For someone who has been working in and around the supply chain for more than 25 years, Lawrence "David" Davis sometimes sounds, well, not like a supply chain kind of guy. He talks about "genetic algorithms" and "evolutionary computation" and "deterministic simulation," which at first blush might seem to have little to do with running a company's supply chain.
But Davis, who is a senior fellow with Charlotte, N.C.-based NuTech Solutions, is one of a group of supply chain thinkers on the forefront of applying advanced science to solve thorny supply chain challenges. And one of the thorniest at present, in Davis' view, is that 20th century supply chain solutions are not up to the challenges that companies face in managing their supply networks in the 21st century.
Those challenges include supply networks that are becoming increasingly extended and complex; integration between companies and their trading partners that is becoming deeper at the systems and process levels; and emerging technologies like radio frequency identification that are producing ever-growing mountains of supply chain data. These and other factors threaten to overwhelm the systems that companies rely on to monitor and manage their flows of goods. Moreover, 20th century systems may be inhibiting companies from moving toward a 21st century supply chain.
Debugging the Supply Chain
Davis sees several problems with current supply chain technologies. First, he believes that contemporary solutions do not allow companies to optimize at the appropriate level of aggregation. By that, he means that companies should be able to use solutions to optimize across their sourcing and procurement, production and distribution processes all at the same time. Instead, supply chain solutions tend to break those functions out into separate modules, each of which runs separately from the others. "The answer you get when you first do production and then do distribution or any of these functions independently is not as good as the answer you get when you do them together," Davis says.
Second, software solutions typically optimize based on deterministic assumptions about how long it will take for any given process to be completed. As a result, the solutions produce "perfect" schedules that do not allow for breakdowns of machinery, traffic jams, defective parts and other real-world circumstances. "If you have a schedule that is built on the assumption that all your assumptions will be correct, the failure of any one assumption can make the whole thing fall down like a house of cards," Davis says.
To produce a plan that can be robust in the face of the many possible failures of assumptions, Davis asserts that supply chain solutions must employ stochastic simulations that simulate the effects of plans and schedules again and again and again, running through a series of "what if" scenarios to determine how well a particular schedule will perform against the range of possible futures. The final plan might sacrifice a small degree of optimization that a company would never be able to capture anyway, but the plan would be able to withstand inevitable changes in assumptions.
Third, Davis believes that these kinds of stochastic simulations must have embedded agents that follow the company's business rules. "At every point in a stochastic simulation when things go off the rails, we need to refer to the business rules of the enterprise in order to ascertain what the effects will be of a deviation from our expectations," he says.
For example, if a dispatcher is expecting to consolidate three loads at a dock in Chicago and one of the three loads winds up being late, that dispatcher likely has a set of procedures that he or she follows to decide whether to send the consolidated load on minus the late one or hold the shipment and send all three later, with all the subsequent downstream effects on the schedule. A simulation of that series of events should take account of the company guidelines that the dispatcher would logically follow.