then Rakesh’s response.

I am surprised at your objection to the article. I found it broad and the article brings about certain critical element of planning. Why should everybody be a proponent of DDMRP.

The DDMRP proponents needs to be accommodative
Scmproknowledge is a knowledge portal and we will keep publishing and posting all views. I have published another article on DDMRP by dr rakesh sinha. Let’s discuss than say what we can’t do as a portal.

Best regards and you are welcome to contribute and I will post it here. – Rakesh

And then Chad’s response.

I am not objecting to the article at all. Please carefully read my post. At no point in any of my posts do I object to you posting this article. It did foster discussion (albeit a discussion that has occurred, at least for me, in many other forums, at many other times over the last few years). What seemed odd to me is that you say you want a broader discussion but posted a specific article about a specific replenishment method in a specific industry. Why not start with the broader question/article if that is the discussion you want to foster as per you post that I replied to? – Chad Smith

And this is where I entered the conversation.

This comment by Chad…

“Please, do not just make this a DDMRP discussion. This is where so many of us are missing each other. DDMRP is simply a supply order generation and management engine. We constantly try to tell people is only a part of a bigger picture – a part that is a starting point (albeit it a rewarding one) to a larger transformation.”

Is worthy of some type of propaganda award.

If any person wishes to write an article about DDMRP, they have the right to do it without being policed to writing about a different or “broader topic.”

The second part of this quote is pure misdirection, also known as a pivot — it proposes that the specifics of an item (like DDMRP) should not be critiqued, because of some more significant issue proposed by an organization (DDI) that does not want that item critiqued.

I could do the same thing. If someone were to critique an article at Brightwork Research & Analysis, I could state, “please do not critique the website, its just a “starting point — albeit it a rewarding one — to a larger transformation.”

If the item itself cannot be critiqued because it is part of a larger transformation that is not actually part of the item, then the item itself has been converted into something that is permanently unfalsifiable or is called an untestable hypothesis. That is, the hypothesis cannot or should not be tested because it is just a cog in a larger machine. But if a hypothesis cannot be tested or analyzed outside of a group that has an incentive to promote it (DDMRP/DDI in this case) — then it is given a protected space — that no hypothesis should have, and that no other hypothesis has in supply planning. I know something about this having engaged in numerous debates with Chad Smith and other DDMRPers. There are only two outcomes of this discussion. You either agree with DDMRP, or you will be accused of not understanding DDMRP. Chad has policed me, Stefan Do Kok, Joannes Vermorel, and now you those are just the ones I can immediately recollect. You are in the early phase of the discussion Rakesh. If you do not submit, you can expect a personal attack. DDMRPers will read this and then make cutting comments. Chad Smith will then jump in double team you — while pretending to be above the fray. – – Shaun Snapp

This is Chad’s response.

I would invite anyone on this forum to review the “debates” that you and I have had in which I have always said people should thoroughly explore and decide for themselves. I would also invite people to review the way you conduct yourself online. We have spent ten years building out a framework that is proving itself over and over again. My point in making the specific post you are responding to was to say that much of the critique of DDMRP has been that lacks this or that. The larger framework is where most of what is claimed to be missing is often found and people miss that and often don’t explore that at all. Have a great day – I think you need one. – Chad Smith

And here is my response.

Let us review the quote because it is very difficult to come to that conclusion from your quote.

“Please, do not just make this a DDMRP discussion. This is where so many of us are missing each other. DDMRP is simply a supply order generation and management engine.”

There is nothing here regarding a specific part of DDMRP.

Chad’s comment here…

“DDMRP is part that is a starting point (albeit it a rewarding one) to a larger transformation.”

It can only be interpreted to mean that he thinks DDMRP itself is not to be analyzed, but part of something bigger.

But, as we cover in the article How Accurate is the Criticism of Lokad DDMRP Video?, Chad did make the statement he said he made above in a critique of Joannes.

“Joannes also misses the point that DDMRP is a package that is greater than the sum of its parts. By only comparing a part at a time, Joannes fails to understand or address how the components work together to produce a result..”

This attempts to defect the critique of any one part of DDMRP — and that it can only be viewer holistically — not in total.

This means that according to Chad’s/the DDI’s logic, the following rules apply.

The Rules Laid Out by the DDI

  1. Rule #1: A DDMRP cannot be critiqued because it is part of something greater.
  2. Rule #2: Specific areas of DDMRP cannot be critiqued because each part can only be analyzed within the construct of a larger part (which is DDMRP).

Let us review this logic graphically.

I will prove that both of these attempts at deflecting criticism cannot be valid.

But I will do so without proposing a normative standard. Instead, I will simply use Chad’s/DDI’s own critiques.

In their book and in articles Chad Smith and Carol Ptak critique MRP. However, they critique without concern for Rule #1. When critiquing MRP, they never consider that MRP is part of something larger. They critique MRP as its own isolated item (as they should, but not as they say DDMRP should not be critiqued.)

They break Rule #2 by specifically criticizing specific parts of MRP.

  1. They say that safety stock is not effective.
  2. They state that forecasts should not be used for operational planning, but to set buffer stocks.

This is a critique of a specific part of MRP. In fact, their commentary and the commentary of other DDMRP followers on MRP is littered with specific critiques of MRP that could also be defended by stating that MRP must be viewed as a comprehensive whole.

Chad Smith receives our Golden Pinocchio Award for creating a fake set of rules that try to place DDMRP beyond critique. 

Conclusion

Neither argument nor rule that sub item to a greater item cannot be critiqued because it is part of a greater “sum of parts” nor that the greater item is part of either larger item is a legitimate defense or rule against criticism. And the DDI breaks these rules when critiquing MRP. These arbitrary rules are put in place to stop DDMRP from being criticized. This is part of a long term pattern by DDI as we cover in the article The Demand Driven Institute’s Approach to Suppressing Dissent on DDMRP.

DDI uses a “thug” mentality to try to critique the person so that they stop critiquing DDMRP. Chad Smith is the primary enforcer, and he employs a “Mean Girl routine,” but there are many DDMRP devotees who will critique those that do not blindly accept DDMRP.

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References

The Demand Driven Institute’s Approach to Suppressing Dissent on DDMRP

Executive Summary

  • The Demand Driven Institute spends a good chunk of its time trying to suppress voices that dissent with DDMRP.
  • This article will explain the strategies they use.

Introduction

The Demand Driven Institute, led by Chad Smith and Carol Ptak makes significant efforts to stop those with extensive experience in the MRP, forecasting, and supply planning space from critiquing their creation DDMRP. Through observing multiple interactions and debates, mostly on LinkedIn, the strategy used by the Demand Driven Institute has become apparent.

In this article, we will layout these techniques. We will call these “Chad’s Rules” as they have be articulated by Chad Smith.

Chad’s Rule #1: DDMRP Cannot be Critiqued in Isolation Because it is Part of a Bigger Picture?

This comment by Chad…

“Please, do not just make this a DDMRP discussion. This is where so many of us are missing each other. DDMRP is simply a supply order generation and management engine. We constantly try to tell people is only a part of a bigger picture – a part that is a starting point (albeit it a rewarding one) to a larger transformation.”

Is worthy of some type of propaganda award.

If any person wishes to write an article about DDMRP, they have the right to do it without being policed to writing about a different or “broader topic.”

The second part of this quote is pure misdirection, also known as a pivot — it proposes that the specifics of an item (like DDMRP) should not be critiqued, because of some more significant issue proposed by an organization (DDI) that does not want that item critiqued.

I could do the same thing. If someone were to critique an article at Brightwork Research & Analysis, I could state, “please do not critique the website, it’s just a “starting point — albeit it a rewarding one — to a larger transformation.”

Chad’s Rule #2: The Item of DDMRP Itself Cannot be Critiqued: And the Untestable Hypothesis

This comment is also from Chad in a comment to Rakesh Paras Singh.

Please, do not just make this a DDMRP discussion. This is where so many of us are missing each other. DDMRP is simply a supply order generation and management engine. We constantly try to tell people is only a part of a bigger picture – a part that is a starting point (albeit it a rewarding one) to a larger transformation.

Chad does not like DDMRP to be critiqued on its specific merits, but instead only wants it praised on its specific merits.

If the item itself cannot be critiqued because it is part of a larger transformation that is not actually part of the item, then the item itself has been converted into something that is permanently unfalsifiable or is called an untestable hypothesis. That is, the hypothesis cannot or should not be tested because it is just a cog in a larger machine. But if a hypothesis cannot be tested or analyzed outside of a group that has an incentive to promote it (DDMRP/DDI in this case) — then it is given a protected space — that no hypothesis should have, and that no other hypothesis has in supply planning.

I know something about this having engaged in numerous debates with Chad Smith and other DDMRP proponents. There are only two outcomes of this discussion. You either agree with DDMRP, or you will be accused of not understanding DDMRP.

Chad’s Rule #3: Chad Smith Is Not Opposed to Articles on DDMRP, Except When He Opposes Them

Chad is opposed to this article Can DDMRP Work in the FMCG Sector?, but then states he is not opposed to it. Notice the following comment on this article shared on LinkedIn.

I am not objecting to the article at all.(emphasis added) Please carefully read my post. At no point in any of my posts do I object to you posting this article. It did foster discussion (albeit a discussion that has occurred, at least for me, in many other forums, at many other times over the last few years).

What seemed odd to me is that you say you want a broader discussion but posted a specific article about a specific replenishment method in a specific industry. Why not start with the broader question/article if that is the discussion you want to foster as per you post that I replied to? – Chad Smith

In a comment to Rakesh, he states that Rakesh needs

“to carefully read his posts,”

This is because Chad contradicts himself from one response to the next.

The reason for the contradiction is simple. Chad wants to give the impression he is for freedom of expression, but that part of his comment is not meant for that individual, it’s meant for the audience.

Chad only wants comments written that promote DDMRP, and wants and actively suppresses comments from those that would critique DDMRP.

He can then say something like the following..

“DDI has never ever tried to restrict anything written about DDMRP, and anything said to the contrary is just salacious gossip.”

He then also wants authors to know he does not want this type of article published.

That is what the dog and pony show was about the scope of the article not being correct, and placing the hypothesis of DDMRP into the unfalsifiable space. (see Chad Smith Rule #2 above)

Chad’s Rule #4: No One Should State a Pro or Con Observation About DDMRP, as it Reduces Objectivity

Dr. Rakesh Paras Singh, you are entitled to your opinion. I agree that many of these threads degenerate to personal attacks. It brings down any meaningful discussion. I would refer you to the discourse that has happened between Stefan de Kok , and myself. I think we have had a lot going dialogue. Others try to stir the pot in many ways. Once again, people should do their homework and pick a method that resonates with their situation. I also think that someone that is trying to be impartial should not comment for or against a particular direction. – Chad Smith

If Chad only defended his work, as Chad stated was his right, then it would not be an issue. Notice that Rakesh’s comment is that DDMRP proponents have been conducting themselves in a manner that violates the principles of discussion. This is not equivalent to defending one’s work. The idea that Chad would complain that debates on DDMRP frequently devolve into personal attacks is hypocrisy as Chad can barely write a message to someone he disagrees with without including several personal digs at the individual.

And obviously, I agree with Rakesh. I have been pointing out for several years that DDMRP proponents behave in ways that seek to stifle debate. And as I pointed out previously, Chad has been extremely dismissive of critiques of DDMRP by Stephan De Kok and Joannes Vormel. DDMRP proponents have stated that the only reason that Joannes critiqued DDMRP was that he was “jealous that DDMRP was adopted by SAP.” What a cheap shot. Secondly, how does this DDMRP devotee know this? I have seen this repeatedly, DDMRP proponents can not tolerate criticism of DDMRP without lashing out and making the discussion personal.

This comment is interesting..

“I also think that someone that is trying to be impartial should not comment for or against a particular direction.”

This translates impartiality into silence. However, as with nearly all of Chad’s rules, they are selectively applied. I believe this translates into the idea that Chad would like people to not comment on DDMRP – but the only time I have ever heard Chad say this is to a person who has critiqued DDMRP.

Firms like Camelot can make all manner of exaggerated claims about DDMRP, (and about SAP), and this is, apparently fine with Chad. Carol Ptak (also of DDI) is seen right here in the video nodding along. 

This appears to be a message to Rakesh to stop analyzing or commenting on DDMRP — because then he won’t be “impartial.”

The evidence that Rakesh is partial?

Well his criticism of DDMRP. Rakesh needs to keep quiet, or else he will break Chad’s rule.

The Broader Implications of Rule #4

This rule is insanity that one can only imagine in some 1984-esque society or something the current Chinese government might propose.

The entitlement of someone to say something like this is off the charts. This seems like a rule we would have to follow….if we all lived in Chad’s DDMRP Prison. Through this statement, Chad has unilaterally removed freedom of speech. Truly amazing, as I thought the 1st Amendment had nothing to do with Chad’s approval or disapproval of the said amendment. I thought the Demand Driven Institute was just an organization that promoted DDMRP, but it appears to have much larger ambitions.

Chad’s Rule #5: Critiques of DDMRP Are Not to be Published

More so in the past than the present, but there was a time when critiques of DDMRP according to Chad Smith — should be “taken off-line.” This means that the critique should not be published as a comment, but instead, critiques should be taken to Chad so they can be discussed in private without anyone else seeing them.

Chad has repeatedly asked people to bring up their concerns to him directly. I had my obligatory conversation with Chad “off line,” but then continued to critique DDMRP after the conversation.

Chad’s Rule #6: The Time of the BLM Riots Created a “Cone of Silence” Which No Critique of DDMRP Should Violate

There were riots during the BLM protests, and therefore Brightwork should observe some type of cone of silence. I violated Chad’s 6th rule when I responded to a rude comment from a DDMRP proponent. Curiously, the DDMRPer did not get into trouble with Chad and was not critiqued for breaking the BLM Cone of Silence. However, I was.

