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With the increasing digitization and networking of medical data and personal health information, information security has become a critical factor in vendor selection. However, limited understanding exists regarding how information security influences vendor selection. Drawing from the attention‐based view (ABV), this study examines the potential impact of data breaches on hospitals' selection of electronic medical record system (EMRS) vendors. To test our hypotheses, we compile a unique dataset spanning 12 years of observations from US hospitals. Utilizing a coarsened exact matching (CEM) technique combined with a difference‐in‐differences (DiD) approach, our study shows that hospitals tend to replace their EMRS vendors after experiencing data breaches. Moreover, breached hospitals tend to prioritize information security in such a vendor replacement process by switching to star vendors and migrating towards a single‐sourcing configuration. Further post‐hoc analyses reveal that these impacts of data breaches are mitigated as the relationship between breached hospitals and vendors matures or when hospitals belong to large healthcare systems. Additionally, we find that the effects of data breaches are contingent on the scale of the breach and are short‐term in nature. This research underscores the significance of information security as a crucial consideration in vendor selection for both academia and practitioners. Highlights: We find that hospitals tend to change their electronic medical record system (EMRS) vendors following data breaches. During this replacement, they are more inclined to select star vendors and migrate towards a single‐sourcing configuration.We also find that the impacts of data breaches can be mitigated as the relationship between breached hospitals and vendors matures, or when hospitals are integrated into larger healthcare systems.Additionally, we find that the effects of data breaches are dependent on the scale of the breach and are typically short‐term in nature. [ABSTRACT FROM AUTHOR]

Various multi-criteria decision-making methods have been utilized in literature to address vendor selection challenges in various contexts. This study reflects on these decision-making techniques for vendor selection in the software ecosystem during software outsourcing. For the same, firstly, a requirement framework for the decision-making process for software vendor selection is proposed. Afterwards, five selected different multi-criteria decision-making techniques are compared and critically analysed against this requirement framework. This study supports software practitioners and decisionmakers by providing information about which decisionmaking method to choose, considering the trade-off between the benefits and drawbacks of adopting each method or using hybrid approaches by combining two or more techniques with fuzzy logic. It is found that decision-making techniques generally lack in modelling the problem itself and handling the conflicts and uncertainties that may prevail during the decisionmaking process. Furthermore, it has been identified that artificial neural networks (ANN) and analytic network process (ANP) can support the dependencies between alternatives and criteria while handling complex interactions in the software ecosystem. On the other hand, due to its comparative ease of use, analytic hierarchy process (AHP) can serve better in scenarios where decision-makers are not experienced, or past data to train the ANN or ANP models is unavailable. [ABSTRACT FROM AUTHOR]

Vendor selection is a strategic activity in order to support the achievement of the company`s success and competitiveness. Significantly, the company has some specific standards in the selection. Therefore, evaluation is needed to see which vendors match the company's criteria. The purpose of this study was to evaluate and select the proposed vendor in a decision support system using the AHP and TOPSIS approaches. The AHP method is used to determine the importance of the criteria, while the TOPSIS method is used to rank alternatives. The results show that Provider 1 has the highest score compared to other alternatives with a value of 0.852. Sensitivity analysis shows that the proposed AHP and TOPSIS methods are robust, suitable for this problem, and have a low rate of change.

Sensors for health are a dynamic technology and sensor-based medical devices (SMD) are becoming an important part of health monitoring systems in healthcare centers and ambulatory care. The rapid growth in the number, diversity and costs of medical devices and Internet of Things (IoT) healthcare platforms imposes a challenge for healthcare managers: making a rational choice of SMD vendor from a set of potential SMD vendors. The aim of this paper is to develop a hybrid approach that combines a performance evaluation model and a multi-objective model for the SMD vendor selection problem. For determining the criteria weights in the performance evaluation model, an original version of the best worst method (BWM) is applied, which we call the flexible best worst method (FBWM). The multi-objective model has two objective functions; one is to maximize the SMD performance and the other is to minimize the SMD cost. A case study for the application of the hybrid approach for SMD procurement in a healthcare center is analyzed. The hybrid approach can support healthcare decision makers in their SMD procurement decisions. [ABSTRACT FROM AUTHOR]

