Vendor Selection Model Research Articles

Supply Chain Management of Outsourcing Module Components Decision-Making using Analytical Hierarchy Process and Visual Basic Application in Automotive Body-in-White Welding Assembly Line

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.

An integrated decision model for cloud vendor selection using probabilistic linguistic information and unknown weights

Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision-making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models.

Probability-based Vendor Selection Model for the Hungarian Automotive Supply Network

The aim of this paper is to investigate the structure of the Hungarian automotive supply network and provide a possible solution that mathematically describes the connections between the interested parties. In the study an approximate model is introduced to determine the links between hubs (car manufacturers), nodes (Tier1 suppliers) and edges, combining probability random graph and scale free network theory. During the simulation some main drivers were applied for selection purposes, such as location, turnover, product profile. As a result of the study a potential tool has been designed to support decision-making.

Composite Techniques of Structural Equation Modeling and Analytic Hierarchy Process for Information Technology Vendor Selection

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.

The optimization model of the vendor selection for the joint procurement from a total cost of ownership perspective

Purpose: This paper is an attempt to establish the mathematical programming model of the vendor selection for the joint procurement from a total cost of ownership perspective. Design/methodology/approach: Fuzzy genetic algorithm is employed to solve the model, and the data set of the ball bearings purchasing problem is illustrated as a numerical analysis. Findings: According to the results, it can be seen that the performance of the optimization model is pretty good and can reduce the total costs of the procurement. Originality/value: The contribution of this paper is threefold. First, a literature review and classification of the published vendor selection models is shown in this paper. Second, a mathematical programming model of the vendor selection for the joint procurement from a total cost of ownership perspective is established. Third, an empirical study is displayed to illustrate the application of the proposed model to evaluate and identify the best vendors for ball bearing procurement, and the results show that it could reduce the total costs as much as twenty percent after the optimization.

A Grey-based approach for ERP vendor selection in small and medium enterprises in Qatar

This paper aims to develop a multi-criteria decision model for the purpose of ERP vendor selection. To tackle the multi-criteria nature and the presence of both qualitative and quantitative factors, Grey-based model was proposed. Grey theory was utilised as it can effectively solve the multi-criteria decision making problems with small datasets. The major contributions of this work have been summarised as follows: development and implementation of a multi-criteria decision model for ERP vendor selection; group based decision making for comparing among attributes and ERP alternatives and efficient use of the concept of grey numbers to facilitate the solution of the model for SMEs in Qatar. The proposed method will help information system managers to weigh the ERP alternatives before actually implementing them, which will in turn save money and time.

Strategic vendor selection criteria discussed in relation to demand and supply perspectives

Vendor selection (VS) is a decision-making problem involving strategic management. In the VS approach, criteria and importance weights must be carefully selected to meet supply chain competitive strategies. Most existing VS models use buying-firm’s perspectives for VS criteria formulation. This study examines the demand and supply views of 108 valid respondents regarding the importance weights of VS criteria under four operations strategies. The multinomial probability distribution analysis and variance weighting method are employed to categorize the individual relative importance weights. This study derives exploratory explanations by conducting in-depth interviews with 12 functional managers focusing on their divergent perspectives on importance weighting of VS criteria. This study also discusses the derived applications of Herzberg’s two-factor theory, supplier integration, and the operating philosophy of total quality management to mitigate the influences of the widely used explicit operational criteria in existing VS models. Finally, this study provides recommendations for future research on VS.

Vendor selection and supply quantities determination in a bakery by AHP and fuzzy multi-criteria programming

One of the possible conceptions for the survival in a turbulent world market is a chain cooperation among the producer, his suppliers and customers, according to the concept of supply chain management. The problem of vendor selection and determination of material quantities supplied is one of the most important activities in the supply chain. The purpose of this article is to propose a model for vendor selection and determination of supply quotas in the area of flour supply by using the analytic hierarchy process (AHP) and multi-objective linear programming (LP) model solved by fuzzy LP method, based on the decision-making in a fuzzy environment, and to point to the advantages of the proposed model in comparison to some other vendor selection and supply quantity determination models. The proposed model applied in the concrete problem of vendor selection and determination of supply quotas proved to be very efficient in determining the priority of alternatives, taking into account all the criteria and sub-criteria, as well as in defining criteria weights that express the decision-maker's preferences, which allows a simple sensitivity analysis of the obtained solutions.

A MCDM-based model for vendor selection: a case study in the particleboard industry

We investigated procurement of raw materials for particleboard to minimize costs and develop an efficient optimization model for product mix. In a multiple-vendor market, vendors must be evaluated based on specified criteria. Assuming sourcing from the highest-scoring vendors, annual purchase quantities are then planned. To meet procurement needs, we first propose a model to describe the problem. Then, an appropriate multi-criteria decision making (MCDM) technique is selected to solve it. We ran the model using commercial software such as LINGO® and then compared the model results to a real case involving one of the largest particleboard manufacturers in the region. The model run based real data yielded a procurement program that is more efficient and lower in cost than the program currently in use. Use of this procurement modelling approach would yield considerable financial returns.