This seemed unfair to me. If we violated this unwritten rule, shouldn’t both of us have been sent to the DDMRP principle’s office?

Chad’s Rule #7: Debates Become Toxic and Should be Exited

Chad Smith has said that he does not like debates where “toothless lions” (that is when people disagree with him in public) and therefore should no longer continue the debate.

By Defining Discussions as Toxic

According to Chad, our debate had become “toxic,” and therefore the discussion should be ended, but then he reentered the debate.

Chad has opted out repeatedly from discussions, only to come back in at a later date. The opt-out request coincidentally occurs whenever Chad is unable to answer an argument — for instance when I showed that Chad was making DDMRP an untestable hypothesis.

Chad’s Rule #8: The Only People that Critique DDMRP Do Not Understand DDMRP

Chad states that all disagreement with DDMRP is based upon ignorance. This quote is found in one comment to Rakesh.

“We have made many many things freely available and will continue to do so even though people refuse to put the time into exploring those things.”

I have put considerable time into understanding DDMRP. This is just one of my articles on DDMRP How is DDMRP Buffer Stock Different from Safety Stock?

Furthermore, the author of the article Can DDMRP Work in the FMCG Sector?, Steve Allanson, also put effort into understanding DDMRP. And we both see shortcomings in DDMRP. I go further than Steve, and consider DDMRP not useful enough to warrant being used.

However, the problem is not our ignorance of DDMRP; it is the fact we don’t see DDMRP the way Chad expects us to.

Furthermore, couldn’t this argument be applied to anything? For instance, ISIS has published a rape manual. It explains how to rape, when to rape women, girls and young boys who are prisoners of war. ISIS states that this is entirely supported by the Koran, which views the rape of POWs as a type of spoil of war (if the person is an unbeliever).

I don’t like this manual. However, if I were to debate a proponent of ISIS, could they not use the exact same defense? That is I don’t understand the ISIS rape manual, which is why I don’t like the ISIS rape manual.

If the only test of something being true, is whether everyone agrees with it, this is a non-falsifiable standard.

Chad’s Rule #9: The Existence of Material Published by DDMRP Equal Evidence of its Accuracy

This is found in the following quotation from Chad.

We have made many many things freely available and will continue to do so even though people refuse to put the time into exploring those things.

Chad is correct that he has made things available for free online—however, everyone who is trying to promote something that does the same thing.

There is lots of free material on the IBM or Accenture website.

How about Infosys? Don’t they make free material available on their websites?

Are we to praise the world’s worst companies, because they have free stuff on their website?

I found this on the Scientology website. It’s free. But does its existence make it true? 

Chad has a habit of overstating what the things he shares prove. He thinks that the existence of a DDI output ends the discussion. Don’t you like it, look Chad “put it out there.”

The assumption that Chad would like to be accepted is that because he wrote something or because the DDI funded some study, that it must be true. That is the existence of the material, which is all the evidence that is required.

On what planet does this assumption apply? To note, I have found numerous false statements about forecasting and MRP and inconsistent and word games used to describe the benefits of DDMRP. I do not consider DDI’s website to be a source of anything, except what the DDI thinks about things.

Conclusion

Chad Smith and the DDI are entirely opposed to anyone who disagrees with DDMRP voicing this view and claims the right to censor those that are not convinced by DDMRP. Chad’s intent is not to engage the criticism, but to shut down the critic. That is where most people who debate Chad miss the boat. He has no interest in understanding the criticism. He questions the critics right critique DDMRP. Chad says exactly otherwise, but Chad does not tell the truth about his intentions.

Brightwork does not back down from corrupt vendors like SAP or consulting firms. Our research calls out what we think is true, without consideration for whom it might offend. We also do not allow ourselves to be censored, or to self sensor out of a concern for engaging in confrontations with those that make non-evidence-based assertions. This means that — we will not be following Chad Smith’s Rules.

What We Do and Research Access

Using the Diagram

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References

*https://www.nytimes.com/2015/08/14/world/middleeast/isis-enshrines-a-theology-of-rape.html

How Accurate is the Criticism of Lokad DDMRP Video?

Executive Summary

  • Lokad produced a video critiquing DDMRP.
  • We check this Lokad video and how logical are DDMRP commenters on their critique of the video.

Introduction

I analyzed DDMRP in the article Repackaged Lean as DDMRP and concluded that it is not an improvement on MRP, and is just repackaged Lean with a few tweaks. In this article, I review both the video and the critique of the video by Lokad on DDMRP.

Disclosure

I have to state that I have had aggressive debates with both Chad Smith and Carol Ptak – the leaders of the Demand Driven Institute or DDI, and various DDMRP devotees. I have also been on the receiving end of a large number of personal attacks from DDMRP proponents. The intent appears to be to censor those that oppose DDMRP or that are simply unconvinced by DDMRP. This is somewhat similar to by debates with Six Sigma proponents but dialed up a few notches.

I read comments by Chad Smith and other DDMRP proponents on a video filmed by Lokad (Lokad is a software vendor). And I found similarities that I thought it would be insightful to note between the comments on the video on YouTube and my debates with the DDMRP crowd.

The starting point, of course, is reviewing the Lokad DDMRP video.

The Lokad Video

The following video consists of Joannes Vermorel of Lokad being interviewed on the topic of DDMRP.

The DDI leaders like Chad Smith have a severe inability to have DDMRP critiqued. This is a comment from Chad Smith on the DDMRP video produced by Lokad. 

This video suffers from the same assumption that has been plaguing industry for decades – that increasing environmental complexity can only be combated by the application of more complexity. Many times in life we must walk a fine line between oversimplification and overcomplication – both create less than desirable effects for systems. DDMRP is a package that sits right in the middle. But it needs to be understood that DDMRP is only the beginning and not the end of a journey. DDMRP is intuitive for people and, at least for the time being, people still run supply chains. When people don’t understand or don’t trust the system, they begin to work around it. Applying more complexity to the system will only exacerbate that lack of understanding or distrust. Joannes seems to not understand that DDMRP is simply a starting point and the supply order generation and management aspect of a larger framework.(emphasis added) Thus, he is significantly under-representing the ability to use appropriate and focused advanced mathematical tools in the tactical and strategic side of the Demand Driven Adaptive Enterprise Model. Joannes also misses the point that DDMRP is a package that is greater than the sum of its parts. By only comparing a part at a time Joannes fails to understand or address how the components work together to produce a result.(emphasis added) This linear comparative approach is ironic given the author’s emphasis on complex mathematics and systems. Now, let’s talk real world – check out the case studies of companies across a wide array of industries that are winning big with DDMRP in a fairly quick fashion: https://www.demanddriveninstitute.com/case-studies. Additionally, you will find research projects from organizations such as the Massachusetts Institute of Technology (MIT) that find DDMRP is highly effective: https://www.demanddriveninstitute.com/research

Our Analysis

This is textbook Chad Smith. Everyone but those that support DDMRP is to be disregarded.

Notice in the highlighted green portion of the quote, Chad Smith states that DDMRP is a starting point. So DDMRP is not a complete method but is a starting point? Is there a forthcoming book called DDMRP: The Rest of It?

If a person can say that critics don’t understand a method, because the technique is not released in its entirety — and it is a “starting point,” that makes the hypothesis untestable. Furthermore, Chad Smith is always claiming the superiority of DDMRP over MRP. So is DDMRP superior in its current form, or is it just a “starting point?” A person cannot be accused of not understanding something if you write several books on a topic, but then state that the issue that is being analyzed is just a starting point.

Notice in the highlighted orange portion of the quote, Chad Smith states that Joannes Vermorel of Lokad should not be trying to decompose the different components of DDMRP and analyzing them one by one. You cannot talk about everything at once when analyzing any system. Decomposition is a fundamental part of the analytical process. Any item will not have a series of problematic components or components with faulty assumptions and then have a positive outcome.

For example, we are analyzing a car, and

  1. The car has an underpowered engine for the purpose or weight of the car.
  2. The car has poor quality tires, a steering wheel which is too small — that car will not have a positive outcome in performance.

If we move the analogy to a baseball team — it would mean that the analysis of the individual players is not a legitimate avenue of analysis — because the team must be analyzed only as a functioning unit.

However, how is such an analysis even possible?

It would mean not mentioning the contributions of each of the individual players — their strengths and weaknesses. While teamwork or how the components work together is essential, the best teams also tend to be comprised of excellent players. And both the integrated nature of the elements as well as the individual components are valid avenues of analysis.

If Joannes Vermorel were to follow Chad Smith’s advice, he would not be able to analyze DDMRP, because the discussion of any one component would open him up to the criticism that he should not be talking about that one component.

This is not a fair critique of Joannes Vermorel’s analysis. It sets an impossible expectation that is not only nonsensical but is not applied to any other item that is analyzed.

The fact that Chad Smith things that you can demand that your items cannot be decomposed into its constituent parts are disabling to analysis. It is an enormous red flag regarding the logical foundation of the person (Chad Smith) making this claim.

I can only conclude one of two things:

  1. Either the person in question has deeply flawed logical processes.
  2. Or is gaslighting Joannes Vermorel as a type of false argumentation.

I can decompose any method used in demand, supply, or production planning into its constituent parts. And the ability to decompose a topic is one of the hallmarks of a person who fully understands a topic.

At the end of the quote, what Chad Smith calls now “let’s talk real world” is where he pivots to a master’s thesis by two people who appeared to have little work experience in planning. Chad Smith is aggressive in censoring any view that he does not like. He tends to want criticism of DDMRP to be taken “offline” so that it never finds itself in a published form. Naturally, if you bring your criticism to Chad “offline,” then, of course, he will convince you the criticism is not correct. One on an offline conversation, he uses his ability to create a fast “personal relationship” to try to get the critic not to publish their criticisms. But if you take one of these conversations but then continue to critique DDMRP, he takes it personally.

Overall, DDI, through Chad Smith’s behavior, has demonstrated a strong need to limit critiques of DDMRP, which I see as a problem and inappropriate. In any method used, one should be free to critique that method — without having some central authority that states this criticism is not appropriate. If, for instance, one wants to critique statistical forecasting or machine learning (like the Statistical Forecasting Institute or the Machine Learning Institute). There is not a “central” body that seeks to quelch criticism of these areas.

The Cult of DDMRP?

DDI intends to create some type of cult around DDMRP. And part of this is based around both inaccurate criticisms of MRP and exaggerated statements around DDMRP. Its members repeat DDMRP assertions without thinking very deeply about what they are asserting.

Here is a second comment from another proponent of DDMRP on the same video.

So Lokad, a competitor in the Supply Chain tools space, makes a “webisode” or “podcast” that completely bags on a competitor, and calls it fact? This is just drivel. What a ridiculously pompous spokesperson. I am sure they are just jealous that SAP adopted DDMRP and not Lokad’s “QUANTITATIVE SUPPLY CHAIN OPTIMIZATION SOFTWARE ” So because DDMRP doesn’t use the buzz words of “machine learning,” “AI,” or the other SC buzz words, they are crap?

Come on man!

All I heard at the Gartner SC Exec. conference a few weeks ago was a consistent message of these buzzwords. Nobody has a clue how to leverage these, but theyll happily sell them to you! Keep it up DDMRP. You guys are awesome. I can’t get my company to bite, yet, but we are getting there. (emphasis added)

Lokad, you should spend some time in the world. I work implementing ERP and SC solutions, and you wouldn’t believe how many businesses, throughout the world, don’t even use ANY automated planning tools. MRP, DDMRP, APS, nothing. They manually plan each order individually. Most of these businesses have NO S&OP process that is linked to SC or even an S&OP process at all. No Forecasting process. They plan each order manually and have the overstock inventory and bad working capital metrics to prove it. (emphasis added)– Mike Bradshaw

Our Analysis

The fact that Lokad sees DDMRP as a competitor is not that easy to see. Furthermore, Brightwork is not a competitor with DDMRP, and we share a similar view of DDMRP as Joannes Vermorel of Lokad, except we are more critical of the DDMRP method.

Second, the fact that SAP adopted DDMRP is not evidence that DDMRP works. We cover SAP extensively as one of the only analyst/research entities that cover SAP but is not paid by SAP ( Gartner and Forrester and IDC and many others are paid by SAP). And we can say with confidence that SAP does not care what is true. We rank them along with Oracle as the two least trustworthy vendors in the enterprise software space, as we cover in the article How to Understand the Honest Vendor Ratings – SAP.

SAP will adopt any approach that has a market interest. Mike Bradshaw critiques machine learning and AI. We cover how SAP’s IBP product is a buzzword adoption machine in the article How SAP IBP, aka “Zoolander” is Going All in on Trendy. However, what Mike Bradshaw does not observe is that DDMRP is just another trendy term they think they can use to get more application sales. That is, while Mike Bradshaw lauds SAP for adopting DDMRP, and then praises DDMRP for not using buzzword or trendy items like machine learning, what Mike Bradshaw misses is that SAP has aggressively adopted machine learning and AI. So if the adoption of DDMRP by SAP is evidence of the legitimacy of DDMRP, then what do SAP’s even greater adoption of machine learning and AI?

According to Mike Bradshaw’s logic, this must mean that these methods are now also legitimized. The standard of evidence is if SAP adopts something.

If Mike Bradshaw knew SAP’s broader marketing, and even how SAP IBP application — which is directly in the same software category as DDMRP, he would not have made that observation and conclusion.

