Academic journal article Journal of Management Information and Decision Sciences

Agility in Large Volume, Small Lot Manufacturing

Academic journal article Journal of Management Information and Decision Sciences

Agility in Large Volume, Small Lot Manufacturing

Article excerpt


We have learned much about mass production and lean production systems from automobile manufacturing. Indeed the concepts of JIT, waste reduction and space utilization have had a profound effect on many industries such as electronics, clothing and furniture. As Christian and Zimmers (1999) indicated, managers of lean facilities are primarily concerned with eliminating waste; minimizing inventories to keep tight control over quality and production resources. Agile managers appear more concerned with meeting customer demands through product variations and delivery performance. However, agility does not have to compromise lean operations. Nor does being lean compromise a company's ability to respond to market demands for variety and expanded customer services.

Simulation is the most robust and realistic way of evaluating the performance of a system of multiple queues. Its primary use is to test changes in a system before they are implemented. Combined serial and parallel queue disciplines are difficult if not impossible to be treated by analytical methods. According to Hall (1999), testing of different probability distributions and various parameter changes found in many production systems cannot be accommodated except by simulation.

Discrete object-oriented computer simulation has been used to identify and help solve problems in an ever increasing number of applications. The on-going research on hundreds of assembly lines at General Motors by Alden et al. (2006) has led to many simulation models and observations that have saved millions of dollars. Simulation saves considerable time and money by viewing the dynamics of a system and providing insight into and a better understanding of those dynamics. Kline et al. (1972) cites the use of simulation as an operations research tool in analyzing a hardwood processing system that produced cabinets and similar products. The simulation helped illustrate the feasibility of alternative solutions by observing the animated flow of products through the processes. Simulation can also offer genuine excitement by pre-testing ideas and introducing realistic ''what-if'' changes in the parameters. As Keller et al. (1991) and Spedding and Sun (1999) concluded, simulation can also be useful in enhancing a cost accounting system by evaluating manpower, space and equipment requirements.

Enormous amounts of money continue to be spent by companies and industries to improve small-lot production. McRainey (1977) observed, as have others, that manufacturers are constantly being challenged by the demands of the distribution systems for quick response and just-in-time (JIT) requirements of customers. Manufacturers and certainly their marketing personnel, seek small-lot production with processes changed over quickly from one product to another to better serve customers. However, as Katayama and Bennett (1999) conclude per the classical economic models, i.e. EOQ/EMQ, an emphasis on agility must simultaneously focus on changeover costs when producing in smaller lots. Whitehead (2000) restates an underlying principle from the JIT concept that agile, small-lot systems can exist in concert with lean manufacturing systems. Both focus on reducing waste through lower inventory investment, space savings, better material handling, and reduced changeover and processing times. Thus small-lot sizes are fundamental to flexible JIT systems and enhance superior customer service.

The simulation study from Baykoc and Erol (1998), Inman and Bulfin (1999), and Ozcan et al. (2010) examined the performance of a multi-item, multi-line, multi-stage JIT system and demonstrated how the systems react under different circumstances. The variability of processing time and arrival demands of subsequent operations were studied. Sianesi (1998) demonstrated that the flexibility inherent in JIT production applied to ''mixed-model'' systems reduces WIP inventories in make-to-order environments.

The system described in this study is more complex in that the subassemblies are produced on separate but parallel lines and linked to a specific mixed-product batch. …

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