Finding the best supplier, at the right price, is a common problem in business. Using the best analytics to get a solution is no problem.
Whether in government or the private sector, choosing the right vendors is an essential of doing business. In many cases, it’s not just finding the low bidder or the supplier closest to the project, it is a matter of many variables coming together at the right time, cost and location, and the decision-maker needs to do priority assignment and risk analysis.
In the case of the School District of Philadelphia, several vendors will normally compete for contracts during the bidding process, and it is up to the department administrator to make a careful and impartial decision. With many variables to weigh in the decision, optimization software can be called upon to solve the relationships between the variables and find the optimal solution. This method is both impartial and faster than human analysis but often requires advanced knowledge of analysis methodology, not always found in every department.
Charles Lowitz, the fiscal coordinator for the transportation office of the School District at the time, understood this. He looked for software that could be applied to his Microsoft Excel contract model. Using Excel had been convenient for the district since it was already in place to collect information about the vendors. Lowitz was able to use its formulas to express relationships in his business model, simplifying the early stages of the project.
The School District of Philadelphia operates about 40 percent of the school bus routes itself, but needs to contract out the rest to meet its obligation to transport students in the sixth largest district in the country. Ultimately, vendors would be selected by experience and capacity, but the District needed to determine the optimal mix of vendors—each supplier had unique limitations in how many buses they would supply, what routes they would serve, and at what cost.
Approximately 725 bus routes were on the table to be contracted out to private vendors. It was Lowitz’s responsibility to come up with a way to maximize the return on investment and improve the process by which the routes were awarded.
In previous years, the process had been done by figuring out, by hand, which contracts would make the most sense given the constraints of budget and time. The school district would then need to figure out a way to distribute bus routes between the bids.
For various external reasons, the district found that it was preferable to keep 30 to 40 percent of the routes in-house with existing buses and then to outsource the rest of the fleet. The transportation department had to find companies willing to take on the rest of the routes within the constraints of the model. In the end, the analytic power of Premium Solver Platform (now known as Analytic Solver Optimization) from Frontline Systems in Incline Village, Nev., was employed to find the partnerships that would be most beneficial, from a financial and operational perspective.
The main variables associated with the vendors chosen were:
- Cost: The district had to determine which route sets to award to which vendors at the lowest possible cost. Some vendors had a minimum number of routes they would bid on, at a certain cost, and if they weren’t awarded that minimum, their prices would increase.
- Vendor capabilities: The district needed to make sure a vendor could accomplish what it put in its bid. If a company only had 50 buses, then the school district could not feasibly award them a contract that included more than 50 routes.
- Vendor reliance: The district needed to make sure the company was reliable, but at the same time, they didn’t want to rely on too few businesses. Some school districts award all their contracts to one vendor and that can quickly turn into a mess if that vendor were to fold, experience financial difficulties, or run into serious equipment problems with its fleet.
Previous experience with the district, financial stability, and exhibited business acumen were also factors taken into consideration when awarding these contracts. Vendors were allowed to bid on any number of routes – from one or two to all of them—but the district stipulated that it would not award more than 300 routes to any one vendor, to protect its own interests.
“In our contract awards process, we are required to have a manual process to verify the solution suggested by the optimization software,” Lowitz explains. “Fortunately, it all worked out, and I don’t think there would have been an assurance that it was the optimal solution without the software. The strategy employed by our procurement office necessitated having to have a product like Premium Solver Platform to come up with the right answers.”
The Optimization Model
During the first year Lowitz used the software, there were 16 requests for proposals submitted to the district. Using an optimization model created in Excel, he tracked which of these vendors would offer the school district the best opportunity. The optimization model he created took into consideration all of the variables that made up the bid and the needs of the district. The final product included a surprising 1,552 binary integer variables. The binary variables expressed the values of 1 for yes and 0 for no. After the model was ready to run, it only required minutes to gather the necessary data to plug into the program and determine the best contract placement.
Lowitz used a standard linear programming model, one that best fit the number of integer variables to be analyzed. There were approximately 100 route sets total, grouped by geographical region in the school district, and there could be anywhere from two to 15 routes per set. When all the analysis was done, 12 of the 16 vendors were awarded contracts. The size of the contracts reached both ends of the spectrum, with one vendor taking four routes and another getting almost 300 routes.
When he required help, involving the input of minimum integers within Premium Solver, Lowitz discovered that customer support for the platform was readily available. The analytics experts at Frontline Systems were able to help him solve the problem. “I had one issue that I had to call Frontline about, and they were great with support,” he reports.
It became clear that the idea of entering a minimum into the model was a bit tricky. For example, a company might indicate it would only accept a contract for a minimum of 14 routes. Realistically, 14 isn’t the minimum that vendor could be awarded—if the district didn’t award any routes to that vendor, which was possible, then the actual minimum would be zero. “Once we got over that, the only hiccup in the model, the rest fell into place,” Lowitz recalls. “It may have taken a day to construct the model, then just minutes to come up with the optimal solution.”
In the end, the School District of Philadelphia was able to award an optimized number of contracts to privately owned bus companies without resorting to handwritten notes and untrustworthy trial-and-error strategies. By implementing Premium Solver Platform analytic tools, creating a model with the proper variables, and then running the program, the school district saved both time and money.
“I can’t say enough about it,” Lowitz says. “It had been a long time since I had had to use an optimization product, so I was rusty. But the software was very intuitive, making it very easy to come up with the solution. It was relatively quick to put the model together.”