To address production-scheduling decisions at Owens-Corning Fiberglas at three distinct levels—aggregate planning, disaggregate planning, and job scheduling—a three-phase hierarchical model was developed. A production-switching rule is used to determine aggregate inventory levels, production levels, and work-force levels. Lot sizes, line assignments, and inventory levels are determined for individual products via linear programming. Final job sequencing is accomplished by use of an efficient heuristic. Implementation of the model has resulted in annual savings of over $100,000 during the first two years of operation.
This paper considers the problem of selecting matched pairs of observations for the reduction of bias in statistical hypothesis testing. A Euclidean distance function is suggested for measuring the similarity between paired observations. The matching process is then formulated initially as an assignment problem. Alternative formulations of the problem that would reduce computational difficulty are considered. INTRODUCTONMany observational studies are designed to investigate the relationship between a continuous variable (often some measure of performance) and a dichotomous variable used to define group membership. The dichotomous variable (independent variable) is often thought to influence the continuous (dependent) variable. The frequent purpose of such studies is to investigate the consequences of alternative courses of action in order to make better decisions.Consider a commercial bank attempting to decide whether to seek membership in the Federal Reserve System. A prime consideration in the decision might be the effect of membership (the dichotomous variable) upon bank profitability (the continuous variable). A possible decision rule would be to select the alternative (membership or non-membership) that is expected to result in higher profitability. A prerequisite to an informed decision would be to investigate the relationship between membership and profitability, perhaps by contrasting the average profitability of member banks with non-member banks.'Other studies of this type include the following examples: a study to determine whether the viewing of a television broadcast affects viewer attitudes [2], a comparison of certain characteristics between financially sound and problem firms [I41 (151, and a study to determine the effect of a specific personnel program on worker turnover.A complicating factor often encountered in this type of study is the presence of additional variables, called covariables or concomitant variables (e.g., bank size and age in our first example), that are not of direct interest in the study but 'Studies of the relationship between membership in the Federal Reserve System and bank profitability have been undertaken by Gilbert and Peterson [7] and Sinkey and Walker [lS]. 62
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.
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