Building the Lean + Tech Dream Team in Supply Chain Problem-Solving

Supply-chain shop talk has changed. Today, when I work with supply chain professionals, I hear about they AI planning tools they are using. They discuss AI in the procurement space with supplier contracts and supplier compliance. They talk about where and how to integrate automation into their manufacturing plants, how much robotics cost and the potential internal rate of return (IRR) of their latest automation project.

I seldom hear discussions about when to do a time study, or whether a value stream map is needed, or where to use Six Sigma in a process. 

So, does lean still matter?

Legacy Lean Uses

Up until a few years ago, when I led supply chain operations at La-Z-Boy, my team and I worked diligently to make our operations as efficient as possible.  We used every lean tool we had to remove even seconds from a process.  We had maps of every process in every plant and distribution center. 

We value-stream-mapped (VSM’d) all our processes to identify and eliminate waste—and when we finished, we did it again, every year. There always seemed to be an ongoing Six Sigma class for process improvement, and we worked to mistake-proof (poke yoke) and optimize our product designs for manufacturing through design-for-manufacturing-assembly (DFMA).

 Seconds really mattered, and using lean concepts was how we engineered seconds out of our operations. 

Case in point: I remember grappling with the dilemma of whether to add a shift to one of our manufacturing plants.  We needed additional production to support the business, but not enough to justify adding an entirely new shift—a very costly move.

After much discussion, we decided to get our continuous improvement engineers (CI) involved at a deeper level. Their job became finding a way to get the production we needed through our existing equipment, on the same shift, by leveraging lean methodologies (primarily VSMs and kaizen events). When they finished their work, they had accomplished their goal and found a way to achieve the needed production throughput without adding a shift.  Lean mattered then.

Supply Chains Look Different 

I wonder whether a supply-chain team would take a similar approach to solving this type of problem today. Do supply chain leaders still use lean tools to reduce labor cost, boost productivity or improve quality and service?  How does lean fit into today’s hyper-technology driven supply chain? 

From what I’ve observed, lean still matters. Supply-chain teams use lean to reduce labor cost, boost productivity and improve quality. It’s likely the meeting looks and feels different than meetings my team participated in—with AI and predictive modeling to game-plan the task—but the goals and outcomes are much the same.

It is still about solving the problem with the best tools available—in lean, it’s VSM, Poke Yoke, Kaizen, 5S, among others. AI is also problem-solving tool, as are machine learning and a host of other tech-driven tools. 

Where to Leverage Tech in Lean

If lean still has a place in the high-tech supply chain, where might leaders look to leverage it effectively?  I mentioned how we used DFMA with new products; AI can perfectly model a host of DFMA scenarios. Then, once the product design is chosen, traditional lean tools such as ergonomics, 5S and time studies—observations to measure the time to complete a task—can help engineer the best workstation layout to optimize productivity and quality.

Additionally, with the rapidly increasing usage of robotics and automation in factories across the globe, lean principles like VSM, poke yoke and heijunka—leveling the type and quantity of production to more efficiently use assets—can help with the best integrated process layout for efficiency, speed and quality. 

Starting the Synergy

How do supply chain leaders integrate lean tools with new technology? 

First, understand the strengths and limitations of each to help you select the best tool for the job.  (Like with most technically driven tools, for AI, automation and robotics to work well, they need good data; the “garbage in, garbage out” adage still applies).

 A case in point: 5S is a classic lean tool where workplace organization is applied to more efficiently categorize all tools used at a worksite; the principial of everything has a place and everything should be in its place.  Here, traditional lean tools clearly work better, as AI cannot effectively visually assess and organize the work site as well as a human using lean. 

However, AI can augment the process by suggesting good questions to ask during the 5S process or suggesting best practices to consider when beginning a 5S exercise.  This scenario optimizes lean and AI for a better solution that neither would have delivered separately. 

Second, start where operational costs are high or critical processes exist. These are areas where the greatest value can be created or preserved. 

When you leverage lean and technology, you not only solve the problem; you likely will solve it more quickly. 

For instance, a great candidate for marrying lean and AI is reducing equipment changeover times. After developing a well-crafted prompt requesting AI to create options for minimum changeover times, a supply chain leader can choose the best scenarios, test them and study the results to determine optimal outcomes. The best solution can then be implemented across the manufacturing network. 

At the end of the day, certain universal truths remain: Time is money, and idle equipment expensive.

So, the next time you need to improve cost, quality, or service, don’t simply limit your thinking to AI, automation or machine learning; think lean, too.  A healthy approach to problem-solving can include a comprehensive suite of tools, both contemporary and traditional.