Search results

Vendor rating is a process having strategic implications for managing a supply chain. Vendor rating can be done using analytic hierarchy process either by a single decision maker or by a group of decision makers. This approach may suffer from some drawbacks including bias in estimation process. A methodology is proposed in this paper which makes use of estimation of the rating by a group on an individual basis following the principle of anonymity. A statistical analysis is carried out to determine the confidence intervals for the estimates of the composite rating of the vendors. The procedure presented here helps in identifying those members whose opinions may significantly deviate from that of the group. Emphasis is placed on establishment of confidence limits in group decision‐making. Participants in group decision making, whose opinions fall outside the group’s confidence limit, are further studied to understand the source of variation. Implementation guidelines have also been provided to account for the dynamic nature of the vendor evaluation process and to take appropriate actions in managing the vendor rating process in the overall supply chain.

The aim of this paper is to identify the criteria that are used for vendor or supplier rating, prioritize these criteria based on the industry inputs and develop a vendor rating model. The data were collected from an Indian start-up working in product development using three-dimensional printing (3DP).

Factors of importance for vendor rating were identified through industry visits, and interacting with the industry experts from the start-up under consideration, substantiated by extensive review of relevant literature. A questionnaire-based survey was carried out to further narrow down the factors important to the industry, prioritizing them with a pairwise comparison analysis as envisaged in the analytical hierarchy process (AHP) technique along with the calculation of consistency ratios. Technique for order preference by similarity to ideal solution (TOPSIS) methodology was further used for data aggregation.

This research brought forward the criteria that are useful for rating vendors or suppliers with reference to 3DP sector.

This paper integrates AHP and TOPSIS to solve a multi-criteria vendor rating problem. The attempt was made to make vendor rating process universal so that it can encompass all the vendors of the firm.

This paper aims to select key criteria for sustainable vendor assessment and spare-parts supplies in the Indian petroleum refining sector using stochastic fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS).

The criteria for sustainable vendor evaluation and selection are identified from the review of the literature and further; it is finalized using the Delphi method. Eight supply chain (SC) experts from the Indian petro refining sector were identified as having more than five years of experience and agreed to participate in this study (known as decision-makers (DM)). Five vendors supplying spare-parts are shortlisted from the market with the discussion and consent of procurement experts from petroleum refineries. Subsequently, criteria and vendors are rated based on relative importance in linguistic terms from the group of eight DMs. As ratings involve uncertainties in the decision-making, the SFTOPSIS method is applied to determine criteria weight and vendor ranking at a distinct significance level (α). The ranking of the vendors is obtained for sustainable supply of spare-parts in the Indian petro refining sector using the SFTOPSIS method.

The ranking of sustainable vendors is obtained through the integrated application of the fuzzy and stochastic approach to capture the uncertainties in the ratings of DMs. The sensitivity analysis is carried out at distinct confidence limits of a normal distribution to obtain a robust ranking of the vendors. In this paper, a case application of SFTOPSIS in the Indian petro refining sector is presented in which key criteria and the vendor ranking are found to be changing with confidence limit for sustainable vendor evaluation.

The fuzziness and randomness in relative ratings collects from a group of DMs are taken in the proposed methodology. The distinct approaches are compared with changing significance-level under stochastic, fuzzy and deterministic TOPSIS to acquire robustness in the ranking. The proposed SFTOPSIS model can be useful to practitioners from the petroleum sector.

The originality of the paper contributes to an application of the SFTOPSIS method that is the extension of FTOPSIS in the petro refining sector of a developing country. The sensitivity analysis with distinct significance-level shows the uncertainties in the collected ratings from the DMs that supports robustness in the ranking. It might be helpful for SC professionals from the petro refining sector, who assess the rank of the vendors at different confidence limits for sustainable supply of spare-parts. Further research in the petroleum industry from emerging economies needs to be undertaken to broaden its scope and applicability.

