How to Understand Segmentation vs Inventory Optimization

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Executive Summary

  • Supply chain segmentation is a method of inventory classification.
  • Supply chain segmentation is employed in niche supply chain vendors.

Introduction to Segmentation

Segmentation is a method of dividing the product location database. In this aricle, you will learn how segmentation compares to inventory optimization.

What is Inventory Optimization

There are some problems in the inventory optimization space relating to what is being communicated to potential clients.

One issue relates to what is claimed to be inventory optimization and what is not.

This is because some vendors that perform product database segmentation, calling their solutions inventory optimization. This is a problem for the following reasons:

  1. Segmentation has many benefits, which happen to be different from inventory optimization. By calling the software inventory optimization, segmentation will not become known in its right.
  2. Buyers, already confused, will end up further confused.
  3. Segmentation is not the same thing as inventory optimization.

What is Supply Chain Segmentation?

Supply Chain Segmentation is a method of selecting portions of the product database and applying changes to its control parameters.

  • For instance, one could select all SKU-Locations or product locations that are above a certain number of inventory turns per year and apply on economic order quantity to them.
  • The ability of Supply Chain Segmentation is critical because it allows planners to more efficiently control their products, and because it is a very direct method of filtering and control.

Along with filtering capability comes an ability to report on the products within the planning system. By performing filtration on different characteristics, the planner can gain a better appreciation and understanding for their products overall. It can fulfill many of the master data maintenance of the planning system.

Supply Chain Segmentation Versus Classification of Inventory

Supply Chain Segmentation should not be confused with the classification of inventory. Classification of inventory places inventory into categories such as A, B, C. Supply Chain Segmentation is about the categorization of product locations for treating them differently with the application different inventory parameters (lot size, reorder point, etc.) and even different supply planning or forecasting methods.

How Is This Segmentation Different From Inventory Optimization?

Inventory optimization is the ability to derive stocking levels from service levels. The definition continues to be a problem regarding being understood by prospective buyers. There are some reasons for this. Several vendors have confused its meaning through directly hijacking the term to be trendy, while many consultants have overused the term, and in fact, do not know themselves the term’s actual definition.

The more flexible and abstract the setting of the service level is, the more powerful the inventory optimization software can be considered to be. The lowest level of inventory optimization is at the product location, and the highest is the customer. However, not every customer wants, needs or is capable of managing their service levels by the client. Thus MEIO software selection is only partially about the level of the service level specificity of the application.

Supply Chain Segmentation, on the other hand, is about parameter control functionality, and an entirely different category of software from inventory optimization. For instance, the ability to apply a particular inventory parameter to a segment of the product database should not even be on the list of selection criteria for inventory optimization. However, it would be central to the selection of segmentation software.

Discerning the Difference Between Inventory Optimization and Supply Chain Segmentation

It is important to have questions ready when interviewing different vendors. The questions below can help prospective clients to understand what type of software they are dealing with in this space.

  1. Is this product segmentation or inventory optimization software?
  2. Where can the service level be set in the application (i.e. product/location, customer, location)?
  3. What specifically is being optimized?

Forecast-ability of the Product Location Combination

One of the first places to start is with the forecast-ability of the product location combination or PLC. Forecast-ability is a measure of how predictive the selected forecast model is. This is often presented within forecast systems as the fitted R-Square. Applications can sometimes provide the fitted R-Square for not only the individual PLC.

They can also do this for the complete product database — or a weighed PLC value. Variability factors can be used to determine the forecast-ability of a PLC with a formula as is explained in this article.
Forecast Error Assignment A PLC can then be coded for whether it is forecast-able. If it is forecast-able, this leads to another set of questions, and if it is not forecast-able, 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 forecast-able and unforecast-able to see how the rest of the calculation form changes. 

Forecast Model Used

For the PLCs that are not assigned a level forecast, the forecast model that is assigned should be part of the PLC coding. In the calculation form below,  this selection is available.