Supply Chain Management (SCM) has gained the attention of business organizations as it affects corporate efficiency and competitive advantage. Component outsourcing is an important element of SCM practices and is considered to be a multi-criteria decision-making problem (MCDM) in vendor selection processes. However, decision-making on the selection of vendors is crucial, as the outsourcing program is influenced by multiple technical and commercial criteria of vendors. Developing an Excel-based Analytic Hierarchy Process (AHP) model for vendor selection therefore helps to simplify selection processes. In this paper, a Visual Basic Application (VBA) Excel-based AHP model was developed, which multiple criteria, sub-criteria and vendors and simultaneously compared with multiple main criteria, sub-criteria and alternatives for vendor selection. The model was validated with a case study of selecting a vendor for module components of body-in-white (BIW) in automotive industry as industrial application. As a result, vendor A is the best vendor since it has the highest overall score of 0.35206. Whereas vendor B is 0.239, vendor C is 0.126, vendor D is 0.146 and vendor E 0.135. In addition, the model is capable of resolving other MCDM problems. This paper provides resources such as AHP methodology, validating method and VBA code programming to readers in developing their own VBA Excel-based model. [ABSTRACT FROM AUTHOR]

With the increased digital usage, web visibility has become critically essential for organizations when catering to a larger audience. This visibility on the web is directly related to web searches on search engines which is often governed by search engine optimization techniques liked link building and link farming amongst others. The current study identifies metrics for segregating websites for the purpose of link building for search engine optimization as it is important to invest resources in the right website sources. These metrics are further used for detecting websites outliers for effective optimization and subsequent search engine marketing. Two case studies of knowledge management portals from different domains are used having 1682 and 1070 websites respectively for validation of the proposed approach. The study evolutionary intelligence by proposing a k-means chaotic firefly algorithm coupled with k-nearest neighbor outlier detection for solving the problem. Factors like Page Rank, Page Authority, Domain Authority, Alexa Rank, Social Shares, Google Index and Domain Age emerge significant in the process. Further, the proposed chaotic firefly variants are compared to K-Means integrated firefly algorithm, bat algorithm and cuckoo search algorithm for accuracy and convergence showing comparable accuracy. Findings indicate that the convergence speeds are higher for proposed chaotic firefly approach for tuning absorption and attractiveness coefficients resulting in faster search for optimal cluster centroids. The proposed approach contributes both theoretically and methodologically in the domain of vendor selection for identifying genuine websites for avoiding investment on untrustworthy websites. [ABSTRACT FROM AUTHOR]

Sensors for health are a dynamic technology and sensor-based medical devices (SMD) are becoming an important part of health monitoring systems in healthcare centers and ambulatory care. The rapid growth in the number, diversity and costs of medical devices and Internet of Things (IoT) healthcare platforms imposes a challenge for healthcare managers: making a rational choice of SMD vendor from a set of potential SMD vendors. The aim of this paper is to develop a hybrid approach that combines a performance evaluation model and a multi-objective model for the SMD vendor selection problem. For determining the criteria weights in the performance evaluation model, an original version of the best worst method (BWM) is applied, which we call the flexible best worst method (FBWM). The multi-objective model has two objective functions; one is to maximize the SMD performance and the other is to minimize the SMD cost. A case study for the application of the hybrid approach for SMD procurement in a healthcare center is analyzed. The hybrid approach can support healthcare decision makers in their SMD procurement decisions.

