Production loss functions and subjective assessments of forecast errors: untapped sources for effective master production scheduling

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Abstract
Master production scheduling is complicated by demand uncertainty. The problem from uncertain forecasts is scheduling either too few or too many components relative to actual demand. To cope with this problem, schedulers often form judgments about future forecasts and they adapt production schedules to reflect beliefs and opinions about forecast errors. The research literature on scheduling has largely ignored formal methods for capturing these informal judgments and injecting them explicitly, rather than informally, into the master scheduling process. The current research demonstrates a method for using subjective assessments of forecast accuracy to improve master scheduling. First, a production loss function is derived using performance data from computer simulations of the production environment. Second, subjective assessments of forecasts errors are integrated with the loss function to reveal how forecasts should be adjusted to minimize expected scheduling losses. The procedure enhances intuitive judgmental scheduling; it reveals how managerial beliefs can be formally used to intentionally and optimally bias forecasts. In applying the procedure to 44 simulated MRP configurations, the optimally-biased forecasts are superior to unadjusted forecasts and provide an objective benchmark for using judgmental adjustments. The results demonstrate opportunities for enhancing master schedules by using subjective assessments of forecasts compared with scheduling without subjective assessments.