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The paper's aim is to provide a tool for decision makers to make more informed decisions regarding their investment in advanced technologies.

Selection of an advanced technology for adoption requires evaluation of several competing alternatives. The difficulty in the evaluation process arises when the ranking of the alternatives is not possible from the result of the financial analysis alone. The purpose of this study is to overcome such difficulty by considering other factors that allows distinction among alternatives with similar economic evaluation results. This is accomplished by identifying the risks or undesirable consequences of technology implementation and including them in the evaluation process. These are costs that are not measurable for inclusion in the financial analysis but could have major impact on the final selection. Owing to uncertainties involved with adoption of a new technology, the involvement of human expertise and judgment seems to be essential in the evaluation process. To accomplish this a process is proposed where decision maker's perceptions on the performance of technology alternatives along with the importance of the relevant risks are solicited in linguistic terms. Fuzzy numbers are then used to represent these linguistic perceptions. Fuzzy arithmetic operators are applied to calculate an aggregate fuzzy score for each alternative. These fuzzy scores are then converted to crisp scores to allow the ranking and selection of the best alternative.

Addition of subjective perceptions to the purely quantitative approach provides a more realistic evaluation process.

The proposed procedure can help practitioners with their technology adoption decisions.

The value of the paper is the inclusion of the decision maker's judgment in the evaluation process by use of fuzzy logic.

The purpose of this paper is to change the traditional supplier selection methods by shifting the emphasis from using a single model to using multiple models in the unstructured decision‐making context and to provide a tool for decision makers to make informed decisions of supplier selection in the multiple perspectives.

There are various supplier selection models available in the literature. However, using the result of a single model as a basis for making the final decision could lead to a biased decision given the fact that any model has its limitations. The qualities of the decision‐making process and the decision itself increase by applying a multiple perspectives approach rather than a single model. The multiple perspectives decision‐making allows collaboration and knowledge sharing among the participants which leads to a less‐biased decision. This study examines commonly applied supplier selection models, formulates general perspectives of these models, and proposes a framework of multiple perspectives decision making for supplier selection. It further provides a structure of supplier selection system based on the proposed approach. Through a prototype of web portal, the study demonstrates the usefulness of the proposed multiple perspective system approach in the decision context of collaboration and knowledge sharing.

The general finding from this study is that the multiple perspectives approach to supplier selection enables the decision makers to actively participate and fully understand the decision‐making process through knowledge sharing which in turn ensures high quality of the final decisions.

Supplier selection decision makers can make more informed decisions through collaboration among all decision‐making participants in the multiple perspectives. It informs supply chain managers of the potentially positive effect of knowledge sharing on the decision‐making process in supplier selection.

Multiple perspectives decision making provides a novel approach that emphasizes on the knowledge sharing and collaboration between the experts, who are familiar with the supplier relations, and the decision makers who are responsible for the final decisions.

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 aims to provide a tool for decision makers to help them with selection of the appropriate supplier.

Companies often depend on their suppliers to meet customers' demands. Thus, the key to the success of these companies is selection of the appropriate supplier. A methodology is proposed to address this issue by first identifying the appropriate selection criteria and then developing a mechanism for their inclusion and measurement in the evaluation process. Such an evaluation process requires decision maker's preferences on the importance of these criteria as inputs.

Human assessments contain some degree of subjectivity that often cannot be expressed in pure numeric scales and requires linguistic expressions. To capture this subjectivity the authors have applied fuzzy logic that allows the decision makers to express their preferences/opinions in linguistic terms. Decision maker's preferences on appropriate criteria as well as his/her perception of the supplier performance with respect to these criteria are elicited. Fuzzy membership functions are used to convert these preferences expressed in linguistic terms into fuzzy numbers. Fuzzy mathematical operators are then applied to determine a fuzzy score for each supplier. These fuzzy scores are in turn translated into crisp scores to allow the ranking of the suppliers. The proposed methodology is multidisciplinary across several diverse disciplines like mathematics, psychology, and operations management.

The procedure proposed here can help companies to identify the best supplier.

The paper describes a decision model that incorporates decision maker's subjective assessments and applies fuzzy arithmetic operators to manipulate and quantify these assessments.

The purpose of this paper is to provide a tool for decision makers to make more informed decisions regarding their outsourcing policies.

In this study it is assumed that recycling or remanufacturing of these returns will soon be a must for original equipment manufacturers. The paper presents a multi‐phased decision model for strategic analysis of outsourcing such activities.

The proposed decision model provides a comprehensive tool for effective decision making by considering economic as well as strategic factors.

The procedure proposed here can help practitioners with their outsourcing decisions. The model presented is conceptual. Application and validation is still required.

The paper describes a new decision model that considers both strategic and economic issues of outsourcing reverse logistics activities.

The purpose of this paper is to develop a decision model to help decision makers with selection of the appropriate supplier.