Master Data Review Cycle

As explained earlier, the PLCs must be reviewed and updated on a periodic cycle. PLCs differ with their review. Before computers were available, PLCs were placed on a review cycle. This review cycle was for actually calculating order quantities.

A review cycle might look something like this:

  • Products 11234 to 11500 – 2nd Monday of the Month
  • Products 11500 to 1534 – 2nd Tuesday of the Month

Computers did not compute the order quantities; this was something that had to be performed my inventory analysts. When computers did arrive, 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. Before computers, safety stock had to cover not only 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. The instantaneous calculation will cause a new order to be generated.
  • In a manual periodic review system, that product may need to wait until it was recalculated by an inventory analyst. That is unless the analysts reviewed the large orders and then recalculated just those PLCs ahead of the rest of the PLCs in their rotation.

The book Decision Systems for Inventory Management and Production Planning proposes 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. 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 is often not changed back.

What is Forecastability?

The first thing to understand is that the term forecastable does not refer to whether an item will or will not be forecasted. Rather the term applies to whether it makes sense to add extra effort to performing forecasting.

Forecastability is a measurement of how much likely benefit can be obtained by putting forecasting resources into a forecasted item. The measurement of forecastability moves the forecasted items into three potential categories. I quote from the book Supply Chain Forecasting Software.

“Many of the products that I have analyzed over the years from different companies are clearly unforecastable. There is a simple reason for this. Many difficult to forecast products have no discernable pattern in their demand history, and without this, no mathematical algorithm can create a good forecast. This is not generally understood. I believe that part of the reason misallocate effort on very hard to forecast products is due to a misimpression about when statistical forecasting can add value, and when it can’t.

Most companies actively increase the intermittency of their demand and reduce its forecastability by doing things like creating promotions and instituting end of quarter sales “pushes.” Customers respond to these behaviors by further batching their demand. It is well known that eventually customers become habituated to end of quarter price reductions, and postpone their buying in anticipation of the end of quarter. These are just a few examples, but there are a host of programs often initiated by companies, which make demand less forecastable than it ordinarily would be if natural or true demand were received.”

The testing for forecastability does not mean those elements that are declared as tough to forecast will not still receive a forecast.

The Question to Ask Regarding Forecastability

One of the first questions to ask is whether there is value to actively generating a forecast. In some cases, the answer is no. However, instead of recognizing that a product is not forecastable and adjusting to this reality, more sophisticated mathematics are often employed in a vain attempt to improve the forecast. Clients I have worked for in the past have adopted this philosophy, as have the majority of consultants and vendors I have worked with, and this philosophy is also reflected in forecasting academic papers I have read. Since so many well-educated people agree on this thinking, it must be correct—right? Well, actually they don’t all agree.

A number of academics have written on the concept of unforecastable products, but for some reason, their research does not seem to get sampled and disseminated. However, the scholarly literature is not objectively sampled. In fact, most consultants in don’t read it at all. Proven approaches like turning off forecasting for unforecastable products leads to short, and insufficiently lucrative consulting engagements.

Success Through Using More Sophisticated Forecasting Models/Mathematics for Poor Demand Histories?

However, there is little evidence that sophisticated mathematics can improve the forecast of difficult-to-forecast products, and this is a problem. Some studies do not show improvement from more advanced methods. But first, the improvement is never very large, and secondly, other studies come by later to contradict the original studies.

In addition, complex methods should have to exceed a higher bar. Academics can apply complex methods in a laboratory environment over a few products far more easily than can be done by industry. This fact, along with the point that sophisticated methods are much more expensive for industry to implement than simple methods, is rarely mentioned.

This point is made very well by J. Scott Armstrong:

“Use simple methods unless a strong case can be made for complexity. One of the most enduring and useful conclusions from research on forecasting is that simple methods are generally as accurate as complex methods. Evidence relevant to the issue of simplicity comes from studies of judgment (Armstrong 1985), extrapolation (Armstrong 1984, Makridakis et al. 1982, and Schnaars 1984), and econometric methods (Allen and Fildes 2001). Simplicity also aids decision makers’ understanding and implementation, reduces the likelihood of mistakes, and is less expensive.”