A hybrid model using analytic network process and gray relational analysis for bank's IT outsourcing vendor selection

PurposeConsidering the limitation of the single stage vendor selection model, this paper proposes a two‐stage vendor selection framework for IT outsourcing in microfinance banks.Design/methodology/approachThe paper attempts to realize a complete analysis at company level using grey systems theory for shaping the relations among variables. With the social choice function – Dodgson function, the first stage is a trial phase that helps the decision‐maker find the potential vendors, then, the decision‐maker employs those chosen vendors for the final selection with modified grey relational analysis (GRA) integrated analytic network process (ANP), which emphasizes the interrelation among those selection criteria, and avoids the subjective estimation of experts and practitioners. The case of a microfinance bank IT outsourcing vendor selection is used to verify the proposed approach.FindingsThe results of the empirical study show that the proposed method is practical for ranking competing vendors in terms of their overall performance with respect to multiple interdependence criteria for the bank's IT outsourcing.Practical implicationsThe method exposed in the paper can be used for other supplier selection by modifying some criteria and weight.Originality/valueThe paper provides a method for ranking competing vendors in terms of their overall performance with respect to multiple interdependence criteria for IT outsourcing.

Fuzzy multi-objective vendor selection under lean procurement

This paper investigates a fuzzy multi-objective vendor selection program under lean procurement based on cost minimization, delivery schedule violation minimization, and maximizing the quality level of the purchased quantity. Specifically, the paper incorporates the vendor production capacity uncertainty into the model to identify an appropriate selection policy for vendors under practical operating conditions. The use of a soft time-window mechanism for the vendor selection model enables decision makers to further incorporate a time based performance metric for vendor evaluation, based on the degree of urgency or need for a part. A solution algorithm using fuzzy AHP is proposed. The results of a numerical example suggest that decision makers prefer vendors who can promise tighter delivery schedules rather than on cost or quality. A sensitivity analysis of the soft time-window on the achievement of the lean procurement objectives is also conducted.

A soft time-windowed mechanism for vendor selection model

This paper presents a vendor selection model for buyers practicing Just-in-time management strategy. Five vendor evaluation criteria including quality, delivery, net price, geographical location, and production capacity are utilized for the selection of vendors and determination of associated purchasing quantities. A soft time window mechanism is incorporated to allow purchased parts been delivered within a grace period of time with a specified penalty function. A mixed-integer programming model is developed for this vendor selection problem. The proposed model encourages the buyer to select vendors capable of providing quality in-time products which will minimize the total cost of purchasing, quality, transportation, and penalty charge for violating the delivery time window. An illustrative example is given to demonstrate the implementation of the time-window model.

A Soft Classification Model for Vendor Selection

A Soft Classification Model for Vendor Selection

Hierarchical economic information filtering model concerning vendor selection

This paper proposes a new hierarchical economic information filtering model for vendor selection, consisting of three phases in which leading enterprises select their vendors, namely, the phase of collecting and releasing information, the phase of selecting vendors and the phase of dynamic rating of vendors. Rating and search criteria are different for the three levels of search. It analyses the weights for individual criteria and their relative importance in accordance with the hierarchical economic information filtering model, multiple rounds of feedback from the management staff in manufacturing industries and the method of analytical hierarchy process. Hierarchical economic information filtering model defines the information search criteria of vendor selection, which contributes to increasing information search efficiency, and reduces information search time and cost. Also, vendor information can be dynamically filtered, selected, treated and responded to real time. The establishment and application of this model can help to define tasks and foci in relation to selection and rating of vendors in varying phases. It might help leading enterprises to accommodate vendors dynamically in terms of the rating results, and avoid noisy information and data when selecting and rating vendors in different phases. It can be considered as an extension of filtering economic information in varying phases and modules.

A Hybrid Method for Vendor Selection using Neural Network

Problem of vendor evaluation and selection has always been viewed as the most important responsibility of purchasing department and for such reason, received a great deal of attention from practitioners and researchers. This solution has always been a complex process as various criteria, known and half known are involved in making a decision. This work attempts to develop a rule based model, to evaluate the performance of vendors, supplying components and raw materials to a multinational organization engaged in designing, manufacturing and delivering a range of products covering various stages of electric power transmission and distribution system. To select the vendors, there is a need to rank all the potential vendors according to a performance measure because in this industry almost all items are outsourced from vendors and input material cost constitute almost 80% cost of the product. For such reason any organization is required to select suitable vendors who can supply input materials and components to the organization as per the need timely with right specification and requisite quantity. This paper presents a hybrid model using analytic hierarchy process (AHP) and neural networks (NNs) theory to assess vendor performance. The model consists of two modules: Module 1 applies AHP using pair wise comparison of criteria for all vendors, In the process the importance of the criteria is also obtained using an iterative algorithm. Module 2 utilizes the results of AHP into NNs model for vendor selection. The results yield the best vendor and appropriate score to compare the performance of each vendor. Selection of alternative vendors also can be carried out by using the historical data. Validation of the entire developed algorithm has been carried out separately.

