Prioritizing Offshore Vendor Selection Criteria for the North American Geospatial Industry

The U.S. market for geospatial services totaled US $2.2 billion in 2010, representing 50% of the global market. Data-processing firms subcontract labor-intensive portions of data services to offshore providers in South and East Asia and Eastern Europe. In general, half of all offshore contracts fail within the first 5 years because one or more parties consider the relationship unsuccessful. Despite the high failure rates, no study has examined the offshore vendor selection process in the geospatial industry. The purpose of this study was to determine the list of key offshore vendor selection criteria and the efficacy of the analytic hierarchy process (AHP) for ranking the criteria that North American geospatial companies consider in the offshore vendor selection process. After the selection of the initial list of factors from the literature and their validation in a pilot study, a final survey instrument was developed and administered to 15 subject matter experts (SMEs) in North America. The SMEs expressed their preferences for one criterion over another by pairwise comparisons, which served as input to the AHP procedure. The results showed that the quality of deliverables was the top ranked (out of 26) factors, instead of the price, which ranked third. Similarly, SMEs considered social and environmental consciousness on the vendor side as irrelevant. More importantly, the findings indicated that the structured AHP process provides a useful and effective methodology whose application may considerably improve the quality of the overall vendor selection process. Last, improved and stabilized business relationships leading to predictable budgets might catalyze social change, supporting stable employment. Consumers could benefit from derivative improvements in product quality and pricing

Sun, Sea and Shrines: Application of analytic hierarchy process (AHP) to assess the attractiveness of six cultural heritage sites in Phuket (Thailand)

In order to make recommendations for the diversification of tourism products on the Thai island of Phuket, this paper applies the analytic hierarchy process (AHP) method to rank the attractiveness of six cultural heritage sites in the island of Phuket to make recommendations for sites that could be incorporated in to cultural tourism development in the region. In addition, it applies a quantitative-qualitative evaluation structure with weighted criteria, based on local expert opinion. The research identified which of the many potential cultural tourism sites would be the most attractive to tourists and shows the utility of the AHP method, combined with quantitative-qualitative evaluation, for decision making in tourism destination development contexts

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

An analysis on integrated supply chain management in academic university library

Supply chain management has been widely used in manufacturing industries and proven by researchers and practitioners as a best practice. It can satisfy stakeholders, increase revenues, and decrease the total costs. However, very few service industries, particularly the academic library, have implemented supply chain management. Several problems related to the academic library supply chain should be addressed, such as how to model the entities and their functions for the general practice of an academic library supply chain, and the linkage among these entities as an integrated model. This research develops an integrated academic library supply chain model, which can be used for the strategic planning of the academic library. It identifies entities and their functions for the academic library supply chain, constructs a conceptual model of the integrated supply chain of the academic library, and analyses the interrelationship among the newly developed model entities. This research used both qualitative and quantitative approaches to achieve the research objectives. The process of identifying the entities and their functions of the academic library supply chain was accomplished by using a theoretical literature review and content analysis techniques. The proposed conceptual supply chain model was developed based on the system thinking method. Eventually, it was validated through the Fuzzy Delphi method, an expert judgment technique. Three conceptual models for the supply chain academic library were successfully developed: The Holistic View of Supply Chain Model for Academic Library, the Material Purchasing Decision Making Model, and the Integrated Academic Library Supply Chain Model. These three models were validated by the academic librarians. This research expands the knowledge of supply chain theory, particularly in the supply chain academic library. It also contributes to the academic library management in planning and formulating a roadmap for the library to increase its quality services for all stakeholders

Fuzzy multicriteria analysis and its applications for decision making under uncertainty

Multicriteria decision making refers to selecting or ranking alternatives from available alternatives with respect to multiple, usually conflicting criteria involving either a single decision maker or multiple decision makers. It often takes place in an environment where the information available is uncertain, subjective and imprecise. To adequately solve this decision problem, the application of fuzzy sets theory for adequately modelling the uncertainty and imprecision in multicriteria decision making has proven to be effective. Much research has been done on the development of various fuzzy multicriteria analysis approaches for effectively solving the multicriteria decision making problem, and numerous applications have been reported in the literature. In general, existing approaches can be categorized into (a) multicriteria decision making with a single decision maker and (b) multicriteria group decision making. Existing approaches, however, are not totally satisfactory due to various shortcomings that they suffer from including (a) the inability to adequately model the uncertainty and imprecision of human decision making, (b) the failure to effectively handle the requirements of decision maker(s), (c) the tedious mathematical computation required, and (d) cognitively very demanding on the decision maker(s). This research has developed four novel approaches for effectively solving the multicriteria decision making problem under uncertainty. To effectively reduce the cognitive demand on the decision maker, a pairwise comparison based approach is developed in Chapter 4 for solving the multicriteria problem under uncertainty. To adequately meet the interest of various stakeholders in the multicriteria decision making process, a decision support system (DSS) based approach is introduced in Chapter 5. In Chapter 6, a consensus oriented approach is presented in multicriteria group decision making on which a DSS is proposed for facilitating consensus building in solving the multicriteria group decision making problem. In Chapter 7, a risk-oriented approach is developed for adequately modelling the inherent risk in multicriteria group decision making with the use of the concept of ideal solutions so that the complex and unreliable process of comparing fuzzy utilities usually required in fuzzy multicriteria analysis is avoided. Empirical studies of four real fuzzy multicriteria decision making problems are presented for illustrating the applicability of the approaches developed in solving the multicriteria decision making problem. A hospital location selection problem is discussed in Chapter 8. An international distribution centre location problem is illustrated in Chapter 9. A supplier selection problem is presented in Chapter 10. A hotel location problem is discussed in Chapter 11. These studies have shown the distinct advantages of the approaches developed respectively in this research from different perspectives in solving the multicriteria decision making problem