The middle part of the quote, highlighted in orange might be true, but has nothing to do with Joannes Vermorel of Lokad’s critique of DDMRP. At Brightwork, we are probably the best-known critic of Gartner, and we have the most material on how Gartner functions to deceive its clients. However, whether Gartner understands something (for instance, in this article we cover in one example of how Gartner failed on analyzing SAP HANA How Gartner Got HANA So Wrong,) is not a defense of a critique of DDMRP.

And the latter part of the comment, which I have highlighted in green, where Mike states that many companies don’t use any automated planning tool, does not have anything to do with Lokad’s critique of DDMRP. I also highly doubt that Joannes Vermorel of Lokad does not know this. This means that roughly 1/2 of the quote has nothing to do with the content of the Lokad video.

This would be like finishing off a critique of the video by talking about the composition of the linebackers that play for the Green Bay Packers. It may be an accurate representation of their players — but is not related to anything.

The Pattern of DDMRP Responses to Criticism

These comments follow a pattern of doing the following to critics of DDMRP.

  1. Proposing that the person does not “understand” DDMRP.
  2. Personally attacking the person criticizing DDMRP.

This last comment is quite rude. However, these types of comments are endorsed by DDI.

How do I know this?

I had one DDMRP proponent write a rude response to one of my comments. This comment was promptly liked by Carol Ptak. Carol Ptak is the second most prominent member of DDI.

This and my online debates with Chad Smith, where he routinely ignores the underhanded behavior of DDMRP followers. Indicate to me that both Chad Smith and Carol Ptak want DDMRP followers to make rude and ad hominem statements against DDMRP critics, while staying a bit above it all. In these debates, Chad Smith tends to puts himself in the position of the master of ceremonies — and likes to “mediate” discussions on DDMRP. This mediation takes the form of critiquing the DDMRP critic while pretending to be neutral. He gives me advice on how I should and should not answer comments. As I write this, I am 50 years old, but with DDMRP debates, I pick up a new parent I never knew I needed. How Chad Smith could even claim to be a neutral party when he is one of the chief proponents of DDMRP as its co-originator is very odd.

Conclusion

I found the explanation of DDMRP and its weaknesses by Joannes Vermorel to be roughly in line with my analysis of DDMRP. The one area where I would depart with Joannes is with his proposal that advanced planning methods are desirable versus MRP. The primary reason I say this is that the history of advanced planning methods is poor. And I say this as a long time consultant in advanced planning who up until a decade ago generally thought of MRP as “below me.” Starting my career in advanced planning, I had close to no interest in MRP and only wrote articles on it when I was asked. At that time, I recall saying to people.

“Why am I writing articles on MRP?”

Most companies that attempt to implement advanced planning methods tend to fail. And some of the planning methods, like cost optimization, are presented misleadingly as to their technical merits, and their underlying logic and fit to the environment.

Secondly, both vendors and consulting companies that have promoted these advanced methods have aggressively understated the maintenance and effort required to keep such systems operating functionally. I worked for one, i2 Technologies that lied unceasingly about the capabilities of their optimizers — and as an independent consultant for SAP APO, where SAP and SAP consulting firms did the same.

As for the arguments made against Joannes Vermorel’s analysis/critique of DDMRP by Chad Smith and Mike Bradshaw — they did not make any sense, and many of them were contradictory. They also fell into a pattern I have seen with DDMRP of attempting to question the understanding of DDMRP and attempting to censor those that would critique DDMRP.

What We Do and Research Access

Using the Diagram

Hover over each bullet or plus sign to see more explanation. To move to a different bullet point, just “hover off” and then hover over the new bullet.

 

Research Access

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    Put our independent analysis to work for you to improve your spend.

References

https://139b7ba6-57f5-40f4-a9cb-3c134e42ba5c.filesusr.com/ugd/390972_dec6ac7b537549c0a5a2e9e5c0340775.pdf

How Convincing is the MIT DDMRP Study?

Executive Summary

  • The Demand Driven Institute proposes a master’s thesis at MIT proves DDMRP’s efficacy.
  • We analyze this thesis.

Introduction

We analyzed DDMRP in the article Repackaged Lean as DDMRP and concluded that it is not an improvement on MRP, and is just repackaged Lean with a few tweaks. In this article, I analyze the validity of an MIT master’s thesis, which is used to try to demonstrate the validity of DDMRP.

Disclosure

I have to state that I have had aggressive debates with both Chad Smith and Carol Patak – the leaders of the Demand Driven Institute or DDI, and various DDMRP devotees. I have also been on the receiving end of a large number of personal attacks from DDMRP proponents. The intent appears to be to censor those that oppose DDMRP or that are simply unconvinced by DDMRP. This is somewhat similar to by debates with Six Sigma proponents but dialed up a few notches. However, in this article, I analyze each web page on DDMRP vendors and consulting firms on their own merits, and based on what they publish on the topic of DDMRP.

The Validity of DDMRP MIT Research

The research that Chad Smith refers to is the master’s thesis titled Investigation of Potential Added Value of DDMRP in Planning Under Uncertainty at Finite Capacity. He states that this partially proves the benefits of DDMRP.

We reviewed this publication in the article How Accurate is the Criticism of Lokad DDMRP Video? 

Repetition of What DDI States About DDMRP

This is one of the first quotations in the research.

In 2011 a new planning methodology called Demand Driven MRP (DDMRP) was introduced in response to the new dynamics of supply chain complexity. DDMRP is a multi-echelon supply chain planning approach that combines the best of lean, MRP, six-sigma and the theory of constraints. It relies on the idea that ROI comes from emphasizing the flow of product to the market rather than mere unit cost reductions. DDMRP proposes an intuitive way to manage flows of products and relevant information by strategically positioning decoupling points and managing those with clear inventory policies. DDMRP has a particular focus on managing variability and planning and execution priorities.

This is taken directly from the DDI website. So this is DDI’s assertion.

Camelot Results

The Demand Driven Institute (DDI) has published results of DDMRP implementations that show an increase in service levels by 13%, reduction in inventory by 31%, and a decrease in lead times by 22% (Camelot, 2019). However, these are median results and different industries can have different results.

First, no research produced by any IT consulting company can be trusted. Camelot has a consulting practice that sells DDMRP services.

Consulting firms cannot do research. They are continually introducing “research” which is in reality promotion. We cover in the article Why PwC’s Research Fellows are Fake and Pretend to be Academic, how PwC creates fake academic titles for people posing as researchers. It is well known that any audit results that one desires can be purchased from PwC. On the consulting side, PwC does not produce research — they produce marketing collateral.

I have never come across an IT consulting firm that would not rig any result to increase sales. I was pressured to rig results for several consulting companies. Early in my career, when I had very little leverage in the job market as I had little work experience — and I buckled and presented information that I knew was false for KPMG and Accenture. Beyond this, both firms would present assertions that they did not have any support for making, but which they said when they presented

“No one seemed to challenge.”

Therefore, both KPMG and Accenture would make strongly declarative statements that they knew they had no support for, and which sounded like they did have evidence to present — and if challenged, they would say

“This is what we have seen at clients.”

As far as I could tell, these companies present false results as part of their normal operations. I would never take a study by any company like this seriously.

Secondly, the study in question was for only a small number of product locations and allowed the effort to be placed into the DDMRP run, but for the MRP run to be performed without any assistance. This thesis does not point out these observations.

Long Term Forecasts are Impossible?

The founders of DDMRP, Ptak and Smith (2011), have emphasized that for an MRP system to run, actual customer requirements is required. However, due to lead time, it is impossible to only base the plan on actual demand. This requires the use of forecasted demand. Burbridge (1980) states that it is impossible to make accurate forecasts for long periods. Therefore, incorrect forecasts are fed into MRP systems in place of actual demand causing nervousness.

The forecasts are not incorrect. Each forecast has a specific inaccuracy. MRP and other supply planning systems account for this inaccuracy with safety stock — something that is not mentioned in this quotation. There are just many problems with the assumptions that are accepted by the authors Leo Ducrot and Ehtesham Ahmed in this quotation.

Safety Stock Can Amplify Instability?

Among the proposed solutions to handle nervousness, the most commonly used are safety stock or safety lead-time or safety capacity. Ho et al. (1995), Whybark and Williams (1976), and New (1975) maintain that safety stock is the preferred technique to control quantity uncertainty and is the primary protection against overall uncertainty in the system. However, a study showed that safety stock could also, in certain circumstances, amplify the variability and the instability in the system (Sridharan and LaForge, 1990).

How would safety stock amplify variability? Safety stock accounts for variability. One might be able to find some study that shows this under “certain circumstances,” However, that is not the norm.

APS Improves Outcomes Over MRP?

In the literature, we find that APS provides better results than MRP. In a study conducted by Moscoso, Fransoo, and Fischer (2010), the APS implementation had a positive result. Backlogs were reduced by 84% (in three months) and 97% service levels were achieved. However, they also found that average production lead time increased by 15%. Hvolby and Steger-Jensen (2010) in their study found that delivery accuracy went up from 79% to 99% after implementing an APS system.

Do Leo Ducrot and Ehtesham Ahmed have any work experience on supply planning projects — because if they did, they would probably have a view on the topic. I have over twenty years working on supply planning projects. Why am I reading a master’s thesis where the authors have to work entirely on their reading?

APS systems have been quite problematic, with most cost optimization projects (so APS) underperforming MRP. Heuristics for supply planning essentially emulate MRP, and allocation is only applicable for companies, such as high tech, where order allocation is a requirement. However, allocation is not particularly logical or intelligent. As for multi-echelon inventory optimization, the results have been generally poor as the applications are considered too challenging to implement. My experience indicates that most APS implementations did no pay back their investments.

Is Conventional Planning Obsolete?

Ptak & Smith (2011) stated that the hypotheses and rules used to design ‘conventional planning’ were no longer valid because they rely on low complexity, low variability, and high customer tolerances.

I don’t think this is true. If one has a higher variability, that is managed with more stock. There is no evidence that all conventional planning has been made obsolete.

Buffer Stock…Not Safety Stock?

DDMRP proposes to reduce the variability transferred between the levels by strategically positioning dynamic buffers and promoting a flow-centric approach.

That is precisely what safety stock does. DDMRP seems to be renaming things. “Flow centric” is code for Lean, or not using forecasts.

Lower Stock Levels are Better?

It can be observed that financial performances of companies with lower stock levels better than that for companies with higher stock level (Obermaier 2012).

The authors are making an immense amount of assumptions and then pointing to one study each to support each assertion. As an aggregate that may or may not be true — but it is difficult to see how that relates to a specific situation. There could also be other reasons for this relationship.

MRP Better for Stable Demand?

In cases where demand is constant, MRP performs better with real demand and few forecasts for a short period of time, and is able to accurately absorb spikes. With seasonal variations, DDMRP is more suitable (Miclo et al., 2016). In any case, MRP requires safety stock to account for forecast variability over production lead time (Shofa & Widyarto, 2017) but Miclo et al. (2016) observed that with DDMRP the stock levels are flat instead of following a normal distribution.

This makes little sense. The authors are confusing constant demand or the demand pattern with predictability. If a seasonal pattern is relatively predictable – which many are, MRP has no problem with the pattern. Secondly, MRP systems can also use reorder points, which are activated as the planning method if the forecast error is too high. The authors seem to be leaving out these, and they are available in all supply planning systems.

MRP Has Poor Cash Flow?

MRP has poor cash flow, and service levels keep on declining despite high levels of inventory; revenues also keep on declining for the company (McCullen & Eagle, 2015). Shofa & Widyarto (2017) found that DDMRP compressed the lead time by 94% for a company, McCullen & Eagle (2015) observed that service levels were increased from 90 to 99% for a company and there was a 35% reduction of inventory levels. Shofa, Moeis, & Restiana (2018) observed an average inventory reduction by 11% and stability in inventory levels with DDMRP.

What? Why are service levels continuing to decline with high levels of inventory? Is this for poorly maintained MRP systems?

The Rest of the Study

The study includes a survey as well as a simulation.

Conclusion

The authors put a lot of work into this study — that was clear. However, the study is problematic because the authors don’t appear to have experience in the field. Naturally, they are young, as this is a master’s thesis. After I graduated with my master’s degree in Logistics, I don’t recall many people caring much about what I thought about supply chain planning. When I went into consulting firms, I was just a quantitative analyst. Outside of running numbers, no one asked me what I thought as I did not have work experience.

The authors make several assertions that are not true — and they point to individual studies to support their assertion — but the problem is these studies contradict my work experience with supply chain planning systems.

The study was well written, but it critiques MRP systems in a way that is not believable. I have managed MRP systems in precisely the environment that the authors say it won’t work — so high variability, problematic or intermittent demand, with a high number of new product introductions. My biggest challenges were not MRP per se. They were related to educating my client, to forecast error testing, to parameter optimization. This may seem incredible to many, but most companies have no way of performing a comparative forecast error measurement. They don’t realize their forecast error measurement does not serve them. They set safety stock at a product location combination without consideration of the impact on the other product locations. Supply planning systems are poorly maintained. Earlier in the quote by Mike Bradshaw, he made similar observations about the poor state of affairs in companies. However, he seemed to be referring to smaller companies that did not have automated planning systems of any kind. But my observation is that measuring MRP and forecasting systems that have extensive underinvestment will not tell you much about how effective the methods are. If, for example, you want to critique the effectiveness of hand gliding equipment, you can’t learn this by measuring the outcome of taking out a hang glider that was never maintained, and with a pilot who has never taken a class in hang gliding. Observing that the pilot crashed as soon as they took off from the mountain top is not a proper measurement of the effectiveness of modern hang gliding.

MRP systems are victims of underinvested — with companies preferring to hire inexpensive and inexperienced resources. Any MRP system I have seen can be dramatically improved by simply investing in the system. And DDMRP requires similar, and I would argue even higher investment (particularly around the management of subcomponents.

