In your opinion, how has the supply chain landscape evolved over the years?
There have been a wide variety of changes over the last couple decades. Outsourcing key supply chain functions has allowed for improved scale, higher levels of functional sophistication, and ultimately lower costs for many industries. Offshoring of production, either in conjunction with outsourcing or within the bounds of a company, has led many companies to be able to capture the benefit of lower unit labor costs, although this effect varies by industry. All of these changes have yielded benefits but have also led to a much more fragmented supply base, making it much more difficult to see and manage the entire end-to-end supply chain. In many cases the focus has been on optimizing the links of the supply chain, rather than the chain overall. This effect, along with the longer transportation times necessitated by offshoring, has also made supply chains less responsive to change in either supply or demand, or in other words, less resilient. Practically speaking, in this situation things work pretty well as long as there are no unexpected disruptions, but results degrade quickly when a problem occurs in even one link in the chain. One thing that the last few years has taught us is that problems can occur. From the pandemic to the Texas winter freeze to the tightness in the labor and transportation markets, we’ve had a series of crises to deal with all at the same time. Valvoline has always focused intently on building resilience into the design and operation of our supply chain, but even then, it hasn’t been easy. The technology and tools we use have advanced over that time, and this has helped, but adoption has lagged in many companies and industries for a variety of reasons, and so there are gaps remaining.
What are some of the advantages of the current technological evolution?
If implemented properly, some of the tools available today can help support day-to-day execution, while still allowing a company to better see and diagnose problems quickly. In our industry, Internet of things-type equipment, like remote tank monitors, have become affordable enough to allow use with even modestly sized customers. This gives us a real time signal we can use to more accurately predict demand and trigger local replenishment. It continues to become easier to integrate data streams with both customers and suppliers, which also helps us manage the entire chain, although challenges remain there as well. Supply chain planning systems are becoming much more powerful, and able to incorporate larger amounts of data, including data from the point of sale, into demand prediction models. With the advent of AI and machine learning, incorporating a broad range of other info to drive demand forecasts is becoming more common.