How to Socialize Complex Projects Like Optimization

Executive Summary

  • There are primary limiting factors to socializing inventory optimization.
  • A sample optimization problem.

Introduction

Inventory optimization is the second major type of optimization to be implemented for supply planning and is an excellent example of an implementation requiring the ability to manage a complex project. I work in inventory optimization; however, I also do a fair amount of work in SAP SNP’s optimizer, which is a cost optimizer. That is it attempts to minimize costs while meeting demand and respecting capacity constraints. 

Both of these optimizers are considered complex projects. One would think that companies would be able to manage complex projects like this, but it is quite rare for many, even large enterprises to managing complex projects of the type required for optimization projects

  • Something that I find commonly between all the optimization projects I work on is that the optimizer is not sufficiently socialized within the company.
  • This results in optimization that is considered a black box by the business and by executives.

After having worked on yet another account where optimization was once again not understood by the business and where the results are continually overwritten by the planners, I thought I would write about the need to provide better education to clients. That is managing complex projects by better socializing optimization overall. In this article, I will emphasize the connection between managing complex projects and socialization of solutions I have witnessed first hand.

The Primary Limiting Factor to Socializing Inventory Optimization

My point to the optimization vendors and consulting companies is this; the primary limiting factor is no longer the technology. The factor limiting the uptake of optimization is the transparency of parameters and in the education on the part of vendors and consultants that work in optimization.

I often come into projects after they are live and I am in a good position to evaluate the effectiveness of the education and knowledge transfer that took place before my visit. I interview the business, see how the system is being used in reality, and review the project and the training documentation. I can say confidently, that the job of education of clients on optimization is being done poorly.

The Main Problems with Optimization Projects

One of the main issues that I see is that both vendors and consultants are not getting the business users at their client’s hands-on feel for what optimization is. I think this is greatly related to the limited number of tools that are used to explain optimization to clients. It seems there are just two educational tools.

  1. One is PowerPoint, which explains optimization with boxes and arrows. Most the optimization PowerPoint decks just are not that useful. If I were managing an optimization vendor, I would contract with a company that specializes in technical education to get people who know how to do this right. A few educational decks could repeatedly be used with different clients.
  2. The second education tool is the application itself. However, few optimization tools are themselves good education tools. Secondly, this is starting for a person new to optimization too quickly. Unless they are quantitative analysis people, need to be eased into a new topic by starting from the basics. Production ready optimization tools are not the basics

Luckily, there are tons of tools to select from to begin to acclimate planners and even executives to how optimization works. As with anything else, we want to start at a basic level, listen to gauge the group to make sure that everyone understands. If people begin leaving, the room or start checking email the moderator needs to take a few breaks and or give some one-on-one tutoring to make sure the group can go through as a block.

Using Excel to Teach Optimization

Managing complex projects like optimization projects mean getting the users the right exposure to the tool in the right way. I have had success explaining optimization using a few optimization scenarios in Excel. Excel is a great learning tool because while the number of people who can read scientific notation is very rare in groups of planners, everyone can read formulas in a spreadsheet. This makes me question the point of scientific notation, but I will save that tangent for a different post.

Suffice it to say, people are comfortable with Excel, Excel has solvers that can be installed as add-ins (some of them free), and so it is a great learning tool. Secondly, since all users have Excel themselves, by only walking the class through an installation of a solver on their machines, they can use the optimization scenario themselves in the class and outside the classroom as well. Real learning takes place when the user changes constraints and goals and sees the output. This is why I highly recommend that each user has a working solver on their computer and be able to work along with the moderator.

I have had success explaining optimization using a few optimization scenarios in Excel. Excel is a great learning tool because while the number of people who can read scientific notation is very rare in groups of planners, everyone can read formulas in a spreadsheet.

This makes me question the point of scientific notation, but I will save that tangent for a different post. Suffice it to say, people are comfortable with Excel, Excel has solvers that can be installed as add-ins (some of them free), and so it is a great learning tool.