What We Do and Research Access

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References

https://139b7ba6-57f5-40f4-a9cb-3c134e42ba5c.filesusr.com/ugd/390972_dec6ac7b537549c0a5a2e9e5c0340775.pdf

How Did DDMRP Convert So Many Vendors?

Executive Summary

  • The Demand Driven Institute has been very successful in getting vendors to adopt DDMRP.
  • What does this mean for the validity of DDMRP?

Introduction

We analyzed DDMRP in the article Repackaged Lean as DDMRP and concluded that it is not an improvement on MRP, and is just repackaged Lean with a few tweaks. DDMRP proponents are inconsistent in that they explain DDMRP, and then when you observe the consistencies between DDMRP and Lean, they say that you do not understand DDMRP. DDMRP proponents, much like Six Sigma proponents assume that they know more than those that critique DDMRP — and that there is only one right conclusion on DDMRP, which is that it is highly desirable.

I have to state that I have had aggressive debates with both Chad Smith and Carol Patak – the leaders of the Demand Driven Institute or DDI, and various DDMRP devotees. I have also been on the receiving end of a large number of personal attacks from DDMRP proponents. The intent appears to be to censor those that oppose DDMRP or that are simply unconvinced by DDMRP. This is somewhat similar to by debates with Six Sigma proponents but dialed up a few notches. However, in this article, I analyze each web page on DDMRP vendors and consulting firms on their own merits, and based on what they publish on the topic of DDMRP.

Getting Vendor Buy-In on DDMRP

What is undoubtedly true is that the Demand Driven Institute, the organization that promotes DDMRP, has obtained significant buy-in from DDMRP vendors and also consulting firms.

Let us review a few of the vendors that have incorporated DDMRP into their software and consulting companies that now promote DDMRP and what they say about DDMRP.

SCM Connections

SCM Connections is a supply chain consulting firm.

It is a composite that takes the best aspects of MRP, DRP, Lean, TOC (Theory of Constraints), Pull, Six Sigma, along with additional innovation by the Demand Driven Institute, to create a supply planning process that encourages and promotes Flow in your supply chain. – SCM Connections

This is amusing because it seems that if an entity is friendly towards DDMRP, it is acceptable to point out that it borrows from Lean. Pull is basically Lean, so that is a second reference to Lean. DDI uses the same description on their website, which is where SCM Connections received the information for this quote.

DDMRP combines some of the still relevant aspects of Material Requirements Planning (MRP) and Distribution Requirements Planning (DRP) with the pull and visibility emphases found in Lean and the Theory of Constraints and the variability reduction emphasis of Six Sigma. – DDI

It is not based upon the theory of constraints, as like MRP, DDMRP cannot constrain. Six Sigma, as we cover in the article Will GE’s Decline Impact the Cult of Six Sigma?, Six Sigma is not a valid method of improvement. Therefore if any process is partially based on Six Sigma, it means that method is somewhat invalid. Furthermore, how one method can be an amalgamation of so many approaches quite odd. And the following quotation pushes the merger a step further.

Multi echelon safety stock planning in IO will look at demand and supply variability for each point in your supply chain that you stock material.  This can be finished goods at customer facing DCs, regional hubs, global hubs or manufacturing sites.  It further considers components of bill of materials.  It calculates the least amount of inventory to maintain the required service levels for a customer.  The calculated amount is treated as the zero threshold for MRP.  Meaning, that MRP will always plan to take inventory down to this amount as the next replenishment is due to arrive. – SCM Connections

MEIO systems do that, but that is just the safety stock portion of MEIO. Overall, this is a feeble explanation of how MEIO functions — and it is not clear the author understands who MEIO works. Furthermore, DDMRP does not have multi-echelon math and cannot set stock at some locations based upon the stock at surrounding areas or have the concept of sufficient lead time. This topic we cover in the article How to Understand Multiechelon Planning. It is difficult to see why DDI even chooses to invoke the name multi-echelon.

Every planning system plans multi-echelon networks, but that does not mean the method is multi-echelon. When software is called multi-echelon, it means something specific. For example, MRP and cost optimization software plans networks that are multi-echelon, but the software is also not multi-echelon. For more, see my book Inventory Optimization and Multi-Echelon Planning Software.

DDI uses this same terminology.

Demand Driven Material Requirements Planning is a formal multi-echelon planning and execution method to protect and promote the flow of relevant information through the establishment and management of strategically placed decoupling point stock buffers. – DDI

It is difficult not to see this usage as misleading. Again DDMRP is not multi-echelon software.

Sounds a little like DDMRP strategic decoupling, right?  Yes, and no.  Safety stock from IO is still more vulnerable to the bullwhip effect that snowballs through the supply chain.  It uses upcoming forecast as the trigger to replenish, may be subject to lumpy demand from weekly or monthly buckets, component safety stocks are replenished (based on forecast) even if the demand for finished goods never materializes. – SCM Connections

When a person writes “yes and no” and then phrases a statement as a question in this way — it is a giveaway the person does not understand the subject matter. This is equivocation and not at all clear writing. I don’t interact with anyone who speaks like this, as it is imprecise. This is a good point for the author to list the dimension in which it is not true, and then a second bullet to describe where it is true. Like this…

  1. Conditions where it is like strategic decoupling: A, B, C
  2. Conditions where it is not like strategic decoupling: Z, Y, Z

Instead, the author makes the reader try to figure out which sentence in the paragraph is where the similarity applies — and which sentence or sentences in the article are where the similarity does not apply.

And this is made particularly problematic because the author follows up with sentences that are not true.

Forecasted Product Locations Are More Susceptible to the Bullwhip Effect?

There is also no reason to say that MEIO is more vulnerable to the bullwhip effect. And to address the more general point, using forecasts does not make the system “more susceptible,” and non-forecast based planning “less susceptible.” Secondly, MRP does not have to use 100% forecast based planning. MRP can be set to reorder point for items that have a high forecast error where there is little benefit from forecasting, as we cover in the article How to Access Forecast Forecastability Measurement.

If a product location has a low error, not using forecasting increases the bullwhip effect.

Let us take a simple example.

If a forecast has a seasonal pattern that is highly forecastable — would using a reorder point or other Pull technique reduce the bullwhip effect?

Clearly not.

By using Pull or Lean, the system does not anticipate the rise in demand that could have been predicted with a forecast — and the same is true on the downside of the seasonal peak. Secondly, even if the forecast has a high error — the high error builds a higher safety stock. So again, the variability is accounted for in stock. This is the same thing that DDMRP is doing with its “buffer” stock setting. A level forecast emulates a Pull or Lean consumptive setting in the supply planning system.

Overstatement of the Bullwhip Effect and Supply Chain Consultants

The bullwhip effect comes from observations of supply chains but was popularized by the Bullwhip Game. This game is essentially rigged to try to prove a point about the bullwhip effect, and the game exaggerates the impact of the effect in real-life situations. The term is frequently used by consultants but is rarely used by planners. That should be a concern in terms of setting one’s strategy around mitigating this effect.

Consultants often have several proposals that are designed to create a “burning platform” to hire their services. Six Sigma, for instance, proposed that any manufacturing quality level below 1 in 3.4 million was a serious problem. Yet this is not true by merely calculating the trade-offs in terms of the cost of the defect versus the cost of attempting to achieve absurdly low-quality levels — which are not achievable when one includes the errors that are created at the beginning of production runs.

Let us move on to the next quote from SCM Connections.

DDMRP requires an all-in approach.  If planners, suppliers or manufacturing are not willing to follow the DDMRP supply plan, you will sub optimize and likely end up with the same over stock and under stock problems inherent in an MRP system.  This can be overcome with change management.  DDMRP is set up in a way to make it clear and understandable to users on how and why the plan is suggesting what it does.  DDMRP will not fix your problems for you, you need to embrace and trust the outcomes. – SCM Connections

This is typical of dogmatic initiatives that propose the method requires full commitment (once again, much like Six Sigma) and entirely skips the part where the validity of the process is evaluated.

Chad Smith, one of the leaders of DDMRP, has repeatedly overstated the degree to which DDMRP is proven to improve outcomes. He will state that the DDMRP has documented evidence of improvement — without pointing out the ability of the DDI to exclude bad data points. The one study the DDI shared with did little to prove the case for DDMRP, as the control group had no extra effort made to improve the planning, while the DDMRP product locations did receive effort. And even with this rigging, the improvement was minimal.

However, when DDMRP consultants or followers propose DDMRP benefits, they routinely quote between 30 and 70% inventory reductions — with service level improvement. The study I read had improvements far below this. This is typical in the consulting space. Consultants routinely take either negative benefits or marginal benefits and explode them out to huge benefits to promote selling consulting services.

Conclusion on SCM Connection DDMRP Coverage

The author for the SCM Connections coverage jumped to the benefits of DDMRP without worrying about providing evidence for these benefits. They also misexplained MEIO and made incorrect statements around the relationship between using a forecast and the bullwhip effect. The author finished off with a highly doctrinal paragraph that said the users of DDMRP have to “ride or die” with DDMRP.

This is not encouraging. It looks like a pretext for shutting down criticism of DDMRP after it fails to deliver what it promises. Naturally, once you convert everything thing over to DDMRP, it will be tough to switch back — therefore, by proposing this way of thinking — it seeks to lock the customer in. SAP uses a very similar tactic — they state that because their software contains 100% best practices, any problems with the implementation are due to the customer because they are not accepting these best practices. We cover this in the article How SAP Uses Best Practices to Control the Implementation. Other ERP vendors and consulting firms do the same thing — but SAP has pushed the practice to the most extreme degree.

Shea

Shea is a company that has incorporated the use of DDMRP, but which primarily implements Microsoft Dynamics or Syspro. It should be noted that neither of these applications has good MRP engines, and neither company has much expertise in supply planning. Both of these ERP applications are sold based on their integrated nature — not because they are good at supply planning. While I have never tested Syspro — I have extensively tested Microsoft Dynamics. And supply planning should not be performed in Dynamics.

Shea’s quotes.

Demand Driven MRP removes the overhead of perpetually adjusting order recommendations while dispelling the myth that you just need more inventory to maintain higher service levels.

Is the fact that you need more inventory for higher service levels a myth? This is probably one of the best-known facts in inventory management. Yes, there are distinctions where inventory can be better deployed, but the relationship between more inventory and higher service levels is quite reliable.

Planning in an uncertain world requires a radically different approach to traditional MRP methods that institutionalize forecast errors and order volatility. The simplicity of the Buffer Zone concept underlies a strong analytical and rigorous approach to material planning. – Shea

Why is that true and where is the evidence for this? How did MRP become inappropriate? Is the author aware that many methods have come across over the past few decades and have promised massive improvements (such as cost optimization) and that most of those promised improvements have failed?

Strategically positioned inventory is designed to decouple supply and demand in order to reduce variability whilst at the same time compressing lead times. DDMRP answers the question all finance and operations managers want to know: How to get the right stock in the right place at the right time – Shea

This term “decoupled” is used frequently with DDMRP, but it only means that there is extra stock. That is the “decoupling.”

This is a constant issue with DDMRP. It takes very well known approaches – and then renames them — and then expects to be given some type of innovation award.

The last part of the sentence in this paragraph is not worth analyzing.

Conclusion on Shea DDMRP Coverage

This is just a copy and paste of material published by DDI without much added.

Implementation Consulting Group

This is another consulting firm. They do several things, and supply chain consulting is one of them.

Amongst other things, they argue that no forecast should be used for operational planning, and only confirmed orders should be considered in the operational planning. – Implementation Consulting Group

This is true in that it reflects what DDMRP says — but does not make sense. If only confirmed sales orders are used, a large amount of demand will be missed because, in most cases, the sales order further out than the total lead time. The case where the opposite is true is called make to order — which is usually estimated to be roughly 10% of situations.

Where DDMRP differs from traditional ROP is that it brings a concept for not only calculating the reorder levels, but adds elements such as:

  1. A concept for where in your supply chain network and bill of materials hierarchy you should strategically put reorder point triggered buffer inventories.

  2. A concept for how supply orders upstream the supply chain are triggered based on future orders, history and future spike orders.

  3. Being very visually appealing, which in turn makes the planning a lot more transparent, allowing the planning tasks to become more simple and efficient.

  4. A concept on execution of orders based on priorities and level of current “inventory” penetration, which increases the focus on where actions are most critical. – Implementation Consulting Group

These are all claims — but DDI is very short on explaining why these contentions are true. The idea that something is a concept does not make that concept true. One can notice that in each case, these authors simply assume the idea must be true and must be beneficial.

We believe that the intuitive colour coding in DDMRP (advanced ROP) allows for clear visualisation of the current state of your stock levels, which can easily be recreated to match whatever system your company is currently using, such as APO, IBP or Excel. Combine this with its easily implementable rules and you can enable a deeper understanding of the system and transparency of the processes. – Implementation Consulting Group 

The first part of this paragraph might be true about color-coding. MRP systems often do suffer from lacking transparency. However, the last part is conjecture. This is a matter of opinion as to whether it enables a “deeper understanding” of the system. Although MRP, mainly when run from ERP systems, does lack transparency. It is difficult to tell what is happening, and they force a focus on managing at the product location with little ability to see the overall network.

Conclusion on Implementation Consulting Group DDMRP Coverage

ICG did not seem to perform any verification of the claims made by the DDI. ICG said it could help — but did not give good reasons why.

Prophetic Technologies

For some reasons too numerous to mention here, accurate sales forecasting is becoming more difficult, consuming more effort and requiring more mature recesses and complex data sets than ever before. DDMRP could be the most effective option for some companies, as it can provide great value compare to Material Requirements Planning. (MRP).

