MUFI Rating & Risk – ToolsGroup SO99 (Supply Planning)

MUFI Rating & Risk – ToolsGroup SO99 (Supply Planning)

MUFI: Maintainability, Usability, Functionality, Implement ability

Vendor: ToolsGroup (Select For Vendor Profile)

Introduction

ToolsGroup is a software vendor that is mostly known for its inventory optimization and multi echelon planning software for supply planning.

Application Detail

ToolsGroup supply planning application uses inventory management and multi-echelon planning functionality. ToolsGroup SO99 has what we consider to be the best graphing ability in supply planning for showing the relationship between inventory and service level. The relationship between inventory and service levels is non-linear; higher and higher service levels require disproportionate increases in inventory to support them. The closer service levels come to one hundred percent, the more extreme the costs become. This relationship is one of the best-documented relationships in supply-chain management and is described by the graphic below.

Service Level Inventory Relationship.pdf

This graphic has been seen at one time or another by most supply chain professionals, and it demonstrates the fact that companies must decide what levels of service they can afford and, most importantly, what levels of service their customers are willing to pay for.

ToolsGroup Service Level Inventory Graphic

ToolsGroup has this relationship curve built into their application. As you can see, their graph shows the same relationship as the graph on the previous page. Notice that there is a slider at the bottom of the ToolsGroup application view. This allows the user to see what the resulting inventory would be for different system-wide service levels. (Some other parameters can be changed, as well as observed, from below the main graphic.) 

It is impressive—considering all of the calculations that the ToolsGroup application has to perform to generate this graph—how quickly the graph can be adjusted by just moving the slider along the bottom. Behind the scenes, there is a fully configured model of the supply chain.

ToolsGroup SO99 is an impressive application in many ways, and the application covers both demands as well as supply planning. ToolsGroup brings a very high knowledge level to its projects, and this is another benefit in addition to the software. ToolsGroup is an excellent but complex application, and a company, which intends to use it, needs to make sure it is interested in funding this type of application.

MUFI Scores

All scores out of a possible 10.

Vendor and Application Risk

ToolsGroup SO99 faces the similar issues of the more heavyweight supply planning applications, which is one of being overwhelmed with the available functionality. Overall the SO99 application is well designed, and it has some productivity-enhancing features. As with all inventory optimization applications, there is a risk of implementing the technology without properly socializing what ToolsGroup SO99 is doing under the covers. Emphasis must be made on getting the business to understand inventory optimization.

Likelihood of Implementation Success

This accounts for both the application and vendor-specific risk. In our formula, the total implementation risk is application + vendor + buyer risk. The buyer specific risk could increase or decrease this overall likelihood and adjust the values that you see below.

Risk Definition

See this link for more on our categorizations of risk. We also offer a Buyer Specific Risk Estimation as a service for those that want a comprehensive analysis.

Finished With Your Analysis?

To go back to the Software Selection Package page for the Supply Planning software category. Or go to this link to see other analytical products for ToolsGroup SO99.

References

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 until is sees “serious usage,” and is free for students and academics. See by clicking the image below:

Software Selection Book

SELECTION

Enterprise Software Selection: How to Pinpoint the Perfect Software Solution Using Multiple Sources of Information

What the Book Covers

Essential reading for success in your next software selection and implementation.

Software selection is the most important task in a software implementation project, as it is your best (if not only) opportunity to make sure that the right software—the software that matches the business requirements—is being implemented. Choosing the software that is the best fit clears the way for a successful implementation, yet software selection is often fraught with issues and many companies do not end up with the best software for their needs. However, the process can be greatly simplified by addressing the information sources that influence software selection. This book can be used for any enterprise software selection, including ERP software selection.

This book is a how-to guide for improving the software selection process and is formulated around the idea that—much like purchasing decisions for consumer products—the end user and those with the domain expertise must be included. In addition to providing hints for refining the software selection process, this book delves into the often-overlooked topic of how consulting and IT analyst firms influence the purchasing decision, and gives the reader an insider’s understanding of the enterprise software market.

This book is connected to several other SCM Focus Press books including Enterprise Software TCO and The Real Story Behind ERP.

By reading this book you will:

  • Learn how to apply a scientific approach to the software selection process.
  • Interpret vendor-supplied information to your best advantage. This is generally left out of books on software selection. However, consulting companies and IT analysts like Gartner have very specific biases. Gartner is paid directly by software vendors — a fact they make every attempt not to disclose while consulting companies only recommend software for vendors that give them the consulting business. Consulting companies all have an enormous financial bias that prevents them from offering honest advice — and this is part of their business model.
  • Understand what motivates a software vendor.
  • Learn how the institutional structure and biases of consulting firms affect the advice they give you, and understand how to properly interpret information from consulting companies.
  • Make vendor demos work to your benefit.
  • Know the right questions to ask on topics such as integration with existing software, cloud versus on-premise vendors, and client references.
  • Differentiate what is important to know about software for improved “implement-ability” versus what the vendor thinks is important for improved “sell-ability.”
  • Better manage your software selection projects to ensure smoother implementations.

