For all their many advantages, transportation management systems (TMS) may be too brittle for today's flexible supply chains. This type of software is terrific at determining the lowest-cost carrier to move a shipment between a fixed origin and destination. But in today's volatile world, too often those points are anything but fixed. Because of changing transportation market conditions—for instance, carrier price increases or capacity issues—a company might suddenly want to ship product from another factory in its supply chain network in order to make a timely delivery at the lowest price.
The solution for companies is to embrace "dynamic optimization," according to Brett Cayot, a global lead for logistics and distribution in the advisory practice of consulting firm PricewaterhouseCoopers (PwC). "Take a company having 10 manufacturing sites in the United States," he says. "The decision on what to produce where is dependent on logistics costs. There's no change-over cost for manufacturing."
Cayot contends that companies need a software tool that can "view" all the plants in the supply chain network, take orders in **ital{real time,} and then determine the best location to ship from based on logistics costs. The challenge is that this approach requires "deck shuffling" on the part of the manufacturer. As Cayot notes, if a company decided that it made more sense from a transportation cost perspective to service Customer A from Plant 2 instead of Plant 1, then it faces the issue of bumping Customer B from Plant 2. "That's a challenging optimization problem," he says.
Although TMS solutions are not designed to perform this type of supply chain optimization, network modeling applications are well suited to the job. Cayot says at least half a dozen vendors currently offer applications that can tackle this complex problem.
Up to now, though, network modeling software has used historical data to make projections on future demand flows. In fact, companies have often used modeling for strategic assessments of their supply chain nodes—plants and distribution centers—to determine the optimal location from a shipping cost perspective. But Cayot says that if real-time order requests are fed into a network solution, the software can optimize outbound shipments to customers by determining plant origins, or even optimize inbound movements by choosing the optimal supplier to serve a plant. "This approach can solve transportation capacity and throughput issues," he asserts.
Although this might all sound theoretical, Cayot says his firm is currently working with many of its clients on exactly these kinds of projects. PwC uses modeling software to determine shipment origin points for two weeks out as a way to optimize carrier deliveries and hold down transportation costs. "We start by running the network modeling solution weekly, in line with an organization's production schedule," Cayot explains. After PwC "gets a better feel for the labor requirements and intensity of the day-to-day operations," Cayot says, it then looks at whether it makes sense to run the model centrally or regionally to accommodate different time zones.
Cayot contends that dynamic optimization allows a shipper to select the lowest-cost mode for a shipment, whether it's rail or multistop truckload. Overall, this holistic approach to shipping should reduce the total number of transportation miles in the face of changing business conditions. "Dynamic optimization provides companies with the opportunity to react to customer needs and supply chain changes with minimal to no effect on service or cost," he says.
