How to Best Use The Brightwork Research MRP Calculator

Executive Summary

  • We cover MRP and how it works, as well as material requirements planning in production systems.
  • We have an MRP calculation form and calculate an MRP example.
  • This article is for anyone who wants to validate their assumptions around MRP calculation.

Introduction to the MRP Requirements Calculator

MRP stands for “materials requirements planning.”

“Material requirements planning software (MRP) is used to describe the process of planning manufacturing inventory – what products to make and what items to buy, when, how much, and from who – all based on supply and demand.”What is MRP Software

What is MRP?

MRP is one of the most important methods in supply chain planning. In performing research for this book, I found that MRP is the most commonly used term in supply chain planning, the next closest one being inventory management. This is true even though MRP is an old planning method, and more advanced methods of creating the initial supply and production plan have been created. However, MRP, and its cousin DRP (MRP used for bringing material into the supply network, while DRP pushes material through the supply network), while old, are still the most common methods of performing supply, production and deployment planning. Interestingly, MRP is a much more commonly used term than DRP, or distribution resource planning, which is almost always used in conjunction with MRP and is the other topic covered in this book. In most instances, when a company talks about their MRP system, they actually mean their MRP/DRP system. But while both methods are used, I will be focusing on MRP for this book.

How Does MRP Work

MRP is a procedure for calculating dependent requirements based upon a bill of materials working backwards from the demand (also called “independent requirements”) of a forecasted item (MRP is emphatically a forecast-based planning method) along with sales orders which, when combined with lead times, creates a series of planned production orders and purchase requisitions which are all timed to allow the demand to be met with the planned supply. MRP has these frequently unstated prerequisites:

  1. “Every inventory item moves into and out of stock.
  2. All components of an assembly are required at the time the assembly order is released.
  3. Components are disbursed and used in discrete lots.
  4. Each manufacturing item can be processed independently of any other.” – Orlicky’s Material Requirements Planning.

Material Requirements Planning MRP does some things in one procedure. It is a great time saver because of this ability, and of all the supply planning methods, it is the easiest to understand.

  • It explodes the bill of material.
  • It then assigns production and procurement quantities to the appropriate time buckets.

However, one of its main calculations is the creation of gross requirements and net requirements, which is the topic of this calculator.

Using MRP Calculation in Supply and Production Planning Systems

Material Requirements Planning MRP is still the most common method used in supply and production planning. It does both supply planning because it creates the planned purchase orders (called purchase requisitions).

It also does production planning, because it creates planned production orders.

How the MRP Calculation Form Works as an MRP Example

It is almost always combined with DRP with Material Requirements Planning creating the production and procurement plan and with DRP creating the deployment plan.

  • This is because Material Requirements Planning only brings material into the supply network.
  • It does not move the material through the supply network. This is another method also uses the same calculation approach to gross and net requirements and is often called DRP, but is broadly known as the deployment planning thread.
  • In SAP it also tends to show the same rows as Material Requirements Planning with forecast consumption. That is how sales orders consume the forecast, by working the same way.

How the MRP Calculation Form Works as an MRP Example

This MRP calculation form requires input to provide output. However, it also has default values. You can change any input value and the rest of the formula — the output will change immediately. You can continue making changes, and the form will always update without having to press any button or refresh. You can use the MRP calculation form as an MRP example to explain how the calculation in an MRP system works.

For the dynamic safety, stock calculator see this article.

Learn about the history of MRP at this article.

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Brightwork MRP & S&OP Explorer

Improving Your Supply Planning, MRP & S&OP Software

Brightwork Research & Analysis offers the following supply planning tuning software, which is free to use in the beginning. See by clicking the image below:

 

References

Repairing the MRP System Book

MRP System

Repairing your MRP System

What is the State of MRP?

MRP is in a sorry state in many companies. The author routinely goes into companies where many of the important master data parameters are simply not populated. This was not supposed to be the way it is over 40 years into the introduction of MRP systems.

Getting Serious About MRP Improvement

Improving MRP means both looking to systematic ways to manage the values that MRP needs, regardless of the MRP system used. It can also suggest evaluating what system is being used for MRP and how much it is or is not enabling MRP to be efficiently used. Most consulting companies are interested in implementing MRP systems but have shown little interest in tuning MRP systems to work to meet their potential.

The Most Common Procedure for Supply and Production Planning?

While there are many alternatives to MRP, MRP, along with its outbound sister method DRP, is still the most popular method of performing supply, production planning, and deployment planning. In the experience of the author, almost every company can benefit from an MRP “tune up.” Many of the techniques that the author uses on real projects are explained in this book.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: The Opportunities to Improve MRP
  • Chapter 3: Where Supply Planning Fits Within the Supply Chain
  • Chapter 4: MRP Versus MRP II
  • Chapter 5: MRP Explained
  • Chapter 6: Net Requirements and Pegging in MRP
  • Chapter 7: Where MRP is Applicable
  • Chapter 8: Specific Steps for Improving MRP
  • Chapter 9: Conclusion
  • Appendix A: Calculating MRP

How to Best Use the Economic Order Quantity Calculator

Executive Summary

  • This is an introduction to EOQ calculator or EOQ formula calculator.
  • We cover how to calculate Economic Order Quantity.
  • We cover the Economic Order Quantity Calculator, EOQ Formula Calculator, and a Quantity Discount Model.

Introduction to the EOQ Calculator or EOQ Formula Calculator

  • Economic order quantity (EOQ) is one of the oldest formulas in inventory management.
  • It was first developed by Ford W. Harris in 1913 (interestingly, as with the development of MRP, the originator of EOQ was not an academic).
  • This EOQ calculator or EOQ formula calculator will allow anyone to calculate EOQ quickly. One can calculate EOQ in a spreadsheet for a large number of product location combinations.

Reorder Quantity Formula

EOQ is one type of reordering quantity formula. Any reorder quantity formula can be used. All that has to occur is that the assumptions of the reorder quantity formula must be agreed to by the entity that intends to use it. Most software has a rather small number of options for its reorder quantity formula. However, when calculated externally, an entity can use any reorder quantity formula that it wants. It can then upload the results of the reorder quantity formula to their ERP or other supply planning system.

EOQ for the Production Order Quantity or the Purchase Order Quantity

EOQ can drive production order quantity as well as procurement order quantity. The production order quantity then drives the purchase order for the finished good. But once the finished good production order quantity is determined, the bill of material can be explored to determine the purchase order quantity. One of the criticisms of EOQ is that the bill of materials is not exploded.

That is untrue as what are called requirements strategies can be set differently for every part of the bill of material. This is covered in my book on reorder point planning which is shown at the bottom of this article.

When companies search for a purchase order system or purchase order software

EOQ is not an adjustable formula; unlike the dynamic safety stock calculation, it cannot account for variability. Some have proposed that this means it cannot be used for more product location combinations (PLCs) with more variable demand history — and this is true — if the data provided to the EOQ is not periodically changed. Therefore, it must be periodically recomputed for the entire database.

This is the standard relationship between cycle stock and safety stock with a stable order quantity. The inventory is reduced by orders — hopefully only occasionally dipping into safety stock. 

Economic Order Quantity Calculator, EOQ Formula Calculator, and the Forecast Error

The higher the forecast error, the less use the EOQ value is. This is because was the forecast error increases, the likelihood that the quantity will be consumed declines.

However, this is not different from any other supply planning parameter. Supply planning parameters have the highest value when the forecast is most accurate.

EOQ Calculator with Quantity Discount Model

If there are quantity discounts, the calculation below will not be accurate. For instance, the formula below may propose an EOQ of 184 units. If the price per each at this level is $50, then this is a total cost of (184 * $50) + 45 or $9245.

If the quantity discount kicks it at 200 units and this discount is 15%, then 16 more units could be obtained for $8538. This would be a missed opportunity. This can easily calculate EOQ for an individual item, but this cannot be systematized because supply planning applications do not have EOQ functionality or even step function min lot sizes.

This means that when you purchase a system, it will in almost all cases not have a quantity discount model as part of its EOQ functionality. It is easy to create a quantity discount model as part of your EOQ externally and then import the hard-coded EOQ values into the system. But normally calculating an EOQ with quantity discount model is not done. This is often seen as too much “work.” And rather than calculating things like EOQ with quantity discount model outside the system, there is a strong preference for many companies to use the software as is.

Rather than calculate EOQ, batching typically is handled by procurement as they are up to date on the volume discounts and will up the orders to meet the discount.

How the EOQ Calculator Form Works

This form requires input to provide output for the Economic Order Quantity Calculator. However, it also has default values. You can change any input value and the rest of the formula — the output will change immediately. You can continue making changes, and the form will always update without having to press any button or refresh.

This calculator assumes that the location receives the entire order at one time. However, this assumption does not always hold. For the noninstantaneous receipt, EOQ calculator see this article.

Also, learn about the limitations of EOQ at this article.

Conclusion

This is a way to calculate EOQ easily. The best way to calculate economic order quantity is normally in a system or a spreadsheet. One can calculate economic order quantity as one wishes and then upload the result to any supply planning system. There are many EOQ formulas and many ways to calculate EOQ that are different from the standard model. To calculate economic order quantity in a way that is different from the standard EOQ formula in ERP or other supply planning systems, to calculate economic order quantity is the way to go.

This page is provided to have a fast way to calculate economic order quantity and EOQ formula calculator online.

Search Our Other EOQ Content

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:

 

References

Plossl, George W. Production and Inventory Control: Principles and Techniques, Second Edition. Prentice Hall, 1985.

Plossel, George. Orlicky’s Material Requirement’s Planning. Second Edition. McGraw Hill. 1984. (first edition 1975)

Harris. Ford W. How Many Parts to Make at Once. Factory, The Magazine of Management. 1913.

