Search results

Process flexibility (PF) is seen as a hedging instrument against demand uncertainty. This paper aims to examine capacity decisions for both flexible and dedicated processes under production policies such as make-to-order and make-to-stock. The study identifies some relative benefits, in terms of expected profit, of the process flexible plant over the dedicated ones. Furthermore, the advantage appears to be contingent upon the decision on the preset service level.

Using the sample-based optimization procedure, a detailed computational analysis is undertaken to identify the conditions under which a flexible plant is preferred over a dedicated plant. A combination of genetic algorithm and sample-based optimization procedure is used to capture the effects of preset service level. The factors controlled in this paper include the demand variance, demand correlation, capacity investment cost and the product price.

According to this study, in a dedicated process changing to a flexible process is not justified for the same level of demand correlation even with high demand variance. In fact, a strict control on the preset service level prefers the dedicated strategy. The advantage of a flexible plant increases as the demand correlation decreases, product price decreases, price asymmetry increases or capacity investment cost increases. With a preset service level constraint, a flexible process should be preferred to a dedicated one only when the capacity investment cost is high or the products have low contribution margins.

The PF index is introduced in this paper to measure the benefit of a flexible plant over a group of dedicated plants. The benefits were found to be contingent upon the decision on the required service level.

Several prior studies have investigated the strategy of concurrently deploying different priority rules at different processing stages of a manufacturing system. The purpose of this paper is to investigate the advantage of using such a strategy over that of using priority rules in their pure forms.

Three priority rules were combined in all possible ways in a simulated, three‐stage, flow‐dominated manufacturing system. The performances of these combinations, along with three other simple priority rules in their pure forms, were compared using both mean and variability in waiting, earliness, tardiness, and total costs under two shop load levels and several tardiness to earliness cost ratios.

The results indicate that the combinations between SI^X and shortest processing time (SPT) rules perform well in reducing both mean and variability of waiting cost but do poorly on tardiness cost. On the other hand, the due date rule in its pure form or in conjunction with SI^X or SPT is effective in reducing both mean and variability of both earliness and tardiness costs. While tardiness cost appears to dominate the total cost data, the shop load level registered little impact on the performance of the combination schemes.

The results of the paper have useful practical implications for textile and ceramic industries. However, the conclusions are limited to the cost structure used, although a wide range of cost ratios is included.

The paper offers insights into whether throughput and due date‐related costs can be reduced by using a job sequencing strategy that simultaneously deploys different priority rules at different processing stages of a manufacturing environment.

In today's highly competitive and customer oriented business environment, business firms in all sectors are looking for ways to improve customer satisfaction. In this context, textile firms are no exception. This research simulates the actual operations of a textile firm in order to show the impact of sequencing operational rules on the ability of the firm to improve its responsiveness to customers.