This paper presents a new model formulation that incorporates vendor selection into production and remanufacturing planning subject to emission constraints. The objective is to determine a feasible production and remanufacturing plan at minimal cost. In each period, the given external demand must be satisfied for both new and remanufactured products. Remanufactured products can be substituted by new products, but not vice versa. When a product is (re)manufactured, the appropriate machine must be set up, which entails set-up costs and/or set-up time. The procurement of raw material that is offered by several vendors at different prices is integrated into this planning problem. To solve this integrated production and remanufacturing planning problem, we apply two solution approaches based on mathematical programming: first, a combination of column generation and a period-oriented fix-and-relax heuristic and second, an adapted fix-and-optimise heuristic. The results of our numerical investigation demonstrate the high solution quality of both solution approaches. [ABSTRACT FROM AUTHOR]

This paper had developed an effective information technology (IT) vendor selection model for banking & financial services industries (BFSI). For any business, profitability and growth depends on the right vendor selection in their purchase process. This paper identifies ten most influencing and important criteria with 43 sub-criteria to comprehend the vendor selection process. Furthermore, the authors developed a vendor selection score model using Structural Equation Modelling (SEM) and Analytic Hierarchy Process (AHP). This article intends to inform practice and conclude with practical recommendations to BFSI for vendor selection. The proposed composite technique identified the most eligible vendor to meet up with the buyer's strategies. This article suggests the most desirable vendor for the required benefit. Vendor 1 (Silverlake) with a score of 44% promised to be the most recommended one that also performed consistently in the sensitivity test. [ABSTRACT FROM AUTHOR]

Purpose: Contribution of small and medium scale enterprises is significant in development of any country, specifically developing countries like India. Therefore, synchronization and integration of their business processes and functions through effective and efficient information sharing is vital. Hence, enhancing their competence through the adoption of an appropriate Enterprise resource planning (ERP) system is crucial to improve their competitiveness in the competitive scenario. Under this backdrop, this paper analyses pertinent factors for selecting an appropriate ERP system for SMEs. Design/methodology/approach: This mix-method research involved triangulation design by merging both quantitative and qualitative techniques. Factor Analysis was applied for selection of relevant factors affecting ERP procurement decision. Fuzzy Analytical Hierarchy Process approach, based on triangular fuzzy numbers, was used to rank these factors. Further, sensitivity analysis was performed to ensure the robustness of the FAHP results. Findings: Cost of deployment was identified as the most significant criteria. On the other hand, 'Vendor credibility' was found to be the least significant factor. Criterion 'User friendliness and Security' and 'Need fulfilment' were ranked as second and third. Practical implications: ERP vendors can use these findings in developing appropriate marketing mix strategies. SME owners can also make use of these findings by collaborating with other SMEs for buying an ERP. Originality/value: This study can be seen as first attempt in investigating and ranking various factors affecting ERP adoption decision for SMEs, specifically in Indian context, using FAHP approach. [ABSTRACT FROM AUTHOR]

Selecting the right educational vendors in the Metaverse, particularly those utilizing extended reality (XR), is crucial for creating an engaging and immersive learning environment. Careful vendor selection ensures the delivery of high-quality XR educational content, considering factors such as seamless integration with different XR devices and addressing associated challenges, which can affect the comfort and well-being of learners.This paper proposes a decision-making model to address the complex task of selecting educational vendors within the Metaverse with a focus on extended reality (XR). Recognizing the challenges inherent in such selections, this work presents a consensus-based measurement of alternatives and ranking according to compromise solution (MARCOS) model, incorporating single-valued neutrosophic (SVN) information and SVN Dombi-Archimedean weighted aggregation operators. Attribute weights crucial for decision-making are determined through an optimization tool employing cross-entropy measures with partial weight information. The evaluation encompasses nine criteria, including cost, security, privacy, pedagogical alignment, configuration, maintenance, level of control, output tools, degree of immersion, availability of XR learning contents, and time. Four leading vendors including Victory XR, ARuVR, zSpace, and Varjo Technologies are assessed, with zSpace emerging as the most favorable alternative with the highest overall utility value of 0.6889. The paper further conducts sensitivity analysis and performance comparisons to establish the robustness of the ranking results, providing a comprehensive methodology for educational stakeholders addressing the evolving dynamics of the Metaverse. • A decision-support model is suggested for ranking XR educational vendors under metaverse. • Some robust Dombi-Archimedean weighted aggregation operators are presented under SVN context. • A consensus based SVN-MARCOS methodology is proposed with partial weights information of attributes. [ABSTRACT FROM AUTHOR]