Silver, Edward A. Peterson, Rein. Decision Systems for Inventory Management and Production Planning. Second Edition. John Wiley and Sons. 1985.

The background for the economic order quantity calcualtor is covered 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 Best Calculate Reorder Points

Executive Summary

  • This is an introduction to reorder points and how to use the reorder level vs the reorder quantity.
  • The Brightwork ROP calculator sets a dynamic reorder point.

Introduction to Reorder Points

As discussed in this article, reorder level and reorder quantity are both significantly underused in companies and can be set some ways in systems like SAP SNP. You will learn about reorder points and how they are set in production systems.

The Significant of Reorder Points

Reorder level and reorder quantity planning is significantly underestimated. This is because companies so frequently overestimate their ability to effective forecast. Most companies don’t have the capacity to leverage even basic forecasting functionality which is available. This functionality has been available within forecasting applications for years.

Reorder level and reorder quantity work well for both highly forecastable products and products that are difficult to forecast. Any product which received a level forecast assignment using a best fit procedure can have their supply planning emulated by using a reorder point.

By placing a substantial portion of the company’s product database on reordering level and reorder quantity there are several benefits.

  • It saves time.
  • It allows the company to focus on those products that can be improved through forecasting.

The graphic below explains this.

Forecast Error Assignment

Order Point Versus Reorder Point

The term order point is infrequently used. People most often use the term reorder point rather than order point. However, order point and reorder point are the same thing. An order point is a level of inventory where an order is generated. Rather than an order point, a reorder point simply implies that the ordering is perpetual.

The Uses of Dynamic Reorder Level and Reorder Quantity

The beautiful thing about the dynamically reorder point calculation is that it adjusts across the following dimensions:

  • Service level
  • Lead time and forecast error both in variability (for lead time and forecast error) as well as lead time duration.

Dynamic Reorder point analysis can be used for an SKU or any aggregation level. For instance, this form can be used to model the inventory and supply chain of an entire company. Instead of entering the values for a single SKU, the overall or average values can be used.

This allows one to see the relationships between things like service level and the total amount of stock carried. This can also be used to create a service level to inventory curve. An inventory service curve is something that many companies do not have.

It will not, of course, include the total stocking level. So the total stocking level should be estimated and added. Only the reorder point should flex — while the cycle stock should stay the same.

Using Dynamic Reorder Level and Reorder Quantity in Production Systems

As is discussed in this article, reorder points can be set some ways in systems like SAP SNP, ToolsGroup or Demand Works Smoothie.

Most importantly, they can set to work without a forecast or with a forecast.

One of the confusing things about setting up a reorder level and reorder quantity is what to do with the forecast that has been generated for the products that are moved to reorder level and reorder quantity or reorder point. A prediction is still required for things like budgeting. It still makes sense to generate a forecast for them. Another reason for this is that products that are set on reorder point — don’t necessarily stay on reorder point in the future — and vice versa.

The demand history will tell the company when a product should be moved to reorder point planning. It will also tell the company when a product should be moved away from reordering point planning. That is when it is to become an actively forecasting item.

What Makes This Reorder Point Calculator Unique

Most of the reorder point calculators that online calculate a static reorder point typically based upon just lead time, safety stock, stock, and sales. There is no measure of variability in either lead times or the forecast, and it asks the user to input the safety stock as well as the stock. However, variability dramatically changes the safety stock – which is part of the reorder point calculation. Most of these online calculators are not helpful to people need to see the relationships between multiple factors and who don’t have the stock information and require estimates.

The authors seem to have put in the absolute minimum level of work so that they could say their website has a safety stock calculator.

This calculator below does not ask for existing stock or safety stock but does ask for variability. If the variability is not known, then zero standard deviations can be added to the form — to mostly hold those values static.

ROP Formula, or Reorder Point Formula

  • A ROP formula or reorder point formula are all synonyms for the same thing.
  • A ROP formula or reorder point formula are all used to determine the reorder point.
  • The reorder quantity formula shows the amount to be ordered once the reorder point is reached. A reorder quantity formula is often called an economic order quantity.

How the Calculation Form Works

This reorders point calculator provides the intermediate values, along with explanations so that the overall logic of the dynamically reorder point calculator can be fully understood by both students and practitioners.

This form requires input to provide output. However, it also has default values. You can change any input value and the rest of the formula — the output will change immediately. You can continue making changes, and the form will always update without having to press any button or refresh.

Note: For some reason, the drop down field below does not appear to work in Firefox – so if you are using that browser try a different one. Second, when you enter a decimal into the Standard Deviation of Demand and Standard Deviation of Lead Time, you may receive a message “Please enter only digits.” You can ignore that message. The calculator works fine with a decimal point.

To determine the EOQ, see the EOQ calculator see this link.

The Problem: Efficient Calculation and Maintainance of Large Numbers of Inventory Parameters

This calculator shows how to calculate an EOQ interactively on a one on one basis. However, when it comes to managing a large number of product locations a different method of calculating reorder points is necessary. And this not only applies to reorder points but to other inventory parameters as well.

A major part of replenishment is inventory parameters. These parameters include values like safety stock or days of supply, rounding value, reorder point, lot size/economic order quantity and minimum order quantity. Whatever the planning procedure that is used, these parameters control what the supply planning system does.

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 Reorder Points in ERP or External Planning Systems

Maintaining reorder points in comes with a number of negatives that tend to not be discussed. One issue is that when using supply planning systems like MRP, the reorder points 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. We have developed a SaaS application that sets the inventory parameters for supply planning systems externally, and that allows for simulations to be created very quickly. These parameters can then be easily exported and it allows for far more control over the parameters. Supply planning systems are designed to receive parameters, they are not designed to develop the parameters.

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

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:

 

Search Our Other Reorder Point Content

References

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 Use the Brightwork Internet Based Dynamic Safety Stock Calculation

Executive Summary

  • This article introduces the dynamic safety stock calculation and reviews the current safety stock calculation explanations.
  • There is a common preference for talking about the standard dynamic safety stock calculation rather than diving into its math.

Introduction to Calculating Safety Stock

This dynamic safety stock calculation article has two purposes:

  • To calculate safety stock.
  • To explain the logic behind the mathematics or the safety stock equation of the dynamic safety stock calculator.

Reviewing the Current Safety Stock Calculation Explanations

Before writing this article and creating this calculator I reviewed quite a few of the available safety stock calculation spreadsheets available from the Internet. I found all to be deficient in not fully explaining the formula.

  • Almost every one of them asked for a service level input. They did not even explain what the definition of the service level calculation definition to be used.
  • The vast majority ignored the lead time component of the formula. They only provided input for sales/forecast variability (error).

This is not a good way to explain how dynamic safety stock works. Those that left out part of safety stock calculation formulas or safety stock equation did not even explain that it was left out — giving any person who is trying to understand the entire formula a difficult time! How can one know the right for calculating safety stock without showing those assumptions?

The Preference for Talking About the Dynamic Safety Stock Rather than Diving into the Math

It seems as if many people want to talk about dynamic safety stock. No one was interested in making a clear calculator that explains calculating safety stock in clear terms.

There is a broader problem with the formula in that most of the explanations of the formula are not specific enough as to what the author is referring to.

  • For instance, what is “Average Demand.” Without knowing the duration, this does not mean very much of anything.
  • Is this the average over lead time, or the monthly average, the yearly average?

This safety stock calculator provides the intermediate values. It also provides explanations so that the overall logic of the dynamic safety stock calculator can be understood by both students and practitioners.

Basic Concept of Dynamic Safety Stock

The basic concept of the dynamic safety stock calculation is to adjust the safety stock per multiple factors.

Often the explanation of dynamic safety stock is that it is to adjust for both supply and demand variability — as is expressed in the graphic below:

Safety Stock-2

The Uses of Dynamic Safety Stock

The nice thing about the dynamic safety stock calculation or safety stock equation is that it adjusts across the following dimensions:

  • Service level
  • Lead time
  • Forecast error both in variability (regarding lead time and forecast error) as well as lead time duration.

Dynamic safety stock analysis can be used for an SKU or any aggregation level. For instance, this form can be used to model the inventory and supply chain of an entire company.

That is instead of entering the values for a single SKU; the average values can be used. This helps one see the relationships between things like service level and the total amount of stock carried.

This can create a service level to inventory curve. This is something that many companies do not have. It will not, of course, include the total stocking level. So the total stocking level should be estimated and added. Only the safety stock should flex — while the cycle stock should stay the same.

Using Dynamic Safety Stock in Production Systems

While dynamic safety stock is quite valuable for analysis, one of the problems with dynamic safety stock is that it will often calculate a value that companies consider too high. Typically companies will not hold this level of safety stock as is explained in this article. This is because of the fact that the standard dynamic safety stock formula in textbooks does not work, and consistently overestimates the safety stock required.

What This Calculation Is

  • The standard dynamic safety stock calculator is problematic in actual usage. For this reason, I created this safety stock calculator which adopts some of the same concepts but provides more consistent output.
  • Currently, the calculator does not include lead time variability. In most cases, the vast majority of variability is from the demand rather than lead time side.

How the Brightwork Safety Stock Calculation Form Works

This safety stock calculation is not the standard dynamic safety stock. It is instead of our own customized dynamic safety stock. 

This form requires input to provide output. However, it also has default values. You can change any input value and the rest of the formula — the output will change immediately. You can continue making changes, and the form will always update without having to press any button or refresh. This will accurately support you in calculating safety stock.

