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In the automotive industry, it is thought that agile manufacturing systems will permit fast cost-effective responses to unpredictable and ever-changing product demand, and support rapid product launches for previously unplanned products tailored to meet changing customer desires. We discuss two simple decision models that provide initial insights and industry perspective into the business case for investment in agile manufacturing systems. The models are applied to study the hypothetical decision of whether to invest in a dedicated, agile, or flexible manufacturing system for engine and transmission parts machining. These decision models are a first step toward developing practical business case tools that help industry to assess the value of agile manufacturing systems.

Automotive companies must consider strategic initiatives such as agile manufacturing systems to compete globally and respond to dynamic customer demand. In this paper, we explore agile manufacturing systems for engine and transmission machining applications as a key enabler in an automotive agile manufacturing strategy. We describe two simple decision models that help distinguish agile systems from dedicated or flexible machining systems (FMSs). Gunasekaran (1998) and Gunasekaran (1999a) describes agile manufacturing as “the capability to survive and prosper in a competitive environment of continuous and unexpected change by reacting quickly and effectively to changing markets, driven by customer-designed products and services.” Goldman et al. (1995) have a slightly different definition, with agile manufacturing allowing companies to be capable of operating profitably in a competitive environment of continually and unpredictably changing customer opportunities. Both definitions apply to the automotive industry's goals of operating profitably, and sensing and responding effectively to changing demand trends. Conceptually, an agile manufacturing system allows an automotive company to re-allocate production line capacity to products that are in higher than expected demand, rapidly launch new products (not previously conceived when the manufacturing system was designed), and yet retain production ability for other products with lower than expected demand. Automotive companies are attracted to agile manufacturing systems, because of the potential for equipment reuse and equipment investment cost reductions over time. The promise of equipment reusability has also been associated with flexible manufacturing systems (FMSs). For example, Goranson (1998) defines a flexible system as a production system capable of dealing effectively with a specific (or predictable) scope of product variation. A FMS, as defined by Askin and Standridge (1993), refers to a set of computer numerically controlled (CNC) machine tools and supporting workstations that are connected by an automated material handling system and are controlled by a central computer. Shim and Siegel (1999) characterize a FMS as a computer-controlled process technology suitable for producing a moderate variety of products in a moderate flexible volume. More recently, Hallmann (2003) describes the implementation of an FMS for automotive parts machining that promises cost and time effective implementation of engineering change orders (ECOs), and thus continual process improvement. Agile systems differ from flexible systems in a critical way: the agile system has capability to adapt rapidly and cost-effectively within a predicted scope of product variation (out of scope is ideal but impractical) to allow future unplanned products to be manufactured. Automotive companies have previously experimented with and been disappointed by FMS for machining applications, because the promise of cost reductions for equipment reuse has not materialized as expected. In practice, to respond to changing demands, a FMS requires significant additional expenditures and a long time to convert or adapt to new “unplanned” products. Thus FMS do not meet the “agility criteria,” i.e., rapid and cost-effective reuse in response to changing product demands. The remainder of the paper is organized as follows. In Section 2, we briefly review the technical literature on agile and FMSs. Section 3 summarizes how automotive engineers perceive the differences between dedicated, agile, and FMSs for engine and transmission machining applications. Two key terms, in-family flexibility and cross-family flexibility are introduced to help explain why automotive engineers are interested in agile and FMSs. We propose two simple decision models in Section 4 and use these models to provide insights into the business case for investment in agile manufacturing systems. Section 5 summarizes the insights gained and identifies opportunities for future research.

In this paper, we have discussed agile manufacturing systems from an automotive industry perspective. A descriptive influence diagram, spreadsheet model and a decision tree model are studied to gain understanding about the value of system agility. From the limited study presented here, agile systems seem to meet the promise of rapid and cost-effective response to new (unplanned) product model introductions and dynamic capacity allocation to meet unpredictable demand. The two decision models developed are simple, capture the important features of economic decisions about manufacturing systems, and facilitate discussion with automotive industry engineers about agile and FMSs. However, there is a significant opportunity to extend research to more fully address the business case for agility. The preliminary analysis described here is localized to system selection for one manufacturing site. It could prove interesting to extend decisions to an enterprise perspective—where to use flexible, agile, and dedicated systems to build a most “robust and resilient” manufacturing enterprise. Portfolio analysis tools may be developed to determine the optimal mix of system types to meet fluctuating demand volumes and simultaneously address future model changes. Hybrid systems consisting of both dedicated lines and agile lines should also be considered. Finally, we note that our results are based on the assumption that demand per product model is much less than available system capacity, which is generally the situation in industry. Business case analyses when demand is approximately equal to or greater than system capacity are also left for future research.