Cloud Technology Selection: A structured framework for decision making

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis study aims to get organizations to improve their decision making during the selection of cloud technology process. As the technology evolves alongside an ever-increasing abundance in market offer, it may be challenging to choose the desirable service that encompasses several business approaches. For the purpose of this study to be attained, the reader must first comprehend the definition of Cloud Technology: it is the delivery of IT resources over the Internet, being applications, software, storage, among other services. Furthermore, understanding the current main technologies/architectures and their capabilities/limitations will play an important role in designing and developing the prospected solution. A thoroughly research will be produced to better define the criteria used in the process. Despite the fact that technology is able to be tailored up to a certain level for the organization needs, a higher level of participation will encourage vendors and architecture designers to develop a better knowledge on the companies’ desires, thus delivering more appropriate features to their unique needs

A best-worst-method-based performance evaluation framework for manufacturing industry

Purpose: The purpose of paper is to develop a performance evaluation framework for manufacturing industry to evaluate overall manufacturing performance. Design/methodology/approach: The Best Worst Method (BWM) is used to aid in developing a performance evaluation framework for manufacturing industry to evaluate their overall performance. Findings: The proposed BWM-based manufacturing performance evaluation framework is implemented in an Indian steel manufacturing company to evaluate their overall manufacturing performance. Operational performance of the organization is very consistent and range between 60% to 70% throughout the year. Management performance can be seen high in percentage in the first two quarter of the financial year ranging from 70% to 80% whereas a slight decrease in the management performance is observed in the 3rd and 4th quarter ranging from 60% to 70%. The social stakeholder performance has a peak in first quarter ranging from 80% to 100% as at start of financial year. Originality/value: This paper utilized BWM, a MCDM method in developing a performance evaluation index that integrates several categories of manufacturing and evaluates overall manufacturing performance. This is a novel contribution to BWM decision-making application.Output Status: Forthcoming/Available Onlin

Sustainable supplier selection based on industry 4.0 initiatives within the context of circular economy implementation in supply chain operations

This study proposes a decision framework based on industry 4.0 initiatives within circular economy implementation to evaluate and select sustainable suppliers. In this context, sustainable supplier selection, industry 4.0, and circular economy have emerged as key topics of the contemporary operations management debate. The mix method approach of combining literature review and industrial expert’s inputs was adopted to identify four main categories and twenty-one sub-categories relevant to the supplier selection decision. A multi-criteria decision-making support tool composed of the ‘best-worst method’ (BWM) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) was applied to aid in the evaluation and selection of a sustainable supplier in Pakistan’s textile manufacturing company. The BWM approach was first applied to determine the relative importance weights, and then, VIKOR used to rank the suppliers. The findings of the study suggest that, the Pakistan’s textile manufacturing company places much emphasis and importance on ‘Technological and Infrastructure (TI)’ with weight of 0.356 and ‘a positive organizational culture towards implementation of industry 4.0 and circular economy initiatives’ (OG3) with global weight of 0.139 when embarking on such decisions, and ranked supplier 2 as the top sustainable supplier. Managerial and post-selection benchmarking negotiations and future research directions are also introduced

An integrated decision support framework for the adoption of lean, agile and green practices in product life cycle stages.

In order to stay competitive in today’s overly competitive market place, businesses must be engineered to match product characteristics and customer requirements. This increased emphasis on achieving highly adaptive manufacturing with reduction in manufacturing costs, better utilization of manufacturing resources and sound environmental management practices force organisations to adopt efficient management practices in their manufacturing operations. Some of the established practices in this context belong to the Lean, Agility and Green (LAG) paradigms. Adopting these practices in order to address customer requirements may require some level of expertise and understanding of the contribution (or lack of it) of the practices in meeting those requirements. Primarily, the wide choice of LAG practices available to address customer requirements can be confusing and/or challenging for those with limited knowledge of LAG practices and their efficacy. There is currently no systematic methodology available for selecting appropriate LAG practices considering of the product life cycle (PLC). Therefore, this research provides a novel framework for selecting appropriate LAG practices based on PLC stages for reducing costs, lead time and generated waste. The methodology describes the application of analytic hierarchy process (AHP), statistical inference and regression analysis as decision support tools, ensuring a systematic approach to the analysis with appropriate performance measures. The data collected were analysed with the aid of SPSS and Excel using a variety of statistical methods. The framework was verified through a Delphi study and validated using a case study. The key findings of the research include the various contributions of lean, agile and green practices towards improving performance measures, the importance of green in improving performance measures and the importance of selecting appropriate practices based on product life cycle stages. This research makes a clear contribution to existing body of knowledge by providing a methodological framework which could serve as a guide for companies in the FMCG industry to systematically integrate and adopt lean, agile and green to better manage their processes and meet customer requirements in their organisations. However, the framework developed in this research has not been tested in other areas.N/