Buy Now

Chapters

  • Chapter 1: Introduction to Software Selection
  • Chapter 2: Understanding the Enterprise Software Market
  • Chapter 3: Software Sell-ability versus Implement-ability
  • Chapter 4: How to Use Consulting Advice on Software Selection
  • Chapter 5: How to Use the Reports of Analyst Firms Like Gartner
  • Chapter 6: How to Use Information Provided by Vendors
  • Chapter 7: How to Manage the Software Selection Process

MUFI Rating & Risk – ToolsGroup SO99 (Forecasting)

MUFI Rating & Risk – ToolsGroup SO99 (Forecasting)

MUFI: Maintainability, Usability, Functionality, Implement ability

Vendor: ToolsGroup (Select For Vendor Profile)

Introduction

ToolsGroup is a software vendor that is mostly known for its inventory optimization and multi echelon planning software for supply planning. However, while less known for the forecasting functionality that is part of their SO99 product, it is a very capable application.

Application Detail

ToolsGroup in everything that it does is very focused on what is known as stochastic modeling. This means modeling in a way that represents the variable nature of reality. This comes across in their forecasting application and is on display in the following screenshot.

ToolsGroup Interface

Most forecasting applications have a single line representing the forecast. ToolsGroup’s S099 application produces a range of values indicating the probabilistic nature of the forecast.  This is a very innovative concept and gets people thinking of a forecast as not a single value.

ToolsGroup Seasonality

ToolsGroup has a very nice seasonality management functionality, which allows a seasonality index to be created, which helps adjust the forecast up or down based upon the indice. A fractional value below 1 for an indice, such as in October, (.80), brings the forecast down – a fractional value above 1 for an indice, such as February, (1.03), brings the forecast up. Some forecasting applications have similar functionality. However, ToolsGroup regularly displays a combination of ease of use long with a high degree of sophistication.

Lifecycle Planning in ToolsGroup SO99

Supersession is the substitution of one product for another product. In lifecycle planning, this often takes the form of phasing out one product and replacing it with a newer version. Seeing the demand for both products in an integrated way is one of the challenges. ToolsGroup has made this very clear in their user interface, which is why I wanted to include them in this section.

ToolGroup can show both the phased out product, and the phased-in product on the same screen. See below, and notice the transparent portion of the block graphs.

ToolsGroup Supersession

If the selected item supersedes another item (i.e., it assumes the demand of the superseded item in addition to its own), the demand bar on the graph displays both the original demand for the selected item (solid green bar) and the acquired demand associated with the superseded item (green perimeter with no fill): the superseded demand. – Kristen Nordstrom, ToolsGroup

ToolsGroup SO99 and Lumpy Demand

Of the vendors I have analyzed, probably the one (that does not specialize in service parts) with the greatest focus on demand intermittency is ToolsGroup. This is consistent with ToolsGroup ‘s orientation towards detailed planning, as is demonstrated in the screenshot below:

ToolsGroup Lumpy Demand

Notice that with ToolsGroup, the lumpiness shows in the demand history. However, the future lines in purple and blue are very long. This is called the prediction interval, which is the range over which a forecast is likely to fall. On a lumpy product like this one, notice how broad the range is when compared to the more predictable demand pattern below:

ToolsGroup Normal Variability

The more predictable item has a smaller range of values. It is inherently more forecastable. ToolsGroup’s interface allows to me to demonstrate a basic concept about forecasting, which is what a good user interface should be able to do.

ToolsGroup’s SO99 is a forecasting application for companies that are very serious about forecasting.  It takes a different approach in some of the areas, and a good fit for ToolsGroup implementations will be companies that buy into the ToolsGroup vision. Some of the functionality in ToolsGroup SO99 for forecasting do not exist in any other supply chain forecasting application.

MUFI Scores

All scores out of a possible 10.

Vendor and Application Risk

ToolsGroup SO99 faces the similar issues of the more heavyweight forecasting applications, which is one of being overwhelmed with the available functionality. Overall the SO99 application is well designed, and it has some productivity-enhancing features ranging from how it performs lifecycle planning to substitution and supersession. The SO99 user interface is of good quality easing user adoption. Users must become acclimated to the concept of probabilistic forecasts.

Likelihood of Implementation Success

This accounts for both the application and vendor-specific risk. In our formula, the total implementation risk is application + vendor + buyer risk. The buyer specific risk could increase or decrease this overall likelihood and adjust the values that you see below.

Risk Definition

See this link for more on our categorizations of risk. We also offer a Buyer Specific Risk Estimation as a service for those that want a comprehensive analysis.

Finished With Your Analysis?

To go back to the Software Selection Package page for the Demand Planning software category. Or go to this link other analytical products for ToolsGroup SO99.

References

Brightwork Forecast Explorer for Error Calculation

Improving Your Forecast Error Management

Did you know that most companies don’t know what their forecast error is? If a company knows an error percentage but not the interval or the aggregation level measured, that means they don’t know. Most forecasting applications make getting a weighted forecast error extremely difficult. That is why we developed a SaaS application that allows anyone to find out their forecast error with a simple file upload.

The Brightwork Forecast Explorer is free to use in the beginning. See by clicking the image below:

Software Selection Book

SELECTION

Enterprise Software Selection: How to Pinpoint the Perfect Software Solution Using Multiple Sources of Information

What the Book Covers

Essential reading for success in your next software selection and implementation.

Software selection is the most important task in a software implementation project, as it is your best (if not only) opportunity to make sure that the right software—the software that matches the business requirements—is being implemented. Choosing the software that is the best fit clears the way for a successful implementation, yet software selection is often fraught with issues and many companies do not end up with the best software for their needs. However, the process can be greatly simplified by addressing the information sources that influence software selection. This book can be used for any enterprise software selection, including ERP software selection.

