This talk involves the need for control towers, data centers that provide real-time metrics across the supply chain. These data can be used to identify potential disruptions, suggest alternative sources, and track product flow, among other things. But DeHoratius says the effectiveness of control towers depends on the integrity of the data they utilize, and her prior work indicates that data accuracy continues to be a problem. Overall, she notes an “accumulation of errors” across the chain that include inaccurate purchase orders, port arrival data, and item descriptions for customs clearance.
Iakovou concurs that a lack of good information sharing has hindered the ability to detect problems ahead of time and respond to crises faster. Indeed, visibility, transparency, and mapping are a few of the tenets for developing resilient supply chains. His research with International Hellenic University’s Dimitrios Bechtsis, University of Cambridge’s Naoum Tsolakis, and Aristotle University of Thessaloniki’s Dimitrios Vlachos outlines a three-layer framework for supply-chain managers to improve data, integrity, and flows (of products, information, cash, and processes) across networks.
In their framework, at the cloud layer, several data streams relevant to a company’s supply chain are collected, managed, and shared across the chain according to various confidentiality levels. A Blockchain layer stores all these data securely, and the A.I. layer analyzes and processes data from the Blockchain layer to create algorithms that help supply-chain managers make decisions.
Blockchain can be used to improve transparency by streamlining transactions, notes Auburn’s Glenn Richey. He says the technology helps companies to make some data easily accessible and to keep them updated, which improves internal forecasting.
And once good data are available and accessible, they also can be shared selectively. Research that DeHoratius conducted with the Ohio State’s Elliot Bendoly and Nathan Craig provides an example of how that could help, arguing that companies can reduce the cost of uncertainty in crisis times by tracking two specific metrics: consistency (the ability of a supplier to fulfill orders repeatedly) and recovery (the ability of the supplier to fulfill orders after a service lapse). These two concepts, says DeHoratius, “have been critical in today’s dialogue about managing the pandemic disruptions to the supply chain.”
Make it resilient
Opacity can hide risks in the chain. Before Japan’s devastating earthquake and tsunami in 2011, Toyota had close contacts with its tier-one suppliers and assumed that they were using a large variety of tier-two suppliers, DeHoratius says. After the disaster, Toyota took a close look at its tier-one suppliers and found that most were themselves using the same suppliers. The diversification of risk Toyota assumed was there actually did not exist, she says.
Iakovou argues that improving digital approaches to mapping extended supply networks is crucial. Then companies can make changes so they’re able to pivot more easily to other suppliers if needed. “Qualifying and engaging multiple suppliers of course has a cost, but, in times of disruption, those costs can be paid off multiple times over,” says Booth’s John R. Birge.
Having multiple suppliers can help both reduce the costs associated with finding and onboarding a new supplier in the midst of a crisis and avoid expensive production delays. “If more companies react to current conditions by increasing redundancy in their supplier base, that should lead to more resilience in the overall supply-chain network,” says Birge.
But building in redundancy costs more. And how much redundancy is enough? That requires accurately assessing the risk of a shock to the chain. What’s the likelihood of a tornado, energy-price shock, or pandemic that would make it worth the price of carrying extra inventory or having backup production capacity?
And what are the costs associated with assessing the risks, or not doing so? Before COVID-19, the World Health Organization put the risk of the spread of infectious diseases as below-average likelihood but above-average impact. Many companies failed to prepare, in part because of potential pushback from shareholders, and are reassessing how much it’s worth to be ready for a relatively unlikely event.
