By now, we’ve all either heard of or personally experienced the impact of the global chip shortage. But how did our automotive supply chain get to this distressed state? In the simplest of terms, what we’re seeing is the result of strong demand with essentially no supply. This concept is nothing new. In fact, supply chains can be specifically built to withstand shocks to the system. However, the pandemic was something that our supply chain simply wasn’t prepared for, and we’re still feeling its impact, two years later. Here’s what you need to know:

Are we through the thick of it yet? As of the beginning of February 2022, the chip shortage is still a thing. The current prediction is that the chip shortage will come back online over the next 6+ months as the volume of chips continue to increase creating more supply to meet demand. In theory, that’s true, but analysts believe that the pent up demand will most likely take an additional 18 months before we see a supply and demand rebalance. Shocks to the supply chain commonly cause supply and demand imbalances, creating new pricing and buying dynamics that take time to stabilize.

What needs to happen to make a full recovery? If I were an automotive manufacturer, this would keep me up at night. There are many variables at play, some of which we’ve never seen before, let alone have an understanding of their full impact. On the production side, judgment calls need to be made about which chips to prioritize based on availability, cost, and the profitability of the cars and trucks. Then there is a massive effort needed to coordinate the queue of cars and trucks partially built without chips. The coordination effort to complete and test these nearly final vehicles is monumental!

In order to strengthen our supply chain, each upstream supplier needs to be carefully organized for turning back on their production. As the flow of cars and trucks increase to full volume, dealerships will be actively gearing up (after over a year of sleepy sales), matching potential customers with the vehicle availability. Over time, it seems inevitable that specialized sales and marketing campaigns will be needed to help better shape the demand to supply volumes. Advanced analytics can help guide this search. There is a lot to consider in the supply chain with dependencies around every corner, while making decisions based on what we know now.

But how do we optimize what we don’t understand? Knowing that all these decisions have dependencies (both up and downstream) in the supply chain, we want to understand the effects of these decisions. But how can we do this when there are so many unknowns?

The answer is: modeling.

Mathematical modeling techniques leveraging optimization and simulation are a perfect answer to this challenge. Optimization technologies can be used to help trade-off between different utility functions. This can then be followed by discrete event simulation, enabling the study of detailed what-if scenarios to better understand the cause and effects, as chip production ramps up. Doing this will help us understand the decisions that can be made and then, through modeling, we can understand what the effects of these decisions are. As different solutions to the problem are studied, we can best understand the choices that we should make while sifting through the sea of possible solutions to these unforeseen problems.

How can the industry be better prepared in the future? We all want there to be lessons learned. The challenges of cost minimization and just-in-time manufacturing are not new. Competition for consumers, especially in price sensitive markets, creates a rightfully strong focus on waste reduction and cost elimination. The trick is to minimize your costs within a certain margin of risk. Full disclosure – we as humans are not very good at risk assessments. Generally, our mental models replace the difficulty of nailing down risk with familiarity. What we are unfamiliar with must be risky. However, this can often be nothing more than a mirage misleading us down familiar paths.

How can we remain profitable and tolerant to shocks in the system? Our utility functions for what we value needs some correcting. For instance, we know that in many markets, automotive included, market share is a big deal. If it had mattered enough that one automotive manufacturer had previously focused on resiliency in their supply chain and as a result continued producing cars at a blockbuster rate while their peers were hemorrhaging for chips, they would have successfully captured portions of the market with greater ease than ever before. After all, they would have been essentially the only supplier in a hungry market! Companies and industries’ utility functions for what they value need to use this information to drive their supply chains. This means we need studies of resiliency to ensure market capture in a supply shortage. The outcomes of these studies will likely be slightly higher costs, but with significantly lower risk. To quantify this, I would again go to simulation to help provide answers to the what-if questions that exist.

As we get through this chip shortage, where should we turn our attention? Interestingly, the place we should look at as a “gold standard” for studying and understanding risk is the U.S. military. For decades the U.S. military has leveraged and studied risk to ensure their pilots are always trained, their supply chains remain functional to support the troops, and that missions are maximizing not only risk but safety as well. Companies would be wise to emulate the simulation modeling that the military uses, repurposing the simulations for understanding our supply chain, product, and demand volatility (and thereby resiliency).

With all the unknowns with chips coming back online, taking the preparation time to understand the effects to our decisions is key. Optimization and simulation modeling are the tools for the job. As we prepare for tomorrow’s problems, we need to better understand our entire utility function, not just our cost sensitivities to ensure that our supply chains are resilient for all the metrics we care about—be it service, risk or resilience—in addition to cost.

About the author:

Jason Judd

Jason is an experienced Supply Chain optimization leader and is the Senior Vice President of Engineering at Optilogic. Driven by innovation and impact, he takes pride in providing the most optimal solution while keeping it simple. As an innovator, his goals include mentoring the next generation of people, tools and technologies to continue to disrupt the optimization industry. Jason also earned his Ph. D. in Operations Research/Industrial and Systems Engineering from Virginia Tech.