Data-Driven Simulation and Optimization Approaches To Incorporate Production Variability in Sales and Operations Planning

We propose two data-driven, optimization-based
frameworks (simulation-optimization
and bi-objective optimization) to account for production variability
in the operations planning stage of the sales and operations planning
(S&OP) of an enterprise. Production variability is measured as
the deviation between historical planned (target) and actual (achieved)
production rates. A statistical technique, namely, quantile regression,
is used to model the distribution of deviation values given planned
production rates. Scenarios are constructed by sampling from the distribution
of deviation values and used as inputs to the proposed optimization-based
frameworks. Advantages and disadvantages of the two proposed frameworks
are discussed. The applicability of the proposed methodology is illustrated
with a detailed analysis of the results of a motivating example and
a real-world production planning problem from a chemical company.