This book is a how-to guide for improving the software selection process and is formulated around the idea that—much like purchasing decisions for consumer products—the end user and those with the domain expertise must be included. In addition to providing hints for refining the software selection process, this book delves into the often-overlooked topic of how consulting and IT analyst firms influence the purchasing decision, and gives the reader an insider’s understanding of the enterprise software market.

This book is connected to several other SCM Focus Press books including Enterprise Software TCO and The Real Story Behind ERP.

By reading this book you will:

  • Learn how to apply a scientific approach to the software selection process.
  • Interpret vendor-supplied information to your best advantage. This is generally left out of books on software selection. However, consulting companies and IT analysts like Gartner have very specific biases. Gartner is paid directly by software vendors — a fact they make every attempt not to disclose while consulting companies only recommend software for vendors that give them the consulting business. Consulting companies all have an enormous financial bias that prevents them from offering honest advice — and this is part of their business model.
  • Understand what motivates a software vendor.
  • Learn how the institutional structure and biases of consulting firms affect the advice they give you, and understand how to properly interpret information from consulting companies.
  • Make vendor demos work to your benefit.
  • Know the right questions to ask on topics such as integration with existing software, cloud versus on-premise vendors, and client references.
  • Differentiate what is important to know about software for improved “implement-ability” versus what the vendor thinks is important for improved “sell-ability.”
  • Better manage your software selection projects to ensure smoother implementations.

Buy Now

Chapters

  • Chapter 1: Introduction to Software Selection
  • Chapter 2: Understanding the Enterprise Software Market
  • Chapter 3: Software Sell-ability versus Implement-ability
  • Chapter 4: How to Use Consulting Advice on Software Selection
  • Chapter 5: How to Use the Reports of Analyst Firms Like Gartner
  • Chapter 6: How to Use Information Provided by Vendors
  • Chapter 7: How to Manage the Software Selection Process

Enterprise Software TCO Calculator – ToolsGroup SO99 (Supply)

How it Works

Fill out the form below for a your TCO estimate. The form does not have a “beginning or end.” The form is constantly calculating, so feel free to make constant changes and the application will auto-adjust.

Details

  • Vendor Name: ToolsGroup (See for Vendor Rating)
  • Software Category: Supply Planning
  • Company Headquarters: 75 Federal St, Suite 920, Boston, MA
  • Site: http://www.toolsgroup.com
  • Contact number:  617.263.0080
  • Delivery Mechanism: On Premises

Finished With Your Analysis?

Once complete, goto this link to see other analytical products for ToolsGroup.

Enterprise Software TCO Calculator – ToolsGroup SO99 (Forecasting)

How it Works

Fill out the form below for your TCO estimate. The form does not have a “beginning or end.” The form is constantly calculating, so feel free to make constant changes and the application will auto-adjust.

Details

  • Vendor Name: ToolsGroup (See for Vendor Rating)
  • Software Category: Demand Planning
  • Company Headquarters: 75 Federal St, Suite 920, Boston, MA
  • Site: http://www.toolsgroup.com
  • Contact number:  617.263.0080
  • Delivery Mechanism: On Premises

Finished With Your Analysis?

Once complete, go to this link to see other analytical products for ToolsGroup.

References

Risk Book

Software RiskRethinking Enterprise Software Risk: Controlling the Main Risk Factors on IT Projects

Better Managing Software Risk

The software implementation is risky business and success is not a certainty. But you can reduce risk with the strategies in this book. Undertaking software selection and implementation without approximating the project’s risk is a poor way to make decisions about either projects or software. But that’s the way many companies do business, even though 50 percent of IT implementations are deemed failures.

Finding What Works and What Doesn’t

In this book, you will review the strategies commonly used by most companies for mitigating software project risk–and learn why these plans don’t work–and then acquire practical and realistic strategies that will help you to maximize success on your software implementation.

Chapters

Chapter 1: Introduction
Chapter 2: Enterprise Software Risk Management
Chapter 3: The Basics of Enterprise Software Risk Management
Chapter 4: Understanding the Enterprise Software Market
Chapter 5: Software Sell-ability versus Implementability
Chapter 6: Selecting the Right IT Consultant
Chapter 7: How to Use the Reports of Analysts Like Gartner
Chapter 8: How to Interpret Vendor-Provided Information to Reduce Project Risk
Chapter 9: Evaluating Implementation Preparedness
Chapter 10: Using TCO for Decision Making
Chapter 11: The Software Decisions’ Risk Component Model

Project Planning Package – ToolsGroup SO99 (Supply)

How it Works

Fill out the form below for your project planning estimate. The form does not have a “beginning or end.” The form is constantly calculating, so feel free to make constant changes and the application will auto-adjust.

Details

  • Vendor Name: ToolsGroup (See for Vendor Rating)
  • Software Category: Supply Planning
  • Company Headquarters: 75 Federal St, Suite 920, Boston, MA
  • Site: http://www.toolsgroup.com
  • Contact number:  617.263.0080
  • Delivery Mechanism: On Premises

Finished With Your Analysis?

Once complete, go to this link to see other analytical products for ToolsGroup.

