مکمل هایی در اجرای فن آوری های تولید پیشرفته

The purpose of this analysis is to use complementarity analysis to explain why some implementations of advanced manufacturing technology (AMT) provide a high return on investment while others do not. By analysing the engineering environment, as well as the technology used in the manufacturing process, we hope to provide further insight into the necessary environmental conditions for high returns on investments in AMT. This paper aims to advance current understanding of the impact of organizational fit through complementarity analysis of 26 AMT and 12 engineering management practices. The results reveal that analysis on the dependencies of implementation of AMT must be conducted at the industry and plant size levels, otherwise the environmental differences may lead to inconclusive or misleading results for the majority of senior managers engaging in strategic AMT investment decision making.

In today's economic climate, there is an increasing emphasis on cost reduction and increased efficiency in manufacturing that is driving the adoption of advanced manufacturing technologies (AMT) (Udo and Ehie, 1996 and Sohal et al., 1999). AMT are a group of computer-based technologies including: computer-aided design, robotics, group technology, flexible manufacturing systems, automated material handling systems, storage and retrieval systems, computer numerically controlled machine tools, and bar-coding or other automated identification techniques. Unfortunately, Chung (1996) found in his review of over 15 studies that the balance of AMT implementations result in failure (no substantial increase in process flexibility, responsiveness, reliability, or quality) 50 to 75 percent of the time. This represents a large number of firms that invested millions of dollars only to obtain minimal returns; in some cases, organizations had a negative return on their investment. Given the correct organizational fit, AMT have given some adopters a strong competitive advantage in their industry. The debate over the value of AMT continues (Swink and Nair, 2007). DeRuntz and Turner (2003) found that careful selection of management practices was conducive to AMT success. Given the high cost of AMT and the importance that such investments have on firm performance (Da Silveira, 2005), it is vital to understand the impact that management practices have on success. The literature on the nature of technology implementation uses two primary modes of analysis — technology adoption/diffusion and complementarities between technologies. Technology adoption/diffusion research focuses on exogenous factors affecting implementation. Bocquet et al., 2007 and Hollenstein, 2004 are examples of this stream of research applied to information and communication technologies and Astebro et al., 2005 and Battisti et al., 2004 investigate CAD/CAM. There has also been some research using a diffusion approach to the organizational and environmental factor impacting electronic data interchange (EDI) implementations (Chau and Hui, 2001, Chau and Tam, 2000 and Premkumar et al., 1994. These studies consider internal aspects of the organization such as infrastructure and prior experience, as well as external variables such as the market conditions and support networks (Kuan and Chau, 2001). These studies do not consider specific organizational practices which significantly influence the value the can be derived from the technologies due to their impact on technology usage. The studies also focus on a specific system or technology and do not consider the interaction between the various systems in the organization. By using the complementarity approach as conducted in this study, it is easier to measure the impact of AMT adoption and organizational changes together. Fundamentally, the impact of complementarity means that there is a marked benefit for making simultaneous changes involving AMT and organizational practices together. When AMT and practices are not complementary the implication is that a new “cost saving” measure may result in the opposite effect. This type of study identifies the interaction effects between the various forms of AMT and engineering management practices in order to identify the optimal technology-organizational fit. Complementarity analysis does not focus on longitudinal diffusion of the technologies but on the impact these technologies and their interaction with other variables has on a value measure such as productivity or profitability. By better understanding the AMT-engineering management practice fit, senior managers will be able to make more informed AMT investment decisions to better match their technology investments, engineering management practices, and strategic goals (Goodhue and Thompson, 1995). The purpose of this study is to aid senior managers in making strategic AMT investment decisions by identifying complementarities between installed advanced manufacturing technologies and engineering management practices in order to obtain an improved AMT-organizational fit. If AMT and engineering management practices were standard inputs into the manufacturing process (i.e. continuous variables such as labor, energy and materials) we could simply use standard economic techniques such as the elasticity of substitution, to measure interaction effects. Since AMT adoption and management practice adoption are binary variables another technique has to be used. We apply the theory of complementarity (Milgrom and Roberts, 1990 and Milgrom and Roberts, 1995) to a performance function. This study advances current knowledge on the impact of technology-organizational fit by analyzing 26 forms of AMT and 12 engineering management practices while previous research has been restricted to only one or two forms of AMT and one or two internal organizational variables. The paper is organized as follows. In Section 2 we discuss the theory behind complementarity and summarize previous research. In Section 3 we propose hypotheses about the complementarities that we expect to find in this research. Section 4 discusses the data and in Section 5 the econometric model is presented. Section 6 presents the results. Finally, Section 7 draws conclusions and gives recommendations for future research.

