Communication and control in small batch part manufacturing

This paper reports on the development of a real-time control network as an integrated part of a shop floor control system for small batch part manufacturing. The shop floor control system is called the production control system (PCS). The PCS aims at an improved control of small batch part manufacturing systems, enabling both a more flexible use of resources and a decrease in the economical batch size. For this, the PCS integrates various control functions such as scheduling, dispatching, workstation control and monitoring, whilst being connected on-line to the production equipment on the shop floor. The PCS can be applied irrespective of the level of automation on the shop floor. The control network is an essential part of the PCS, as it provides a real-time connection between the different modules (computers) of the PCS, which are geographically distributed over the shop floor. An overview of the requirements of such a control network is given. The description of the design includes the services developed, the protocols used and the physical layout of the network. A prototype of the PCS, including the control network, has been installed and tested in a pilot plant. The control network has proven that it can supply a manufacturing environment, consisting of equipment from different vendors with different levels of automation, with a reliable, low cost, real-time communication facility

Estimating process batch flow times in a two-stage stochastic flowshop with overlapping operations.

Processes; Time;

Clips: a capacity and lead time integrated procedure for scheduling.

We propose a general procedure to address real life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its own route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on a Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four phase hierarchical operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real life shops. We therefore report on a real life application.Model; Models; Applications; Product; Scheduling;

Analyzing the Tradeoff Between Efficiency and Flexibility in Cellular Manufacturing Systems

A limitation of Group Technology (GT)-based cellular manufacturing systems is that their limited routing flexibility offsets the setup and material handling efficiencies they offer. Virtual Cellular Manufacturing (VCM) systems do not encounter the problem of limited routing flexibility, but do not yield the same efficiencies as GT-based cellular systems. This study compares the performance of a GT-based cellular manufacturing system that utilizes operations overlapping to further improve material flow efficiency with that of a virtual cellular manufacturing system. Results suggest that while the use of operations overlapping in a GT-based cellular manufacturing system can to some extent compensate for the system’s low routing flexibility, it cannot fully overcome the high flow time variance that results from the permanent dedication of machine resources. As a result, GT-based cellular manufacturing performs comparably to VCM only under a limited set of conditions

Lot Splitting in Stochastic Flow Shop and Job Shop Environments

In recent years many firms have been implementing small lot size production. Lot splitting breaks large orders into smaller transfer lots and offers the ability to move parts more quickly through the production process. This paper extends the deterministic studies by investigating various lot splitting policies in both stochastic job shop and stochastic flow shop settings using performance measures of mean flow time and the standard deviation of flow time. Using a computer simulation experiment, we found that in stochastic dynamic job shops, the number of lot splits is more important than the exact fonn of splitting. However, when optimal job sizes are determined for each scenario, we found a few circumstances where the implementation of a small initial split, called a "flag," can provide measurable improvement in flow time performance. Interestingly, the vast majority of previous research indicates that methods other than equal lot splitting typically improves makespan performance. The earlier research, however, has been set in the static, deterministic flow shop environment. Thus, our results are of practical interest since they show that the specific method of lot splitting is important in only a small set of realistic environments while the choice of an appropriate number of splits is typically more important

The impact of lean practices on inventory turnover

Lean manufacturing (LM) is currently enjoying its second heyday. Companies in several industries are implementing leanpractices to keep pace with the competition and achieve better results. In this article, we will concentrate on how companies can improve their inventoryturnover performance through the use ofleanpractices. According to our main proposition, firms that widely apply leanpractices have higher inventoryturnover than those that do not rely on LM. However, there may be significant differences in inventoryturnover even among lean manufacturers depending on their contingencies. Therefore, we also investigate how various contingency factors (production systems, order types, product types) influence theinventoryturnoveroflean manufacturers. We use cluster and correlation analysis to separate manufacturers based onthe extent of their leanness and to examine the effect of contingencies. We acquired the data from the International Manufacturing Strategy Survey (IMSS) in ISIC sectors 28–35