References

Risk Book

Software RiskRethinking Enterprise Software Risk: Controlling the Main Risk Factors on IT Projects

Better Managing Software Risk

The software implementation is risky business and success is not a certainty. But you can reduce risk with the strategies in this book. Undertaking software selection and implementation without approximating the project’s risk is a poor way to make decisions about either projects or software. But that’s the way many companies do business, even though 50 percent of IT implementations are deemed failures.

Finding What Works and What Doesn’t

In this book, you will review the strategies commonly used by most companies for mitigating software project risk–and learn why these plans don’t work–and then acquire practical and realistic strategies that will help you to maximize success on your software implementation.

Chapters

Chapter 1: Introduction
Chapter 2: Enterprise Software Risk Management
Chapter 3: The Basics of Enterprise Software Risk Management
Chapter 4: Understanding the Enterprise Software Market
Chapter 5: Software Sell-ability versus Implementability
Chapter 6: Selecting the Right IT Consultant
Chapter 7: How to Use the Reports of Analysts Like Gartner
Chapter 8: How to Interpret Vendor-Provided Information to Reduce Project Risk
Chapter 9: Evaluating Implementation Preparedness
Chapter 10: Using TCO for Decision Making
Chapter 11: The Software Decisions’ Risk Component Model

Project Planning Package – ToolsGroup SO99 (Forecasting)

How it Works

Fill out the form below for a your project planning estimate. The form does not have a “beginning or end.” The form is constantly calculating, so feel free to make constant changes and the application will auto-adjust.

Details

  • Vendor Name: ToolsGroup (See for Vendor Rating)
  • Software Category: Demand Planning
  • Company Headquarters: 75 Federal St, Suite 920, Boston, MA
  • Site: http://www.toolsgroup.com
  • Contact number:  617.263.0080
  • Delivery Mechanism: On Premises

Finished With Your Analysis?

Once complete, goto this link to see other analytical products for ToolsGroup.

Software Category Analysis – Demand Planning

Introduction

When forecasting applications were first developed external to the ERP system, a narrow spectrum of applications was developed for demand planning and these were primarily designed for the statistical forecasting process. However, statistical forecasting is only one of several forecasting processes, and over the years the variety of demand planning applications has increased significantly. Therefore, attempting to force all of a company’s forecasting needs through a single application is no longer necessary or desirable. The diagram below represents the various forecasting processes:

Major Forecasting Processes

A common mistake companies seem to make is trying to use statistical forecasting packages to manage the other forecasting processes. Part of the reason that companies do this is because both the major vendors (who tend to only offer statistical packages) and the major consulting companies incorrectly advise companies that statistical demand planning software can be used to manage non statistical process forecasting. This analysis package will primarily be focused on statistical forecasting applications, but where the application can branch out into other areas, we will point this out.

It is important to understand where demand planning fits among the different supply chain applications, as shown in the graphic below.

Common Supply Chain Application Categories Demand Planning

Demand planning one of the major categories of supply chain software. When companies implement an external supply chain planning module to be connected to their ERP system, they most often start with demand planning.

If you read most forecasting books they tend to focus very heavily on the mechanics of forecasting. They talk about forecasting methodologies (simple exponential smoothing, regression, etc.), however not enough get into the business process or how forecasts are used in real life. The first wave or generation of applications leveraged the ability to place not so sophisticated and quite sophisticated forecasting methods in software. These applications had high failure rates because they were generally not designed to be easily implemented or easily maintained. Applications in this category are what we refer to as first generation advanced planning products. A good marker of a first generation advanced planning forecasting application was if it expended most of the development effort in the application of complex methods and would often have a proprietary forecasting algorithm. During the period when first generation the prevailing wisdom was that the application mostly came down to how complex forecasting methods could be applied in an automated fashion. This period in software, which has been the dominant software development approach lead to some very bad habits. In fact, there little evidence that sophisticated mathematics can improve the forecast of difficult-to-forecast products, and this is a problem. Some studies do not show improvement from more advanced methods, but firstly the improvement is never very large, and secondly other studies come by later to contradict the original studies. In addition, complex methods should have to exceed a higher bar. Academics can apply complex methods in a laboratory environment over a few products far more easily than can be done by industry. This fact, along with the point that sophisticated methods are much more expensive for industry to implement than simple methods, is rarely mentioned. This point is made very well by J. Scott Armstrong:

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

This idea, that forecasting software just came down to most sophisticated forecasting algorithm has been incredibly durable, especially since the research into this area clearly demonstrates that when actually implemented (that is not in a controlled academic environment where only a small number of items are being forecasted), more complex methods have a hard time defeating simple methods. The major limiting factor that companies face in leveraging forecasting functionality? That would be getting these types of applications to be understandable and to work consistently. That is making them easier to use. Unfortunately, statistical forecasting software vendors do not tend to compete on the basis of how easy their applications are to use, instead the competition tends to center around the sophistication of the mathematics that is within the forecasting methods. Having covered this subject extensively and worked in the field for quite a few years, we can say with confidence that simply having sophisticated mathematical forecasting algorithms/methods within the application is nowhere near enough to obtain consistent forecast accuracy. This leads many companies to have a false sense of security with regards to their statistical forecasting application – and it leads to companies asking themselves “We have statistical forecasting software, why can’t we get a decent forecast?” A certain cynicism has crept into statistical forecasting – however, forecasting is a disciplined endeavor and many implementing companies are not used to performing the type of data accuracy and discipline and testing that is required for forecasting. Forecasting vendors have also been responsible for overselling the ease by which a forecast can be improved by a system without the required work. Finally, many forecasting software vendors have been let down by major consulting companies who seem to primarily be able to staff IT resources for forecasting – that can configure the system, but lack very much experience or an understanding of forecasting beyond textbook knowledge.

