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SCDigest Expert Insight: Supply Chain by Design

About the Author

Dr. Michael Watson, one of the industry’s foremost experts on supply chain network design and advanced analytics, is a columnist and subject matter expert (SME) for Supply Chain Digest.

Dr. Watson, of Northwestern University, was the lead author of the just released book Supply Chain Network Design, co-authored with Sara Lewis, Peter Cacioppi, and Jay Jayaraman, all of IBM. (See Supply Chain Network Design – the Book.)

Prior to his current role at Northwestern, Watson was a key manager in IBM's network optimization group. In addition to his roles at IBM and now at Northwestern, Watson is director of The Optimization and Analytics Group.

By Dr. Michael Watson

May 7, 2013



Four Steps for Thinking About An Optimization Problem

Optimization Can Help you Get Value from your Supply Chain Data - Here’s How to Think About an Optimization Project


Dr. Watson Says:

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These four steps will help lead you to better decisions, fewer surprises, and a more successful implementation.
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As the amount of supply chain data continues to increase, there is a greater need to use that data to make better decisions.  Optimization can help here—whether it is for better planning systems or to even react to new events.

In the previous post, we talked about a definition of optimization and how it can be used in many areas within your supply chain.

In this post, we want to cover the four steps for thinking about an optimization problem.  Thinking about these four steps helps you make better decisions when running an optimization project.  It will help lead you to better decisions, fewer surprises, and a more successful implementation. 

Step 1:  Determine the Decisions you Want to Make

The result of an optimization run is a decision you can take action on.  For example, you want to determine where to locate a warehouse, you want to know the sequence to run jobs on your lines, or you want to know who should be scheduled to work each shift in the warehouse. 

In this step, you want to determine what action the optimization engine should recommend.

Step 2:  Quantify the Objective

In this step, you are quantifying how you will know if one solution is better than the other.  That is, the first step gives you the action you should take, this step quantifies how one potential solution compares to another.  It could be cost, it could be service, or any number of things.  The main point of this step is that you do not want to be ambiguous.  You want a formula to say how one solution compares to another.

Columns by Dr. Watson

Four Supply Chain Lessons from the Amazon book The Everything Store

Supply Chain by Design: What Toyota, Schneider National, PayPal, and Palantir Got Right

Supply Chain by Design: Service Level Measures in the Supply Chain, Part 2

Supply Chain by Design: Service Level Measures in the Supply Chain

Supply Chain by Design: Nike's Phil Knight on the Importance of Supply Chain

Supply Chain by Design: Four Lessons from Hau Lee's Green Car Story Updated for the Era of Machine Learning

Supply Chain by Design: Profitability of Your Assets Depends on how you use Them

Supply Chain by Design: The Most Overlooked and Underestimated Data in Supply Chain Design

Supply Chain by Design: Self Driving Trucks May Create New Roles and New Types of Jobs

Supply Chain by Design: Why Driverless Trucks May Create the Need for More Drivers

Supply Chain by Design: A Non Obvious Way Self Driving Trucks May Impact Your Network Design Strategy

Supply Chain by Design: How You Should be Using Multi-Echelon Inventory Tools

Supply Chain by Design: You Don't Need the Optimization in Multi-Echelon Inventory Optimization

Supply Chain by Design: On Network Modeling - Blaspheming the Baseline

Supply Chain by Design: Profit Maximization Feature and Amazon’s Focus on Lead Time to Grow Revenue

Supply Chain by Design: Using Profit Maximization to Minimize Cost

Supply Chain by Design: Two Big Reasons You Don't Want to Maximize Profit in your Supply Chain Model

Supply Chain by Design: You Can Set Inventory Levels and Other Such Myths

Supply Chain by Design: Six Organizational Issues in Tactical Inventory Planning

Supply Chain by Design: Four Things Nick Saban Can Teach us About Inventory Planning

Supply Chain by Design: Seven Ways You Can Think About Christmas Capacity to Avoid Having the Press Blame Your Supply Chain for Missed Deliveries

Supply Chain by Design: Machine Learning and High Quality Potato Chips

Supply Chain by Design: Simple Models to Solve Complex Fast Delivery Problems

Supply Chain by Design: CSCMP Report - Five Take-Aways on Natural Gas Trucks from the AB InBev and Owens Corning Talk

Supply Chain by Design: CSCMP Report - Six Insights from LLamasoft and JLL

Supply Chain by Design: CSCMP Report - Five Supply Chain Design Lessons from Benjamin Moore and How One is Used by Amazon in their 1-hour Delivery Service

Supply Chain by Design: CSCMP Report - Two Supply Chain Design Lessons from Starbucks CEO

Supply Chain by Design: Four Groups that Need to Step Up to Help Make Network Design More of a Profession

Supply Chain by Design: Top Four Best Practices for Multi-Objective Optimization

Supply Chain by Design: Caesars Entertainment's Customer Data is Worth $1B - How Much is Your Supply Chain Data Worth?

