New Supply Chain Mindset and Examples

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The New Supply Chain Mindset: From Just-in-Time to Just-In Case

EVER GIVEN SUEZ CANAL STUCK SUPPLY CHAIN DISRUPTION OLD.jpg

November 15, 2022

Valerie Tardif, SCB Contributor

Many of those in supply chain management learned early on the principles of just-in-time (JIT) manufacturing. Inspired by the work of Taiichi Ohno at Toyota Motor Co., the JIT revolution tightly coupled production and demand, resulting in little or no work-in-process inventory. Subsequently, it went on to change how supply chains ran. JIT fostered demand-driven, agile and lean processes, and contributed to breaking down functional silos inside an organization.

The adoption of JIT principles resulted in more than four decades of economic prosperity for global manufacturing and distribution. Yet much of that success relied on enabling factors that were existing at the time. To anticipate customer demand, companies relied on past sales data. They also assumed that customers would act rationally, with trade-offs that could be easily evaluated, and would respond logically to incentives.

To reduce inventory, companies depended on predictable transportation, with fast loading and unloading at ports around the world, and high availability of road, rail, barge and final-mile transportation. They were able to synchronize supply and demand by relying on abundant raw materials and components flowing through integrated supply chains, dedicated suppliers and manufacturing capacity.

Many of those assumptions no longer hold. COVID-19 has changed sales patterns and created holes in historical data. The new generation of consumers — digitally native, socially and eco-conscious — is changing demand patterns, making them more unpredictable than ever before.

This new world brings fresh opportunities to rethink how we plan and run supply chains. Succeeding under these conditions begins with three simple changes.

Adopt a probabilistic mindset. Probabilistic modeling is a technique that factors possible events or actions and their probabilities. Probabilistic planning involves predicting future outcomes and making plans that lead to desired cost and benefits. The most common example is a probabilistic demand forecast that outlines expected demand, while including a confidence interval or a probability model around it.

In fact, many parameters in planning should be modeled probabilistically. Lead times in master data are often inflated to represent worst-case figures. Planning leaders should demand that their systems model all the variability around lead times to factor the impact of shipments arriving early or late. Think beyond a future described by “one number,” and take in the wide range of potential outcomes.  ... '