Attribute-based Forecasting

Attribute-based forecasting is one of the most important developments since enterprise demand planning software began being used. I know this is a big statement to make, but I make it based upon research into the history of demand planning and in light of my research I am quite confident that my statement is true. After one uses an application capable of attribute-based forecasting, it’s difficult not to come to the same conclusion. And perhaps most interesting is the fact that attribute-based forecasting is still only used in a minority of companies (even though so many vendors say their applications are good at dealing with attributes). Attribute-based forecasting can allow different groups and departments to perform forecast aggregation as they are interested in seeing the data, and does not require that one single static hierarchy be used for all users. With attribute-based forecasting systems, the implementation approach for forecasting and analytic projects, which currently focuses on the technical details of complex database setup, can be changed. Instead of explaining the concept of realignment and spending seemingly endless hours debating what the “one” static hierarchy should be, that time can now be refocused onto determining and explaining how the business can get the most out of the forecasting application. This is an enormous benefit to forecasting system implementation projects, which have tended—along with many other supply chain planning software implementations—to become overly technical affairs with more emphasis on meeting deadlines and IT objectives than on adding value to the business.

Is the Word Out on Attribute-based Forecasting?

I cannot find a good explanation for why the term is searched for in search engines so infrequently. According to SEOMoz.com, the number of searches typed into Google per month for either the term “attribute forecasting,” or “attribute-based forecasting” or other derivations of these terms is negligible. There few Internet articles on this topic as well. Even a search through Google Books does not bring many results (this is usually a very comprehensive way to search for a topic). Attribute-based forecasting may not be used that commonly now; but I predict it will be in the future.

Forecast Disaggregation

Every statistical forecasting vendor that I am aware of states that they can perform forecast aggregation and disaggregation; however, there is a large gap between statistical forecasting vendors in terms of capability. This functionality is too important for clients to simply accept the statement from a vendor that “our product can do that.” In fact, aggregation and disaggregation should both be extensively demonstrated and tested by the company’s planners prior to selecting an application for purchase, in order to discern how easy the aggregation functionality is to use in competing systems. Aggregation and disaggregation capability cannot be an afterthought. Instead these capabilities must be designed into the application from the database layer up. The following quote on this topic is instructive.

Demand Sensing

We point out all trends in each software category that we cover, the valid and invalid. One of the invalid trends that we have been tracking is called demand sensing. The information on the definition of demand sensing is currently and primarily controlled by software vendors. Demand sensing did not come out of the academic community, so there have been few unbiased descriptions of what demand sensing is. Demand sensing is the adjustment of forecasting inside of the lead-time of the product, and therefore when the supply plan cannot respond. If our lead-time is 2 weeks, then demand sensing means changing the forecast less than 14 days out. Demand sensing is the adjustment of forecasting inside of the lead-time of the particular product, and therefore when the supply plan cannot respond. Because demand sensing changes the forecast within the lead-time, demand sensing cannot be considered a forecasting approach. To understand this, its important to understand that while broadly speaking a forecast is a prediction of a future event; in practical terms a forecast is a prediction of a future event that is given with sufficient advanced notification to be worthwhile. For instance, a forecast could be improved for a football game by waiting until 1/2 the game is over. However, when half the game is over, it’s too late to place a bet on the game. Therefore the forecast is not particularly useful because it occurs within the lead-time of when a some benefit from be received from it. The forecast of the game could be further improved by waiting until the minute before the game ends, but again its hard to see how anyone would accept this as a forecast. One could not want to compare the forecast accuracy of a person who forecasts games while in progress versus those that forecast the game before the game begins. This of course brings up the topic of demand sensing and forecast accuracy “improvement.”

So Where Does Demand Sensing Belong?

Instead, it is a method of creating the illusion of improving the forecast accuracy by change the forecast in way that can never translate into an improvement in supply chain performance. This is extremely appealing to any demand planning group that cannot meet its forecasting goals (which is not to say they are realistic or unrealistic).  Interestingly, the vast majority of articles on demand sensing describe it as a method of improving the forecast, and categorize it as a forecasting approach. While its true that forecast accuracy can be improved by waiting until the last minute, this is blurring the line of what forecasting actually is. Vendors and IT analysts like Gartner have completely confused the issue by combining demand sensing with demand shaping.

The list of basic things that most companies cannot do in their forecasting systems is often amazing. A company that should start with doing proven things like those above to improve the forecast, will instead choose to go with the latest “trend,” and buy demand sensing software. They prefer to use an approach that is untested and has no academic research to support the contentions that the vendors in this area (notably Terra Technologies and SAP) make about it.

Software Category Summary

All companies would like to improve their forecast accuracy. Forecasting continues to be one of the great areas of opportunity within companies. Many companies are still using some combination of ERP (which is not a good platform for forecasting) combined with Excel. This is a difficult way to improve forecast accuracy. There have been many mistakes made on demand planning projects, and gaining value from a demand planning application means analyzing those mistakes and adjusting the implementation methodology. Application of the same approaches will lead to the same outcomes.

Overall this software category has several very compelling applications, and also a growing application where we are not sure how it will develop in the future.

The links to the specific research you have paid for is included at the beginning and end of this Software Category Analysis. You will only be able to access the pages that apply to your subscription.