Supply Chain by Design: Using Transactional Data to Estimate Truckload Market Conditions in Near-Real-Time

Supply Chain by Design: Routing Optimization is Hard: Lessons from UPS

Supply Chain by Design: Eli Goldratt's Book "The Goal" is on Jeff Bezos (Amazon) Reading List

Supply Chain by Design: Top Five Rules for Cleaning Data for a Strategic Analysis

Supply Chain by Design: Network Design and Accounting Data

Supply Chain by Design: Sears Case Study - Same Day Delivery

Supply Chain by Design: Three Ways UPS and FedEx Handled Christmas 2014

Supply Chain by Design: Deepen Your Optimization Knowledge Over the Holidays with Free E-book

Supply Chain by Design: Calculating Supplier Lead-Time Variability - Not as Easy as It Seems

Supply Chain by Design: The Myth of the Market Rate and Network Design Projects

Supply Chain by Design: Three Ways the Supply Chain Wastes Big Data

Supply Chain by Design: Three Supply Chain Lessons from the book Scaling Up Excellence

Supply Chain by Design: What to Do About the Rise in Costs Because of the Trucker Shortage

Supply Chain by Design: Modeling Your Competitors

Supply Chain By Design: Just Because the Feature Exists, Doesn’t Mean You Should Use It

Supply Chain by Design: Just Because People are Talking about Big Data Doesn’t Mean it is Clean Data

Supply Chain By Design: Can Western Manufacturing Be Saved: What Does it Mean to Your Firm?

Supply Chain By Design: Optimized Baseline and the "Perfect" Network Design

Supply Chain By Design: Become more Analytics-Driven to Recruit Talent

Supply Chain By Design: Step Up Your Preventative Maintenance with Predictive Analytics

Supply Chain By Design: Demystifying Stochastic Optimization

Supply Chain By Design: More on Big Data in the Supply Chain

Supply Chain by Design: Should You Extend Your Network Design Capability with a Map Portal?

Supply Chain by Design: A New Trend in Network Design: Flow Path Modeling

Supply Chain By Design: Top 5 Skills You Need in a Supply Chain Modeler

Supply Chain By Design: Comment on Biggest Supply Chain Planning Technology Challenges

Supply Chain by Design: Beyond the Square Root of N Rule

Supply Chain by Design: UPS's Christmas Problem Explained in One Graph

Supply Chain By Design: Your One Network Design New Year's Resolution

Supply Chain By Design: 80/20 Rule for Supply Chain Design

Supply Chain By Design: What Makes a Good Inventory Buffer

Supply Chain by Design: Some Things Do Not Change: Cost and Service Trade-Offs with Air Shipments

Supply Chain By Design: The Impact of Natural Gas Trucks On Your Supply Chain Design and Capabilities

Supply Chain By Design: Controlling Inbound Transportation with Inventory

Supply Chain by Design: Three Types of Supply Chain Buffers

Supply Chain by Design: Systems Thinking and the "Limits" of Optimization

Supply Chain By Design: 3D Printing and Robotics - Disrupting the Dominant Supply Chain Model

Supply Chain by Design: Future Supply Chain- Airships and the Physical Internet

Supply Chain By Design: Avoiding Capital Investments - A Hidden Benefit of Network Design

Supply Chain by Design: Three More Reasons the Impact of the New Hours of Service Rules May Not Be So Drastic

Supply Chain By Design: Three Ways to Handle the Lag Time from Modeling to Implementation

Supply Chain By Design: Three Quick Steps for Analyzing Big Data

Supply Chain by Design: What is Big Data?

Supply Chain By Design: Using Optimization to Compare Facilities or Internal Benchmarking

Supply Chain By Design: Four Steps for Thinking About An Optimization Problem

Supply Chain By Design: Don't Let the Term "Optimization" Become a Buzzword

Supply Chain By Design: Supply Chain Models Can Go Wrong - A Different Perspective

Supply Chain By The Numbers: Top Three Ways Supply Chain Models Can Go Wrong

Supply Chain By Design: Supply- and Demand-Centered Modeling: A Follow Up to 2013 Priorities

Supply Chain By Design: Three SCDigest Predictions You Should Be Modeling

Supply Chain by Design: Cost to Serve Modeling

Supply Chain By Design: Top Five Models You Should Build in 2013

Supply Chain By Design: Should You Source from China or the US? Why Not Both?

Supply Chain By Design: Same Day Delivery and Network Design

Supply Chain By Design: Political Supply Chain and Network Design

Advanced Analytics in Supply Chain - What is it and is it better than Non-Advanced Analytics?

If we are evaluating two sequences for jobs running down the line, we might want to pick the one with the lowest cost.  Or, we might want to pick the one with the least amount of set-up time.

Step 3: Determine the Constraints or Limits in the System

In this step, we are defining what makes legitimate solutions. For example, we may want to define a constraint that says we need to meet all customer demand. So, this is not a criteria for comparing solutions, but a requirement for any solution.


Step 4: Understand the Data Available

With an optimization project, you need to keep an eye on the data requirements. Either you need to gather data that you do not yet have or you need to revise earlier steps if some data will not be available.


Final Thoughts

You definitively need to iterate through the steps—decisions at one step can impact another. And, it will be tempting to do multiple steps simultaneously. But, without this framework, it can be very difficult to keep track of the different aspects of the optimization project.

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