Supplier selection problem is the key process in decision making of supply chain management. An effective selection of vendors is heavily responsible for the success of any organization. Vendor selection problem (VSP) reflects a more practical view when the decision makers involved in the problem are present on different levels. Moreover, vendor selection consists of various random parameters to be dealt with in real life. The purpose of this paper is to present a decentralized bi-level VSP where demand and supply are normal random variables and objectives are fuzzy in nature. Decision makers are present at two levels and are called as leader and follower. As the next purpose, this paper extends and presents a solution approach for fuzzy bi-level multi-objective decision-making model with stochastic constraints. Different scenarios have been developed within a real-life case study based on different sets of controlling factors under the control of leader.

This study uses chance-constrained programming and fuzzy set theory to generate the results. Stochastic constraints are converted into deterministic constraints using chance-constrained programming. Decision variables in the bi-level VSP are partitioned between the two levels and considered as controlling factors. Membership functions based on fuzzy set theory are created for the goals and controlling factors and are used to obtain the overall satisfactory solutions. The model is tested on a real-life case study of a textile industry and different scenarios are constructed based on the choice of leader’s controlling factors.

Results showed that the approach is quite helpful as it generates efficient results producing a good level of satisfaction for the decision makers of both the levels. Results showed that on choosing the vendors that are associated with worst values in terms of associated costs, vendor ratings and quota flexibilities as controlling factors by the leaders, the level of satisfaction achieved is highest. The level of satisfaction of solution is lowest for the scenario when the leader chooses to control the decision variables associated with vendors that are profiled with minimum vendor ratings. Results also showed that higher availability of materials and budget with vendors proved helpful in obtaining quota allocations. Different scenarios generate different results along with different values of satisfaction degrees and objective values which shows the flexible feature of the approach based on leader’s choice of controlling factors. Numerical results showed that the leader’s control can be effectively incorporated maintaining satisfaction levels of the followers under various scenarios or conditions.

The paper makes a certain contribution toward the study of vendor selection existing in a hierarchical manner under uncertain environment. A wide set of data of different factors is needed which can be seen as a limitation when the available time is short for the supplier selection process.

VSP which is generally adopted by most of the large organizations is characterized with hierarchical decision making. Moreover, dealing with the real-life concern, the data available for some of the parameters are not complete, representing an uncertainty of parameters. This study is quite helpful for decentralized VSP under uncertain environment to reduce the costs, improve profit margins and to create long-term relationships with selected vendors. The proposed model also provides an avenue to explore the decision making when the leader has control over some of the decision variables.

Reviewing the literature available, this is the first attempt to present a multi-objective VSP where the decision makers are at hierarchical levels considering uncertain parameters such as demand and supply as per the best knowledge of authors. This research further provides an approach to construct scenarios or different cases based on the choice of leader’s choice of controlling factors.

The purpose of this paper is to demonstrate the application of vendor rationalization strategy for streamlining the supplies and manufacturing cycle time reduction in an Indian engineer-to-order (ETO) company. ETO firms are known for a large number of vendors, co-ordination hassles, rework problems and its impact on cycle time and operational excellence.

The research demonstrates the case-based application of Kraljic’s matrix for supply and leverages items, on-the-job observations, field visits, discussions and analysis of supplies reports.

The study guides on the rationalization of supplies and the necessary strategic alignments that can significantly reduce supply risk, costs, manufacturing and delivery cycle time along with co-ordination hassles. The study depicts the challenges of ETO environment with respect to supplies, and demonstrates the effectiveness of vendor rationalization application for the case company and weaknesses of commonly practiced vendor management approaches.

To be competitive, companies should rationalize supply items and vendors based on the nature of items and their subsequent usage by applying Kraljic’s matrix-based classification. The immediate implication of vendor rationalization is misunderstood as reducing supply base, but it does much more and includes review of supplies, nature of items and strategic alignments, leading to win-win situation for company and suppliers.

For the rationalization of supplies, while procuring and dealing with vendors, executives should envisage engineering nature of components, considering cross-functional requirements and integration of components in context to ETO products/projects environments. There is a dearth of studies focusing on vendor rationalization aspects in ETO setups in fast-developing country context.