Vendor selection in supply chain is a multi-criteria problem that involves a number of quantitative and qualitative factors. This paper deals with a concrete problem of flour purchase by a company that manufactures bakery products. The selection process is driven by the price, reliability and quality objectives of the buyer and is subject to the capacity constraints of the vendors in the situation where all suppliers offer several kinds of flour (multiproduct situation). Vendors often offer quantity discounts, i.e. the purchasing price of flour depends on the quantity ordered. The multiproduct situation complicates the mathematical formulation of the problem and in this context it is much more convenient to consider the discounts of the money volume of business with every vendor. This problem has been solved using an integration of analytic hierarchy process (AHP), weighted sum model (WSM) and fuzzy multi-objective mixed-integer programming to define the optimum quantities among the selected suppliers. [ABSTRACT FROM AUTHOR]

In this article, we consider a service supply chain where two vendor firms compete to win an information technology outsourcing contract from a client firm, which faces uncertain demand. The vendors' variable operations costs are private information. The vendors also serve an external market with uncertain demand. After the award of the contract through bidding competition, vendors invest in capacity to meet their service requirements. We derive the optimal sourcing strategy for the client firm and the capacity investment decisions of the vendor firms. We characterise a threshold policy on the client's outsourced requirements with respect to demand variability. We also observe that as the vendors' mean cost increases, the requirements outsourced by the client firm decrease. In addition, we observe a threshold policy on the client's outsourced requirements with respect to cost variability through numerical studies. [ABSTRACT FROM AUTHOR]

Many factors in the current scenario have influenced manufacturing organisations to have a competitive edge by concentrating on entire supply chains. Sourcing decisions are one of the strategic decisions because they enable companies to reduce costs and improve profit figures. The main task in sourcing is vendor selection. Recent challenges such as shortened product life cycle, just-in-time environment, and the importance of strategic partnerships in upstream chains always influence companies to prioritise vendor selection. In addition, outsourced parts and components account for a significant contribution in the cost of finished goods. Thus evaluating and selecting the right vendor is the key to business. Vendors are selected merely on the basis of cost factors in the traditional approach. However, companies eventually have understood that their approach which emphasises costs as the sole criterion is inefficient and needs to be changed. To deal with the complex process of vendor evaluation, multiple criteria decision-making techniques have evolved. This study presents the integrated approach of multiple multi criteria decision making (MCDM) techniques such as fuzzy logic, strength-weakness-opportunity-threat (SWOT) analysis, and data envelopment analysis. The efficacy of the proposed approach is evident from the case study of an automotive component manufacturer involving 20 vendors, comprising of pre-qualification by fuzzy SWOT and final selection by DEA. [ABSTRACT FROM PUBLISHER]

Information system (IS) procurement history is replete with poorly executed, multimillion dollar procurement decisions. Yet we barely understand what effective IS procurement should look like. IS procurement is highly challenging, as it requires the client to simultaneously select a technology and vendor. This paper explores the technology‐vendor selection process through the sensemaking perspective. Our study develops a sensemaking model for technology‐vendor selection that connects the multiple rounds of client‐vendor communicative actions with the client's sensemaking process. We show how the client reconciles fragmented and sometimes conflicting cues and information through three intertwined cycles: immediate, retrospective, and decision. Sensebreaking occurs as a separate process (and not a communicative action) when disruptive cues occur persistently and from different vendors over multiple rounds of sensemaking. We derive a set of critical factors, on the basis of the sensemaking perspective, for selecting an appropriate vendor and technical solution. These insights in turn help explain many poorly executed IS procurement decisions. [ABSTRACT FROM AUTHOR]

