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Performance evaluation of the LDR and the PSH with forecast errors
Author(s) -
Barman Samir,
Tersine Richard J.,
Burch E.Earl
Publication year - 1990
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/0272-6963(90)90006-y
Subject(s) - flexibility (engineering) , aggregate (composite) , econometrics , standard deviation , forecast error , heuristic , statistics , production (economics) , sensitivity (control systems) , computer science , operations research , forecast skill , factory (object oriented programming) , operations management , economics , mathematics , engineering , microeconomics , programming language , artificial intelligence , composite material , materials science , electronic engineering
Despite its ability to produce optimal solutions, the Linear Decision Rule (LDR) has not had a significant impact in the business environment. The Production Switching Heuristic (PSH), which has shown promising results when compared with the LDR, has experienced some business application because of its practicability and flexibility. During aggregate production planning, forecast errors are almost unavoidable, but the sensitivity of these models to such errors has not been thoroughly tested. Insufficient attention has been paid to truly understand the cost effects of forecast errors and other important interactions. The study investigates these issues by analyzing the results of 740 simulated problems. Using the famous “paint factory” cost data, the sensitivity of the LDR and the PSH are examined under various experimental conditions. The factors controlled at different levels are: forecast error mean, forecast error standard deviation, demand pattern, demand variability, and cost coefficients. The results show that 1) the PSH is generally less sensitive than the LDR to forecast errors, 2) both forecast error mean and standard deviation effectively measure the severity of forecast errors, and 3) underforecasts cause less cost penalty than overforecasts. The outcome of the study has helpful managerial implications for aggregate planning related decisionmaking. It suggests that the use of the PSH could result in potential cost savings even if significant forecast errors are envisioned as long as the period‐to‐period demand variability is not substantially high. Also, BIAS warrants more attention than MSE in evaluating the extent of forecast errors and their eventual cost impact on aggregate production planning.