While it has long been recognized that vendor selection is multi‐objective in nature, little has been done to develop techniques for measuring vendors’ performance on multiple criteria. Demonstrates the use of data envelopment analysis (DEA) as a tool for measuring the performance of vendors on multiple criteria and for use in vendor negotiations. Describes the DEA model, develops a DEA formulation for measuring vendor efficiency and, finally, shows how a baby food manufacturer applied the DEA technique in a just‐in‐time environment. Shows how application of the DEA technique can provide savings in monetary and other measurable terms.

The purpose of this study is to examine the simultaneous effects of the product, brand, and vendor trust beliefs on consumers' online intentions, i.e. the intention to purchase and the intention to provide personal information online.

The study uses an online 2×2×2 between‐subjects factorial experiment design with two vendor trust beliefs levels (high/low), two brand trust beliefs levels (high/low), and two product trust beliefs levels (high/low). Multivariate analysis of covariance, linear regression, and the SOBEL test were used to analyze the hypotheses.

The results suggest that brand trust beliefs affect online intentions, and may be needed to increase online sales. The influence of vendor trust beliefs on online intentions varies with brand trust, beliefs for products and for services is augmented by brand trust beliefs.

The limitations are those typically applied to experimental methodology. Intentions were used as surrogate for behavior, and a fictitious e‐tailer selling two products with fictitious brands was used.

E‐tailers are encouraged to carry reputable brands and prominently display information about these brands on their web sites. This will improve consumers' trust beliefs, increasing conversion rates, and reducing shopping cart abandonments.

The influence of trust beliefs on online behavior, notwithstanding its importance, remains under‐researched. The paper addresses this gap in the literature. Specifically, it addresses the effects of the simultaneous influence of vendor, brand, and product trust beliefs on shoppers' online intentions. And it decomposes online intentions into its components, i.e. intentions to provide personal information and intentions to purchase, to understand these simultaneous effects not addressed before. The results contribute to the growing literature on trust and consumer online behavior.

To provide decision makers (DMs) an option for addressing problems involving finite alternative sets and multiple criteria, where criterion weighting is difficult or impossible.

The multicriteria decision problem is described, and a typically descriptive (rather than prescriptive) tool, data envelopment analysis (DEA), is summarized, along with a hypothetical but typical example of a multicriteria decision (vendor selection). The DEA approach is modified to incorporate weight constraints and is used to rank the available vendors. Results are compared with those from the use of a popular multicriteria decision tool (SMART) and a naïve averaging approach.

The modified DEA approach yields results very similar to those produced using SMART; these results are quite satisfactory in spite of the fact that DEA requires less involvement on the part of the DM. In addition, non‐dominant optima (a possible anomaly with DEA) are avoided, and often a single alternative, rather than a non‐dominated set, will result, thus providing a unique optimum.

Results are based on the analysis of a single data set. Future investigation should examine the performance of the DEA approach when other data sets involving more like as well as more unlike alternatives are involved.

With DEA the burden on the DM is reduced, as the need for eliciting criterion weights is obviated. DEA should thus provide an acceptable alternative to prescriptive modeling tools when multiple DMs are involved and/or criterion weight determination is unfeasible.

This paper demonstrates how DEA, a tool used more typically in post hoc evaluations, can be used also, with some modifications, as a prescriptive decision support tool.

This article addresses some of the critical issues with respect to public procurement of information technology (IT). The article provides results from surveys of state public information managers and technology vendors who provide services to government to determine if IT procurements have improved in the ten years since the Kelman study on public procurements and suggests opportunities to improve public IT procurements.

Just‐in‐time (JIT) is thought by managers to be an important key to improving operations planning. JIT can be defined as a systematic approach which minimizes inventory by having supplies arrive at production and distribution points only when needed. Reports on a study to examine a supply performance evaluation strategy for implementing a just‐in‐time/total quality management (JIT/TQM) purchasing system for a major chemical company. The innovative strategy includes a vendor survey plan (VSP) with an evaluation system and a supplier self‐assessment. Results show the average quality performance of suppliers improved after implementing the new JIT/TQM strategies.