The Inconvenient Truth About Statistical Forecasting

For statistical forecasting, the only products that can be forecasted are those that have a discernible pattern to their demand history, and not all products have this pattern. Forecastability can usually be determined—or at least indicated—without any math by simply observing a line graph of a product’s three-year demand history. If there is no discernible pattern, it is unlikely that the product is forecastable with mathematical methods. (Products that are using just the last few periods to create a forecast are the exception to this rule.) An algorithm that can appear to be predictive can be built for unforecastable products, but more often than not this is an illusion created by the forecaster who over-fitted the forecast.

As is pointed out by Michael Gilliland, just because a model can be built to match the past, does not mean it should be used to perform forecasting:

“The statistical approach is based on the assumption that there is a structure or pattern in the behavior we are trying to forecast. As human beings, we are very good at finding structure and pattern in the world around us—even when none exists. Clouds look like poodles, a burnt cheese sandwich reveals the Virgin Mary, and an ant’s innocent meandering in the sand caricatures of Winston Churchill. We readily come up with lucid explanations of the ups and downs of the stock market and of demand for our products and services. Unfortunately, the patterns we see may not be real, and even if they are, we have no assurance they will continue into the future.”

Stable Product Forecasting

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. Very stable and very unstable products converge in their forecasting approach, as is evidenced by the fact that a many-period moving average is equally useful for products with both a stable demand history and products with an unstable demand history.

When both stable and unstable demand history products are run through a best-fit forecasting procedure, the normal result is that both will be fitted with a stable or level forecast. Companies have a very strong tendency to actively forecast all items in their product database without first asking the following question:

“What is the value added by forecasting for the different product categories?”

The Rule of Thumb

The rule of thumb is simple:

“A forecast adds value to the supply planning process when the demand planning system is creating a forecast for a product for which there is a discernible pattern for demand and if the forecast is not simply a constant or relatively constant value.”

Creating forecasts for the entire product database for S&OP forecasting or for other purposes may be important and necessary. However, the forecasting process that results in a demand plan being sent to supply planning can be segregated based on the rule of the value added to supply planning.

What is Being Tested?

The concept behind this test is that for product location combinations with sufficiently intermittent demand, it is not useful or value-add for companies to be forecasting them. For a title to be forecastable, there must be some discernible pattern.

Without a pattern, the best that any forecasting engine can do is create a level forecast which only averages the previous demand volumes. The issue of the diminishing marginal utility to forecasts was brought up by George Plossl in his book Production and Inventory Control over twenty-five years ago.

“The present rational approach recognizes that forecasts will always be subject to error and that, while there are tools available to improve the art of forecasting, the amount of money and effort put into applying such tools rapidly reaches a point of diminishing return. Beyond this point, it is far more profitable to develop flexibility to cope with forecast inaccuracy instead of trying to improve forecasts. The best solution is to develop a formal forecasting program and a system that detects and measures forecast errors and then to react quickly to correct for such errors. This solution is covered in this chapter in detail.” – George Plossl

Forecasts Being Generated at a Sufficient Level of Accuracy

At the point where forecasts cannot be generated with a sufficient accuracy level, it no longer makes sense to forecast the item, and instead, it is recommended that the item is removed from the forecasting system and be managed purely with consumption logic in the ERP system. It is not always the case that these items should move towards consumptive logic, but it is often the case. This is an extremely rarely made recommendation, so I have this method thoroughly explained here.

When to Switch to Reorder Point Planning

Reorder point planning is activated by populating product location fields in either SAP ERP’s Material Master, in SAP SNP’s Product Location Master, or in the material object in any supply planning engine. Sometimes people will say something like “reorder point with forecasts,” however a reorder point is a method used without projections.