MUFI Rating & Risk

See the MUFI Ratings & Risk below for all of the applications we cover.

Vendor NameApplication
Big ERP
SAPMUFI Rating & Risk – SAP ECC
OracleMUFI Rating & Risk – JD Edwards EnterpriseOne
EpicorMUFI Rating & Risk – Epicor ERP
SageMUFI Rating & Risk – Sage X3
InforMUFI Rating & Risk – Infor Lawson
Small and Medium ERP
SAPMUFI Rating & Risk – SAP Business One
OracleMUFI Rating & Risk – JD Edwards World
ProcessProMUFI Rating & Risk – ProcessPro
RootstockMUFI Rating & Risk – Rootstock
ERPNextMUFI Rating & Risk – ERPNext
OpenERPMUFI Rating & Risk – OpenERP
MicrosoftMUFI Rating & Risk – Microsoft Dynamics AX
Financial Applications
IntacctMUFI Rating & Risk – Intacct
IntuitMUFI Rating & Risk – Intuit Quickbooks Enterprise Solutions
FinancialForceMUFI Rating & Risk – FinancialForce
NetSuiteMUFI Rating & Risk – NetSuite OneWorld
PLM
SAPMUFI Rating & Risk – SAP PLM
Arena SolutionsMUFI Rating & Risk – Arena Solutions Arena PLM
Hamilton GrantMUFI Rating & Risk – Hamilton Grant Recipe Management
Demand Planning
SAPMUFI Rating & Risk – SAP APO DP
TableauMUFI Rating & Risk – Tableau (Forecasting)
Business Forecast SystemsMUFI Rating & Risk – Forecast Pro TRAK
Demand WorksMUFI Rating & Risk – Demand Works Smoothie
JDAMUFI Rating & Risk – JDA Demand Management
ToolsGroupMUFI Rating & Risk – ToolsGroup SO99 (Forecasting)
Supply Planning
SAPMUFI Rating & Risk – SAP SNP
SAPMUFI Rating & Risk – SAP SmartOps
ToolsGroupMUFI Rating & Risk – ToolsGroup SO99 (Supply Planning)
Demand WorksMUFI Rating & Risk – Demand Works Smoothie SP
PlanetTogetherMUFI Rating & Risk – PlanetTogether Galaxy APS Superplant
Production Planning
SAPMUFI Rating & Risk – SAP APO PP/DS
DelfoiMUFI Rating & Risk – Delfoi Planner
PreactorMUFI Rating & Risk – Preactor
AspenTechMUFI Rating & Risk – AspenTech AspenOne
PlanetTogetherMUFI Rating & Risk – PlanetTogether Galaxy APS
BI Heavy
SAPMUFI Rating & Risk – SAP BI/BW
SAPMUFI Rating & Risk – SAP Business Objects
OracleMUFI Rating & Risk – Oracle BI
SASMUFI Rating & Risk – SAS BI
MicroStrategyMUFI Rating & Risk – MicroStrategy
IBMMUFI Rating & Risk – IBM Cognos
TeradataMUFI Rating & Risk – Teradata
ActuateMUFI Rating & Risk – Actuate ActuateOne
BI Light
SAPMUFI Rating & Risk – SAP Crystal Reports
QlikTechMUFI Rating & Risk – QlikTech QlikView
TableauMUFI Rating & Risk – Tableau (BI)
CRM
SAPMUFI Rating & Risk – SAP CRM
OracleMUFI Rating & Risk – Oracle RightNow
OracleMUFI Rating & Risk – Oracle CRM On Demand
InforMUFI Rating & Risk – Infor Epiphany
Base CRMMUFI Rating & Risk – Base CRM
SalesforceMUFI Rating & Risk – Salesforce Enterprise
SugarCRMMUFI Rating & Risk – SugarCRM
MicrosoftMUFI Rating & Risk – Microsoft Dynamics CRM
NetSuiteMUFI Rating & Risk – NetSuite CRM

Software Category Analysis – Supply Planning

Introduction

Supply planning uses the methods of MRP, DRP, heuristics, allocation, and optimization (both cost and inventory) in order to make the recommendations of what, when and how much to bring material into the supply network, the what, when and how much to move material between the locations of the supply network, and in some applications what, when and how much create the production recommendations. Supply planning was performed exclusively in ERP systems using the twin methods of MRP and DRP up until the mid 1990s when companies began investing in advanced planning systems. These advanced planning systems used more sophisticated methods that up until that point hardware was insufficiently powerful to leverage. These applications were introduced with great fanfare, but also had high failure rates because they were generally not designed to be easily implemented or easily maintained. Applications in this category are what we refer to as first generation advanced planning products. And a number of software vendors are still selling these applications as if they are state of the art. A good marker of a first generation advanced planning applications is if it uses a cost optimizer. During the period when first generation the prevailing wisdom was that any supply chain domain could be “optimized” with by focusing on minimizing costs. Some of the advanced planning applications have still not progressed beyond this state. Optimizers are run with constraints, which limit the result to what is actually feasible.

Constraints

When used in planning, constraints limit the solution to what is feasible, or what is actionable by the business. By limiting the solution, the time and processing spent evaluating recommendations that cannot be converted in reality are also limited.