Note: For some reason, the drop-down field below does not appear to work in Firefox – so if you are using that browser try a different one. Second, when you enter a decimal into the Standard Deviation of Demand and Standard Deviation of Lead Time, you may receive a message “Please enter only digits.” You can ignore that message. The calculator works fine with a decimal point.

For the reverse safety stock calculator sees this link.

Search Our Other Supply Chain Calculators

The Necessity of Fact Checking

We ask a question that anyone working in enterprise software should ask.

Should decisions be made based on sales information from 100% financially biased parties like consulting firms, IT analysts, and vendors to companies that do not specialize in fact-checking?

If the answer is “No,” then perhaps there should be a change to the present approach to IT decision making.

In a market where inaccurate information is commonplace, our conclusion from our research is that software project problems and failures correlate to a lack of fact checking of the claims made by vendors and consulting firms. If you are worried that you don’t have the real story from your current sources, we offer the solution.

Brightwork MRP & S&OP Explorer for Safety Stock

Safety Stock Calculation

The way safety stock is set and sometimes calculated is highly imprecise and problematic. What is needed is a way of calculating safety stock dynamically that is performed in an integrated manner with both constraints and service levels. 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:

 

References

Safety Stock and Service Level Book

Safety Stock

Safety Stock and Service Levels: A New Approach

Important Features About Safety Stock

Safety stock is one of the most commonly discussed topics in supply chain management. Every MRP application and every advanced planning application on the market has either a field for safety stock or can calculate safety stock. However, companies continue to struggle with the right level to set it. Service levels are strongly related to safety stock. However, companies also struggle with how to set service levels.

How Systems Set Safety Stock

The vast majority of systems allow the setting of safety stock by multiple means (static, dynamic, adjustable with the forecast in days’ supply, etc..). However, most systems do not allow the safety stock to be set in a way that is considerate of the inventory that is available to be applied.By reading this book you will:

  • Understand the concepts and formula used for safety stock and service level setting.
  • Common ways of setting safety stock.
  • Service levels and inventory optimization applications.
  • The best real ways of setting both service levels and safety stock.

Chapters

Chapter 1: Introduction
Chapter 2: Safety Stock and Service Levels from a Conceptual Perspective
Chapter 3: The Common Ways of Setting Safety Stock
Chapter 4: The Common Issues with Safety Stock
Chapter 5: Common Issues with Service Level Setting
Chapter 6: Service Level Agreement
Chapter 7: Safety Stock and Service Levels in Inventory Optimization and Multi-Echelon Software
Chapter 8: A Simpler Approach to Comprehensively Setting Safety Stock and Service Levels

To Whom Should Supply Chain Planning Report?

Executive Summary

  • Who supply chain planning reports to versus who it should report to?
  • The current problem is time horizon orientation of supply chain planning.

Introduction to Who Supply Chain Should Report

I was recently asked where a potential newly created supply chain-planning department should report. Online you can find answers to the question of where supply chain planning departments normally report. But this does not answer the question of where the Supply Chain Planning department should report.

In the area of supply chain planning, there are truly very few companies to look up to. Companies want the benefits of supply chain planning. They want better forecasts, lower inventories, more efficient manufacturing, etc.. But they do not want to set up the necessary preconditions to obtaining these benefits.

Regarding the history of supply chain planning – there is little doubt that planning goes back since as long as there have been supply chains. In its modern incarnation, supply chain planning charts it’s growth along with the growth of computerization.

History of MRP and DRP Systems

MRP was developed in the early 1960’s but not implemented in companies in any significant number until the mid-1970’s. By the mid-1980’s ERP systems – which contained both MRP and DRP – had become popular, but these were by in large black box MRP/DRP systems. That is they did not give people the ability to aggregate and control the information in a way that was conducive to planning.

Even with the development of much more sophisticated systems, because most MRP and DRP that is run is typically run from an ERP system. This is still a problem for many companies, as this article explains.

I would propose that supply chain planning became what we think of it today in roughly the mid-1990s. This was when planning systems began to be broadly implemented. This means that modern supply chain planning is recent. Perhaps it should not be all that surprising that it has not been mastered by companies. One of the questions is how can the organization of supply chain planning be made more effective.

Who Supply Chain Planning Normally Reports To

Who an entity reports to controls its orientation and its bias. In researching this article, I was shocked to learn that some companies have their supply chain planning report into sales. This is an undeniable place you do not want supply chain planning reporting to.

  • First, a salesperson and a planner are two different ends of the spectrum.
  • A salesperson wants things in the short term, while a planner is all about the long term.

A second place that supply chain planning most often reports is to operations. There is some logic to this assignment, but it’s not optimal because operations also have a near-term orientation. So while there is some match in the knowledge-based of the two entities as both talk the language of inventory and efficiency, there is a major disconnect regarding timeline orientation. When supply chain planning reports to operations, the VP of Operations will almost never be out of planning. The VP of Operations sets the agenda for those that report to him/her.

The Common Issue of Time Horizon Orientation

Upon analyzing many planning organizations, it is clear that one of the major problems with them is that they are oriented around too short of a horizon. If you look at supply planning systems, it is quite common for supply chain planning not to have total control over the parameters.

At most companies, those in operations can go in and adjust the parameters. In some companies, salespeople also have this ability. The logic that is presented for this is that each group is trying to do something that will “meet customer demand.” Typically this is an oversimplification. One may meet one customer demand, but shorting another customer demand. Or one might meet customer demand in the short term and short a future customer.

The problem is that supply chain planning parameters are not designed to be changed interactively. They should be periodically reviewed, but not changed to meet a short-term objective.

There are ways of placing orders in the ERP system that can meet the need of simply adding more supply to the system. A major problem with making these types of changes to parameters is that after the event passes they are rarely changed back.

Conclusion

The problem with making supply chain planning report to any of the possible entities (sales, operations, IT, etc..) is that they all have time horizons that are shorter that supply chain planning’s time horizon. Supply chain planning cannot be effective at taking advantage of the software tools that it uses if it continually has its timelines shortened.

Planning is supposed to take the long view, and it is difficult to do this if the entity that one is reporting to does not share this as a KPI. For instance, it is quite common for VP’s of Operations to be hostile to planning – and this undermines the resources that the company has put into planning. In my view, supply chain planning should not report to any of these entities, but should instead report directly to the CEO.

Something that this would allow is for the more strategic use of supply chain planning output. Supply chain planning can run long-range simulations that will provide the analytical support that the CEO requires to manage the business better. Unfortunately, few companies leverage their supply chain planning in this way.

The Necessity of Fact Checking

We ask a question that anyone working in enterprise software should ask.

Should decisions be made based on sales information from 100% financially biased parties like consulting firms, IT analysts, and vendors to companies that do not specialize in fact-checking?

If the answer is “No,” then perhaps there should be a change to the present approach to IT decision making.

In a market where inaccurate information is commonplace, our conclusion from our research is that software project problems and failures correlate to a lack of fact checking of the claims made by vendors and consulting firms. If you are worried that you don’t have the real story from your current sources, we offer the solution.

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References

Repairing the MRP System Book

MRP System

Repairing your MRP System

What is the State of MRP?

MRP is in a sorry state in many companies. The author routinely goes into companies where many of the important master data parameters are simply not populated. This was not supposed to be the way it is over 40 years into the introduction of MRP systems.

Getting Serious About MRP Improvement

Improving MRP means both looking to systematic ways to manage the values that MRP needs, regardless of the MRP system used. It can also suggest evaluating what system is being used for MRP and how much it is or is not enabling MRP to be efficiently used. Most consulting companies are interested in implementing MRP systems but have shown little interest in tuning MRP systems to work to meet their potential.

The Most Common Procedure for Supply and Production Planning?

While there are many alternatives to MRP, MRP, along with its outbound sister method DRP, is still the most popular method of performing supply, production planning, and deployment planning. In the experience of the author, almost every company can benefit from an MRP “tune up.” Many of the techniques that the author uses on real projects are explained in this book.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: The Opportunities to Improve MRP
  • Chapter 3: Where Supply Planning Fits Within the Supply Chain
  • Chapter 4: MRP Versus MRP II
  • Chapter 5: MRP Explained
  • Chapter 6: Net Requirements and Pegging in MRP
  • Chapter 7: Where MRP is Applicable
  • Chapter 8: Specific Steps for Improving MRP
  • Chapter 9: Conclusion
  • Appendix A: Calculating MRP

How to Appreciate The Four Supply Planning Threads and Their Timing

Executive Summary

  • There are four major supply planning threads which are distinct from planning runs.
  • We also cover how a rough schedule or rough plan differs from a detailed schedule and S&OP, as well as the deployment plan, redeployment plan and the timing of various supply planning threads as well as the differences between the threads.

Introduction

There is often a good deal of confusion as to what are the primary planning threads for supply planning. Therefore, I have spelled them out in this article.

Different threads make up the supply planning portion of the supply chain process. The supply chain process would include demand planning, supply planning, production planning, etc..

Planning Runs or Planning Threads?

These are often referred to as “planning runs.” However, this is a less accurate way of defining them.

This is because, in system terms, there are often multiple jobs or runs scheduled within each thread. For instance, one may schedule one grouping of product location combinations to be placed into one run, and then another group to be placed in a second run and so on. For instance, one may hear someone say that after the “MRP run” the results were XYZ.

However, the MRP run that is referred to may, in fact, be comprised of one MRP run for a particular product group, another MRP run for another product group and so on. But when the person is using the term, MRP run they are in fact describing all of the MRP runs.

Therefore, I use the term threads instead of the more common “run” because there can be multiple planning runs within any one thread as part of that supply chain process.