The first part of this paragraph is true. However, it does not follow that MRP is not useful. I simply mean using more reorder points or using a level forecast. To supply planning, a level forecast emulates a reorder point.

Conclusion on Prophetic Technologies DDMRP Coverage

Prophetic Technologies is making claims that it does not appear to make much effort to substantiate. It simply asserts great value versus MRP, but without providing evidence as to why this is true.

QAD

QAD is a software vendor. They are an ERP system for smaller companies.

To understand DDMRP requires one to think differently about supply chain flows and the decoupling of specific points in the supply chain. This is easier said than done, but if this hurdle is overcome, then DDMRP becomes surprisingly uncomplicated to understand and simple to use. The challenge however lies within the “if.” – QAD

What is so different here? Safety stock already “decouples” demand from the supply. It does not require thinking differently about the supply chain — and what does that even mean?

DDMRP is not a buzzword, it is not a marketing spin on an existing concept. The buzz comes from the unique approach to modeling supply chains and the process of triggering a supply event from a demand signal. With DDMRP a supply chain is not a chain, but rather a network of networks. The networks within a network are decoupled, meaning there is no immediate and direct action and reaction between them. The flow of goods is buffered in strategically located positions with dynamically updated targets. The proverbial bullwhip impact is thereby controlled. – QAD

Well, it is sort of a buzzword and a marketing spin on existing concepts. DDI lists the “existing concepts” on which it is based. The definition of DDMRP by DDI is that it is an amalgamation of different pre-existing methods.

Once again, safety stock already “decouples,” there is no benefit from obsessing over the fact that extra stock is carried to deal with unexpected demand. The assertion about a reduction of the bullwhip effect is repeated — but as I explained earlier, it depends upon the forecastability of the product location combination.

DDMRP offers a tangible reduction in obsolete and excess inventory (emphasis added) in addition to a proven track record of customer service improvements. Of course, reduced inventory and higher service levels are value claims not unique to DDMRP. For many QAD customers, these benefits are augmented with the unique value proposition of simplicity. Once one has embraced the demand driven basics and the strategic configuration components are in place, DDMRP is remarkably easy to plan with. The execution component is simple and intuitive. DDMRP borrows from the Theory of Constraints (TOC) approach of buffer level alerts using the colors of a traffic light. In caution of oversimplification, red is bad, yellow requires some replenishment action and green is ‘hold.’ Onboarding a new supply planner has never been easier. – QAD

There is no proven track record of this. This is asserted by the DDI, but every vendor says they have this. It is not independently verified information. I know of many SAP products that fail or deeply disappoint customers nearly every time they are implemented, and SAP keeps making the same claims about them for over a decade.

DDMRP may have beautiful graphics to identify stock — but DDMRP is a high maintenance approach. It reduces the automation on the part of MRP by breaking the BOM, so primarily the sub-components can be manually managed.

Conclusion on QAD DDMRP Coverage

QAD is an ERP company. I would never listen to an ERP company about what works for supply planning. How would they know? ERP companies provide sub-par supply planning functionality and pretend to their customers that it is a “leading edge.” Even the largest ERP vendors have weak MRP/DRP and supply planning functionality as I cover in the article Understanding MRP & Why Use MRP in ERP?

Camelot

Camelot is a consulting firm that specializes in SAP and has been one of the biggest proponents of DDMRP.

Increasing network complexity and enlarged product portfolios result in higher planning effort. Furthermore, shorter product lifecycles and higher customer expectations with respect to delivery periods lead to increasing volatility and uncertainty. In such an environment, demand forecasts are always wrong and often deviate by more than 50% from the actual demand. Consequently, the wrong quantities are sourced, made or shipped to the wrong places resulting in low customer service levels. In order to avoid such problems, companies often spend a lot of money and time moving products and materials through their factories and supply chains, resulting in high inventories. However, companies still often fail to meet the target service levels. – Camelot

This explanation of what has been happening is true. However, it makes it seem inevitable. These are decisions made by companies — my marketing, by sales, etc.. that are insensitive to the impacts of these changes on operations.

Let us take the example of shorter life cycles.

Yes, marketing keeps introducing new products — shortening lifecycles. However, over 95% of these “new products” are just recycling previous products, and the forecast history of the old product can be superimposed on the “new product.” Not that companies do this sufficiently as they tend to be overwhelmed by the number of new products. This reduces forecast accuracy — but this does not mean that forecasting is not useful or value add — and the statement

“forecasts are always wrong”

..is the type of statement made by a person who is not familiar with forecasting. However, it is repeatedly stated by both Lean proponents and DDI proponents. Is a forecast with an accuracy of 95% “wrong?” Yes, by 5%. But it does not mean there was no value in creating the forecast.

This is an actual company data that shows the assignment of forecasting methods to product location combinations. The sales history for this database is intermittent, meaning that they are pushed to more simply (except for Croston’s) forecasting methods — and of course, the error is higher. This decline in sales history quality is increasingly common. But this does not mean that one can dispense with forecasting. 

Forecasts have an error. A low error is good; a high error is bad. Not all products need to be forecasted — but value is still obtained from forecasting those that can be forecasted at a reasonable error.

This setup by Camelot is illogical — because no supply planning method can be constructed to absorb these issues. Every supply planing method will have higher inefficiency if you design the business in a way that inherently maximizes waste. And within MRP systems already exists the mathematical methods to deal with this — they are called reorder points and safety stock.

Dynamic adjustments in the third step of the demand-driven operating model allow the previously calculated buffer zones to be modified. This allows the knowledge and experience of the planners to be considered as well as other changes and short-notice adjustments of input parameters. The planners’ input is especially relevant in order to react to dynamic market effects like trends, seasonality, or significant promotions. The first three steps of the DDMRP concept include the overall parameterization of the supply chain and, thus, set the framework for further operational planning and execution. Therefore, they are crucial for the successful application of the DDMRP methodology. – Camelot

Yes, that is fine, but it is not new.

We cover in the article Brightwork Explorer and Safety Stock Calculation that safety stock can and should be externally calculated and recalculated at the frequency of choice by the company. There is no reason to accept safety stock calculated inside of MRP or external supply planning systems that only calculate the safety stock in isolation at the product location combination.

The innovative DDMRP concept represents the biggest paradigm shift in supply chain planning in the last decades. In the solution “DDMRP for SAP IBP”, the concept has been implemented by Camelot using SAP’s latest and state-of-the art planning technology SAP IBP.

That is quite an exaggerated claim on both counts. And SAP IBP is not a particularly effective planning tool as we cover in the article Is SAP IBP Good?

This solution is being continuously improved and enhanced by additional functionalities. Hence in addition to DDMRP, “Demand-Driven Supply Chain Management” (including Segment & Strategize, Flow Metrics, and DDS&OP, for example) will be available in future. Thus, Camelot offers an implementation of the DDMRP concept on all major SAP platforms (SAP SCM, S/4HANA, and SAP IBP) as part of its “Demand-Driven LEAN Planning Suite for SAP”. This combination of a revolutionary concept and state-of-the-art technologies offers considerable added value to the customers. They benefit from the conceptual advantages of the demand-driven planning approach, significant competitive advantages based on comprehensive efficiency enhancements, a trendsetting solution, and thus, from investment reliability.

Well, Camelot lies a lot.

This video is filled with the same types of claims almost always made about these projects, that do not come true. They even worked in the term “paradigm” into the video. Bravo. 

The statement from one of the Camelot partners that…

All the SAP (inaudible) for all platforms IBP, S/4HANA, SCM, there is no excuse anymore.

This statement makes zero sense. First, neither IBP nor S/4HANA are platforms — they are applications. And what is the excuse that this man is speaking of? S/4HANA has already had a very problematic implementation history. This is just content free boasting — it implies that because SAP has introduced an application that it must be good and must be used.

They are clearly in 100% promotion mode, and I am quite familiar with SAP consulting firms that are very comfortable lying. Nearly all SAP consulting companies provide zero critical thinking and just serve to try to sell SAP projects. And they are very prone to exaggeration as they spend most of their time just repeating SAP marketing literature.

Look at this product list on the Camelot website. Many of these items Camelot guaranteed does not implement. SAP Leonardo is dead as we cover in the article Our 2019 Observation: SAP Leonardo is Now Dead, SAP does not have AI. IoT is the same thing as Leonardo.

I could go on, but Camelot is saying it does things that it does not do. This is par for the course with SAP consulting firms. Consulting firms cannot do research. They are continually introducing “research” which is in reality promotion. We cover in the article Why PwC’s Research Fellows are Fake and Pretend to be Academic, how PwC creates fake academic titles for people posing as researchers. It is well known that any audit results that one desires can be purchased from PwC. On the consulting side, PwC does not produce research — they produce marketing collateral. And they will rig the results whichever way allows them to sell more consulting projects.

Conclusion

There was not much quality of thought put into the web pages that cover DDMRP. Most of them were just repeats of information that is published on the DDI website. I could have reviewed more websites of vendors and consulting companies that covered DDMRP, but the others I read were about the same as those presented in this article.

The issue is that these software vendors and consulting companies don’t care what is true.

They think that DDMRP can help them sell software or consulting services, so they are “onboard.” None of these entities can be trusted to evaluate DDMRP, and most of them are not qualified to do so. There may be implementers who disagree entirely with DDMRP, but the marketing departments that approve the web pages I read, will tell prospects it is excellent.

So while it seems impressive at first glance that DDMRP was able to onboard several vendors and consulting firms — it is not evidence that what DDMRP says is true.

What We Do and Research Access

Using the Diagram

Hover over each bullet or plus sign to see more explanation. To move to a different bullet point, just “hover off” and then hover over the new bullet.

 

Research Access

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References

https://www.demanddriveninstitute.com/ddmrp

*https://scmconnections.com/intro-ddmrp-sap-ibp-right/

*https://sheaglobal.com/solutions/ddmrp-2/

*https://implementconsultinggroup.com/lift-your-operational-planning-with-ddmrp/

*https://blog.camelot-group.com/2019/05/success-factors-for-ddmrp-in-a-constraint-manufacturing-environment-i/

*https://blog.prophetic-technology.com/what-is-so-different-and-unique-aboutddmrp

*https://blog.qad.com/2018/03/ddmrp-beyond-acronym/

*https://www.camelot-itlab.com/en/company/press-releases/press-articles/demand-driven-material-requirements-planning-ddmrp-the-new-paradigm-in-supply-chain-planning/

How to Achieve Lean Supply Chain

Executive Summary

  • Many people want to know how to achieve a Lean supply chain.
  • We cover important background on Lean.

Introduction

When people try to find out how to achieve a Lean supply chain, the first thing they run into is a large quantity of false information about the Lean supply chain. The following quotation is a good example.

Top management knows that lean can add value, but many still haven’t moved past the initial education stage into full-scale lean supply chain implementation. One reason may be that they haven’t made the paradigm shift as to how to implement lean. The Lean Supply Chain is a system of interconnected and interdependent partners that operate in unison to accomplish supply chain objectives. There should be metrics involved to monitor these objectives to ensure success across the supply chain. These metrics should be reviewed frequently to ensure supply chain success. – Cerasis

Lean proponents have a way of presenting perfect world scenarios that change things that Lean programs don’t have control over. Companies that I have reviewed that follow Lean approaches don’t match what the Lean consulting firms say that Lean accomplishes.

In this article, we will cover Lean from a realistic perspective instead of presenting a fantasy world about how to implement Lean.

Lean Planning Versus MRP

Procedural versus Lean planning is one of the most enduring debates in the area of supply planning. Lean is both a philosophy and several techniques. Lean adherents propose that the standard mathematics of everything from the usual supply and production planning methods to Economic Order Quantity are incorrect and can be improved upon by reducing order quantities.

Within Lean, there are several techniques, with reorder points being just one of them. This book proposes that the strongest approach leverages both schools of thought. The trick is determining which segments of the product location database should go out on which school of thought.

A significant difference between Lean and MRP or more accurately Lean versus procedural-based supply and production planning primarily has to do with the replenishment trigger. Supply and production planning procedures such as MRP, heuristics, allocation, cost optimization, and inventory optimization work off projections, while Lean replenishment works off an immediate need. At least this is most often the case, as there are some reorder points which calculate based upon projections.

Reorder points can be calculated in several ways. For instance, they can be calculated differently based upon whether the demand history is lumpy or stable.

Lean adherents propose that the standard mathematics of everything from the standard supply and production planning methods to Economic Order Quantity are incorrect and can be improved upon by reducing order quantities. Lean proponents make accurate points that the way that MRP and other supply and production planning software is implemented and run in most companies leaves a significant opportunity for improvement.

It is also true that there is less than meets the eye with some of the most popular and well-known inventory calculations. For instance, EOQ often leaves out some considerations outside of ordering and holding costs. While there are many varieties of EOQ formulae that can be pulled from research papers, there is also considerable complexity involved in using these more advanced formulae. It is difficult to find a formula that incorporates all the dimensions of factors that should set the economic order quantity – and of course, only the more basic EOQ formulae tend to be available in enterprise software.

The standard dynamic safety stock formula is also problematic and much less useful than generally proposed – as is covered in the book Safety Stock and Service Levels: A New Approach.

Reorder points are a major method used by proponents of Lean. Reorder point planning is an early approach to supply chain planning; however, while often dismissed as passé, it has applicability to several circumstances. Reorder point planning can be used effectively for products that are both easy and difficult to forecast.

What works well for products with erratic demand history works equally well for products with extremely stable demand history. Reorder points were the primary planning approach used by companies before MRP and DRP were developed and offered in the software.

