Agility and production flow layouts: An analytical decision analysis
Highlights
► Introduced an objective method of evaluating the most often subjective decisions. ► Able to bring relevant qualitative factors in the selection process of production layouts. ► Quantification of the impact of various qualitative factors associated with production layouts on the agility of a company. ► The long term impact of the selection of production layouts on the competitiveness and performance of any company.
Introduction
This paper contributes to our understanding of how to create supporting production and operations systems for agile manufacturing by investigating the characteristics of three alternative production flow layouts, Process layout, Product layout, and Hybrid Cellular Layout (Agarwal and Sarkis, 1998, Al-Mubarak et al., 2003, Dilworth, 1996, Drira et al., 2007, Francis et al., 2002, Satoglu and Suresh, 2009, Shambu and Suresh, 2000) are investigated. These layouts provide a spectrum of various aspects of flexibility, production flow, and process characteristics. These three production flow layouts are based on differing manufacturing concepts and philosophies (see Table 1). These layouts are also selected in response to a case study evaluation of a manufacturing company located in India. The paper more fully describes and discusses the Analytical Network Process (ANP) technique and model, the case study environment, the production flow layouts and the results of our analysis. Conclusions and implications are also drawn from these initial results.
Market forces have become increasingly dynamic requiring manufacturing and operations activities to respond quickly to demand changes, production volume and product mix. New products in a typical organization represent an average of 40% of a company’s sales (Page, 1991). Responding to dynamic customer demands in terms of volume, variety and innovation requires careful development and management of manufacturing strategies and technology in order to thrive in this hostile competitive environment (Drira et al., 2007). In response to these market forces the past two decades has seen growth in the concept of agility and agile manufacturing (Bessant et al., 2001, Yusuf et al., 1999). Current research has sought to further understand and evaluate these concepts (Hasan, Shankar, & Sarkis, 2008).
Agile manufacturing and agility were terms initially introduced by the Iacocca Institute study in 1991 and have been defined and refined in a number of ways. Essentially it focuses on the capability to respond quickly and effectively to current and future configurations of market demand, and also to be proactive in developing and retaining markets in the face of extensive competitive forces (Bessant et al., 2001, Hasan, Shankar, Sarkis, 2009, Hasan, Shankar, Sarkis, Suhail, 2009, Sarkis, 2001, Yusuf et al., 1999). The term ‘agile manufacturing’ refers specifically to the operational aspects of a manufacturing company which translates into the ability to produce customized products at mass production prices and with short lead times (Baker, 1996, Hillman-Willis, 1998, Prince and Kay, 2003). Therefore, a core issue faced within agile manufacturing is the need for appropriate and supporting production and operations systems (Hasan, Shankar, Sarkis, 2009). Many design dimensions of agility and agile manufacturing exist. Some of these dimensions respond to specific aspects of agility, one of which is product mix flexibility. To help attain this goal, operations infrastructure and capacity must be carefully planned to manage production flow and thus production layout planning takes on an increasingly important role (Drira et al., 2007). Given the importance of these dimensions in response to agility, this paper seeks to make a contribution by providing insights into a decision aid for evaluating production flow layouts that support and enhance the agile manufacture of products.
In a more strategic evaluation of production layouts both qualitative and quantitative factors (managerial, organizational, and technical) need to be incorporated. Traditional quantitative evaluation tools (e.g. cost-based optimization methodologies) may not rise to this challenge, thus this paper makes use of ANP. ANP is a general form of the Analytical Hierarchy Process (AHP) (Saaty, 1996). ANP captures interdependencies among different criteria, sub-criteria and dimensions, an evident characteristic of production flow layouts in complex agile manufacturing environments. We more fully describe the capabilities and limitations of ANP in a later section.
Section snippets
Literature review
Layout generation and evaluation is often challenging and time consuming, due to its inherent multiple objective nature and its data collection process (Lin and Sharp, 1999a, Lin and Sharp, 1999b). Simulation studies are often used to measure the benefits and performance of given layouts (Aleisa and Lin, 2005, Drira et al., 2007). Layout design has a significant impact on the performance of a manufacturing or service industry system (Agarwal and Sarkis, 1998, Apple, 1997) and has been an active
The Analytical Network Process (ANP)
As seen in the previous section there are a variety of strategic and operational considerations in evaluating facility layouts. These types of decisions require the careful balance and consideration of not only the strategic and operational dimensions, but also the various tangible and intangible factors affecting these decisions. Effective decision support of this decision environment can rely on various modeling approaches. A popular approach for multi-attribute decision-making problems with
The ANP decision network
The objective of the ANP model and approach described and overviewed in the previous section is to rank the production flow layouts on agility. Even though many dimensions of agility exist (Goldman et al., 1995, Groover, 2002, Hasan, Shankar, Sarkis, 2009, Iacocca Ins, 1991), we limit the factors to Ability to modify product/process (AMP), Schedule Reaction (SR), Human factors (HF), and Agility Enrichment Factors (AEF). This limitation is made to more closely focus on product layout
A small organization illustrative example: company background
The case company is a manufacturing unit within an India-based holding group established in 1961. This group and its associate companies are well diversified with interests in: auto components, agro foods processing, chemicals and machine tools. This manufacturing unit has current product demand for auto components including hand brakes, door components, and jacks. The auto-components’ operations strategy of the company is to develop agile manufacturing and mass-customization capabilities,
Sensitivity analysis
The ANP approach is based on perceptions of the decision makers. In order to better understand the implications of the possible variations of these subjective weights on the results of our ranking procedure, a sensitivity analysis is completed. The sensitivity analysis is executed by varying the weight of a given controlling criteria, while maintaining consistently equal weights in the other criteria. The sensitivity analysis will be completed for each controlling criterion. Table 6 presents
Results and discussion
The final weights of the three alternatives (production flow layouts) as obtained from the converged supermatrix are 0.3152 for product layout; 0.3532 for process layout; and 0.3292 for hybrid cellular layout. Thus, the case organizations views process layout as the most preferable layout when all four controlling criteria are deemed to have equal importance by the decision makers. Product layout is the least preferable.
In the sensitivity analysis, we observe that the results are sensitive to
Conclusion
In this paper we introduced a strategic decision model to help manufacturing managers strategically implement agility in their organization by selecting the most appropriate production layout. This issue is important because implementing a particular production flow layout has far reaching consequences especially in a competitive environment faced with unanticipated and sudden changes in the product as well as product-mix. Implementation of a particular production flow layout is a long-term
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