Supplier selection is a multi‐criteria decision‐making process encompassing various tangible and intangible factors. Both risks and benefits of using a vendor in supply chain are identified for inclusion in the evaluation process. Since these factors can be objective and subjective, a hybrid approach that applies to both quantitative and qualitative factors is used in the development of the model. Taguchi loss functions are used to measure performance of each supplier candidate with respect to the risks and benefits. Analytical hierarchy process (AHP) is used to determine the relative importance of these factors to the decision maker. The weighted loss scores are then calculated for each supplier by using the relative importance as the weights. The composite weighted loss scores are used for ranking of the suppliers. The supplier with the smallest loss score is recommended for selection.

Inclusion of both risk and benefit categories in the evaluation process provides a comprehensive decision tool.

The proposed model provides guidelines for supply chain managers to make an informed decision regarding supplier selection.

Combining Taguchi loss function and AHP provides a novel approach for ranking of potential suppliers for outsourcing purposes.

The purpose of this paper is to analyze the factors affecting the implementation of unmanned aerial vehicles (UAVs) in Indian humanitarian logistics. The factors listed are significant as they are hindering the incorporation of this new technology into the humanitarian supply chain, thus creating inefficiencies in the humanitarian logistics sector.

This research is approached using a two-step process. In the first step, the particular barriers for UAV implementation are determined by a literature review and consultation with experts. Next, the proposed framework, a combination of grey-decision-making trial and evaluation laboratory (grey-DEMATEL) and analytic network process (ANP), i.e. g-DANP, is used to determine a hierarchical structure for the factors and sub-factors. The grey hypothesis provides sufficient analytical data to an otherwise lacking DEMATEL technique. Also, the use of ANP gives weightage to each factor, allowing us to categorize their importance further.

This study reveals that factors like expensive commercial solutions and high transport energy costs are significant factors of the “cause” group, whereas the uncertain cost for maintenance and repair and deficiency of high-level computing are crucial factors of the “effect” category. The mentioned factors, along with many others, are the main reasons for the delayed incorporation of UAVs in humanitarian logistics.

The results of this study present insights for humanitarian supply chain managers, UAV producers and policymakers. Those in the humanitarian logistics sector can use the findings of this study to plan for various challenges faced as they try and implement UAVs in their supply chain.

This research is unique as it analyses the general factors hindering the implementation of UAVs in Indian humanitarian logistics. The study enriches existing literature by providing an analytic approach to determine the weightage of various interrelations between the identified factors affecting UAV incorporation in the humanitarian supply chain.

The purpose of this paper is to provide a tool for decision makers to consider both tangible and intangible factors while making decisions regarding investment in advanced manufacturing technologies (AMT).

Traditional financial approaches are often used for evaluation of advanced technologies. However, the difficulty arises when the result of financial techniques cannot provide a conclusive recommendation for a technology adoption. The current research is an attempt to address this issue by including factors that allow distinction between technology alternatives with similar financial results. This task is accomplished in a two step process. First, a process is developed for identifying all potential benefits associated with adoption of an AMT. These are the benefits that were not measurable for inclusion in the financial analysis. Second, a mechanism is developed for quantifying these benefits to be used for ranking of the technology alternatives. This task is done by soliciting decision maker's input on importance of the benefits, required benefit goals, and his/her perception of how well each technology meets the benefit goals. This information is then used in Taguchi's loss functions to assign ranks to technology alternatives.

Investing in new technologies is the only way for manufacturers to survive in today's competitive market. Thus, there is a need by these manufacturers to have access to a decision model that will help them with their investment decisions.

The procedure proposed here helps companies to rank the technology alternatives and identify the best technology for adoption.

The identification of intangible benefits associated with adoption of a new technology and use of Taguchi's loss function to quantify these benefits.

This paper seeks to provide a tool for decision makers to make more informed decisions regarding their outsourcing decisions and selection of the appropriate supplier.

The method uses the Taguchi loss function for the inclusion of intangibles in the evaluation and selection of suppliers. Intangibles are defined as factors that have an impact on the selection of an appropriate supplier but are not easily quantified to be included in the financial evaluation. These intangibles are classified as the benefits and risks of using a supplier to perform the outsourcing function. A decision maker has certain expectations regarding these intangibles and a loss occurs when a supplier's performance does not meet the decision maker's expectations. The Taguchi loss function has been selected as a means of measuring the loss. The decision maker defines the target value and the specification limits for each benefit and risk category. The weighted loss scores are calculated where the weights are the importance ratings assigned to benefit/risk categories by the decision maker. Based on this analysis each supplier will receive a weighted loss score for all the pertinent benefit categories and one weighted loss score for all the risk categories. To achieve a single measure, the aforementioned weighted loss scores are combined to determine a single aggregate loss score for each supplier, which is then used to rank them. The supplier who receives the highest ranking (minimum loss score) will be selected to perform the outsourcing function.

The procedure proposed here can help companies to identify the best supplier to perform an outsourcing function.

The paper presents a phased decision model that begins with economic evaluation and then uses Taguchi functions to measure the impact of intangibles.