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.

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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

Software Ratings: Supply Planning

Software Ratings

Brightwork Research & Analysis offers the following free supply planning software analysis and ratings. See by clicking the image below:

software_ratings

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.

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Supply Planning Research Contact

  • Interested in Our Supply Planning Research?

    The software space is controlled by vendors, consulting firms and IT analysts who often provide self-serving and incorrect advice at the top rates.

    • We have a better track record of being correct than any of the well-known brands.
    • If this type of accuracy interests you, contact us and we will be in touch.

References

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:

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

 

The Probability of Success of Different Supply Planning Methods

Executive Summary

  • What are the Different Supply Planning Methods?
  • How does each Supply Planning Method Stack up in Terms of Implementation Success?
  • What does the Complexity of the Supply Planning Method used Have to do with Implementation Success?

Selecting software, and the method within the software primarily by both brand and simply the hypothetical capability of the software, without considering the company’s ability to implement complex systems, and the experiences and difficulties that other companies have had with the more complex supply planning methods do not make a lot of sense.

Introduction

The various methods of supply planning for advanced planning and scheduling are very different from one another. These methods, which are heuristics, allocation and cost optimization also differ greatly in their likelihood of being implemented successfully. During the software selection phase, typically a method is selected based on how well it meets the business requirements. Something which is left out of this analysis is the probability of success of the methods.

If one method provides all the things your business wants, but the company lacks the funding or expertise, or sustainable orientation to bring up a solution, it makes more sense to select a solution which can be implemented. It is extremely rare that I find that companies correctly estimate their abilities to bring up complex solutions. For instance, in the area of support, it is becoming increasingly common for companies to outsource support to India. 

However, the outsourced model was never designed for complex solutions like supply planning APS solutions which are some of the most complex systems that a company has.

Resolving issues requires a detailed understanding of the issues, domain expertise, the configuration history, etc.. It is not simply performing a password reset. Therefore, if a company wants to use outsourced support, to also not provide sufficient internal personnel for the implementation, etc…then the company should move towards an easier APS method and one which has a higher probability of success.

How the Different APS Methods Compare in Terms of Probability of Success

Heuristic

Heuristics have a very high success rate. The SAP SNP Network Heuristic is about as easy to use as MRP but has extra settings that require some analysis and troubleshooting. The SNP Heuristic is extremely fast and can be run as many times as per day because the heuristic provides the same result if it is run for the overall network as it does if it is run for a single location or a single product location combination. The SNP heuristic can also be run interactively which allows it to provide an instant update on the new situation.

CTM and Cost Optimization

However, there is a significant drop off in the success of the more complex methods which include allocation and cost optimization. Few companies have success with allocation or cost optimization. This is because this method is complex. There are both many screens of settings on each of these methods, but also these methods require detailed configuration and master data maintenance in the area of resources, as most companies that select these methods are interested in performing constraint-based planning.

Additionally, allocation requires the development and maintenance of a table which declares which customers should receive inventory over others. In cost optimization, costs must be developed and maintained for the transportation costs between locations, the storage costs at a location, the costs of violating (dipping into) safety stock, the cost of production, and the costs of missing a demand.

All of this entails work, and this work must be appropriately staffed. It also requires a clear understanding and clear declaration of the policies and communication of these policies to the individuals who are maintaining the configuration and master data.

Conclusion

While APS offers many more settings and more functionality, it has not had as high implementation success rates as MRP/DRP, which at this point are nearly universal within companies of any substantial size. The reasons why can vary as much as the specific method of APS implemented (heuristic, cost-based optimization, or allocation), and the probability of success differs very significantly between with heuristics being on one side of the continuum, and cost-based optimization and allocation being on the other.

However, as a general statement, the APS has been criticized for being overly complex, which I also agree with. Part of the complexity is due to the method. However, the fact that some solutions like SAP SNP are so complex has to do with the decision made by a development organization. This is connected to another topic, which is that companies do not seem to be selecting for software that has been naturally designed to be easily implemented.

Companies that try to reach for a more complex method, without providing the necessary preconditions, are worse off than if they had selected a more simple method. This is why it is so important to understand the differences in the probability of success between the different methods. It is also important to know how easy or difficult the particular software application is to configure and maintain and relating this back to an honest appraisal of how effective the company has been in the past in implementing complex systems before making a software selection decision.

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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