However, there are now many quite sophisticated reorder point formulae – although it is rare that anything but the basic reorder point formula finds its way into an enterprise software application. A reorder point is simply a quantity of stock or an interval at which a “reorder,” or order is to be created. In reorder point planning, orders are not triggered by a specific requirement (such as a forecast or dependent requirement), but instead by the depletion of stock over time, eventually triggering the minimum stock level or reorder point. Reorder points can be used with any of the supply planning methods, or they can be used to exclusively control the supply plan without any of the methods. However, when they are used solely to control the supply plan, the company is said to be performing reorder point planning, as opposed to forecast-based planning. MRP/DRP and APS (heuristic, allocation, cost optimization, inventory optimization) methods are forecast-based planning.

Advice on Enjoying the Reorder Point Quiz

To see the full screen, just select the lower right-hand corner and expand. Trust us, expanding makes the experience a whole lot more fun.

 

Conclusion

The article tried to answer the question of how to achieve a Lean supply chain. It is, however, essential to consider when and where Lean should be applied.

A lot of energy is spent on debating between Lean and procedural planning. However, both camps tend to provide too little in the way of evidence for their claims. Coming up from the procedural school of thought, I was guilty of this myself. For some time, I believed that the standard inventory formulae were reliable – until I tested them in great detail and found flaws in their output, which depended upon the circumstance.

Instead of spending time in either of the camps, I would recommend testing both Lean and procedural planning techniques to see which are most appropriate for different product location combinations and then using whatever works. Without testing, it is too easy to simply fall back to whatever one’s background is. However, through testing, one can not only drive to the use of the best technique per circumstance but also increases one’s knowledge level. Most supply planning systems have both types of functionality available to them. Therefore it is just a matter of knowing which kind of functionality to apply to which product location combinations.

Intermittent – or “lumpy” – demand is one of the most common features of a product’s demand history that makes a product unforecastable. Unfortunately, as is covered in the book Promotion Forecasting: Techniques of Forecast Adjustment in Software, many factors are combining to reduce the forecastability of product databases. This includes factors such as the increase in the number of SKU’s carried – called product proliferation, reduced product lifecycles and higher turnover, and increases in promotions. The less forecastable the product database, the less than an MRP, or any other supply planning method, can do to provide a good supply plan. With Sales and Marketing running strategy at most companies, companies are making it increasingly difficult for themselves to have a manageable supply chain. It also means that some maintenance areas must be performed with increased frequency on the MRP system. This is a good segue into our next topic, which is how to improve MRP systems.

One of the best ways to understand how to set reorder points externally, we have developed a system called the Brightwork Explorer that is both designed to improve parameters with reorder points being one of them.

We developed an approach where reorder points are calculated externally, which allows for a higher degree of control. And for the average inventory to be coestimated in a way that provides an observable total system inventory, holding cost, service level and a picture of what is happening to the overall system. Calculating individual parameters like reorder points without an appreciation for the systemwide does not make any sense.

Brightwork MRP & S&OP Explorer for Order Optimization

Order Sizing and Optimization

Order optimization is necessary in order to get the predicted value from ERP and other supply planning applications. The Brightwork MRP & S&OP Explorer does exactly this, and it is free to use in the beginning until it sees “serious usage.” It is permanently free to academics and students. See by clicking the image below:

 

What We Do and Research Access

Using the Diagram

Hover over each bullet or plus sign to see more explanation. To move to a different bullet point, just “hover off” and then hover over the new bullet.

 

Research Access

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References

*https://cerasis.com/lean-supply-chain/

https://en.wikipedia.org/wiki/Quotations_from_Chairman_Mao_Tse-tung

This topic is covered in depth in the following book.

Lean and Reorder Point Planning Book


Lean and Reorder Point 2

Lean and Reorder Point Planning: Implementing the Approach the Right Way in Software

A Lost Art of Reorder Point Setting?

Setting reorder points is a bit of a lost art as company after company over-rely upon advanced supply planning methods to create the supply plan. Proponents of Lean are often in companies trying to get a movement to Lean. However, how does one implement Lean in software?

Implementing Lean in Software

All supply planning applications have “Lean” controls built within them. And there are in fact some situations where reorder points will provide a superior output. With supply planning, even within a single company, it is not one size fits all. The trick is understanding when to deploy each of the approaches available in software that companies already own.

Are Reorder Points Too Simple?

Reorder points are often considered to be simplistic, but under the exact circumstances, they work quite well.

There are simply a great number of misunderstandings regarding reorder points – misunderstandings that this book helps clear up.

Rather than “picking a side,” this book shows the advantages and disadvantages of each.

  • Understand the Lean Versus the MRP debate.
  • How Lean relates to reordering points.
  • Understand when to use reorder points.
  • When to use reorder points versus MRP.
  • The relationship between forecastability and reorder points.
  • How to mix Lean/re-order points and MRP to more efficiently perform supply planning.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: The Lean versus MRP Debate.
  • Chapter 3: Where Supply Planning Fits Within The Supply Plan
  • Chapter 4: Reorder Point Planning
  • Chapter 5: Lean Planning.
  • Chapter 6: Where Lean and Reorder Points are Applicable
  • Chapter 7: Determining When to use Lean Versus MRP
  • Chapter 8: Mixing Lean and Reorder Points with MRP-Type Planning

How to Mix Reorder Points and MRP in SAP APO

Executive Summary

  • The question of how to mix reorder points and MRP is what sets part of the planning strategy.
  • We cover when to use reorder points versus MRP planning.

Introduction

The question of when to use Lean versus MRP is critical to determine when to use a non-forecast based method of supply planning (reorder points) and forecast based planning (like MRP).

Mixing Lean and Reorder Points with MRP Type Planning

Much time is spent debating among the different supply planning methods, but far less is spent on how to properly integrate multiple methods into a cohesive strategy. That is unfortunate because all methods and method modifiers can be of value in some circumstances. Arriving at the right combination of supply planning methods requires a detailed study of all the requirements and data necessary to drive the method. Without that upfront effort and knowledge, a method can end up being selected, and then simply be perpetuated because of the strong tendency not to change decisions after they have been made. I will use the example of SAP APO to illustrate how to configure the use of various methods. This is from a real project, and while the system that is used will change, the approach outlined in this chapter would apply to any supply planning application.

Here is a screen shot taken from the SCM Focus Press book Multi Method Supply Planning in SAP APO. It shows the association of each product location combination with either the advanced method (either CTM – Capable to Match or the optimizer) or consumption-based replenishment methods.

In SAP APO, some methods need to work with what I refer to as method modifiers. In SAP APO, or more specifically SAP SNP (the supply planning module of APO), reorder point planning is a modifier of the heuristic method. The reorder point is just one of the modifiers that works with the SNP heuristic. Not all modifiers work with all of the methods. How this works is explained in the following graphic:

In the matrix above, the method is documented per product location. As you can see, the reorder point and the target stock level (TSL) only work with the SNP heuristic and are ignored by CTM and the optimizer. An important consideration for determining when to use one supply planning method over another is whether the product location combination is forecastable.

The above matrix can be used to help explain how to make selections between the various supply planning methods. Once this has been explained, the business subject matter experts can go off and code the entire product location database for each method that is used. There is much more to a successful configuration than simply assigning product locations to profiles and then running the profiles.

There is a sequence in which the profiles of the different methods must be run in order for the system to work properly. As such, testing must be performed to make the desired assignments between the supply planning method and the product location combination workable and to set up the profiles in the proper sequence. We will get into all of the detail on this topic in the following chapter.

Background on the Configuration of Multiple Supply Planning Methods

Once a company decides to use multiple supply planning methods, the next question is how to implement this in the system’s configuration. How to do this is not widely understood or even explained. The book Multi Method Supply Planning in SAP APO is one of the first to describe how to implement multiple supply planning methods in the configuration of any external supply chain planning system. It would be most convenient if using multiple supply planning methods could simply be accomplished by assigning every product location a supply planning method and have the supply plan be created in a logical and consistent manner. Unfortunately, making multiple methods work together is a good deal more complex than this because of how APO was developed. In this chapter, I will describe how combining multiple methods can be accomplished with the following methods and method modifiers:

  1. Capable to Match (CTM)
  2. The SNP Heuristic with:
    1. A Reorder Point
    2. A Target Stock Level
    3. A Target Days’ Supply

The same principle and testing as discussed in this chapter applies to collaboratively using other supply planning methods; however, for any SNP heuristic method modifiers (reorder point, target stocking level, target days’ supply), either of the other two modifiers (target stock level or target days’ supply) could be set the same way as the reorder point I describe here. Within SNP, there are several ways of setting up a reorder point, target stock level, or target days’ supply. For the purposes of this demonstration, I will show the most basic settings for each. Here is a screenshot showing the reorder point as set in the product-location master:

There are six different reorder point settings in SNP. However, for our purposes, there is no need to use anything but the simplest method. The time-dependent maintenance reorder points are used when the company has an interest in allowing planners to alter the reorder point per time.

On an actual project, it is important to provide all of the alternatives for each setting that are within the scope of the project.

When following a multi-method approach, it’s important to have a good tool for assigning which method (and method modifier) is to be applied to which product location combination, as well as for keeping track of these assignments. Even though companies have a very large number of product location combinations, the assignment of methods and method modifiers does not have to be an onerous task.

First, a company already has method modifiers set up in their ERP system or external planning system (if they are migrating from another system such as Manugistics). These modifier values can be extracted from the existing system and used. Product location combinations can be grouped based upon any criteria and have any value applied to them. I am unaware of any approach that is faster or of higher quality than the one I will describe in this chapter.

A major challenge of all application implementations is how to keep track of the settings in the application. This is particularly true of SAP implementations because SAP development takes such a comprehensive approach to developing functionality that every application ends up with a very large number of fields. However, in the vast majority of cases, only a small percentage of the fields are actually used in an implementation. Therefore, one of the most important steps to be performed during the implementation is to determine which fields should have values assigned to them by the business.

In SNP, there are a wide variety of fields in different locations. However, many fields are stored in the product location master, which is equivalent to the material master in SAP ERP (this would be assigned to a plant to bring in the location dimension).

There are hundreds of fields in the product location master in SAP APO. Most companies only have the values in these fields managed in SAP or whatever system they use. However, that is not the best way to manage their fields. The parameters should be kept in an external database for the following reasons:

  1. Comparing and Contrasting: Product location data cannot be easily compared and contrasted inside of the system, beyond bringing up the product location master for two combinations in different windows.
  2. Productivity: Productivity is greatly enhanced when planners have access to the product location data in an easily accessible form.
  3. Visibility: A common problem with product location data is enhanced with this approach, reducing the likelihood that this data will become out of date.
  4. Metadata: A product location spreadsheet can include descriptions and comments in a way that SAP cannot. (Descriptions can be found by hitting F1 from any field. However, descriptions can be placed right into a spreadsheet, and can also be customized – and typically truncated to just the information that is of interest.)

My preferred way of storing this data is in a spreadsheet, which allows for the filtering of values, the use of Pivot Tables for analysis, and other advanced data capabilities in Excel. This approach applies to setting the overall policies during the initial implementation and to continued maintenance. Once changes have been made to the spreadsheet, they can be made in most systems with a mass maintenance transaction.

This is just a sample of the fields in the product location spreadsheet, and is all that I could fit into this screenshot. There are, of course, many fields on the product location master. Not all of them are filled in, but it is beneficial to note all of them in the spreadsheet, along with their definitions, so that planners can chose which to enable. While it can seem intimidating to fill in all the fields of a spreadsheet like this, in fact the fields are rarely filled in one-by-one. It is much more common to group the products for a specific field. Spreadsheets also allow for the applications of IF/THEN formulae, which can auto-populate some field values based upon the values of one or more other fields.

Steps to Creating the Product-Location Spreadsheet/Database

On the following page, I list the steps to creating this product location spreadsheet, as well as the uses of the product location spreadsheet.

Once created, the product location spreadsheet can be reused for many purposes. The following are uses of the product location spreadsheet:

This way of managing settings for applications is quite sustainable, provides a great deal of visibility into the settings and allows for analysts to easily compare and contrast the parameters. There are some applications, such as BarloWorld’s Optimiza application, that build similar functionality right into the user interface, but these types of applications are few and far between. Therefore, in most cases, companies implement software that does not offer this type of functionality. This approach to application settings is quite unusual, as can be seen in the screen shot of Barloworld on the following graphic:

Planners are often told to keep the master data parameters up to date by finding issues and then transferring the values to the master data team for adjustment. Companies generally have no problem understanding the need to have effective planning systems, but often miss the fact that they also need applications to enable these planning parameters to be analyzed and updated, and that they need to provide an effective way for those that have this responsibility to do so. Barloworld’s applications provide the following capabilities:

  1. Allows planners to view the overall supply network.
  2. Notifies planners about what to focus their attention on.
  3. Allows the planners to manipulate supply planning parameters.
  4. Provides planners with an understanding of the relationship between different supply planning elements. A number of Barloworld’s screens are examples of what I am referring to when I describe supply chain visibility. The following view highlights the obvious areas to be addressed by the planner. The categories F, M, and N on the next page are carrying far too much inventory according to what the Barloworld model recommends. Armed with this information, the planner can then go into the necessary details to determine why this is the case and take corrective action.

Advice on Enjoying the MRP Quiz

To see the full screen just select the lower right-hand corner and expand. Trust us, expanding makes the experience a whole lot more fun.

 

Conclusion

We tried to answer the question of when to use Lean versus MRP in this article.

Arriving at the right combination of supply planning methods requires a detailed study of all the requirements and data necessary to drive the method. Without that upfront effort and knowledge, a method can end up being selected and then simply be perpetuated because of the strong tendency not to change decisions after they have been made. A major challenge of all application implementations is how to keep track of the settings in the application. Therefore, one of the most important steps to be performed during the implementation is to determine which fields should have values assigned to them by the business. In this chapter, I highlighted an approach that maintains many of the master data fields outside of the supply planning application.

