Simulation of flow lines in clothing manufacture. Part 2: credibility issues and experimentation

Abstract

Explains that the improvement of flowline assembly systems provides the context for this research: to understand and improve the design and control of manually‐intensive flowline assembly in the clothing industry. Constructs a simulation model of the progressive bundle system, incorporating operator performance variations and learning effects, machine failure and repair, operator absenteeism, quality failure and supervisory control. Notes that, while the operator performance data and the stochastic variables are handled satisfactorily within the simulation, the problems of control are not handled well by conventional discrete event modelling techniques. Adopts a knowledge‐based approach to control, in which an online computerized supervisor exercises control over the execution of the simulation run. As complex system models are not easy to validate, uses a four‐stage approach to demonstrate conformance with real‐world systems: qualification, face validity, modular validation and time‐series system behaviour. Discusses applications of the model and the results of experiments with a line starting work on a new style.

Analysis of Impacts of Production Order Complexity and Lot Size on Apparel Production Systems Using Simulation Technique

“…Extensive research has been done concerning the performance of pull and push systems regarding the impact of the number of operators (Guner & Unal, 2008), supervisory control (Fozzard et al,1996a;Fozzard et al, 1996b;Spragg et al, 1999), production order size (Leung et al 2006) and machine breakdown (Fozzard et al, 1996a;Fozzard et al, 1996b) on system performance. However, investigations concerning the impact of production order complexity, lot size and their interaction on the production system performance are very limited.…”

A simulation model of quick response replenishment of seasonal clothing

“…However, industry has been slow to put QR into practice. The above authors have identified several reasons for these delays:the length of time needed for the development of truly trusting partnerships within the pipeline (including sharing the financial benefits of QR);the difficulty and cost of assessing specific store and product‐line rewards;the considerable cost and risk of installing QR procedures; anda lack of understanding of how best to manipulate POS data.In the past decade, a group at the Manchester Metropolitan University in UK has developed a simulation model of flow lines in clothing manufacture (Fozzard et al ., 1996a, b; Spragg et al ., 1999). Models incorporated operator performance variations and learning effects, machine failure and repair, operator absenteeism, quality failure and knowledge‐based supervisory control.…”

Constraint based reactive rescheduling in a stochastic environment

“…The implementation of the framework described here has been tested in a simulated environment described in 11 12 .…”