Understanding the Threads of Supply Planning

These planning threads are most typically discussed independently from one another. However, they, in fact, have an everyday basis. They are the following:

  1. S&OP & Rough Cut Capacity Plan
  2. The Network/Initial Supply Plan
  3. The Deployment Plan
  4. The Redeployment Plan

S&OP & Rough Cut Capacity Plan

These are used for long-range planning and in most cases are off-line analyses and is not part of the live environment. The term rough schedule or rough plan or rough capacity schedule is used to differentiate it from a detailed schedule. A rough plan is aggregated and will have only high-level resources information if it uses any resource information at all.

A rough plan, schedule or capacity plan is intended to allow higher-ups to gain an overview, but the rough plan, schedule or rough-cut capacity plan is only a high-level representation of what will actually occur. A rough plan or rough schedule or rough cut schedule is the starting point.

The Network/Initial Supply Plan: (performed by MRP in ERP systems)

Produces initial production and procurement plan. Is focused on bringing stock into the supply network, and in creating stock with planned production orders. Can also be called the master production schedule (MPS), if the initial supply plan is run under certain criteria. This is covered in this article.

The Deployment Plan: (performed by DRP in ERP systems)

Focused on pushing stock from locations at the beginning of the supply network to the end of the supply network.

The Redeployment Plan: (performed by specialized applications with redeployment functionality or with a custom report)

Focused on repositioning stock, which is already in the supply network to locations where it has a higher probability of consumption. More on this in this article.

Timings of the Supply Chain Process Planning Threads

The following are the general frequency of the different supply planning processes.

  1. Rough Cut Capacity Plan / S&OP Run / MPS Run / Unconstrained Capacity Run: Weekly to Monthly
  2. Initial Supply Plan: Daily to Weekly
  3. Deployment Plan: Daily to Weekly
  4. Redeployment Plan: Weekly to Quarterly

Similarities Between the Supply Chain Process Planning Threads

similarities-between-sop-mps-and-rccp

The Differences Between the Supply Chain Planning Threads

differences-between-sop-mps-and-rccp

More Details on the Supply Planning Threads

This article will be very different from what you may have read on this topic. This is because I see S&OP, MPS, and RCCP as all different cuts or derivations of the initial supply plan (all of which also contain the forecast and at least production, but in some cases supply planning constraints).

They are also defined by their level of granularity, whether they are a rough plan or a detailed plan.

  • A series of supply planning method and method modifiers were developed over time to create the initial supply plan.
  • The MPS and RCCP are direct copies of the initial supply plan, with changes to their thread characteristics. For instance, a copy of the initial supply plan configuration may be put into a simulation version.
  • The planning time the horizon may be lengthened, and its resources made unconstrained. (This will be demonstrated in just a few paragraphs.)

Conclusion

The S&OP thread is not a complete copy of the initial supply plan, as extra information is required, for instance from finance. There are just a few adjustments and additions necessary to convert an initial supply plan into an S&OP plan and to fit within the supply chain process of planning.

The vast majority of supply planning applications are not designed to support S&OP within their applications natively. And the penetration of specialized S&OP applications is low. In most cases, supply planning applications support S&OP by providing extracts.

The Necessity of Fact Checking

We ask a question that anyone working in enterprise software should ask.

Should decisions be made based on sales information from 100% financially biased parties like consulting firms, IT analysts, and vendors to companies that do not specialize in fact-checking?

If the answer is “No,” then perhaps there should be a change to the present approach to IT decision making.

In a market where inaccurate information is commonplace, our conclusion from our research is that software project problems and failures correlate to a lack of fact checking of the claims made by vendors and consulting firms. If you are worried that you don’t have the real story from your current sources, we offer the solution.

Search Our Supply Planning Articles

Brightwork MRP & S&OP Explorer for Constraining

Improving Your Constraint Planning

Brightwork Research & Analysis offers the following supply planning tuning software with a new approach to managing capacity constraints, which is free to use in the beginning. See by clicking the image below:

 

 References

The various supply planning threads are covered in the following books.

Sales and Operations Planning Book

S&OP

Sales and Operations Planning in Software

Getting Clear on S&OP

S&OP is a commonly discussed, yet infrequently mastered area of planning. S&OP continues to be one of the most misused and overused terms in business.

S&OP is a type of long-term planning that attempts to match supply and demand and provides input to a financial plan to support the firm’s overall strategy. S&OP is in part a subcategory of consensus-based forecasting. It means driving to a consensus on what are branches within the company or entity that are often more competitive with one another than actually collaborative.

No Problem on Getting Consensus?

Obtaining this consensus is no easy task, and beyond the political aspects of S&OP, S&OP comes with its unique software challenges because it means both planning at a higher level of aggregation than other planning processes, while also exposing the specific constraints so that those constraints can be evaluated for possible alteration.

All of these issues and more are addressed in specific detail in this book. By reading this book you will learn:

  • What is the difference between S&OP and IBP, and how does this relate to the difference that is often described in the marketplace?
  • What are important features of S&OP applications and how do some standard S&OP applications differ in their design?
  • What are the implications of aggregation to S&OP application and process?
  • What are the political considerations that are required to be understood to be successful with S&OP?
  • What are the natural domains for executive adjustment versus lower level planning adjustment?

Chapters

  • Chapter 1: Introduction
  • Chapter 2: The Relationship Between Planning Systems and S&OP Systems
  • Chapter 3: S&OP Versus Integrated Business Planning
  • Chapter 4: SAP IBP, ANAPLAN & SAP Cash Management
  • Chapter 5: The Impact for SAP IBP with HANA
  • Chapter 6: S&OP, Aggregation, and Forecast Hierarchies
  • Chapter 7: Challenges in S&OP Implementation
  • Chapter 8: How Misunderstanding Service Level Undermines Effective S&OP
  • Chapter 9: Conclusion

How to Best Understand Supply Chain Multisourcing

Executive Summary

  • Multisourcing is the use of sourcing from multiple providers and uses logic to make a selection.
  • We cover how multisourcing works.

Introduction

Within supply planning systems, in the vast majority of instances, single locations are assigned to fulfill the demand of internal locations. Multisourcing is the opposite of this and can mean the fulfillment of demand from more than one, but up to many sources of supply.

  • Multisourcing is the ability for a supply planning system to intelligently choose between alternate sources of supply. This can apply both to the selection of suppliers as well as to the selection of a source of supply along with the supply network for internal locations.
  • The functionality’s driving logic can be multidimensional, from total costs to meeting order dates, etc..

The Reasons for Multisourcing

  • One common reason for multisourcing is when one location is the primary location, but cannot handle the capacity of the order, then the supply planning system would move to a second or even third location in order to satisfy the demand.
  • Another reason can be to spread, by rough percentages, the total demand among various sources of supply in order to meet contract responsibilities.

The Reality of Multisourcing

Very few supply planning applications can actually perform multisourcing. And I am unaware of any ERP system that can do this. Some applications like SAP SNP that are sold on the ability to perform multisourcing actually cannot practically do so because of the computation time, and therefore after great expense, the functionality is turned off by companies that implement it.

For this reason, in the vast majority of instances, multisourcing continues to be a manual decision and a change made to the purchase order or stock transfer. Sourcing teams within companies contain individuals who know what the various sources of supply are. They alter the purchase order or stock transfer to account for the need.

Multi sourcing is a motivator for companies to implement cost optimization, as it is stated that the cost optimizer can be used to perform multi-sourcing. It should not be simply assumed that your company will be able to get multi-sourcing to work. Still, multi-sourcing is a major motivating factor for the selection of cost optimization as a method of supply planning.

Multi-Sourcing Due Diligence

Before merely assuming your company will be able to enable multi-sourcing successfully, and therefore choosing cost optimization, it makes sense to evaluate the problems other companies have had in activating this functionality and the likelihood your company has in doing the same.

Introduction to Multi-Sourcing

One of the exciting features of software selection is why companies select one method of supply planning over another.

Primary reasons companies select cost optimization are:

  • To perform constraint-based planning.
  • To perform multi-sourcing.

Constraint-based planning is the ability to restrict capacity. Primarily in the production resources. Although hypothetically companies are told, they will be constrained by other supply chain constraints. Constraints like transportation and warehousing.

Multi-Sourcing

Multisourcing is the ability to pull sourcing from multiple locations and to make decisions based upon costs. It is easy to setup locations as sources of supply for an area, and this is performed in all supply planning systems, through the master data setup by making the locations valid to and from shipping point. The logic for when to source from one location versus another and making this match the business requirements is where the trick comes. The way that cost optimization accomplishes this is with the combination of transportation lane costs and resource constraints.

The Multi-Sourcing Requirement

In the perfect state, one location would have a higher cost to supply the second location. However, when the primary sourcing location runs out of capacity, the optimizer, in concept, will then move to the secondary source of supply, without the planner having to do anything. The diagram below can be used to help understand this.

In this scenario, two producing locations have been set up as sources of supply for Location A, which is a DC. If the requirements are within location B’s capacity, location B fulfills the requirements from location A, because the transportation lane cost is only $1 per mile, versus $2 per mile as with location C. When the costs are set up in this way, nothing further is needed to be done. The system will naturally source from location B.

However, if in any one period, the requirements are higher than 100 units, location C will begin to serve as a source of supply to location A.

If the resource that produces the product for location A goes down for maintenance, the resource has no capacity in location B, and C becomes the sole source of supply for this material to location A.

A major reason this is so appealing is that this hypothetical example auto-adjusts. Executive decision makers love this idea and foresee great cost savings from such a system. However, the reality of what tends to occur with multi-sourcing is quite a bit different from this hypothetical example.

The Reality of Multi-Sourcing in SAP

The fact is, in SAP SNP, at least few companies make the jump to multi-sourcing. There are several reasons for this, and these reasons should be considered when selecting both a supply planning method as well as selecting software.