The Real Story Behind Constrained Planning

The ability to use constraints is a major differentiator between applications in the minds of buyers. However, the ability of buyers to actually implement constrained systems is much more limited. One of the important factors in how well constraint based systems can be implemented is how easily constraints can be modified and assigned – something which buyers rarely look for. The best application at managing constraints that we have seen is PlanetTogether, which is primarily a production planning and scheduling software vendor, although they are moving into supply planning. Curiously enough, while applications like SNP can hold supply planning constraints, by far the most common constraints used in supply planning are actually production constraints. Supply planning applications can be differentiated partially based upon whether they can incorporate production constraints. When they do, and when they are used, the planning system generates both the initial production plan along with the supply plan (stock transfers and purchase requisitions).  After this point, the supply plan and initial production plan are passed to the production planning and scheduling system where the more detailed planning and scheduling is performed, and adjusted, before being passed back to the supply planning system. This is the standard approach used by the supply planning applications that can incorporate production constraints. We refer to it as the sequential approach, because first the supply plan is run, then the production planning and scheduling is run. However, after several decades of working this way, our observation the standard approach is not actually effective. PlanetTogether, a software vendor we rate as the most innovative in the production planning space, are currently developing ways in which the supply plan and production plan are created in a single planning run – something which allows production to be managed much more flexibly than in the standard sequential approach.

Choosing 2nd Generation Planning Applications

Because of the numerous implementation problems with first generation supply planning systems, we tend to score applications significantly higher if they were new – which would mean being developed on new principles within the past 10 years. This is not because we are focused on newness for simply the sake of newness, but because there have been observations about what has and has not worked in supply planning, and older applications – while they may make some changes, have difficulty in being adjusted to work a more modern and effective way. In addition to the modernity of the methods used, we give significant weight to how well the system can be adopted by users. One of the highest rated applications in this category does not actually use complex methods for generating the supply plan, but is highly rated because software vendor focused on usability and maintainability. This should demonstrate our view, which we can support with project experience, that the complexity of the mathematics used to generate the supply plan is only one measurement of the application. This is actually a consistent observation across many enterprise software categories, if not all of them that complex solutions often do not lead to good outcomes.

Inventory Optimization and Multi Echelon Planning

Several of the applications profiled in this software category section use the inventory optimization and multi echelon planning (MEIO for short) method. MEIO is an innovative use of two separate forms of optimization: inventory optimization and multi-echelon inventory optimization (but which I simply refer to as multi-echelon planning to reduce confusion). MEIO applications are an example of the 2nd generation planning applications that we just discussed. While cost optimizers are generic and adapted to different supply chain planning domains, MEIO was an attempt to adjust the optimizer for the specific environment of supply planning; each component of MEIO answers a separate supply chain-planning question.

  1. Inventory Optimization: Answers the question of how much to keep in inventory.
  2. Multi-echelon Planning: Answers the question of where to keep inventory in the supply network.

Developers of MEIO applications have very smoothly combined both sets of mathematics (which were developed separately) to work in unison during a single supply planning run. Unlike supply planning techniques that use sequential processing or calculation, MEIO calculates the service level impact of carrying one additional item at every product location combination and then sorts the list of options by their contribution to service levels and selects the best contributor. It is a powerful technology that is still only understood – beyond a high level understanding – by a small portion of the people that work in supply planning. Some important concepts behind MEIO are included in the following paragraphs.

Multi Echelon Aggregate Purchasing

While most companies have multi-echelon networks, not all supply networks are of equal complexity from the perspective of multi-echelon. The more locations, and the more echelons (regional DCs, which feed DCs, which feed forward stocking locations, etc.), the more complex the multi-echelon problem becomes, and the more beneficial the implementation of MEIO software would be. Service parts networks are known for having a high number of echelons due partially to their need for forward stocking locations that can quickly service expensive equipment, thus minimizing equipment downtime. Service parts networks have the additional complexity of needing to move repairable items to locations in the supply network where they can be serviced. Therefore, it is not surprising that one of the early innovators in multi-echelon inventory optimization was MCA Solutions, a vendor that focused on the service parts market. Unfortunately they were acquired by Servigistics. Furthermore, the earliest papers on multi echelon and inventory optimization were directed towards optimizing service parts networks, not finished goods networks.  For any application to be multi echelon, it must have the concept of effective lead time – that is instead of viewing lead time as a static input, effective lead time is situational, and dependent upon other conditions in the supply network.

Effective Lead Time

In this example, the effective lead-time is the lead-time experienced by a customer at a retail location. Note the chain effect: their effective lead-time increases as the quantity demanded increases as more demand stocks out locations higher in the supply network. The numbers here are kept small in order to improve the clarity of the explanation. A key assumption is that this is a single product supply chain.  All locations carry only one product. The example above shows that that while the actual lead time stays the same between the locations, depending upon the demand vs. what is stocked at the various locations the effective lead time changes depending upon how high the multi echelon supply chain it is necessary to go to obtain inventory. Service levels can be set at various places, which vary depending upon the particular vendor. Companies that intend to select and implement MEIO software should have the necessary internal discussions as to how and where they want to control their supply planning with service levels. The way the implementing company is current performing supply planning will if unaltered, will not be able to fully leverage MEIO’s capabilities. The decision of where to set service levels should be known prior to any MEIO software selection. Of course this means understanding the options for service levels. To this end I have listed the options below. In MEIO software, service levels can be set by the following:

  1. At the location
  2. At the product-location combination
  3. At the group location
  4. At the customer
  5. At a product mix, and
  6. *At the contract/equipment

Degrees of Serice Level Control

Where the service level can be set in the application makes a very big difference in terms of how the company can use the application as well as how powerful the application is, but also in the degree of maintenance required after the application is installed. MEIO applications can be categorized partially by how easily the service level is set and how high in the hierarchy—contract, customer, location, and product location—the service level can be set

Multi-source Planning

Multi-source planning (or multi sourcing) — the ability to have the system flexibly choose from external sources of supply — has been a consistent requirement at many companies. However, many companies have also had a problem getting multi-source planning to work the way they want it to work, and so it is not implemented as commonly because of issues with functionality robustness. Buyers must do a better job of having the software vendors actually make proof of concepts with regards to multi sourcing work if the application is being partially purchased on the basis of this.

