Environmental modeling has been an increasingly active area of OR over the past few years (see the greenOR blog for many examples). As companies strive to do good, either for economic or other reasons, they are thinking more about environmental impact in all that they do.
ILOG has just announced a “Carbon Footprint” extension to its supply chain modeling software (which in turn is based on the software from David Simchi-Levi’s previous company LogicTools). InfoWorld is one site that has picked up on this:
In an effort to aid companies as they struggle to balance profitability and environmental responsibility, vendors are rolling out increasingly sophisticated tools. Among those vendors is ILOG, which this week released a Carbon Footprint extension to its LogicNet Plus XE supply-chain application. This remarkable tool serves a valuable function: It’s designed to help companies evaluate the impact that various supply-chain network configurations and transportation strategies would have on their carbon footprint.
David Simchi-Levi is quoted regarding a case of a company trying to decide how many distribution centers to have:
Using LogicNet Plus XE with the Carbon Extension, the company cranked out various scenarios that involved adding between two and seven new distribution centers. Turns out that moving to four distribution centers would have resulted in the highest costs savings. Thus, a company that wasn’t thinking about green metrics might have gone with that option.
The company found, however, that by going up to six distribution centers, it would have slightly higher costs (1.6 percent), but it would reduce transportation distances by 20 percent and overall carbon emissions by 11 percent.
Those results might come as a surprise: How could adding six more energy-consuming distribution centers result in less carbon waste? The answer: With the six-center model, the company relies more on trains for transporting goods inbound than it does on trucks to ship products outbound. Trucks have a significantly higher environmental impact than trains, according to Simchi-Levi.
I don’t know whether a 1.6% cost increase is worth an 11 percent reduction in carbon emissions, but having the information makes it possible to make an informed decision.
This reminds me of work being done in robust optimization, of various forms. David Ryan of the University of Auckland spoke two years about his efforts to devise airline schedules that are less susceptible to delays (I wrote on this in 2006). He found that a very small cost increase allowed a huge improvement in the robustness of the schedules. Many of these supply chain optimization problems are relatively “flat” with many near-optimal solutions (cases where there is only one optimal solution with everything much worse tend to have obvious solutions). In such a situation, “secondary” issues such as environmental impact, customer perception, or robustness to variation in data take on a higher importance.