The APO or ERP Formula

The determination of whether a product will be exclusively kept in APO, or kept both in APO and in SAP ERP can be determined by a formula which will run off of several key measurements that can be obtained from the statistical export file from a forecasting environment.

The Forecastable or Unforecastable Formula

The formula I have developed is listed below. It certainly is not be used in a pure form for every company and should be adjusted per company. However, one can start by using it, and tune it through showing the results with a group of people with the domain expertise to provide input as to whether the results of the adjusted formula “seems” correct.

=IF(R-Square>0.7,”Forecastable”,IF(AND(R-Square>.15,Standard Deviation/Series Mean<3,Series Mean>3, MAD>Series Mean*0.333333),”Forecastable”,”Unforecastable”))

The Flow of Forecasts

The entire flow of this approach can be described in the graphic below. Then the formula above, or some modification of the formula can be run against each item to determine if the material will be placed in the forecasting engine, or instead only reside in ERP or the supply planning engine and be entirely controlled by the consumptive logic settings.

How Companies and Implementers Ignore the Research

System implementers are not adjusting their approach to be considerate of the research in this area. The results for many decades now is consistent in showing that more complex forecasting techniques do not improve forecastability of difficult to forecast items and certainly do not pay off given their implementation investment, and long-term maintenance costs. A perfect example of this is the Crostons method, a method that Wayne Fu of Servigistics and myself discuss in this are an article as a highly complex formula which is very popular in software, but which has extremely limited areas where it can defeat many periods moving average.

Reorder Points Don’t Provide Visibility?

Comments that reorder points “don’t provide visibility” miss the point that the supply planning system can only provide visibility which is commensurate with the demand plan, which is the quality of the demand plan.

Even the most sophisticated cost optimizer solution for supply planning will not provide visibility if the forecast is of poor quality. On the other hand, products that have very low forecast error and are primarily a level forecast also do not benefit from forecasting effort beyond simple automation. This is because a level forecast is emulated with a reorder point. That is they provide the same result to supply planning.

  • Many companies are spending time forecasting items that are not “forecastable.”
  • Much of the history of computerized demand planning has been spent in attempting to apply very complex methods under the assumption that there is always a method of developing a good forecast, as long as enough effort is placed into forecasts.
  • This approach has had decades to prove itself out and has failed to improve forecast accuracy both at any company I have consulting with and in meta-analysis in the academic literature.
  • What is not recognized is that not every product in the database is forecastable.

The Benefits of Applying No Statistical Forecast

There are many circumstances where creating a statistical forecast is not helpful. This is because there is no pattern that any statistical forecast can use to create a reasonable projection. In most cases, the standard approach is to go ahead and create the forecast in any case and populate the planner user interface with values — values that the planner will typically ignore.

In this article, I am going to question this orthodoxy regarding the forecast. The reason for this is I have come to the conclusion that items that cannot have a good statistical forecast generated for them do not help planners. Secondly, it miscommunicates to the planner. That is — the implication is that the statistical forecasting system is adding value to the process — when in fact isn’t. One of the most significant forecasting issues in supply chain departments is the use of planners for non-value added activities. Creating a forecast tends to both underuse statistical forecasting and make their planners perform too many adjustments and overrides.

Communicating the Limits of a Statistical Forecast

By creating no forecast, this approach is honest with the planners and tells them

“The statistical forecasting methods has nothing to offer on this particular item.” 

This helps in two ways:

  1. It clearly communicates that the planner must control the forecast for this item.
  2. It reinforces the concept that different things need to be forecasted with various approaches. A few examples of these different approaches are listed below:
  3. 100% statistically forecasted items
  4. Statistically forecasted + manually adjusted items
  5. 100% manually adjusted items

Other authors are quite clear that different items require different approaches – and my experience shows that this is always true after I analyze the company’s product database. While this is a constant, very few companies incorporate this knowledge into how they forecast. Yes, the term forecastability has become standard at this point, how many companies use forecastability? The answer is very few. Segmentation of the product database is one of the most powerful ways to manage the forecasting process, allowing companies to have a much better chance of improving forecast accuracy.

