Disasters have killed just-in-time supply chains; time to develop something more agile

Empty shelves have become a common sight in American stores. Toilet paper, cough syrup, baby formula and eggs are just a few of the products that have been in short supply since the COVID-19 pandemic started a series of global supply chain disruptions three years ago.

Those shocks exposed weaknesses and vulnerabilities in a supply chain dependent on just-in-time delivery and warehouse strategies that left it in such brittle condition, just one disaster was enough to break it. Under multiple subsequent shocks — a war in Ukraine, inflation, avian flu, a ship stuck in the Suez Canal, to name a few — it had little chance. 

Just-in-time was a boon for manufacturers and retailers when the conditions were right. So efficient, it left little waste and maximized profits. No wonder it was a near-universal strategy.

But we have to acknowledge now that the conditions that made just-in-time strategies so efficient and profitable are not coming back. In response, it’s imperative we develop a new, more agile approach to replace it, one that starts with an assumption that everything involved in the supply chain can change at a second’s notice. We need a strategy that anticipates that change and quickly pivots with the circumstances so that inventories are available when needed and never stockpiled when they are not.

We need to develop a risk-adjusted, agile inventory management system.

In the optimal just-in-time world, manufacturers received materials and retailers their products just when they were needed. When consumers needed more shirts, for instance, the cloth and other raw materials arrived at the factory just in time to make them, and the completed shirts arrived on store shelves just in time to be sold. Money wasn’t wasted with bolts of cloth and unsold shirts taking up costly space in warehouses and retail storerooms.  

Just-in-time became even more efficient as advances in technology and data collection gave manufacturers and retailers the consumer insight needed to know what shoppers wanted and when.

But as we’ve seen in the last three years, a wide range of risk factors — from pandemics to geopolitical issues to hurricanes to the influence of a random social media post — can undermine even the most efficient just-in-time strategy.

Some companies seeing those weaknesses have adopted a Plan B just-in-case strategy, stockpiling products and materials as a hedge against future shortages. But storage costs cause a bullwhip effect that drives up prices throughout the supply chain and forces businesses to dump excess inventory at steep discounts.

So it’s clear we need something that is more agile and adjusts for risk on the fly. But what does that kind of supply chain look like? Businesses need to consider a number of factors, and they can start with the same emphasis on data analytics and technology that made just-in-time so profitable. Managers need to not just understand but be able to predict events that could threaten supply chains, and advances in artificial intelligence can help them monitor and anticipate risks so they can develop contingencies. 

Technology can also help firms remain flexible in how much stock to keep on hand, adjusting stock and inventory locations based on risk factors in the supply chain at any given time.

Finally, companies need to consider the political reliability of where their suppliers are based. Five years ago, it made sense to choose a China-based supplier because of low costs, but ongoing geo-political conflicts and COVID-19 management have made the country much riskier, with lengthy order delays not unusual. Is China the most reliable source country anymore, and if not, what should replace it? Measuring cost versus predictability and cost versus reliability can drive those discussions.

The need for more flexible inventory management began even before COVID-19 because technological advances were already disrupting supply chains. But today’s global realities have made flexibility even more imperative. 

Jennifer Blackhurst is a professor of business analytics at the University of Iowa Tippie College of Business.