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Rocket Science and Dynamic Schedule Optimization

Much has been made of the relationship between "rocket science" and the scheduling of sales associates in retail stores. While the term "rocket science" is misleading, here is the short explanation starting with a historical perspective.

In the epoch Battle of the Atlantic during World War II the good guys struggled to convey supplies to Britain and the Soviet Union through the submarine infested North Atlantic. A question of strategy became apparent: do you group the transport ships together in convoy where they can be more easily protected but are limited to the speed of the slowest ship, or do you send ships separately? A team of mathematicians studied the problem, and concluded that the convoy system would minimize loses. The use of convoys together with the advent of useable radar turned the tide - but for a time the battle was a very close call.

The branch of mathematics stemming from the wartime effort was referred to as operations research. Broadly speaking, the purpose of O.R. is to apply mathematics to real-world problems that are operational in nature. Typically, the goal is to maximize or minimize something (e.g. profit, customer service, wait time) subject to "constraints" (e.g., budget, available sales associates).

Through the mid-1970's business schools placed much emphasis on O.R., and there were success stories. Did you know that the best mix of ingredients in animal feed has been determined using O.R.? The petroleum industry has also made extensive use of O.R. both to help decide where to drill for oil (and when to stop), and to optimize refinery operations. Unfortunately, O.R. proved cumbersome for most real world problems. The data needed to perform the calculations was not available, and the computers of the day could not economically carry out the extensive calculations. By the 1980's, mergers, downsizing, and business reengineering had become the accepted method of obtaining large cost savings - O.R. was rarely discussed.

However by the early 90's the wheel had turned full-circle. The benefits of corporate reorganization and reengineering had been largely realized. And, for the first time, extensive computerization in most industries meant that "input" data (e.g., POS data) was typically available and computer power became inexpensive and vast.

The retail store scheduling problem is ideally suited to an O.R. solution. The objective typically is to minimize selling cost or to maximize productivity. Constraints are in the form of budgets, availability of sales associates, shift composition rules, and numerous other factors.

The scheduling problem is too complicated to solve by hand. This is the case because of the large number of feasible solutions (ones that satisfy the constraints) that would have to be manually evaluated. Supervisory personnel can easily develop a feasible solution that looks "ok." But finding an optimal solution or near-optimal solution by hand is almost impossible, especially when the solution has to be frequently recomputed because of changing conditions. And it is the difference between an "ok" solution, and a near-optimal one that can mean the difference between profit and loss.

Fortunately, store users need not be concerned with the "rocket science" aspects of scheduling. The user accesses QServ screens to enter employee availability and other information; the "rocket science" calculations happen automatically inside the system.

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