A flexible deterministic, stochastic and fuzzy Data Envelopment Analysis approach for supply chain risk and vendor selection problem: Simulation analysis

This study consists of three types of vendor selection models in supply chains and presents a decision making scheme for choosing appropriate method for supplier selection under certainty, uncertainty and probabilistic conditions. These models are, Data Envelopment Analysis (DEA), Fuzzy Data Envelopment Analysis (FDEA), and Chance Constraint Data Envelopment Analysis (CCDEA). In FDEA model we use α-cut method in five levels for α, to convert fuzzy DEA into interval programming. Also, we solve the CCDEA model for two levels of probabilities. It is assumed that inputs are random variables. Under this assumption the efficiency scores of Decision Making Units (DMUs) are random variables. Obtained results form each model is: average efficiency scores of DMUs, variance of efficiency scores, and 95% confidence interval for average. Results from three models are compared. Our decision making scheme allows decision makers to perform analysis among input factors which are expected costs, quality of acceptance levels, and on-time delivery. This is the first study to a present a flexible approach for supply chain risk and vendor selection. The superiority of the flexible algorithm is shown for 10 suppliers. Its features are also compared with previous models to show its advantages over previous models.

Integration of strategic and tactical decisions for vendor selection under capacity constraints

We examine a generalized vendor selection problem of a multi-store firm where the goal is the simultaneous determination of (i) the set of vendors the firm should work with and (ii) how much each store should order from the selected vendors. In addition to the typical costs associated with vendor selection and delivery between the vendors and their assigned stores, we explicitly consider the inventory-related costs and decisions of the stores. We emphasize the relationship between facility location applications and the problem at hand, and we propose an integrated vendor selection and inventory optimization model. Also, arguing that our model creates a venue for precise incorporation of realistic capacity constraints, we model throughput and dispatch capacities, explicitly. The model is a challenging mixed integer nonlinear program for which we propose an efficient solution approach that relies on Generalized Benders Decomposition (GBD).

“16T”: toward a dynamic vendor evaluation model in integrated SCM processes

PurposeThe purpose of this paper is to provide a “trait based approach” for vendor selection and evaluation for expensive procurements in large businesses through a simple and easy‐to‐use mathematical model using safety, quality, delivery and cost criteria. Design/methodology/approach – We use 16 traits addressing safety (S), quality (Q), delivery (D) and cost (C) areas for evaluating performance of a vendor on a linear 10 point scale. We start with evaluating each “supplier‐supply item” combination and compute gross averages for each of the SQDC areas and finally arrive at an “Overall Performance Index” for each supplier‐supply item combination. These indices form a “Vendor Performance Dashboard” for decision making. Findings – The case study shows that the proposed method is quick and easy to adopt, and provides a logical framework for vendor selection and management, based on performance in the four critical (SQDC) areas. Research limitations/implications – A lot of monotonous computations are required for the proposed vendor evaluation process. Hence, the scope for software development warrants further investigation. There is also a need to develop a process for weighting the different SQDC elements for application in different industry/market contexts. Originality/value – This paper presents a unique and simple approach for generating dashboard data for decisions regarding evaluation of vendors and distribution of sub‐contracts in a dynamic technology intensive market with practical examples. The proposed model for vendor selection and determination of order sizes is less dependent on complex algorithms and more practical and logically framed than existing models.

A comparison of stochastic dominance and stochastic DEA for vendor evaluation

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.

A hybrid MCDM model for strategic vendor selection

Proposed in this study is a hybrid model for supporting the vendor selection process in new task situations. First, the vendor evaluation problem is formulated by the combined use of the multi-criteria decision-making (MCDM) approach and a proposed five-step hybrid process, which incorporates the technique of an analytic network process (ANP). Then the modified TOPSIS (technique for order performance by similarity to idea solution) is adopted to rank competing products in terms of their overall performances. The newly developed ANP will eventually yield the relative weights of the multiple evaluation criteria, which are obtained from the nominal group technique (NGT) with interdependence. An example is solved to illustrate the effectiveness and feasibility of the suggested model. The empirical study has demonstrated how the approach can be used for the strategic vendor selection problem.