  1. SNP is a very high maintenance application. This means that there are always many other issues to fix and other things to focus on before multi-source can be reviewed. It can and often is years of fixing problems and focusing on other things until multi-source can be reconsidered.
  2. Multi-source significantly increases the run time of the optimizer.
  3. Several clients, I have had that started out with multi-source turned on, ended up turning it off because of the run-time specifically.
  4. Turning on multi-sourcing in addition to getting resource constraints right and keeping them updated is a heavy burden for even the biggest companies, and both of these capabilities must be present in order for multi-sourcing to work. Therefore, while seeming relatively simple in concept, it is, in fact, one of the most evolved uses of cost optimization for supply planning.

Conclusion

The assumption that a company will be able to multi-source with a cost optimizer drives a decision to the cost optimization method over others. It is not an assumption that is practical. To perform multi-sourcing with SAP SNP, a company must maintain the master data. They must do this for the multi-source option. But must also spend on the servers to make the multi-sourcing model run. In short, multi-sourcing is expensive to do.

If companies are not willing to support this expensive solution, it makes little sense to head down this path. Right now, across the US, there are plans to turn on multi-sourcing in supply planning applications, that may never work properly. This is one of the major areas of cost optimization that promises great things. But which companies are not able to successfully implement.

Connections

Multi-plant planning is considered (by this site) to be the second method within the Superplant Concept (see link for definition).

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Brightwork MRP & S&OP Explorer for Tuning

Tuning ERP and External Planning Systems with Brightwork Explorer

MRP and supply planning systems require tuning in order to get the most out of them. Brightwork MRP & S&OP Explorer provides this tuning, which is free to use in the beginning. See by clicking the image below:

References

Superplant Book

 

SUPERPLANT

Superplant: Creating a Nimble Manufacturing Enterprise with Adaptive Planning Software

What is the Superplant Concept?

This book addresses several production and supply planning software functionalities that are all related to the location-based adaptability of the supply chain planning application (multi-plant planning and subcontracting, and contract manufacturing planning).

Solving a Historic Weakness in Production Planning and Scheduling Software

This adaptability is a historical weakness of both advanced planning applications as well as ERP systems. Some of this functionality is rarely found in commercially-available applications, while other functionality is more commonly found but ‘s hard to implement. This book explains these how these multiple functionalities can be leveraged to provide the ultimate in planning flexibility in both supply and production planning.

Why This Book is Unique

The only book about planning for a “Superplant,” by the author who coined the term.

In an environment of increasingly globalized manufacturing, a very long production line that spans the globe is more common than ever. For an increasing number of corporations, multi-plant planning is a reality. “Superplant” describes the ability to plan separate locations as if they were part of one giant plant – or superplant, and is the more accurate modeling of location interdependencies for production and supply planning than is provided by standard advanced planning functionality.

This book delves into the three advanced functionalities that must be enabled for superplant planning: multi-plant planning, subcontracting and multi-source planning. By reading this book you will:

  • Investigate how multi-site planning works from a design perspective.
  • Learn about the functionality that exists to specifically address multi-plant planning and understand why most supply planning software can do nothing with multiple plants.
  • Explore in-depth the PlanetTogether application, which targets the unique planning requirements of a superplant.
  • Learn how to set up master data objects to support multi-plant planning functionality.
  • Improve Key Performance Indicators (KPIs) through proper deployment of multi-plant planning functionality.
  • Examine how subcontracting, and contract manufacturing fit into the superplant concept

Who is This Book For?

This book was written for those with interest in leveraging leading approaches in the supply network for planning improvement. The particular audience would range from executive decision makers to software implementers to supply and production planners.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: Understanding a Superplant Conceptually
  • Chapter 3: Multi-plant Planning
  • Chapter 4: Single Versus Multi-pass Planning
  • Chapter 5: Multi-source Planning
  • Chapter 6: Subcontracting Planning and Execution
  • Chapter 7: Combining All Three Superplant Functionalities
  • Chapter 8: The Superplant and the Integration Between ERP and the External Planning System
  • Chapter 9: Superplant-enabled Capable-to-promise
  • Chapter 10: Conclusion
  • Appendix A: Labor Pools in Galaxy APS
  • Appendix B: Time Horizons in Galaxy APS
  • Appendix C: Prioritizing Internal Demand for Subcomponents over External Demand

The Problem with SNP Optimizer Flow Control

Executive Summary

  • The SNP optimizer has two types of methods of deployment. The deployment optimizer has major problems with the second method, which is called cost optimization.
  • The deployment optimizer cannot fair sharing stock to locations.
  • This topic is hidden by SAP and SAP consulting companies from customers.

Introduction to Optimization in APO

In its early years, SAP APO was sold on its ability to perform optimization. This is primarily because it was an industry-wide practice to market advanced planning software in this way. In fact, SAP APO, or SAP Advanced Planning & Optimization, had the term directly in its name.

The General Versus Specific Meaning of Optimization

Optimization has two general meanings. One is more of a business nature, which basically means to produce the best outcomes. The other has to do with the area of operations research, from where the supply chain optimization originates. For this article, we’ll define optimization as the use of software tools and processes to ensure the optimal operation of a supply chain, including the optimal location of inventory within the supply chain and the minimizing of operating costs (including manufacturing costs, transportation costs, and distribution costs).

The Two Different Methods of Performing Deployment in SNP

There are two ways to run the deployment in SNP:

  1. The Deployment Heuristic
  2. The Deployment Optimizer

These two methods provide two very different sets of functionality. This article will focus on the optimizer. Details on the deployment heuristic can be found in this article.

Deployment Complexity

Deployment can be considered somewhat of an afterthought to the actual planning run. However, that is not a correct way of thinking about it. Deployment is the planning that essentially takes action to correct either an overage or underage between two locations. There are two ways to run Deployment in SNP; Heuristics and Optimization. In this post, we will only be concerned with optimization. SNP has a very significant number of settings for the deployment, and just this fact means that deployment is far more an afterthought.

Methods of Running the Optimizer

The deployment optimizer is quite flexible. This includes how safety stock deviations are treated (absolutely or relatively), whether discreet or linear optimization is used, whether cost-based or strict prioritization should be used, whether existing orders should be deleted, global push, pull and SNP check horizons.

However, regarding costs, the optimizer can have relative costs setup for:

  • Costs of transporting stock
  • Costs for storage stock
  • Costs for handling stock
  • Costs for violating safety stock
  • Costs for not delivering

Duplication of Optimizer Profile

The following profile will seem extremely familiar; this is because the deployment optimizer uses the same screens as the optimizer configuration. You can see this here.

This is accessible from Profiles. This allows us to apply different Profiles to different Product Location combinations.

SNP ProfilesWe name the profile as well.

General Constraints Tab

Back to the configuration of the Deployment Optimizer Profile. In this tab, constraints can be set up, that is which constraints the optimizer should respect, in addition to whether it should respect lot sizes. One of the most important setups is whether the optimization should perform linear or discrete optimization. Linear optimization is faster, but discrete optimization is more realistic. If we are not simply testing the system to get a general response, we will go with discrete optimization. Safety stock and shelf life can be incorporated or not incorporated in the optimization run.

Deployment Optimizer 0

Discrete Constraints Tab

This provides more constraints, which are respected if the “Discrete Optimization” radio button is selected. Below this costs can be incorporated as well for both transport and for the procurement quantity.

Shelf Life

An interesting feature here is that the SNP optimizer as the strongest recognition of the shelf life parameter, while CTM has only recently added consideration for shelf life, and its degree of respect for this constraint is something we have not had time to research. Thus projects that use CTM and heuristics (which is the vast majority of SNP implementations) can end up losing out on shelf life functionality. However, by running the deployment optimizer with the SNP heuristics or CTM, one can get the benefit of optimization.

Deployment Optimizer 2Model Parameters Tab

This tells the model whether to consider the average stock on hand or look for the stock at the end of the period. I typically do not change these settings or the Bucket Offset During Shipment.

Deployment Optimizer 3Integration Tab

This tab deals with the duration of the horizon. It also allows you to change how the system views Distribution Demand. If it is set as a hard constraint, it must meet the distribution demand. If it can’t be then the solution is not optimal. I do not know this as Regard as a Hard Constraint, but instead, set it regards as a forecast.

Deployment Optimizer 5These are the options available for the Fixed Order Handling. You can select the option below, which determine how the orders should be considered (either part of the demand forecast, or the customer demand) and if the orders should be considered a hard or semi-hard constraint. The lowest two options determine if any orders should be allowed to be deleted.

Deployment Optimizer InsertAutomatic Cost Generation Tab

This is a highly abstract number of settings, which we will skip.

Deployment Optimizer 6Extend Settings Tab

This determines which consistency checks should be run.

Deployment Optimizer 7Deployment Parameters Tab

This determines if the shortage of supply should be handled by which method. This is important because it changes whether the deployment optimizer is based upon costs or based upon the demands and performs a fair share.

It is surprising to many people that the optimizer performs as fair share. However, it does.

Deployment Optimizer 8

This tab supposedly provides the option to perform a fair share with the optimizer, which is very strange as its counter to how cost optimizers work. More on this topic can be read here:

In fact, a check at SDN.SAP.COM shows only a few entries on fair share for the cost optimizer. When I tested it, I did not find it to work, and SAP sells an add-in, which I have also tested and not found to work properly. This is not something which companies should be using the SNP optimizer for.

Running the Optimizer

The optimizer can be run interactively in the planning book. However, it can also be run in the background.

OptimizerThese, of course, can be saved as variants, so that they can repeatedly be rerun, and multiple variants can be created.