Complementarities between AMT and engineering management practices are diverse. Each industry has different complements between AMT and quality control practices, planning practices and design practices whether we consider productivity growth or profit growth in the objective function. We find that for both complements and substitutes, planning practices and quality control practices feature prominently for profit and productivity. For instance, planning practices are complementary with communications technologies, design practices and production design technologies. Quality control practices are complementary with design practices, inspection and storage technologies, planning practices, and robotics technologies. For growth in profit, we further find that design practices and production design technologies feature prominently in terms of complements and substitutes. Our first hypothesis was not supported — that complementarities between AMT will be the same for all plant sizes and that complementarities between engineering management practices and AMT will differ for each plant size. There was no pairing of variables that was complements or substitutes across all plant sizes. This means that senior managers cannot simply copy what larger organizations are investing in if they want a good AMT-organizational fit. In many cases, what is a positive interaction for medium plants is a negative interaction for small plants. We found support for Hypothesis 2; that complementarities between AMT and engineering management practices will differ by industry. This brings forward concerns about previous research that was not conducted at the industry level. Given the large differences in the results found in each industry, analysis that combines multiple industries together in order to obtain a large enough sample is at serious risk for confounding the results. There is no global implementation strategy that can be used to ensure AMT-organizational fit and senior managers should be wary of technology representatives who provide success stories from outside their industry. Hypotheses Hypothesis 3 and Hypothesis 3a had only partial support. These mixed results demonstrate the complexity of making appropriate AMT investment decisions. The importance and interaction between specific types of engineering management practices and AMT differ both by industry and plant size. There is no standard that can be set for optimal implementations across multiple industries based on existing classifications. This means that any government policy to support increased investment in manufacturing technologies needs to be flexible enough to allow firms in each industry to select the specific type of AMT that will best fit within their industry strategic needs as well as their existing engineering management practices. One limitation of the research is sample size. While on the surface 2191 plants may seem large, the independent variables were reduced to seven while the comparisons were reduced to four variables at a time (due to the exponential increase in the number of states). Again, because of small sample size we aggregated 3-digit SIC codes to 2-digits. Finally, the small sample size left us with a number of empty states. Consequently, the complementarity restrictions for those states were removed to allow the econometric model to find a solution. To address these limitations, a more refined method for determining complementarities is needed that would allow for constrained regression analysis of a larger set of variables. The method might use a nested or recursive model where results from one stage would help determine the factors for the next stage of the analysis, thus grouping all complementary variables together. The challenge will be to create a method that still allows for comparison of results between industries and/or size classes because as demonstrated the results will be quite different. Future research could try to determine the magnitude of the complementarity effect. This would enable managers more detailed information to help them select between pairs when an investment is complementary to two others which happen to be substitutes. Although this study does provide some insight into the complementarities and substitutes that exist between AMT and business practices, there is currently no way in which to measure the strength or importance of the complementarity or substitute effect. This is very important information as in many cases there does not exist complete complementarities or substitutes between three or more elements. If we could measure the strength of a complementarity then managers would be able to analyze the trade-off of a potential substitute versus two potential complements for a three element comparison. In some cases, the substitute may be so strong it would not make sense to engage in all three practices. In others, the substitute might be very small making it insignificant when compared to the gains made by the complementarity effects. This is an area which requires future research in both the theoretical calculation modeling as well as empirical analysis of the calculations for application to various industries and technologies. Given the importance of identifying the appropriate technology-organizational fit in order to obtain maximal value from technology investments, further research is needed into the specific organizational elements which impact technology implementations. This should not be limited to the manufacturing sector but should also consider technology enhanced services, as well as government and health care organizations where improved integration of technology can result in improved product offerings and significant operational savings. This research should aid in reducing the number of failed technology implementations and improve the value proposition for technology investment. The results from this study also demonstrate the need for future research into the effect of different technology implementation pathways for the development of complementarities between forms of AMT. Questions remain about whether the order of implementation of the various AMT has an effect on the strength of the complementarity or substitute findings. By better understanding what leads to the development of complementarities, this will help managers determine optimal implementation pathways for projects as well as provide important insight that can be applied to new and emerging technologies.