It is a relatively simple matter in a supply planning system to convert some product location combinations to pure reorder point planning and other product location combinations to being processed with a supply planning method. Furthermore, a product may be planned in one way at one location and planned a second way at a different location.

We developed an approach where reorder points are calculated externally, which allows for a higher degree of control, and for the average inventory to be coestimated in a way that provides an observable total system inventory, holding cost, service level and a picture of what is happening to the overall system. Calculating individual parameters like reorder points without an appreciation for the systemwide does not make any sense.

Brightwork MRP & S&OP Explorer for Order Optimization

Order Sizing and Optimization

Order optimization is necessary in order to get the predicted value from ERP and other supply planning applications. The Brightwork MRP & S&OP Explorer does exactly this, and it is free to use in the beginning until it sees “serious usage.” It is permanently free to academics and students. See by clicking the image below:

 

What We Do and Research Access

Using the Diagram

Hover over each bullet or plus sign to see more explanation. To move to a different bullet point, just “hover off” and then hover over the new bullet.

 

Research Access

  • Do You Need to Access Research in this Area?

    Put our independent analysis to work for you to improve your spend.

References

This topic is covered in depth in the following book.

Multi Method Planning Book

MULTI

Multi-Method Supply Planning in SAP APO

Choosing Supply Planning Methods

Which supply planning method meets your company’s business requirements?

The answer might surprise you! Here’s the truth: There is no one right supply planning method for all situations, even within one company! In fact, it is unnecessary to choose only one method, and using multiple supply planning methods is feasible and in most cases, has many advantages over using a single method.

Multi-Method Uses

This book explains why no one supply planning process meets all requirements and lists the many benefits of using multiple supply planning methods. This book gives practical advice about selecting supply planning methods and method modifiers, and goes deep into the “how to” of implementing mixed methods, and how specifically to setup them up in SAP APO, which are approaches taken from real projects. The book is also useful as a general document on how multi-methods can be used in supply planning applications.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: The Different Supply Planning Methods Available within SAP SNP
  • Chapter 3: Combining Supply Planning Methods Across External Systems and ERP Systems
  • Chapter 4: Preparing for the Prototype for Multi-Method Testing
  • Chapter 5: Prototyping the Multi-Method Supply Planning Model
  • Chapter 6: Coding the Product-Location Database /Spreadsheet
  • Chapter 7: Planning Beyond a Single Supply Planning Method Per Echelon
  • Chapter 8: Creating a Dynamic Master Data Selection for Automatic Product Location Switching Between Methods
  • Chapter 9: Overcoming the Human and Information Challenges of the Multi-Method Approach
  • Chapter 10: Combining SNP with Inventory Optimization and Multi-Echelon Planning
  • Chapter 11: Conclusion

When to Use Lean Versus MRP

Executive Summary

  • The question of when to use Lean versus MRP is what sets part of the planning strategy.
  • We cover when to use reorder points versus MRP planning.

Introduction

The question of when to use Lean versus MRP is critical to determine when to use a non-forecast based method of supply planning (reorder points) and forecast based planning (like MRP).

Differentiating Planning From Physical Shop Floor Movements

Lean is really a combination of techniques that are based around principles that have already been covered up to this point. If we put aside the management and the process aspects of Lean for a moment, in terms of the specific techniques, some of them relate to planning, and are therefore competitive with MRP and other planning procedures, and others are related to execution movements that occur after planning is performed. KANBAN (also developed by Taiichi Ohno) is a perfect example of one of these Lean techniques. KANBAN, as pointed out by Wikipedia, is not an inventory control system. Instead, KANBAN is a technique for scheduling the movement of material within a factory floor. KANBAN and other Lean techniques are very focused on keeping WIP as low as possible. MRP, if capacity leveling is not performed, is only concerned with meeting demand. If capacity leveling is performed, then it is also concerned with scheduling production in a way that meshes with the capacity of the resources. Other planning methods, such as cost optimization, may be more sophisticated in terms of planning while accounting for constraints, but they are also unconcerned or are not designed to minimize WIP.1 The question becomes how to integrate two control mechanisms (MRP/planning and Lean/execution) in a way that leverages the best of each. While reorder points can be used, in most cases it is not desirable to use reorder points exclusively for planning. On the other hand, MRP/planning does not have much to “say” with respect to what happens after the plan is generated.

Certainly, any planning method can be rerun up until the product is produced; however while many companies do this, it does not make very much sense to. MRP and similar planning systems have a frozen period which declares a timeline where changes are not to be made. This frozen period in the supply and production planning system determines the hand-off of responsibility for managing the activity. If a company has a one-week frozen period, then changes should not be made, and in fact MRP should not even be run for this period.

This can be easily controlled in the system in its configuration. Continuing to run MRP for this period produces two undesirable outcomes. First, it of course moves the schedule around – but secondly, it creates confusion as to what mechanism is in control of the process. Frozen periods are very appropriate because the frozen period in the MRP system declares when MRP no longer controls what is happening and the responsibility for the production and procurement shifts to another system, and often another group of people. MRP and Forecastability MRP and all other supply planning procedures are forecast-based planning methods. They all assume a certain level of forecast accuracy, which in turn assumes a certain level of forecastability on the part of the product location combinations. However, if we consider the environment where Lean was developed in Japan, we can see that Toyota was facing a situation of low forecastability:

“Levels of demand in the Post War economy of Japan were low and the focus of mass production on lowest cost per item via economies of scale therefore had little application. Having visited and seen supermarkets in the USA, Taiichi Ohno recognized the scheduling of work should not be driven by sales or production targets but by actual sales. Given the financial situation during this period, overproduction had to be avoided, and thus the notion of Pull (build to order rather than target driven Push) came to underpin production scheduling.” – Wikipedia

Many products that are difficult to forecast have no discernible pattern in their demand history, and no mathematical algorithm can create a good forecast without one. This is a well-observed phenomenon, is increasing as a trend, and is in great part driven by forces inside, rather than outside, of the company.

“Nearly every company is 30 t0 40 percent unprofitable by any measure. In almost every company, 20 to 30 percent of the business is highly profitable, and a large proportion of this profitability is going to cross-subsidize the unprofitable part of the business. The rest of the company is marginal. The most current metrics and control systems (budgets, etc.) do not even show the problem or the opportunity for improvement.” – Islands of Profit in a Sea of Red Ink

“Some managers argue that it is a good idea to accept business that contributes, even marginally, to covering overhead. However, when you take on a lot of business that contributes only marginally to overhead, in almost all cases it will absorb a significant amount of sales and operations resources that otherwise would have been devoted to increasing your “good” business. And it will remain and grow into the embedded profitability that drags down earnings in company after company. “If the underlying reason for taking marginal business is to fill unused capacity, you need a sunset policy to stop taking the marginal business once capacity is filled and to remove it when full freight business is available. Not many companies have the information and discipline to do this.” – Islands of Profit in a Sea of Red Ink

So first, most companies carry far too many products, which reduces forecast- ability. This is referred to as product proliferation.

Product Proliferation

Product proliferation is the increase in the number of products that are carried and this greatly impacts when to use Lean versus MRP. Often the marketing differences between the products are only incidental and illusory. Proliferation would be even worse than it currently is, but retailers only have so much space to offer. An excellent example of product proliferation is toothpaste.

Most of these toothpaste containers essentially contain a similar set of chemical compounds; however, marketing provides customers with different varieties of what is often the same product in order to promote purchases.

Many of the claims are unfounded, but because there is very little regulation (in the US at least), they can say what they like regarding what the toothpaste will do for consumers. Whether something is true or not is barely mentioned (that is what is written on the packaging), and anyone who might bring this up is considered hopelessly naïve, as the primary focus is whether or not the claim will increase sales. This is the problem – when you create incentives for groups that are entirely focused on maximizing sales, it is quite predictable that the company will metastasize into areas that are not profitable. There may be no better example of an industry that has gone to the extreme with unnecessary product proliferation as the grocery industry. The typical US grocery store has between 35,000 and 50,000 SKU’s, which is a massive increase in SKU’s over the past several decades. When standing, one can no longer see over most grocery store shelves. However, one grocery chain takes a different path, and this is a major reason they perform so much better than the industry average. I covered Trader Joe’s in my first forecasting book, Supply Chain Forecasting Software.

A basic principle of Lean is smooth production. This is referred to as Heijunka and the approach to smoothing production through scheduling is called the Heijunka Box which is one of the factors in when to use Lean versus MRP. In fact, one of the “Seven Zeros” described by Edward Deming and which outlines the ideal production environment is Zero Surging. However, Sales and Marketing are destroying the Heijunka! This is because of their insistence in having the company carry so many products and having them available at such high service levels. Toyota controlled its demand variability by controlling the product mix. This is explained in the following quotation from Factory Physics:

“Toyota’s product design and marketing were so successful that demand for its cars consistently exceeded supply. This helped in several ways. First, Toyota was able to limit the number of options of cars produced. A maroon Toyota would always have maroon interior. Second, Toyota could establish a production schedule months in advance. This virtually eliminated all demand variability seen by the manufacturing facility.”

Thus Toyota controlled most of the demand variability, which left it only with manufacturing and supplier variability. Therefore, Lean and the high variability of demand that is self-generated within companies by Sales and Marketing is a poor fit for Lean – something I have never heard brought up. Of course, product proliferation is only one of the negative externalities caused by Sales and Marketing – another is promotions.

Promotions

In many companies, Sales and Marketing increasingly view promotions as a major part of the overall strategy. Promotions have greatly increased in their frequency and, according to Gartner, roughly 20 percent of the revenue of manufacturers is spent on promotions, up from 0.5 percent in 1985.2 This is one of the largest increases of any expense item.

For many consumer packaged goods companies, promotions are the majority of their overall advertising expense. Furthermore, the use of promotions is likely to increase in the future, as one of the limitations to performing more promotions is related to technology – something that vendors of promotion management software are alleviating by increasing the sophistication of their software. We can tell because it’s evident in the marketing literature of software vendors that sell promotion management software to companies. Retalix is one of the software vendors that specializes in this type of software.

“’With hundreds of promotions happening across thousands of items simultaneously, oftentimes more than one department is promoting the same item,’ said Bob Smith, product manager for Retalix Loyalty. ‘This becomes a critical business problem for everyone involved, because not only will the item have a lower margin, it can even sell at a loss.’”

In fact, software vendors ranging from JDA to IBM to Junction Solutions make promotion management software, and this category of software is quite broadly implemented at CPG clients. All of these applications are singularly focused on allowing companies to implement increased numbers of, and increased complexity of, promotions. However, these applications do nothing to update the promotion information in the forecasting system.

Of course, any system can have its data extracted and put into another system with an interface, but this is clearly not the focus of the marketing literature of these software vendors. They also do not bother to mention the fact that there is increased overhead in accounting for promotions in demand history. They are simply focused on selling their software to companies by offering them tantalizing options to run increasingly complex promotions. Once again, the line of reasoning is that promotions are “free.” They are actually “all benefit and no cost.”

What is at least somewhat amusing is that JDA, which sells this promotion management software, also sells forecasting software. However, once again,

JDA’s marketing literature on promotion management software makes no mention of the overhead and complexity that all of these complicated promotions create for forecasting generally. Therefore, just as with the corporate buyers of software that often work towards conflicting objectives, JDA as a software vendor does the same thing in its software lines. It offers functionality to Sales and Marketing that optimizes their needs at the expense of forecasting, while offering a forecasting solution that then attempts to deal with the extra forecasting complexity driven by the promotions that the company can now run more of because they purchased JDA’s promotion management software. All of this is covered in detail in the book Promotions Forecasting: Techniques of Forecast Adjustments in Software.

Sales and Marketing still feels quite hamstrung by not being able to run more promotions. Some in Sales and Marketing question whether there may be too many promotions and whether they have negative consequences, but most of the sentiment lies with increasing the use of promotions.

“CPG companies often spend anywhere from 8 percent to 20 percent of revenue on promotions. Various studies suggest that anywhere from 25 to 70 percent of CPG suppliers’ trade promotion spend (their expenditure) is ineffective. Some quick math suggests that for every billion dollars in revenue, at least $20 million to $50 million, and likely significantly more, is being poorly spent. That is a substantial amount of money that could be better applied to product innovation or other more significant drivers of growth and brand equity.” – Uncovering the Hidden Costs of Trade

The Compexity Added by Promotions

Promotions are an added complexity to any business. There is what is referred to as a churn, which is caused by constant promotions, introducing chaos into the management of products. Lean proponents talk a great deal about reducing mura, which is Japanese for unevenness. However, promotions produce nothing but unevenness. Lean uses a variety of techniques for keeping mura in check such as control charts which leverage statistical process control. However, that can only help control for unevenness that is part of the supply chain process – but promotions are unevenness that is imposed from outside the supply chain process. The entire concept of the control chart is that the root cause of the variability is found and then addressed.

However, the root cause of promotion variability cannot be addressed because, at the majority of US and European companies, supply chain does not have any influence over the number of promotions run by Sales and Marketing.4 No control chart will help with this type of unevenness because it requires confrontation with the Sales and Marketing entities that are responsible for the unevenness.

“We have found that trade promotions can play havoc with the sales forecasting process, creating promotion-driven seasonality in historical sales data when distributors increase their inventories in response to periodic price promotions from manufacturers rather than to anticipated increases in consumer demand.” – Sales Forecasting Management

If you read a typical book or article that covers this topic, there is often a great willingness to point out that the product database has “exploded” and that product life cycles are decreasing, but a great unwillingness to explain why this is the case. The reader is left with the definite impression that all of these changes are driven by the market, when in actual fact, companies themselves, and more specifically Sales and Marketing within these companies, are driving these changes. I believe that it is misleading the reader to not point out that much of these changes are in fact self-imposed by the company itself.