Problematic Outcomes for The SNP Deployment Optimizer

Interestingly, costs control the flow of product throughout the supply network when the cost optimizer is used. However, SNP does not have a “tie-breaking” logic which can essentially share supply among different child locations.

There is no nuance here in this design. The problem with this is that costs are very binary. So there is parent location A, which deploys to child locations B and C. If all other costs are equal, and the cost of nonfulfillment is set as follows:

  1. A = 50
  2. B = 30
  3. C = 25

The stock will be kept back at location A because this is the lowest cost solution. However, the optimizer will keep all the demand and share none of it with B or C. If the optimizer nondelivery charge is changed so that C = 55, then C will simply get all of the stock. If all locations are set to the same cost, SNP seems to decide by random (although it always seems to send to the same location) which one of the locations will receive the stock, and will again ship all of it to that location.

This lack of attention to detail and oversimplification of deployment functionality is frankly shocking.

Background of Fair Share in the SNP Optimizer

I always find it strange when I am asked about performing fair share distribution with the cost optimizer. This is because the optimizer is designed to move product through a supply network based upon costs. I always like to keep the other costs in the optimizer equal when analyzing any one particular cost. So in the scenario, I am discussing all costs are the same. This includes:

  1. Production
  2. Procurement
  3. Prod Cap
  4. Transport
  5. Transport Cap
  6. Storage
  7. Storage Cap
  8. Safety Stock
  9. Handling Cap
  10. Late Delivery
  11. No Delivery (only this is changed in our discussion)
  12. Rec.Bound
  13. Quota Arrangement
  14. Setup C.

Setting up Costs in the SNP Optimizer

While there are a lot of costs listed here, not all of them necessarily need to be used for to run the optimizer. The system will simply use those that you populate and not attribute any costs for those that you do not.

All of these costs can be found in SAP SNP: Profile of Cost Multipliers. These penalties are set in specific locations, for instance, the transportation cost is set at the transportation lane. However, the penalties can be increased in proportion to one another.

Therefore, the penalties can be kept the same, but the but these can be used to multiply the cost categories by.

Here is the cost of the transportation lane. If the cost here was entered as 5 and the multiplier in the SNP Profile of Cost Multipliers were 2, then the cost incurred by moving a truck over this lane would be 10. It’s not very useful to think of this regarding dollars, because it is not actually dollars. I find it more instructive to think of it as points.

The points are all relative. If all the costs/points are increased by a factor of 5 or 10, the same result comes out of the optimizer.

The most commonly populated optimizer costs that I have seen in the SAP optimizer are the following:

  1. Production
  2. Procurement
  3. Transport
  4. Storage
  5. Safety Stock
  6. No Delivery (only this is changed in our discussion)

Fair Share Distribution / Deployment

While SAP SNP has in-built fair share distribution/deployment capability, supposedly, it does not work very well. The concept of fair share is the opposite of cost optimization. Because of these limiting factors, SNP offers a fair share patch, which clients can buy. I analyzed this patch and found a lot of illogical things. From what I could tell, the patch seemed to make the optimizer ignore the case quantity or rounding values.

Therefore if an optimizer deployment run looked like the following when the patch was included:

  1. Location A = 5
  2. Location B = 15
  3. Location C = 100
  4. Location D = 10
  5. Location E = 75

and the minimum case size or other rounding value was 40, the deployment run without the fair share patch would look like the following:

  1. Location A = 40
  2. Location B = 40
  3. Location C = 100
  4. Location D = 40
  5. Location E = None

What happened was that when the rounding value was implemented, the deployment optimizer was able to deploy to fewer locations, because it has to satisfy rounding values. However, the rounding values were in fact necessary. So any deployment optimizer run that produced below case quantity results simply had to be rounded manually after the fact.

Something else I noticed was that there did not seem to be any logic to how the fair share was allocated. There was no proportionality. So if a demand at location A was 20 and the demand at location B was 20, the deployment optimizer fair share patch might send 5 to A and 15 to B., In fact, all the fair share patch seemed to do was violate the case quantity. It was essentially no value add.

Fair Share Distribution / Deployment with Costs

One request that was made of me was to see if the deployment optimizer could be made to fair share between parent and child locations through using costs. So if A which supplies to B and C and they all had demands of 40 (which would appear to A as distribution demands from B and C), the could the deployment optimizer be made to allocate 120 units equally among the three.

Firstly, this is a request, but no documentation states that the deployment optimizer can do this. There is a setting under the Demand at Source Location that is supposed to be able to control for this. This is the “Demand at Source Location” so it can be made to consider either forecasts or sales orders at the parent location. There was some confusion as to where this should actually be set (at the parent or the child), but I tested it at both, and neither worked.

I communicated with SAP on this, and the comments that came back from development contracted this functionality. In my consulting experience, I have never seen this work, but on the other hand, I have also no been asked to configure this requirement.

Getting back to obtaining a fair share of costs, one might think that by setting the costs of non-delivery the same at all locations in the supply network. However, when I tested it, it sent all material to one location and divided none of it. This brings up a general point about the binary nature of how material is flow controlled in cost optimization that is a real concern, and which surprises me that it is not brought up more frequently as a major design problem. It has given me some real concern as to how well cost optimizers match real term requirements and is explained in more in this article.

Problematic Outcomes for The SNP Deployment Optimizer

The optimizer has numbers problems with fair sharing. SAP’s fair share components for the deployment optimizer do not work. Secondly, attempting to emulate a fair share by setting the non-delivery penalty costs the same at the locations where the fair share is desired did not work. It seems this is because the SNP optimizer does not have any tie-breaking logic for when costs are identical, and how it selects which location to use are not apparent to me.

I am curious if anyone has any comments on this. The behavior I see in the deployment optimizer is concerning as I can’t see it matching business requirements. I recall hearing from an IBM consultant several years ago how wonderfully nuanced and capable the optimizer was once it is “tuned.” I do not see this. However, for years my viewpoint has been obscured by the fact that the optimizers I have been working with have been misconfigured. I describe the problem with cost setting in this article.

However, even the simplest location to location movement within one echelon seems to be a problem. It is simply not desirable to send all stock to one location when multiple locations have a demand. It is also not useful to source all stock from one location until the stock is depleted, before shifting to a secondary location. Anyone who has worked in supply chain for some amount of time should know this, which is why I am a bit perplexed why there is not more conversation on this topic.

The Issue with Cost Flow Control

Costs control the flow of product throughout the supply network when the cost optimizer is used. Some consultants will bring up the Fair Share selection on the SNP cost optimizer profile. However, that does not appear to work. This is described in this article.

SAP sells a fair share patch which is added to APO. The results that I have tested that come from this fair share patch do not make any sense. That is in either in how the distribution compared to demand, or in how it ignores the rounding value. For this reason, I reject the common consultant claim that the SNP optimizer has fair share capability. I have concluded that the only way to control the deployment of the cost optimizer is with costs.

Many companies do not realize what I will explain below before they go down the track of using the deployment optimizer.

How the Deployment Optimizer Works

You can use several costs to control the deployment optimizer in this respect, but I have used the cost of non-delivery. SNP lacks a “tie-breaking” logic which can share supply among different child locations. I will use an example to explain how this works. In the example, there is parent location A. A deploys to child locations B and C. If all other costs are equal, and I have set them to be equal in the testing, the cost of nonfulfillment is set as follows:

NonDelivery Penalties for One Product at Three Locations

Test Case 1

  1. Location A = 50
  2. Location B = 30
  3. Location C = 25

In this case, as expected, the stock will be kept back at location A because this is the lowest cost solution. That is it reduces the cost of the objective function.

However, the binary nature of the decision-making of the cost optimizer is evident here when the results of this test are that the optimizer will keep all the demand and share none of it with B or C.

Test Case 2

  • If the optimizer nondelivery penalties are changed so that C = 55, then C will simply get all of the stock. If all locations are set to the same cost, SNP seems to decide at random.
  • But it always seems to send to the same location. This is one of the locations will receive the stock, and will again ship all of it to that location.

The problem is very few supply chains work like this, so what I have described will simply not meet the business requirement.

In fact, the only time it works properly is when there is the sufficient demand, and the deployment is set up as a pull. The inbound or network optimizer works in the same problematic way. If locations E and F are supplied by location D and the costs are as follows:

Test Case 3

  1. Location D = 50
  2. Location E = 30
  3. Location F = 25

The higher unfulfilled demand costs at D will create a pull strategy out to E and F.

Therefore stock will be kept at D until a demand is required at E or F. However, if both E and F require stock at the same time, the stock will always go to E. This is because it has the higher unfulfilled demand penalty, and all of it will go to E until the demand at E is entirely satisfied before F is supplied. Who would want that setup?

Again, the location which is being sent product will 100% of the time chose the lowest cost source location until there is no more capacity and then switch to the next highest and the next highest, as long as it can meet the dates. How realistic is this? I would say not very.

Test Case 4

If the change the scenario the following holds:

  1. Location D =15
  2. Location E = 30
  3. Location F = 25

Because D is now lower, the optimizer will want to keep the supply there. This is so this is now a pull deployment strategy. Again if both E and F have demands at the same time, E will again get all of the supply.

When there is sufficient stock, the system works fine. But in that case costs have nothing to do with the movement and it is strictly based on the demand. A much simpler way to run by demand is to use MRP and DRP. A main benefit of optimization is that it processes sub-networks, as this article describes.

costs-snp-infographic

What Most Companies Want From Deployment

Most would prefer to apportion stock about the demands, more of a fair share situation. The sophisticated optimization functionality in SNP is not particularly usable because for deployment. It is so binary.

People are usually wowed by cost optimization because it’s so complex. 

