Jay Nathan

Master Production Scheduling for a Process Industry Environment

Previous research has not addressed the problem of developing a master production schedule (MPS) for production systems with minimum batch‐size production restrictions. Proposes a weighted integer goal‐programming model for the development of a rolling horizon master production schedule, under conditions of demand certainty, for a process industry environment with multiple production lines and minimum batch‐size production restrictions. The presence of multiple and often conflicting goals prevalent in production planning and scheduling is explicitly incorporated in the model. The model can easily be implemented on a microcomputer and the master production schedule developed is in spreadsheet format and can easily be understood by a practitioner. Uses a case study conducted for a paint company to illustrate and validate the model. Results show that the MPS developed using the proposed model is superior in terms of total cost when compared with actual company performance.

Effect of forecast errors on rolling horizon master production schedule cost performance for various replanning intervals

Master production schedules are usually updated by the use of a rolling schedule. Previous studies on rolling schedules seem to form the consensus that frequent replanning of a master production schedule (MPS) can increase costs and schedule instability. Building on previous research on rolling schedules, this study addresses the impact of overestimation or underestimation of demand on the rolling horizon MPS cost performance for various replanning frequencies. The MPS model developed in this paper is based on actual data collected from a paint company. Results indicate that under both the forecast errors conditions investigated in this study, a two-replanning interval provided the best MPS cost performance for this company environment. However, results from the sensitivity analysis performed on the MPS model indicate that when the setup and inventory carrying costs are high, a 1-month replanning frequency (frequent replanning) seems more appropriate for both of the above forecast error scenarios.

Determination of master production schedule replanning frequency for various forecast window intervals

This paper examines the impact of forecast window intervals on replanning frequencies for a rolling horizon master production schedule (MPS). The problem environment for this study is an actual MPS operation of a paint company and includes features such as multiple production lines, multiple products, capacity constraints, minimum inventory requirements. A mixed integer goal programming model formulated for the MPS problem is used to analyze the impact of forecast window interval length on replanning frequencies and MPS performance in a rolling horizon setting. Given demand certainty, results indicate that the length of the forecast window interval influences the choice of replanning frequency for this company environment. A three‐month forecast window interval with a two‐month replanning frequency provided the best MPS performance in terms of total cost.

Acetaminophen (Tylenol): Johnson & Johnson and Consumer Safety:

Considers, against backdrop of Johnson & Johnsons handling of the Tylenol poisonings of the 1980s, Johnson & Johnsons response to product safety concerns raised about acetaminophen by studies suggesting links to hepatotoxicity and end-stage renal disease.