Software Category Summary

Supply planning has a hangover from the purchase and implementation of many applications, which were overly complex, and developed and implemented by software vendors more focused on sales than on implementation success. The likelihood of receiving a very good supply plan from and ERP system is low. Supply planning systems are very important to the overall solution architecture, but the best applications cannot be found from any of the name brand software vendors. To find a good supply planning application, buyers should be looking for 2nd generation applications and software vendors who have really thought through what has and had not worked in previous approachs. Supply planning software is a complex area to analyze and it’s easy to get tied up in the complexity of some of the applications. Overall this software category has a small number of compelling applications. This is an area where buyers really understand their requirements and their tolerance for complexity before even initiating a software selection.

MUFI Rating & Risk

See the MUFI Ratings & Risk below for all of the applications we cover.

Vendor NameApplication
Big ERP
SAPMUFI Rating & Risk – SAP ECC
OracleMUFI Rating & Risk – JD Edwards EnterpriseOne
EpicorMUFI Rating & Risk – Epicor ERP
SageMUFI Rating & Risk – Sage X3
InforMUFI Rating & Risk – Infor Lawson
Small and Medium ERP
SAPMUFI Rating & Risk – SAP Business One
OracleMUFI Rating & Risk – JD Edwards World
ProcessProMUFI Rating & Risk – ProcessPro
RootstockMUFI Rating & Risk – Rootstock
ERPNextMUFI Rating & Risk – ERPNext
OpenERPMUFI Rating & Risk – OpenERP
MicrosoftMUFI Rating & Risk – Microsoft Dynamics AX
Financial Applications
IntacctMUFI Rating & Risk – Intacct
IntuitMUFI Rating & Risk – Intuit Quickbooks Enterprise Solutions
FinancialForceMUFI Rating & Risk – FinancialForce
NetSuiteMUFI Rating & Risk – NetSuite OneWorld
PLM
SAPMUFI Rating & Risk – SAP PLM
Arena SolutionsMUFI Rating & Risk – Arena Solutions Arena PLM
Hamilton GrantMUFI Rating & Risk – Hamilton Grant Recipe Management
Demand Planning
SAPMUFI Rating & Risk – SAP APO DP
TableauMUFI Rating & Risk – Tableau (Forecasting)
Business Forecast SystemsMUFI Rating & Risk – Forecast Pro TRAK
Demand WorksMUFI Rating & Risk – Demand Works Smoothie
JDAMUFI Rating & Risk – JDA Demand Management
ToolsGroupMUFI Rating & Risk – ToolsGroup SO99 (Forecasting)
Supply Planning
SAPMUFI Rating & Risk – SAP SNP
SAPMUFI Rating & Risk – SAP SmartOps
ToolsGroupMUFI Rating & Risk – ToolsGroup SO99 (Supply Planning)
Demand WorksMUFI Rating & Risk – Demand Works Smoothie SP
PlanetTogetherMUFI Rating & Risk – PlanetTogether Galaxy APS Superplant
Production Planning
SAPMUFI Rating & Risk – SAP APO PP/DS
DelfoiMUFI Rating & Risk – Delfoi Planner
PreactorMUFI Rating & Risk – Preactor
AspenTechMUFI Rating & Risk – AspenTech AspenOne
PlanetTogetherMUFI Rating & Risk – PlanetTogether Galaxy APS
BI Heavy
SAPMUFI Rating & Risk – SAP BI/BW
SAPMUFI Rating & Risk – SAP Business Objects
OracleMUFI Rating & Risk – Oracle BI
SASMUFI Rating & Risk – SAS BI
MicroStrategyMUFI Rating & Risk – MicroStrategy
IBMMUFI Rating & Risk – IBM Cognos
TeradataMUFI Rating & Risk – Teradata
ActuateMUFI Rating & Risk – Actuate ActuateOne
BI Light
SAPMUFI Rating & Risk – SAP Crystal Reports
QlikTechMUFI Rating & Risk – QlikTech QlikView
TableauMUFI Rating & Risk – Tableau (BI)
CRM
SAPMUFI Rating & Risk – SAP CRM
OracleMUFI Rating & Risk – Oracle RightNow
OracleMUFI Rating & Risk – Oracle CRM On Demand
InforMUFI Rating & Risk – Infor Epiphany
Base CRMMUFI Rating & Risk – Base CRM
SalesforceMUFI Rating & Risk – Salesforce Enterprise
SugarCRMMUFI Rating & Risk – SugarCRM
MicrosoftMUFI Rating & Risk – Microsoft Dynamics CRM
NetSuiteMUFI Rating & Risk – NetSuite CRM