However several of the assumptions taken by the SNP optimizer are problematic for implementation. The flow control is just too”twitchy.” It allocates everything to one location while leaving other locations barren. Sure, costs may have been minimized, but to what end? Costs are merely a control device. They are not set to match real costs.

This is so the company does not have to develop costs that are necessarily accurate, as described in this article.

What Cost Optimization Then Means

So costs in any real sense were not minimized. Fake costs which have no relationship to real costs were minimized. And so? This would be like saying you had lost 20 pounds, which you had earlier imagined around your waist. Again, not helpful or beneficial to anyone. A better system would have stock moving in the proportions of demand. And SNP has this as fair share functionality in the optimizer. It also as has it in the SNP heuristic. However, in testing, it did not at all seem to work. Companies that want to use fair share should instead use the deployment heuristic.

Going Down the Path of SNP Optimization

I find many companies that go down the path of fully implementing the SNP deployment optimizer. But they do this without actually having it meet any of the requirements for a fair share. The supposed fair share patch has been used in companies to think they have a way to get the deployment optimizer to do what they want.

The Problem and the Larger Context

The tactical flow of material as controlled by non-delivery penalty costs is a serious disadvantage to cost optimization. It is surprising to me that I had not read any material about this topic and had to come to this conclusion on my own.

Many times in the past I have heard people make excuses for the SNP cost optimizer not giving the desired output. But this is a serious design issue that is getting past companies. For years those on the realistic side of the fence have questioned the reasons for the lack of successful cost optimization projects.  Not all optimizers are created equal. Cost optimization is complex. Without the necessary functionality, it is hard to count a problem implementation against cost optimization.

Linear Versus Discrete Optimization

Optimization works best in situations that are perfectly “linear,” so that inputs can be increased or decreased in a continuous fashion. An example of a linear input is an order quantity. In a perfectly linear optimization, any order quantity from zero to infinity can be placed and fulfilled. But in reality, supply chains are not perfectly linear problems.

For example, the lot size is a discrete value that limits the flexibility of the order quantity. One item may be ordered in units of 50, but if 135 units are desired, and the current inventory is less than 35, then 150 must be ordered to meet this demand. SAP SCM has some techniques, such as lot size, that alter the problem being solved from perfectly linear to discrete, or what is known as a step function. This is very important for making the resulting recommendation realistic

The Reduced Focus on Optimization

Although optimization drove development in SAP SCM at one time, it no longer does. The evidence for this is that optimization is an option in three of the older applications (Supply Network Planning [SNP], Production Planning and Detailed Scheduling [PP/DS], and SAP Transportation Management [SAP TM], formerly known as Transportation Planning and Vehicle Scheduling [TP/VS]). But isn’t an option in any of the newer applications (SAP Extended Warehouse Management [SAP EWM], SAP Supply Network Collaboration [SAP SNC], SAP Event Management, SAP Service Parts Planning [SAP SPP], and SAP Forecasting and Replenishment). Also, the core optimization functionality in SAP SCM has been stabilized for some time. This shift is partly because optimization didn’t meet its originally envisioned potential. So, the newer applications in SAP SCM have tended to downplay optimization in favor of other functionality.

Conclusion

While it may sound like an interesting concept, supply networks are not simply sequences of locations which have cost associated activities. That is storage costs, non-delivery costs, etc. that are modeled in an optimizer. These are not costs that must be minimized.

Optimization may be working well when there is sufficient stock, but then again so will any deployment system based on dates using the SNP heuristic. I have not tested this on other vendor systems. I will be sending this article to several vendors to gain their insights into how their cost optimizers work in this respect.

The Necessity of Fact Checking

We ask a question that anyone working in enterprise software should ask.

Should decisions be made based on sales information from 100% financially biased parties like consulting firms, IT analysts, and vendors to companies that do not specialize in fact-checking?

If the answer is “No,” then perhaps there should be a change to the present approach to IT decision making.

In a market where inaccurate information is commonplace, our conclusion from our research is that software project problems and failures correlate to a lack of fact checking of the claims made by vendors and consulting firms. If you are worried that you don’t have the real story from your current sources, we offer the solution.

Search Our Other Supply Chain Optimization Content

Brightwork MRP & S&OP Explorer for Tuning

Tuning ERP and External Planning Systems with Brightwork Explorer

MRP and supply planning systems require tuning in order to get the most out of them. Brightwork MRP & S&OP Explorer provides this tuning, which is free to use in the beginning. See by clicking the image below:

References

The SNP optimizer is covered in the following book.

Constraint Planning Book

 

CONSTRAINED

Constrained Supply and Production Planning in SAP APO

How Constrained Supply and Production Planning Works

Constraint-based planning generates something that is appealing to all manufacturers: a feasible supply and production plan. However, constraint-based planning software was first implemented over twenty years ago, and yet few companies (as a percentage that all that have tried) have mastered constraint-based planning.

Getting the Real Story

This book provides the background information, detailed explanations, step-by-step examples, and real-life scenarios to assist a company in becoming proficient at constraint-based planning, along with valuable information about what SAP APO can do for supply and production planning in reality, rather than just in theory. Here you will learn about resources-the mechanism for constraining the plan in APO and for determining the feasibility of the plan and how constrained supply and production planning work together (and how they don’t).
Also, this book talks about constraint-based planning at the supplier level: can a vendor’s production be capacity-constrained?
By reading this book, you will learn:
  • The different resources available in APO, how production resources differ from supply planning resources, and the role resources and other significant constraints play in constraint-based planning.
  • How constraints integrate across the supply planning and production planning applications.
  • The areas of disconnect between supply and production planning applications, and between SNP and PP/DS in particular.
  • The difference between unconstrained (or infinite) planning and constraint-based planning.
  • The benefits of constraint-based planning and how it differs from capacity leveling.
  • Various types of demand, and how backward and forwards were scheduling work.
  • The benefits of using production constraints in the supply planning system, and how SNP and PP/DS can be synchronized to produce the desired output.
  • The methods that can do constraint-based planning in SNP and PP/DS–heuristics, CTM, and optimization–and how to configure these methods.
  • The difference between hard and soft constraints, and how to plan using multiple constraints.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: Understanding the Basics of Constraints in Supply and
    Production Planning Software
  • Chapter 3: Integrating Supply and Production Software with Constraints
  • Chapter 4: Constraint-based Methods in APO
  • Chapter 5: Resources
  • Chapter 6: Capacity-constraining Vendors/Suppliers
  • Chapter 7: The Disconnection Points Between Supply Planning and
    Production Planning
  • Chapter 8: Conclusion

How to Use The Simplex Method and Dual Simplex Method with CPLEX and Frontline

Executive Summary

  • There are several ways of solving a supply chain optimization problem with CPLEX.
  • These settings are made in both supply planning applications as well as off the shelf optimizers.
  • There is both a simplex method and a duplex method.

Introduction

The solution procedure is the optimization method that is applied. I often describe and differentiate optimizers based upon their objective function. Therefore, optimizers with an objective function of minimizing costs, I call cost optimizers. Those that attempt to minimize inventory at a set service level, or maximize service level at a set inventory level are called inventory optimizers.

To read about this type of optimization see this article.

CPLEX Options

However, something I have discussed significantly less is the optimization solution selected, which is a subset of the optimization method.

There are some methods, but a small number of them are the most popular. For applications like supply planning, the following would apply.

Where this is set in many optimizers is very clear. This is a screenshot of the Solution Methods tab of the SAP SNP Optimizer. The decomposition methods describe how the problem is segmented to improve run times.

More on this topic can be read about in this article.

However, notice the options at the bottom of the screenshot under LP Solution Procedure.

SNP Optimizer Solution Methods Tab

There are three LP Solution Procedures available to choose from. This is Primal Simplex, Dual Simplex Method and Interior Point Method, which can be used along with either of the first two options. As the CPLEX solver is actually what is being used, these are the same options provided by CPLEX.

These are described by Wikipedia below:

The IBM ILOG CPLEX Optimizer solves integer programming problems, very large[2] linear programming problems using either primal or dual variants of the simplex method or the barrier interior point method, convex and non-convex quadratic programming problems, and convex quadratically constrained problems (solved via Second-order cone programming, or SOCP).– Wikipedia

The methods move from the most simple, being the Primal Simplex to the most complex, the Interior Point Method. The Simplex is the most commonly used. The simplex method must work with equalities, not inequalities, and thus requires the introduction of slack variables, which measures the amount of unused capacity in the resource.

Dual Simplex Method

The Dual Simplex method is used for a particular type of problem where the equality constraints are set up in a specific way. This quote is from Elmer G. Wiens site on operations research:

Like the primal simplex method (or just the simplex), the standard form of the dual simplex method assumes all constraints are <= or =, but places no restrictions on the signs of the RHS (right hand side variables — to read more about right hand side variables see this article. The dual simplex method algorithm consists of three phases.

Phase 0 is identical to Phase 0 of the primal simplex method, as the artificial variables are replaced by the primal variables in the basis. However, the dual simplex method algorithm in Phase 1 searches for a feasible dual program, while in Phase 2, it searches for the optimal dual program, simultaneously generating the optimal primal program. – Elmer G. Weins

The interior point method solves problems differently from the primal or the dual method simplex in that the interior point begins from the interior of the problem, rather than looking across the surface. Where the optimizer starts it search is of great importance as to the final solution it develops.

For instance, MatLab in their documentation (a separate optimizer not associated with SAP), describes how to “change the initial point” of the optimizer in at least one of its online documentation pages.

This is not the only way to change the starting point. The Heuristic First Solution selection will also change the optimizers’ first point by estimating the best solution with a heuristic before the optimizer even begins.

