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The importance of advanced manufacturing technology (AMT) has been increasing because of their competitive advantages. Adoption of AMT involves major investment, and large‐scale issues are involved in the selection and justification of these technologies. Researchers have identified a considerable number of issues and subattributes for implementing various projects. Reviews the literature and then classifies several of these implementation issues.

Globally, manufacturing facilities are constantly being upgraded in order to provide competitive advantages. Parunndekar observes that Indian manufacturing firms have recognized the importance of automating their production systems to meet the challenges posed by the pluralistic market. However, adoption of such automation projects involves large investments and a strategic management style. Meredith and Suresh state that any financial investment in economic activities requires a justification for the purpose of committing capital. The introduction of automation projects (AP) can, and many would say should, include changes in the prevailing management style and in the organizational culture of a firm. Thus, the decision‐making process with regard to the selection of AP is quite complex, because the commercial success of the firm is highly dependent on the mechanics of such a process. In addition to the obvious quantitative benefits accruing from the introduction of AP, they can offer a large number of qualitative (intangible) benefits. Datta et al. observed that these are generally difficult to quantify. Researchers have attempted to convert the relative importance of qualitative benefits into priority weights (PW) for procedures involving the selection and evaluation of AP. Mohanty states that project selection and evaluation involve decisions that are critical to the profitability, growth and survival of the manufacturing firm in the increasingly competitive global scenario. Such decisions are complex and require analysis of tangible and intangible attributes.

The purpose of this paper is to report the findings of an exploratory survey administered in North America on advanced manufacturing technologies (AMTs). The objective of the survey is to compare the status of AMT investment, planning and implementation, and performance in two different regions: Anglo America (developed countries) and Middle America (developing countries).

Responses from 97 Anglo‐American companies (62 from Canada and 35 from the USA) were compared to responses from 125 Middle American companies (85 from Mexico and 40 from Costa Rica). The researchers used different statistical analyses such as exploratory factor analyses, analysis of variance and regression.

In general, Middle American countries representing the developing region show higher AMT investment, planning and implementation activities, and finally, higher performance due to AMT implementation. This phenomenon was hypothesized since developed countries have shifted most of their manufacturing operations into developing countries, while they keep ownership of multinational firms. Therefore, big corporations that previously invested in AMT in their home countries are now investing in AMT in their manufacturing plants in developing countries.

This research provides insights to the growing body of knowledge on AMT. Most AMT research has been done in developed countries. In this study, the researchers show results comparing developed versus developing countries.

Developing countries are heavily dependent on the resources and commitment of foreign providers to ensure successful adoption of advanced manufacturing technology (AMT). The purpose of this paper is to describe the important role of buyer‐supplier relationships (BSRs) in the process of technology selection, acquisition and implementation.

A survey of 147 Malaysian manufacturing firms is the main instrument used in the research investigations and data analysis is carried out by the structured equation modelling (SEM) technique. In particular, the authors examine the impact on performance of different patterns of relationship between technology buyers and suppliers.

Although the majority of the firms reported improvements in their performance since the acquisition of AMT, closer investigation reveals that those demonstrating a closer relationship with their suppliers are more likely to achieve higher levels of technology and implementation performance (IP) than those that do not.

The paper only assesses the strength of BSR from the buyers' perspective and they may have limited experience of acquisition, whereas suppliers may have more experience of selling AMT. Also, the research is undertaken in Malaysia and the findings may be different in other countries, especially where the technology being acquired is not imported but sourced locally.

The findings relating to BSR, technology acquisition and IP have important implications both for customers and supplier firms as well as for industrial policy makers in developing countries.

The result of the research provides useful insights that are especially pertinent to an improved understanding of BSRs in the procurement of capital equipment, about which the current research literature is limited.

The purpose of this paper is to propose a methodological framework that combines several data envelopment analysis (DEA) models to deal with the problem of evaluating and ranking advanced manufacturing technologies (AMTs) without introducing any subjectivity in the analysis.

The methodology follows a two-phase procedure. First, the relative efficiency of every technology is calculated by implementing different DEA cross-efficiency models generating the same number of high-order indicators as efficiency vectors. Second, high-order indicators are used as outputs in a SBM-DEA super-efficiency model to obtain a comprehensive DEA-like composite indicator.

The framework is implemented to evaluate a sample of flexible manufacturing systems. Comparing it to other methods, results show that the methodology provides reliable information for AMTs selection and effective support to management decision-making.

This paper contributes to the body of knowledge about the utilization of DEA to select AMTs. The framework has several advantages: a discriminating power higher than the basic DEA models; no subjective judgment relative to weights necessary to aggregate single indicators and choice of aggregation function; no need to perform any transformation normalizing original data; independence from the unit of measurement of the DEA-like composite indicator; and great flexibility and adaptability allowing the introduction of further variables in the analysis.

To examine the relationship of performance in advanced manufacturing technologies (AMT) to the levels of AMT investments and planning and implementation activities in two regions of America: Anglo‐Saxon (USA and Canada) and Hispanic (Mexico and Costa Rica).

Survey methodology was employed to collect data. The instrument was translated into Spanish for administration in the Hispanic region. Exploratory factor analysis was used to establish discriminant validity of the constructs under investigation. Through multiple regression analysis, predictors for two types of performance (organizational and operational) were examined.