Enterprise risk management (ERM) has become an important topic in today's more complex, interrelated global business environment, replete with threats from natural, political, economic, and technical sources. The development and current status of ERM is presented, with a demonstration of how risk modelling can be applied in supply chain management. Within supply chain management, a major managerial decision is vendor selection. We start with discussion of the advanced ERM technology, i.e. value-at-risk (VaR) and develop DEA VaR model as a new tool to conduct risk management in enterprises. A vendor selection set of data is used to demonstrate how this model can be used to assess supply risks in ERM. Such models provide means to quantitatively improve decision making with respect to risk. [ABSTRACT FROM AUTHOR]

This work presents a linear programming model with fuzzy multiple goals for analysing cost effectiveness during vendor selection. The proposed model considers material preparation for outsourcing firms, technological transition, quality, lead time, and their interactions. The model also uses cost, quality and effectiveness as decision criteria for capacity planning and for determining product requirements for multi-stage planning of the make/outsourcing formulation. Different scenarios involving capacity transitions limited by technical factors, additions, and support shortages are mathematically formulated. In addition to its functions for evaluating cost minimisation and for accommodating the components of lead time, quality, and responsiveness, the proposed model also integrates multi-stage functions for supply chain systems. Therefore, the analytical results of this study, regardless of application outcomes or analysis methodology, provide decision criteria for manufacturing firms and introduce a new area of academic research. [ABSTRACT FROM AUTHOR]

Vendor selection involves decisions balancing a number of conflicting criteria. Data envelopment analysis (DEA) is a mathematical programming approach capable of identifying non-dominated solutions, as well as assessing relative efficiency of dominated solutions. A simple multi-attribute utility function can be applied to a small set of alternatives, providing a tool to assess relative value, but is subject to error if estimated measures are not precise. This paper compares stochastic DEA with a multiple-criteria model in a vendor selection model involving multiple criteria, reporting simulation experiments varying the degree of uncertainty involved in model parameters. [ABSTRACT FROM AUTHOR]

In this article, we propose a new methodology for solving the vendor selection and the supply quotas determination problem. The proposed methodology combines the Analytic Hierarchy Process (AHP) for determining the coefficients of the objective functions and a new multiple objective programming method based on the cooperative game theory for vendor selection and supply quotas determination. The proposed methodology is tested on the problem of flour purchase by a company that manufactures bakery products. For vendor selection and supply quotas determination we use three complex criteria: (1) purchasing costs, (2) product quality, and (3) vendor reliability.

Background: Vendor selection and supply quotas determination is one of the most important issues in the purchasing process in manufacturing. In many situations to solve this problem it is necessary to use the operations research methods.

This paper introduces a novel and practical vendor selection problem of a firm that cooperates with multiple geographically dispersed stores. In this problem, the firm entrusts some of its business process to external vendors, and each store can split the ordered quantity between one or more potential vendors, represented as a multi-sourcing strategies. Moreover, the Cobb–Douglas demand function is utilised to establish a relationship between the market demand and the selling price; representing price-sensitive demand. This paper seeks to choose the best vendors, to allocate the stores to them, and to find the optimal values for inventory-related decisions. The approach is based on the integration of the vendor selection problem and the inventory-related decisions in order to generate additional opportunities for system-wide operational efficiency and cost-effectiveness. The aim is to minimise total cost of the firm consisting of costs associated with the vendor selection and inventory-related decisions. A novel meta-heuristic called the binary-continuous invasive weed optimization (BCIWO) algorithm that is capable of solving both binary and continuous optimization problems is developed to solve the complicated NP-hard problem. As there is no benchmark available in the literature, an efficient genetic algorithm enhanced by a multi-parent crossover operator is designed to solve the problem in order to validate the results obtained using BCIWO. The algorithms are tuned using the response surface methodology, based on which their performances are analyzed statistically. Finally, the applicability of the proposed approach and the solution methodologies are demonstrated. [ABSTRACT FROM AUTHOR]