Frontline Solver

The Frontline Solver offers different options which are listed in the screenshot below:

Something which is interesting is that Frontline recommends only using the Simplex LP method for non-linear problems. However, CPLEX (which is inside of SNP) uses Simplex for non-linear problems (realistic supply planning problems are non-linear).

This discrepancy is something that I will update this post with when I figure out the reason for this.

Conclusion

The solution method is always of great emphasis for those using a general solver, which requires that the users get very much into the detail of the optimization. However, on enterprise optimization projects, often the particulars of the optimizer parameter setup can be overlooked due to other issues and distractions.

However, it is both interesting and relevant to know what solution methods are being employed and to have a good reason for their selection in a documented format.

The Necessity of Fact Checking

We ask a question that anyone working in enterprise software should ask.

Should decisions be made based on sales information from 100% financially biased parties like consulting firms, IT analysts, and vendors to companies that do not specialize in fact-checking?

If the answer is “No,” then perhaps there should be a change to the present approach to IT decision making.

In a market where inaccurate information is commonplace, our conclusion from our research is that software project problems and failures correlate to a lack of fact checking of the claims made by vendors and consulting firms. If you are worried that you don’t have the real story from your current sources, we offer the solution.

Search Our Other Supply Chain Optimization Content

Brightwork MRP & S&OP Explorer for Constraining

Improving Your Constraint Planning

Brightwork Research & Analysis offers the following supply planning tuning software with a new approach to managing capacity constraints, which is free to use in the beginning. See by clicking the image below:

 

References

Supply Planning Book

SUPPLY

Supply Planning with MRP, DRP and APS Software

Showing the Pathway for Improvement

Supply planning software, and by extension supply planning itself, could be used much more efficiently than it currently is. Why aren’t things better?

Providing an Overall Understanding of Supply Planning in Software

Unlike most books about software, this book showcases more than one vendor. Focusing an entire book on a single software application is beneficial for those that want to use the application in question solely. However, this book is designed for people that want to understand supply planning in systems.

  • What methods fall into APS?
  • How do the different methods work and how do they differ in how they generate output?
  • What is the sequence of supply planning runs?

These types of questions are answered for readers in this book.

This book explains the primary methods that are used for supply planning, the supply planning parameters that control the planning output as well as how they relate to one another.

Who is This Book For?

This book as a practical primer for anyone looking to perform a supply planning software selection, any person beginning a supply planning project, or anyone who just wants to understand supply planning software simply better.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: Where Supply Planning Fits Within the Supply Chain Planning Footprint
  • Chapter 3: MRP Explained
  • Chapter 4: DRP Explained
  • Chapter 5: APS Supply Planning Methods
  • Chapter 6: APS for Deployment
  • Chapter 7: Constraint-based Planning
  • Chapter 8: Reorder Point Planning
  • Chapter 9: Planning Parameters
  • Chapter 10: How MRP, DRP, and APS Relate to One Another
  • Chapter 11: Supply Planning Visibility and Master Data Management
  • Chapter 12: Understanding the Difference Between Production Versus Simulation

 

What We Can Learn from the Link Flight Trainer for Simulation

Executive Summary

  • What was the link trainer and how did it work?
  • What are the differences between flight simulation and supply chain planning simulation?

Link-Trainer

A picture of the Link Trainer.

Introduction to the Link Trainer

The Link Trainer was developed back in 1929, although it developed through several incarnations which eventually included instrumentation and a course plotter.  However, the story of how the Link Trainer was eventually accepted is explained in the quote below:

“Link’s first military sales came as a result of the Air Mail scandal, when the Army Air Corps took over carriage of U.S. Air Mail. Twelve pilots were killed in a 78 day period due to their unfamiliarity with Instrument Flying Conditions. The large scale loss of life prompted the Air Corps to look at a number of solutions, including Link’s pilot trainer. The Air Corps was given a stark demonstration of the potential of instrument training when, in 1934, Link flew in to a meeting in conditions of fog that the Air Corps evaluation team regarded as unflyable.[5] As a result, the Air Corps ordered the first six pilot trainers at $3,500 each.” – Wikipedia

The fatalities were no doubt due to the following:

“Only 48 of those selected had logged at least 25 hours of flight time in bad weather, only 31 had 50 hours or more of night flying, and only 2 had 50 hours of instrument time.” – Wikipedia

Desperation always increases the acceptance of unusual ideas. For instance, the Nazi super-weapons were driven by the fact that the Germans knew they were not going to be able to win the war with conventional means. The Air Corps eventually got out of the business of delivery airmail, but the Link Trainer had already proven itself and was soon to become a popular purchase for aviation globally.

How the Link Trainer Worked

The Link Trainer has a number of components, the main flight capsule being the most obvious component, however, I learned from visiting the Cavanaugh Flight Museum in Addison, Texas that there was also a table at which the flight engineer would work and plot the plane’s theoretical location using a course plotter.

LInk Trainer Table

Clearly, the flight engineer, who was sitting at the table could see the same instruments that the pilot inside of the capsule could see. The trainer would speak to the trainee through a microphone. No doubt the flight engineer graded the trainee.

“By relating the position of the student’s aircraft to marks on the chart, the instructor was able to manually control the transmission of simulated radio beacon signals to the trainer.” –  Kevin Moore

The best way to figure out exactly how the Link Trainer worked would be to find an old manual, which still exists. I found one from a listing on eBay. It would be nice if the manual were saved as a PDF document online. If the manual is not eventually digitized it is most likely disappear at some point, which is unfortunate as this is a historically important document.

Although there is a description of its operation from an author who read the patent.

“Rougerie’s patent of 1928 describes a simple trainer, fixed to the ground, consisting of a students seat facing an instrument panel and two sets of controls, one each for the student and instructor. The student’s flight instruments are directly connected to the instructor’s controls. The student, then, flies the trainer in response to commands from the instructor, who in turn modifies the instrument indications according to the students actions – the accuracy of the simulation depends entirely on the instructor.” – Kevin Moore

The type of simulation the Link Trainer provided, which was the understanding and ability to fly from only instruments. This type of training can be performed in an airplane, but it requires that the trained pilot have their vision impaired in some way and that the training pilot take control when the trainee invariably makes errors that put the plane in danger. Instrumentation flying is the perfect form of flight simulation to train in a simulator. Furthermore, the costs of running a flight simulator like this is minuscule compared to using a real plane.

Since the link, flight simulation has become extremely sophisticated as the Boeing flight simulator shown below demonstrates.

Modern simulators are so realistic, they should really be experienced with video in order to understand how good a job in providing an immersive experience. Below is a video of the 787 simulator, the outside of which can be seen above.

The Differences Between Flight Simulation and Supply Chain Planning Simulation

In terms of the lessons for supply chain simulation, is that not only supply chain software by business software simulation in general greatly lags the simulation in flight training. There is much less simulation performed in the supply chain than could or should be. Clearly, air transportation and air warfare have higher stakes than supply chain planning. Therefore, it is not at all surprising that simulation was adopted earliest for training pilots. When we speak of simulation in supply chain planning, it typically refers to changes made to master data that are applied to models, and then a supply chain procedure is run, and the results are checked to see if the master data change (say reduce lead times) should be made permanent. In fact, most supply chain simulation environments end up being hijacked to simply to test narrow technology adjustments which attempt to fix issues. That is the simulation environment is jealously guarded by the IT department to meet their needs. In terms of simulation for the users of the system, this is very rarely done. Users in supply chain planning could certainly benefit from a system to work test with, but they simply, in the vast majority of cases are never provided one.

Conclusion

The Link Trainer was a groundbreaking device. It had a number of experienced pilots who questioned whether a device like this could actually train pilots, however, it proved that it could. The Link Trainer is the precursor to the many increasingly sophisticated flight simulators that have been used since the Link and to the present day. Since the Link flight simulation has been a major area of study and of product sales. Interestingly, several aircraft accidents where the pilot lost orientation have been blamed on the high degree of computerization of modern aircraft and that some pilots have lost the training to fly based upon instruments alone. This demonstrates the importance of training in multiple aspects of flight skill.

References

https://www.cavanaughflightmuseum.com/

https://en.wikipedia.org/wiki/Link_Trainer

https://en.wikipedia.org/wiki/Instrument_time

https://www.goflightinc.com/flightsimhistory.shtml

Supply Planning Book

SUPPLY

Supply Planning with MRP, DRP and APS Software

Showing the Pathway for Improvement

Supply planning software, and by extension supply planning itself, could be used much more efficiently than it currently is. Why aren’t things better?

Providing an Overall Understanding of Supply Planning in Software

Unlike most books about software, this book showcases more than one vendor. Focusing an entire book on a single software application is beneficial for those that want to use the application in question solely. However, this book is designed for people that want to understand supply planning in systems.

  • What methods fall into APS?
  • How do the different methods work and how do they differ in how they generate output?
  • What is the sequence of supply planning runs?

These types of questions are answered for readers in this book.

This book explains the primary methods that are used for supply planning, the supply planning parameters that control the planning output as well as how they relate to one another.

Who is This Book For?

This book as a practical primer for anyone looking to perform a supply planning software selection, any person beginning a supply planning project, or anyone who just wants to understand supply planning software simply better.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: Where Supply Planning Fits Within the Supply Chain Planning Footprint
  • Chapter 3: MRP Explained
  • Chapter 4: DRP Explained
  • Chapter 5: APS Supply Planning Methods
  • Chapter 6: APS for Deployment
  • Chapter 7: Constraint-based Planning
  • Chapter 8: Reorder Point Planning
  • Chapter 9: Planning Parameters
  • Chapter 10: How MRP, DRP, and APS Relate to One Another
  • Chapter 11: Supply Planning Visibility and Master Data Management
  • Chapter 12: Understanding the Difference Between Production Versus Simulation