Both types of performance are reasonably predicted by the AMT investment and planning and implementation factors. Performance predictors are different between the two regions.

There are limitations common to survey research (e.g. subjective perceptions and respondent bias). Also, results depart from the literature in terms of the predictors for operational and organizational performance. This can be due to other complex relationships among the variables not identifiable by regression analysis. Future work should address this by using more sophisticated statistical tools such as structural equation modeling.

The study can help managers understand the factors leading to a successful AMT implementation. This is one of the first studies on AMT in developing countries; and as such, it should encourage more research in these countries.

The purpose of this paper is to develop a decision tool to help managers make more informed decisions regarding their investments in advanced manufacturing technologies.

Selection of a new technology is multidimensional in nature and interdependent relationships exist among various elements of the system. In addition, both quantitative and qualitative factors need to be considered in the evaluation process. The Analytic Network Process (ANP) methodology satisfies these requirements by considering interdependencies among all the factors and by allowing transformation of qualitative judgments into quantitative values for the decision analysis. These capabilities of ANP make it a perfect methodology for use in the development of our decision tool. Once technology alternatives and selection criteria are identified by the decision maker, several pair‐wise comparisons are conducted to determine local priorities for the selection criteria and alternatives. These priorities are then used to determine the overall priorities for the alternatives. The technology alternative with the highest overall priority is chosen for adoption.

Allowing for interdependencies among selection criteria, as well as between alternatives and selection criteria, provides a more realistic evaluation process than other selection processes that ignore such interdependencies.

The model provides decision makers a tool for evaluating several competitive technology alternatives and selecting the most appropriate technology for adoption.

The paper discusses the inclusion of the subjective judgments of the decision maker in the evaluation process and use of ANP methodology for transforming these judgments into quantitative values for rankings of the alternatives.

This paper empirically examines the process of acquiring technology from a source, external to the firm, and incorporating it into a new product or operational process under development. We refer to this key activity in product and process innovation as external technology integration. This paper develops a conceptual model of external technology integration based on organizational information processing theory and a wide range of technology management literature. Field interviews were conducted to evaluate the validity of the model across diverse settings. Our results indicate general support for the conceptual model. We close with a discussion of the implications of this study for both theory and practice.

The purpose of this paper is to visualize the prioritization among essential factors of cellular manufacturing system (CMS) implementation using the analytic hierarchy process (AHP) and analytic network process (ANP) methods.

Based on literature review, 4 enabler dimensions and 17 CM factors were identified which were validated by experts from academia and industry. Then, AHP and ANP models are proposed in evaluating CMS implementation dimensions and factors. The results are validated using sensitivity analysis.

These models give firms a straightforward and simple to utilize way to deal with CMS efficiently. The two strategies were appeared to be powerful in choosing a strategy for CMS implementation. The two strategies brought about nearly similar outcomes. Both methods consider the particular necessities of the organization through its own accessible ability.

The techniques exhibited in this paper can be utilized by a wide range of organizations for adopting CMS that have a higher impact on performance and thus overall productivity. The two techniques are explained in a step-by-step approach for easier adoption by practitioners.

The strength of the present study is that it is one of the first few to be conducted in perspective for CM implementation factors analysis.

Advanced manufacturing system (AMS) offers opportunities for industries to improve their technology, flexibility and profitability through a highly efficient and focused approach to manufacturing effectiveness. Selecting a proper AMS is a complicated task for the managers as it involves large tangible and intangible selection attributes. Failure to take right decision in selecting proper AMS alternative may even lead industry to losses. The purpose of this paper, therefore, is to rank the AMS alternatives by using fuzzy grey relational analysis, which will help managers when choosing an appropriate AMS.

This research proposes a multi‐attribute decision‐making (MADM) method, fuzzy grey relational analysis (FGRA), for AMS selection. The methodology is explained as follows. AMS alternatives and selection attributes will be chosen. The qualitative attributes will be converted into quantitative using fuzzy conversion scale. Then these data will be pre‐processed to normalize every value. This step is done to convert all alternatives into a comparability sequence. According to these sequences a reference sequence (ideal target sequence) is defined. Then, the grey relational coefficient between all comparability sequences and the reference sequence is calculated. Finally, based on these grey relational coefficients, the grey relational grade between the reference sequence and every comparability sequences is calculated. If a comparability sequence translated from an alternative has the highest grey relational grade between the reference sequence and itself, then that alternative will be the best choice. Fuzzy logic is used here to convert linguistic data into crisp score.

The proposed method is validated and compared by taking two examples from literature. The traditional statistical techniques require large data sets for evaluating attributes while grey theory on the contrary solve the multi attribute decision making problems with small data sets. This methodology will significantly increase the efficiency of decision making and overall competitiveness for manufacturing industries. This approach will motivate more and more industries to invest in AMS.

This method will help managers to weigh the AMS alternatives before actually buying them, which will in turn save money and time. This will build confidence of the top management for investing in costly technology such as AMS.

From time to time, various researchers have proposed various techniques to select the AMS. However, a survey on current evaluation methods shows that they are all less objective, lack accurate data processing, involve large calculations because of their complexity. In this paper, the authors attempt to solve the problem of AMS selection with FGRA, which is more logical, axiomatic, generates results in fewer steps with less calculations and is easy to understand. This paper succeeds in getting AMS alternatives' ranking using fuzzy grey relational analysis.