An Evaluation of Forecast Error in Master Production Scheduling for Material Requirements Planning Systems.

Abstract

Spical forecast-error measures such as mean squared error, mean absolute deviation, and bias generally are accepted indicators of forecasting performance. However, the eventual cost impact of forecast errors on system performance and the degree to which cost consequences are explahed by typical error measures have not been studied thoroughly. The present paper demonstrates that these typical error measurn often arc not good predictors of cost consequences in material requirements plarming (MRP) settings. MRP systems rely directly on the master production schedule (MPS) to specify gross requirements These MRP environments receive forecast cmrs indirectly when the errors create inaccuracies in the MPS.Our study results suggest that within MRP environments the predictive capabilities of forecast-error measures are contingent on the lot-sizing rule and the productcomponents structure When forecast errors and MRP system costs are coanalyzed, bias emerges as having reasonable predictive ability. In further investigations of bias, loss functions are evaluated to explain the MRP cost consequences of forecast errors. Estimating the loss functions of forecast errors through regression analysis demonstrates the superiority of loss functions as measures over typical forecast error measures in the MPS.Subject Alaas: F m t i n g , Materhl Requlnmcnts Pbnning, prrfonnancc Evaluation, and Simulation.

Impact of Bias Magnification on Supply Chain Costs: The Mitigating Role of Forecast Sharing

“…The authors concluded that forecast bias had a bigger impact on inventory and past-due demand, than forecast standard deviation. Subsequent studies have supported this finding (Lee & Adam, 1986;Lee, Adam, & Ebert, 1987;Malhotra & Ritzman, 1994;Sanders & Ritzman, 2004).…”

Mitigating forecast errors by lot-sizing rules in ERP-controlled manufacturing systems

“…Lee and Adam (1986) study the impact of forecast errors measured by bias on the performance of a single-product manufacturing system in which an MRP is used. Lee et al (1987) use a regression analysis to show that among all the measurements of forecast errors, bias is the best predictor of MRP system performance. Through two separate case studies, Awate (1989a, b) examines the variability of shop load caused by the propagation of sales forecasting errors, assembly production errors, and component production errors.…”

Uma proposta de modelagem da lista de materiais

“…Sempre para estruturas de pequena complexidade, conforme pode ser comprovado em VERAL & La FORGE (1985), LEE & ADAM (1986), LEE et al (1987), BAHL et al (1987), KRAJEWSKI et al (1987), SUM et al (1993), BENTON & SRIVASTAVA (1993) e BRENNAN & GUPTA (1993.…”