Optimization: Game On

Like the Mousetrap game, optimizing logistics requires precision timing, nerves of steel, and a little bit of luck to reach your goal. While you may not care about catching mice, you certainly care about speeding shipments, reducing inventory, delighting customers or just plain cutting costs. Here are the strategies of several logistics leaders who put their optimizing game face on and play to win.


Using Data to Win the Game

In the integrated world of supply chain management, logisticians at companies large and small—in every industry—work each day to fit the pieces together most efficiently. Some companies strive to optimize their supply chain end-to-end, while others optimize just one subset, such as transportation or warehouse operations.

But whatever they choose to optimize, logisticians and operations managers turn to Six Sigma, lean manufacturing, total quality control, sophisticated computer modeling tools, supply chain planning and execution technology, performance management, and numerous other optimization methods.

What, precisely, is optimization? In the broadest sense, it means “balancing multiple factors to obtain the best overall result,” says William H. Drumm, president and CEO of Establish Inc./Herbert W. Davis and Company, a supply chain strategy consultancy based in Fort Lee, N.J.

“In planning mode, optimization means balancing transportation, operations cost, and inventory levels with service performance; or production and sourcing costs versus fill rates and availability; or the combination of several operations or businesses in order to achieve synergies,” Drumm says.

In transaction processing mode, optimization means using software to choose the best alternative for processes such as shipment routing or product allocation.

The solution that delivers maximum results in a particular situation “may not be the best of all solutions,” Drumm says, because it may be impractical.

Take the example of a company looking to design an optimum distribution network. Computer modeling may indicate an optimum network that places a distribution center in between several major markets, when locating in one of those major markets is a more practical solution, Drumm says.

“Instead of seeking the perfect solution, choose the best practical solution for the specific situation,” he suggests.

Objectives for Optimizing

When it comes to improving operations, “having a clear set of objectives is a top priority,” notes Richard McDuffie, vice president of logistics and merchandising for AutoZone, a Memphis-based auto parts and accessories retailer. The company’s philosophy is reflected in the term WITTDTJR, which stands for “what it takes to do the job right.”

Having clearly defined objectives is a critical factor in doing the job right, McDuffie explains. “Not knowing the ultimate goal is a sure route to failure,” he says. “If you don’t have those objectives in front of you, you don’t know why you’re building your processes, people, and systems.”

McDuffie’s objectives are built on a clear understanding of the business and the issues or impediments in the way of success. He generally tackles five to 10 key objectives that will either break those impediments or improve the company’s overall performance.

“I make sure the supply chain and logistics objectives we develop meet overall corporate objectives,” he says.

Logistics is a high priority for AutoZone, which has more than 3,400 stores across the United States and Mexico. Supply chain objectives are discussed at weekly executive committee meetings, and the retailer has company-wide objectives in place for in-stock rates, fill rates, and on-time performance.

“These high-level objectives flow down to individual cost or service centers where they’re related to their particular location,” McDuffie explains. This involves developing service, cost, and quality objectives that set the standard for performance.

“You want to do things that drive service and sales,” he says. “The second piece is cost—you need to ask: ‘What should the network look like to reduce costs? How can our activities reduce costs?'”

Creating a distribution network that supports the desired service levels while achieving the least possible landed cost is key.

Equally important is having a sound quality process. “Without a good quality program across the board, you will not fulfill the customer’s need at the 100-percent level,” McDuffie notes.

Optimization, he says, is made possible by having a team of top-notch logisticians and other professionals who have the skills, knowledge, and tools they need to do the job.

Optimization means different things to different people. While it is often used to refer to doing something in the best possible way, “an operations researcher will define optimization in a strict mathematical way,” notes Jeff Schutt. As senior principal of Menlo Worldwide Logistics, Austin, Texas, Schutt draws on operations research as a powerful tool for optimizing clients’ supply chains.

