Choosing vendors to serve a business or government organization is essential. Several vendors compete for contracts during the bidding process, and it is up to the administrator to make a wise and impartial decision. With many variables to weigh in the decision, optimization software can solve the relationships between the variables and find the optimal solution. This method is both optimal and impartial, and it is faster than human analysis.
Charles Lowitz of the School District of Philadelphia understood this. He looked for software that could be applied to his contract model in Excel. Excel had been a convenient format for the district to collect information about the vendors and to use formulas to express relationships in his business model.
One program particularly fit the bill: Premium Solver Platform from Frontline Systems, Inc. When he installed the program, he was pleased to see that it was presented as a menu choice at the top of Excel. More amazing, by pointing and clicking, he was able to indicate to the Premium Solver which cell was his object to be optimized.
Case Study of a Transportation Contract Model
The complexity that Charles Lowitz faced makes an intriguing case study. The School District of Philadelphia owned some school buses, but not all the buses it needed for its growing student population. They needed to determine how many additional buses, and on what routes, to bus all the students to school on time. They turned to outsourcing vendors. Yet these vendors had their 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 the primary job of Charles Lowitz, the fiscal coordinator for the transportation office at the time, 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 arduous: figuring out by hand which contracts would make the most sense given the constraints of budget and time. The school district would need to figure out a way to distribute bus routes between the bids.
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 the Premium Solver Platform was able to help the School District of Philadelphia find the partnerships that would be most beneficial, from a financial and operational perspective.
How did the school district determine which RFPs to award with contracts? 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, for instance. 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 a bid for. 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 need to make sure the company was reliable, but the district didn't want to have to rely on any one business. Some school districts awarded all of their contracts to one vendor, which could quickly turn into a mess if that vendor were to fold.
Past experience with the district, financial stability and business acumen were also factors taken into consideration when awarding these contracts. Vendors were allowed to bid on any number of these routes – from one or two to even all of them, if they so desired. However, the district itself stipulated that it would not award more than 300 routes to any one vendor, in order 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. Fortunately, it all worked out, and I don’t there would have been a guarantee that it was the optimal solution without the software," Lowitz said. "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 year Lowitz first used the Premium Solver Platform, there were 16 requests for proposals submitted to the district. With the help of an optimization model created in Excel, he could track which of these vendors would offer the school district the best opportunity. The optimization model he created had to take all of these variables into account. 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, which 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 was said and 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 97 routes.
When Lowitz needed help involving the input of minimum integers within Premium Solver, he discovered that the customer support for the platform is top of the line, as well. The analytics experts at Frontline Systems were able to help Lowitz solve the problem.
"I had one issue that I had to call Frontline with, and they were great with support," he said.
It became clear that the idea of entering a minimum into the model would be a bit tricky. For instance, imagine a company said it would only accept a contract for a minimum of 14 routes. However, 14 really isn't the minimum that vendor could be awarded, because if the district didn't award any routes to that vendor – which was also possible – then the minimum would actually be zero.
"Once we got over that, that was the only hiccup in the model," he said. "It may have taken a day to construct the model, then just minutes to come up with the optimal solution."
Outcome: Advanced Analytics to the Rescue
In the end, the School District of Philadelphia was able to award an optimized number 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 said. "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, very easy to come up with the solution. It was relatively quick to put the model together."