Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility

Layout flexibility is critical for the performance of flexible manufacturing cells, especially in dynamic production environment. To improve layout flexibility, layout optimization should consider more flexible factors based on existed models. On the one hand, not only should the current production demands be covered, but also the future uncertain demands should be considered so that the cell can adapt to the dynamic changes in a long term. On the other hand, the flexibility of machines should be balanced in the layout in order to guarantee that the cell can deal with dynamic new product introduction. Starting from these two points, we formulate a layout optimization model based on fuzzy demand and machine flexibility and then develop a genetic algorithm with bilayer chromosome to solve the model. We apply this new model to a flexible cell of shell products and test its performance by comparing it with the classical two-stage model. The total logistics path of the new model is shown to be significantly shorter than the classical model. Then we carry out adaptability experiments to test the flexibility of the new model. For the dynamic situation of both the fluctuation of production demands and the introduction of new products, the new model shows obvious advantages to the classical model. The results indicate that this advantage becomes greater as the dynamics becomes greater, which implies that considering fuzzy demand and machine flexibility is necessary and reasonable in layout optimization, especially when the dynamics of the production environment is dramatic.

Significant Parameters of Machine Flexibility Contributing to Lean Manufacturing

In today’s highly competitive environment manufacturing industries can’t bear the wastage of resources. Machines and equipments are one of the most important resources of a manufacturing industry. It is necessary to employ efficient processes and practices to hold cost of manufacture and move towards the attainment of lean manufacturing. This paper presents a study carried out in manufacturing industry to assess the status of machine flexibility and its impact on lean manufacturing implementation. A survey was conducted using a specially designed questionnaire carrying multiple choice questions on various aspects of lean manufacturing and machine flexibility. From the analysis of the response, standing of each parameter of lean manufacturing and machine flexibility were found out. The relationships between these parameters were determined from the coefficient of correlation and significant parameters of machine flexibility contributing to lean manufacturing were found out from regression analysis.

An analysis of the status of resource flexibility and lean manufacturing in a textile machinery manufacturing company

Purpose – As the manufacturing industry is under pressure to face the global competition, it is necessary to improve productivity and reduce costs through minimization of wastage of resources for their survival. This paper aims to present an analysis of the status of resource flexibility and lean manufacturing through conducting a case study in an Indian textile machinery manufacturing company and also demonstrate the various areas of future scope for improving lean manufacturing. Design/methodology/approach – The case study has been conducted using the flexible system methodology (FSM) framework (Sushil, 1994). For measuring resource (labour and machine) flexibility and lean manufacturing, various factors contributing towards labour flexibility, machine flexibility and lean manufacturing are identified. To determine their relative weights, an analytical hierarchy process (AHP) has been used. A specially designed questionnaire is used to collect the information during case study on different aspects of resource flexibility and lean manufacturing. SAP-LAP analysis has also been carried out to look in to the ways the company is building up resource flexibility and lean manufacturing. Findings – The status of labour flexibility, machine flexibility and lean manufacturing is merely 49.30, 47.10 and 47.40 per cent, respectively. The most important factors of labour flexibility and machine flexibility attained a value of 59.50 and 61.17 per cent, respectively. Similarly, only 39.09 per cent wastes are eliminated through lean manufacturing. There is a huge scope to achieve a higher degree of lean manufacturing through focusing on continuous improvement, just in time (JIT) and resource flexibility factors. Research limitations/implications – The present study includes only labour and machines to compute the resource flexibility. Other resources may also be included to compute the overall resource flexibility. Practical implications – The present study provides guidelines to analyze the status of resource flexibility and lean manufacturing. According to conclusions, frail areas in the manufacturing system can be identified and a suitable course of action could be planned for the improvement. Hopefully, this study will help the firm’s management to identify the problems to manage resource flexibility and implement an effective lean manufacturing. Originality/value – In this work, the theoretical perspective has been used not only to update the original instrument, but also to study the subject from a perspective beyond that usually associated with resource flexibility and lean manufacturing.

A Dynamic Simulation Approach for Flexible Manufacturing System Design

Achieving the greatest flexibility is the key objective for a manufacturing enterprise to design and install a Flexible Manufacturing System (FMS). Unfortunately, before the contents of “flexibility” is explicitly defined and commonly accepted within the company, the design effectiveness of an FMS will never be formally justified; not to mention evaluating its production performance once the FMS is implemented. The objective of this paper is twofold: first it presents a practical and quantitative measure of performance for an FMS by introducing the Machine Flexibility (MF) and the subsequent System Flexibility (SF). The second objective of this paper is then to develop a generic architecture for optimally designing an FMS which considers not just manufacturing and economic constraints but also dynamic perturbations from the shop floor. Machine flexibility comprises two parts: 1) the descriptive segment provides the operation type information and 2) the quantitative segment uses a weighted relative scaling (WRS) method to evaluate the flexibility based on machine power generation, operation cycle time, machine design mechanics, working volume, machining precision, and controller performance. System flexibility contains five attributes for an FMS: power generation, system design mechanics, working volume, system precision, and dynamic performance. The adaptive architecture for designing an FMS is composed of three modules: the design preprocessor, the reference system generator, and the alternative systems generator..