Merkle is a leading database marketing agency with over 1,000 employees and about $250 million in annual revenues.  One of the services they provide clients is the processing of incoming mail, and Sid took on the challenge of figuring out how to best allocate the incoming mail for 150 clients to three forms of processing:  processing using Opex machines, Aggissar machines, and manually.   Each of the two kinds of machines had limited capacity, and the task was how to allocate mail to the two kinds of machines so as to minimize the use of manual processing while meeting the contractual throughput requirements of each customer.  

Sid quickly recognized this as an opportunity to use Solver, but his first attempt at modeling resulted in a problem too big for Solver to handle.  The modeling challenge was the number of integer decision variables reflecting the reality that within any shift a single machine (station) could not be used to sort mail from multiple customers.   After several rounds of modeling using creative simplifications and reformulations, Sid was eventually able to create a Solver model “small” enough to be solved.  When tested on historical data, the model solutions saved, on average, 20 hours of manual labor per week.