In the operations research sense, optimization involves “a mathematical model representing a description of a set of operations or logistics/supply chain management problems that an organization wants to optimize,” Schutt explains.

The model works to minimize or maximize an objective function. The objective function “represents the objective of what you’re trying to optimize,” Schutt says. It is usually subject to constraints of some sort.

A company’s objective function, for example, might be to meet customer orders at the lowest possible cost subject to certain constraints, such as not exceeding production line capacity on any given day.

By the Numbers

Optimization tools use algorithms, or a specified way of solving a problem.

“A mathematical model enables people to see things that they may not find working by themselves,” Schutt explains. Taking the solution several steps further, a mathematical model considers thousands of costs and constraints more effectively than people can.

Computer-generated solutions that are reviewed and modified by a knowledgeable human being, then reoptimized by the software tool, “typically deliver a substantially better solution than people can on their own,” Schutt says.

Optimization tools can be used to address a broad set of logistics/supply chain issues. Network design, for example, is well-suited to a mathematical model.

One network optimization project, for example, evaluated various ways to consolidate the operations of two computer paper manufacturers. After the companies merged, the new unit had a total of 10 plants and 47 distribution centers. The company ran a series of supply chain optimization models—using more than 200 potential scenarios—with the SLIM modeling and optimization system from SLIM Technologies.

Using the model’s results, the company consolidated its operations over a period of time. It converted to a direct-delivery outbound distribution strategy, and eventually closed five of its plants.

Mathematical optimization also works well for integrated planning, where planners look at production, distribution, and inventory 12 to 16 months into the future.

“Planners usually update on a monthly cycle, and look at the model every day,” Schutt explains. This type of planning needs to be fully integrated with other information systems in the company.

Reaping the Benefits

Logisticians don’t need to be operations researchers in order to reap the benefits of these mathematical models.

“Ideally, the people who use the systems are logistics/supply chain professionals who understand how optimization tools can solve problems, and have some understanding of the scenario,” says Schutt. Optimization tools have evolved into commercially available, off-the-shelf software packages that don’t require a modeling specialist for implementation.

On the following pages, we look at the optimization initiatives of leading companies as they build better supply chain mousetraps:

Of course, every company faces its own optimization challenges—we couldn’t address them all here—but one certain way to fail any challenge is not knowing your path. Whether you’re looking for the practical best of the best, or the mathematically optimal solution, optimization depends on strong leadership and a team of creative, innovative professionals continually striving for improvement.

Using Data to Win the Game

Every level of your optimization effort involves data input. After all, you can’t improve what you don’t know. Here are 10 tips for using data to optimize your supply chain.

Though optimization projects vary from company to company, they share some common elements. Here are 10 tips on using data to optimize the supply chain, from William H. Drumm, president and CEO of Establish Inc./Herbert W. Davis and Company.

1. Timing. Optimization modeling should be a regular part of your overall effort. Do it annually, as you would budgeting or staff reviews.

2. Multiple levels. Model or optimize at multiple levels in the structure of the situation.

3. Average solutions. Don’t search for or adopt solutions that provide the best average for multiple business units—this satisfies no one.

4. Assumptions. Make assumptions carefully—getting them wrong can invalidate results.

5. Accuracy. Don’t get hung up on finding precise data when estimates will do.

6. Focus. Focus on the most impor­tant, driving issues. Estimates aren’t sufficient for key data elements; for these, you need precise data.

7. Test. You can never test enough. Use “what-if’s” and sensitivity analysis to test under changing conditions.

8. Pictures. For best results, use graphics, such as maps and charts, as often as possible. You need the big picture more than the precise numbers.

9. The answer. The conceptual answer, not the precise number, is what’s important. The number will be different by tomorrow, so the answer should prevail.

10. Expertise. Until you develop in-house expertise and capability, consider getting guidance, advice, and assistance from experienced resources.

Leave a Reply

Your email address will not be published. Required fields are marked *