Determining Forecastability

Products that have a very stable history exist at the other end of the continuum of forecast difficulty. Typically, it is very easy to forecast for products with a stable demand history; however, if this is the case, actively forecasting the product does not add very much value to supply planning (the ultimate consumer of the demand plan) because a product with stable demand history does not need to be forecasted. Products with stable demand can be managed effectively and efficiently with reorder point logic, where orders are based upon a reorder point or a reorder period. Intermittent – or “lumpy” – demand is one of the most common features of a product’s demand history that makes a product unforecastable. Service parts are the best-known example of a product with lumpy demand. However, I have come across intermittent demand in many different types of companies. For instance, one of my clients was a textbook publisher. A large percentage of their product database had an intermittent demand history, which would normally not be expected of this type of product. However, due to the fact that different US states buy textbooks in large volumes whenever funding comes through, the demand ends up being quite unpredictable for many books. A school system will not make any purchase for some time and then will buy many textbooks all at once. For example, California is on a seven-year procurement cycle, which means they wait seven years between purchases.

Managing Products With No Forecast With Supply Planning

When speaking of supporting supply planning, it is in fact not necessary to forecast the entire product database. This leads directly to the question of when to use Lean versus MRP. However, the vast majority of companies think that they should and they do so. The only products that can be forecasted using statistical forecasting are those that have a discernible pattern of demand, and not all products have this. Without a discernable pattern, no mathematical forecasting method can beat a many-period moving average. In many cases, the most effective approach of dealing with products that are very hard to forecast (or so easy to forecast that forecasting becomes unnecessary) is to simply remove their forecast from the supply planning process – although they will typically still be forecasted by the demand planning systems – and these forecasts can be used for other purposes.

These product location combinations can be placed on reorder point planning. At one company I consulted for, reorder point planning could have been used for around half of the product database. The percentage varies by the product type and the activities of the company, among other variables. Reorder point planning works for both items with uniform consumption and erratic consumption. In the case of difficult to forecast items, they have an erratic demand history. The reorder quantity is therefore high and the safety stock is high. Because it cannot be predicted when demand will arrive, there is no other solution than to carry a large amount of stock (relative to average monthly demand) in order to be able to fulfill demand when it arrives.

This is simple to do with reorder point planning. For stable items, the amount of stock (average monthly demand) is low. Safety stock is relatively low. Therefore, reorder points actually work for both types of demand history; however the distinction is how high, relative to average demand, the stocking level can be set, as well as the reorder quantity (or reorder duration).

“The reorder point, on the other hand, always orders materials whenever the on-hand balance is below the reorder point, regardless of whether more is actually needed. In this case, there is enough on hand to satisfy the demands. But the reorder point system doesn’t look at what is needed. Instead, it blindly attempts to keep a certain amount of inventory on hand at all times.

“Examples show that reorder points are an obsolete technique – an invalid inventory model – and should not be used in any situations where inventory is maintained. They simply do not provide the visibility to see when product is actually needed and when problems are likely to occur.” – Distribution Requirements Planning

This criticism of reorder point planning only holds for forecastable items. However, for erratic demand items, the system cannot “look at what is needed” because it is inherently unknowable. The fact that the item has such a high forecast error means that the supply planning system is already “blind.”

Applying a supply planning method does not change this fact. For highly stable products, the reorder point that is set can easily order the right amount. Items with a stable demand pattern are easy to model with reorder points, so it is in fact not blind at all. There is simply little benefit to passing a forecast to the supply planning system in this scenario.

“It is normally reserved for products without dependent requirements, like spare parts or consumables. However, it is also possible to use reorder point planning in combination with future requirements. An example of use for products with an erratic demand, like spare parts for customer service.” – Delaware Consulting

This is not necessarily a criticism of reorder point planning; however, I do not see how reorder point planning should be limited to products without dependent requirements. Reorder points can be set in the supply planning system for finished goods, and then the BOM can be exploded with or without MRP for all the dependent items. I do, however, agree that it makes little sense to set dependent products on reorder point planning.

“Manual reorder points are even less effective in larger organizations. When an organization must plan across a large enterprise, particularly a vertically integrated enterprise, it is left with few viable and satisfactory options. The lack of visibility means that organizations of size or even moderate complexity are flying blind to the overall materials and inventory picture. Furthermore, manual reorder points and KANBANs do not consider the bill of materials in its totality. The KANBAN is defined only at each discrete connection. This means that stock positions must be placed at every position in the bill of material. This strategy, in turn, increases the number of stocked positions to manage and potentially raises total inventory.” – Orlicky’s Material Requirements Planning (3rd Edition)

Orlicky criticizes reorder point planning from several directions in this quotation. The clearest way to address each of his points is to list them one by one in a numbered format.

  1. The size of the enterprise is irrelevant to whether reorder point planning should be used and, in fact, Orlicky provides no evidence to support the statement as to “why” this would be organization-size dependent. Rather than the organization, instead it is the attributes of the product demand history that determine whether reorder point planning should be used.
  2. The statement regarding a lack of visibility is, again, true for erratic demand products no matter what method is used. A poor forecast accuracy means that visibility is by definition lacking. Using a supply planning method does not change the basic unforecastability of a product. “Visibility” is, in fact, provided by simply setting the reorder point to something that is consistent with the average demand.
  3. Reorder points do not have to consider bill of materials in their totality because, as I stated earlier, they can only be set for the finished good. All dependent demand can be extrapolated from a BOM explosion without MRP, or a BOM explosion with MRP. Something that needs to be considered by critics of reorder point planning, but seems to be frequently ignored, is that it is unnecessary for all items in the BOM to be planned the same way.

All of the criticisms listed above, as well as the criticisms generally, suffer from a need to make a universal statement regarding the usability of reorder point planning when a universal statement cannot be made. Reorder point planning is useful under certain conditions. One might think that it’s not really possible to simply stop using the forecast generated by the demand planning system for supply planning. In fact, it is quite possible and easy to implement, although there can be a fair amount of complexity in the methods designed to calculate reorder points (something that is not commonly understood by those that oppose reorder point planning on the grounds that it is too simple).

In the book Supply Planning with MRP/DRP and APS Software, I cover reorder point planning differently than it is covered in a number of supply planning books, so I won’t repeat the information here. Suffice it to say that there are many cases where it is better not to send a forecast to the supply planning system, and the supply planning system will still manage quite well. Therefore, a simple moving average forecast can be sent for unforecastable products, or no forecast at all.

Reorder Point Settings

Reorder point setting does not require a forecast because the order is placed when the inventory drops to a certain level. However, there is not one “right way” of doing this. Regardless, the company gets away from continuing to invest effort in forecasting unforecastable products. Analyzing the forecastability of the product database is one of the important steps to moving toward a more effective way of managing the forecasting process. For some products, a more advanced forecasting method cannot reasonably be expected to be an improvement over a simple, long duration, moving average forecast. A number of trends are reducing forecastability of the product database, including actions by Marketing (such as promotions) and SKU proliferation (spreading the same demand over more products).

Interestingly, the connection is not frequently made between these trends and forecastability. The more erratic demand becomes, the less forecasting can add value, and increased amounts of inventory must be carried to ensure that sufficient inventory exists when demand does arrive. This fact is lost on people who are unfamiliar with forecasting.

The Concept of Coding a Product Location Database

Companies tend to not properly code their product location database so that different product locations can be treated appropriately by the system. However, this coding can be valuable so that a short code can tell anyone who works with the product location combination (PLC) both the basic properties of the PLC, but also how the PLC is set up in system. This coding is not static, because the PLC is periodically reviewed, or reviewed based upon market intelligence. However, if the coding is kept up to date, it can be very useful for a number of supply chain planning purposes.

A PLC can then be coded for whether it is forecastable. If it is forecastable, this leads to another set of questions; if it is not forecastable, this leads to a different set of questions. To help people follow this conditional logic, it is programmed in the calculation form below. Try switching the first drop down between forecastable and unforecastable to see how the rest of the calculation form changes.

Master Data Review Cycle

As was explained earlier, the PLCs must be reviewed and updated on a periodic cycle. However, PLCs differ in terms of their review. Before computers were available, PLCs were placed on a review cycle for actually calculating order quantities. A review cycle might look something like this:

  • Products 11234 to 11500 – 1st Monday of the Month
  • Products 11500 to 15340 – 2nd Tuesday of the Month
  • Products 15341 to 16201 – 3rd Wednesday of the Month

Computers did not compute the order quantities; this was something that had to be performed by inventory analysts. When computers did arrive on the scene, software vendors began touting their “perpetual inventory” abilities. This meant that when a goods receipt was recorded, the inventory was immediately recalculated. This also allowed companies to carry less inventory because, prior to computers, safety stock had to not only cover variability in lead time and forecasts, but also the longer period between reviews.

In a computerized system, if a larger than forecasted order comes in, it may reduce the planned stock below the reorder point – and the instantaneous calculation will cause a new order to be generated. In a manual periodic review system, that product may need to wait until recalculation by an inventory analyst (unless the analysts reviewed all of the large orders and then recalculated just those PLCs ahead of the rest of the PLCs in their rotation.) Interestingly, the book Decision Systems for Inventory Management and Production Planning does propose an advantage to periodic review and periodic ordering.

Items may be produced on the same piece of equipment, purchased from the same supplier, or shipped in the same transportation mode. In any of these situations, coordination of replenishment may be attractive. In such a case, periodic review is particularly appealing in that all items in a coordinated group can be given the same review interval. In contrast, under continuous review, a replenishment decision can be made at practically any moment in time; hence the load is less predictable. A rhythmic, rather than random, pattern is usually more appealing to the staff.

This master data review cycle concept is the same concept as was applied previously to inventory management, but of course a lot less work because this review cycle is for setting master data and prevents the settings from falling out of date. This is also important because master data is often changed in reaction to short-term needs, but then not changed back. This is an important quality checking process as well as an important process for adjusting the PLCs as things change over time.

Advice on Enjoying the MRP Quiz

To see the full screen just select the lower right-hand corner and expand. Trust us, expanding makes the experience a whole lot more fun.

 

Conclusion

We tried to answer the question of when to use Lean versus MRP in this article.

Now that we have identified the PLCs that will go out on either the appropriate procedure or Lean approach, the next step is actually implementing this coding in a system. Of course, there are a wide number of systems out there that perform supply planning, so it is difficult to guess exactly the system that you, the reader, will be using. However, by providing an example it should be able to replicate the configuration in your supply planning system. In ERP systems, there are really only two methods that can be used. There is MRP for the initial supply and production planning run, and then DRP for the deployment planning run. Reorder points can also be used at any product location combination. In APS systems, there are many more options for the supply planning method. However, each product location combination can be coded for any of these procedures, or for reorder point planning. How to implement this in systems will be the subject of the next chapter.

It is a relatively simple matter in a supply planning system to convert some product location combinations to pure reorder point planning and other product location combinations to being processed with a supply planning method. Furthermore, a product may be planned in one way at one location and planned a second way at a different location.

We developed an approach where reorder points are calculated externally, which allows for a higher degree of control, and for the average inventory to be coestimated in a way that provides an observable total system inventory, holding cost, service level and a picture of what is happening to the overall system. Calculating individual parameters like reorder points without an appreciation for the systemwide does not make any sense.

Brightwork MRP & S&OP Explorer for Order Optimization

Order Sizing and Optimization

Order optimization is necessary in order to get the predicted value from ERP and other supply planning applications. The Brightwork MRP & S&OP Explorer does exactly this, and it is free to use in the beginning until it sees “serious usage.” It is permanently free to academics and students. See by clicking the image below:

 

What We Do and Research Access

Using the Diagram

Hover over each bullet or plus sign to see more explanation. To move to a different bullet point, just “hover off” and then hover over the new bullet.

 

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References

This topic is covered in depth in the following book.

Lean and Reorder Point Planning Book


Lean and Reorder Point 2

Lean and Reorder Point Planning: Implementing the Approach the Right Way in Software

A Lost Art of Reorder Point Setting?

Setting reorder points is a bit of a lost art as company after company over-rely upon advanced supply planning methods to create the supply plan. Proponents of Lean are often in companies trying to get a movement to Lean. However, how does one implement Lean in software?

Implementing Lean in Software

All supply planning applications have “Lean” controls built within them. And there are in fact some situations where reorder points will provide a superior output. With supply planning, even within a single company, it is not one size fits all. The trick is understanding when to deploy each of the approaches available in software that companies already own.

Are Reorder Points Too Simple?

Reorder points are often considered to be simplistic, but under the exact circumstances, they work quite well.

There are simply a great number of misunderstandings regarding reorder points – misunderstandings that this book helps clear up.

Rather than “picking a side,” this book shows the advantages and disadvantages of each.

  • Understand the Lean Versus the MRP debate.
  • How Lean relates to reordering points.
  • Understand when to use reorder points.
  • When to use reorder points versus MRP.
  • The relationship between forecastability and reorder points.
  • How to mix Lean/re-order points and MRP to more efficiently perform supply planning.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: The Lean versus MRP Debate.
  • Chapter 3: Where Supply Planning Fits Within The Supply Plan
  • Chapter 4: Reorder Point Planning
  • Chapter 5: Lean Planning.
  • Chapter 6: Where Lean and Reorder Points are Applicable
  • Chapter 7: Determining When to use Lean Versus MRP
  • Chapter 8: Mixing Lean and Reorder Points with MRP-Type Planning