Conclusion

The inventory optimization space is crowded, with everyone declaring they have inventory optimization. Among true inventory optimization vendors there are significant design approach differences that make them better or worse fits for different environments. However, the first step in an inventory optimization software selection is removing segmentation vendors. Segmentation software is still quite a value, but it is important to know what you are buying and what you can expect from your software purchases.

The Problem: Preparing for Inventory Optimization

Inventory optimization projects tend to take a long time and to be a significant expense. As with most complex implementations, the actual effective usage of inventory optimization software will significantly lag after whatever the initial project plan predicts. For companies that are interested in inventory optimization, we have a simpler solution that should be tested first before investing in inventory optimization software.

A major part of getting supply planning right is setting these parameters. Testing of the extracted parameters of ERP and external supply planning systems clearly shows that these values are poorly maintained. The result is far worse planning results than could be obtained otherwise.

Being Part of the Solution: Our Evolution of Thinking on Maintaining Inventory Parameters

Maintaining inventory parameters like rounding values and lot size in systems comes with a number of negatives that tend to not be discussed. One issue is that when using ERP systems, inventory parameters are typically managed on a “one by one” basis. This leads to individual planners entering values without any consideration for how inventory parameters are set across the supply network. After years we have given up managing safety stock or other inventory parameters in we now calculate inventory parameters in our application, the Brightwork Explorer, and then simply upload the data into the ERP system. See our link below. We have developed a SaaS application that sets the inventory parameters that allow for simulations to be created very quickly. These parameters can then be easily exported and it allows for far more control over the parameters. In our testing, the approach, which is within the Brightwork Explorer is one of the most effective methods for managing planning in any system. This approach is laid out in the book How to Repair Your MRP System.

In our testing, the approach, which is within the Brightwork Explorer is one of the most effective methods for managing planning in SAP applications.

Brightwork Explorer for Service Level & Safety Stock Setting

Service Level & Safety Stock Setting

Setting service levels and inventory can be performed far more easily than is done at the vast majority of companies. Click the image to see how.

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References

MEIO Book

What is MEIO?

This book explains the emerging technology of inventory optimization and multi-echelon (MEIO) supply planning. The book takes a complex subject and effectively communicates what MEIO is about in plain English terms. This is the only book currently available that describes MEIO for practitioners, rather for mathematicians or academics.

The Interaction with Service Levels

The this book explains how inventory optimization allows the entire supply plan to be controlled with service levels, and how multi-echelon technology answers the question of where to locate inventory in the supply network.
This is the only book on inventory optimization and multi echelon planning which compares how different best of breed vendors apply MEIO technology to their products. It also explains why this technology is so important for supply planning and why companies should be actively investigating this method.
The book moves smoothly between concepts to screen shots and descriptions of how the screens are configured and used. This provides the reader with some of the most intriguing areas of functionality within a variety of applications.
Chapters
  • Chapter 1: Introduction
  • Chapter 2: Where Inventory Optimization and Multi-Echelon Planning
  • Fit within the Supply Chain Planning Footprint
  • Chapter 3: Inventory Optimization Explained
  • Chapter 4: Multi-Echelon Planning Explained
  • Chapter 5: How Inventory Optimization and Multi-Echelon Work
  • Together to Optimize the Supply Plan
  • Chapter 6: MEIO Versus Cost Optimization
  • Chapter 7: MEIO and Simulation
  • Chapter 8: MEIO and Service Level Agreements
  • Chapter 9: How MEIO is Different from APS and MRP/DRP
  • Chapter 10: Conclusion
  • References
  • Vendor Acknowledgements and Profiles
  • Author Profile
  • Abbreviations
  • Links in the Book
  • Appendix A: MEIO Visibility and Analytics
  • Appendix B: The History of Development of MEIO Versus MRP/DRP