What do self-driving cars and supply chains have in common?
On the surface, the process for both may seem entirely automated. But when you dig a little deeper, both rely on a heavy backlog of collected and analyzed data so they can deal with any anomalies that come up on the fly. You need to put as much in in advance as possible and work with exceptions as you go.
Blake Johnson, consulting assistant professor of management science and engineering at Stanford University, anticipates that having this built-in knowledge in supply chain management will lead to significant business outcomes.
“If I’m an executive … the more I can bake in the same kind of self-driving controls, rules and priorities, that would be great for me,” he says. He imagines a future where businesses can catalogue different circumstances and make recommendations to management on what the next steps should be the vast majority of the time.
In supply chain’s history there has been a lot of uncertainty — uncertain demand, uncertain supply, uncertain manufacturing yields, regulations and so on that were hard to predict, according to Johnson.
“Add on top of that that there are a lot of entities in our own company — sales, supply chain, procurement, logistics. They only get part of the puzzle, information wise or incentive wise,” he says. And once you add external entities to the mix, then supply chain companies face a siloing of information across a complex network. This can leave supply chain companies feeling like they are stuck in L.A. traffic.
The shift to smart automation
But the supply chain of the future will shift away from former business intelligence norms, where industries could just capture a picture of what is happening right now. Instead they will shift to an approach that blends knowledge of past successes and errors with automation, where they can stay one step ahead by knowing future demands in markets.
At its highest level, Johnson says supply chains will be able to know what action should be taken to meet future demand and then assign who owns that action. And there are two fundamental steps to getting there.
First, businesses must understand that uncertain circumstances are not going to go away — but they can quantify them better with better analysis of past events. By setting some operating boundaries, a supplier can opt to place an order within a certain range of numbers given a circumstance. By putting a minimum and maximum in place, it sidesteps today’s scenario, full of over-ordering for the sake of a happy sales department but then having a ton of change orders later on.
“Typically, as a supplier, I share a forecast with you. You know it’s gonna be wrong, and I know it’s gonna be wrong,” he says.
Second, these new parameters can help supply chain partners make a demand forecast. And then these analytics hold everyone accountable.
The most effective automation requires a lot of pre-committed actions, just like self-driving cars, which means more upfront work. But that leads to complete alignment, says Johnson. “Everyone knows exactly what to do based on the circumstances they’re in.”
It will take a cultural shift, to move from a situation where there is a plan in theory for an exact order to an approach that estimates many possible projected orders with automated triggers that carry out the best case scenario for each of those possibilities, but this equals savings down the road, says Johnson. And automation that leverages and learns from past data will allow companies to find better tasks for their employers.
“I think this makes people’s lives better. I work with one company that all 300 employees just negotiate change orders,” he says. “We can have these people do something interesting and add value instead.”