Secondly, since all users have Excel themselves, by simply walking the class through an installation of a solver on their machines, they can use the optimization scenario themselves in the class and outside the classroom as well. Real learning takes place when the user changes constraints and goals and sees the output. This is why I highly recommend that each user has a working solver on their computer and be able to work along with the moderator.

Choosing a Solver

For this example, I am using the Frontline Solver for Excel. There are many solver plug-ins for Excel, and I happen to like this one because it works with Mac as well as for PC. When using it I found this particular plug-in very easy to use so that I would recommend it, plus the basic version is free. Frontline offers a basic solver at no charge, and then sells a more advanced solver or pro version that they do cost around $900 for.

This Pro version can handle up to 2000 variables and a production type solver. I tip my hat to Frontline for offering a free basic version, which allows a person to cut their teeth on optimization without having to commit to buying a product. It also allows an unlimited number of users to download a basic solver for teaching without having to place an optimizer in the budget for short-term use.

A Sample Optimization Problem

Below I have set up a simple optimization scenario. This scenario asks the solver to resolve the following problem.

  1. Minimize the transportation dollars spent for 375 pounds of material.
  2. Respect the constraints for the minimum number of pounds to be shipped by truck, and the maximum number of pounds to be shipped by rail.
  3. Allocate material to the different modes (which move at different speeds) such that the average pound of material is moving at greater than or equal to 75 miles per hour.

Below is the initial view of the optimizer scenario. We start with the values that the optimizer will change, the pounds shipped, as set to “1,” next I will go through each of the values and describes how the optimizer will see each of them and use them to come to an optimal solution.


Next, we need to bring up the solver by selecting it from the Tools menu.

When the solver window comes up, we will setup the objective, the variables to be changed and the constraints. I find it useful first to name the cells so that the cell names show up in the Solver Parameters window. Having “TotalTransCost” in the window is far clearer than “C13.”

I want the all of the screens to be as clear as possible so that the user can intuitively understand what is happening based on their business experience. Later, when we get into adjusting parameters in the real model, I need complete clarity because ultimately, the business user must provide the right values for the parameters.

Here is our result below.


After the users are comfortable with this first example, I will make a change to the scenario. I will change the average speed to 85 miles per hour and rerun the optimizer.

The users will quickly notice that the optimizer moved roughly 10 pounds from the truck mode to the plane mode to meet the constraint of a higher average speed per pound. The users will also note that this decision cost an extra 78 dollars (roughly).

Conclusion

There are big opportunities to improve the education of clients on these complex projects. This education is in my estimation the number one reason many optimization projects either fail to meet their potential or simply fail overall. In addition to implementing the software, a big push must be made to educate everyone involved with the optimizer output. Since supply chain directors and vice presidents often issue policies on inventory management, this includes educating the executive decision makers. They must be motivated to get into the details of managing complex projects like optimization projects, and there is a lot to know. I have yet to find an account where the users or executive decision makers were educated up to their potential and had been to some accounts where the business does not understand the solution at all.

I have found success in using simple tools to socialize optimization concepts. The example I have provided is just one of these. Starting with the basics, and showing users and executives multiple optimization scenarios can rapidly increase their understanding of a topic that is unfamiliar to most. Vendors and consulting firms that follow a similar approach will significantly improve the success ratio of their projects, and improve the utilization of projects that are already considered successful.

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References

Brightwork Explorer for Service Level & Stock Management

Faster and More Straightforward Approach than Inventory Optimization

Inventory optimization started with a bang but failed to live up to the hype that was built up for it. Inventory optimization software is normally overly complex and while the idea is right, successes with inventory optimization are rare.

After seeing many failed inventory optimization projects we developed a simpler and less invasive way of modeling service levels and inventory. The Brightwork Explorer is free to access until it sees “serious usage” and is free for academics and students. Select the image below for more details.

MEIO Book

What is MEIO?

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

The Interaction with Service Levels

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