Govindan Kannan

Green supplier development program selection using NGT and VIKOR under fuzzy environment

We investigate the problem of green supplier development program evaluation.The approach can be applied under limited or no quantitative data.Program evaluation uses linguistic ratings (fuzzy numbers) obtained from experts.An integrated approach based on NGT and fuzzy VIKOR is proposed.Sensitivity analysis is performed. Developing environmental performance of suppliers is critical for green supply chain management. Organizations are nowadays investing in various green supplier development programs to enhance their supplier performances. The decision to select the right program for green supplier development is often a challenging decision due to lack of prior experience, limited quantitative information, specific context of the organization, and varying supplier backgrounds. This paper addresses the problem of evaluating green supplier development programs and proposes a fuzzy NGT (Nominal Group Technique)-VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) based solution approach. NGT is used to identify criteria for evaluating green supplier development programs. Fuzzy theory is used to address qualitative (linguistic) ratings for the alternatives and the selected criteria used under lack of quantitative information. VIKOR is used to generate green supplier development program rankings and recommend the best program(s) for implementation. Sensitivity analysis is performed to determine the influence of modeling parameters on ranking results of alternatives. A numerical application is provided.

Incorporating risk measures in closed-loop supply chain network design

This paper considers a location-allocation problem in a closed-loop supply chain (CLSC) with two extensions: first, demand and prices of new and return products are regarded as non-deterministic parameters and second, the objective function is developed from expected profit to three types of mean-risk ones. Indeed, design and planning an integrated CLSC in real-world volatile markets is an important and necessary issue. Further, risk-neutral approaches, which are considered expected values, are not efficient for such uncertain conditions. Hence, this paper, copes with the design and planning problem of a CLSC in a two-stage stochastic structure. Besides, risk criteria are considered through using three types of popular and well-behaved risk measures: mean absolute deviation, value at risk and conditional value at risk (CVaR). Consequently, three types of mean-risk models are developed as objective functions and decision-making procedures are undertaken based on the expected values and risk adversity criteria. Finally, performances of the developed mean-risk models are evaluated in various aspects. Results reveal that the inefficiencies of risk-neutral approaches can be overcome. In addition, in terms of quality of solutions, the acceptability of CVaR is proved when it is compared to other risk measures.

Benchmarking the interactions among barriers in third-party logistics implementation: An ISM approach

Purpose – The purpose of this paper is to analyze the interaction among some of the major barriers that may hinder the implementation of third-party logistics (TPL) in manufacturing industries. Design/methodology/approach – This paper uses an interpretive structural modeling (ISM) methodology to analyze the interactions among the barriers. Findings – It is beneficial for the management of any firm to be aware of significant barriers and to diagnose those that could be integral to the organizations future survival. Many works have focused on identifying barriers for TPL implementation, but a model for such barriers is lacking. This paper attempts to develop a model for the barriers using an ISM methodology and analyzes the mutual interactions among the barriers. The model differentiates between the barriers so that driving barriers, which can intensify other barriers, and dependent barriers are identified separately. Originality/value – In this research, eight barriers are considered. Interactions between...

An application of the Analytical Hierarchy Process and Fuzzy Analytical Hierarchy Process in the selection of collecting centre location for the reverse logistics Multicriteria Decision-Making supply chain model

This paper aims at developing an effective and efficient Multicriteria Decision-Making (MCDM) model for selecting the collecting centre location in the Reverse Logistics Supply Chain Model (RLSCM) using the Analytical Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (FAHP). The paper aims at demonstrating how the model can help in solving such decisions in practice. The effectiveness of the model is illustrated using a case study taken in a tyre manufacturing industry located in the southern part of India and validated the results of AHP model using the result obtained from FAHP model. The proposed model helps the industry to select the collecting centre location effectively for the RLSCM.

Analytical hierarchy process-based framework for VMI adoption

In recent years, the collaborative supply-chain management techniques have been widely adopted in various enterprises, and vendor-managed inventory (VMI) practice is one of those adopted techniques. Also, the VMI adoption process differs from company to company and industry to industry. In order to adopt the VMI, a generic framework is needed to understand the various adoption issues. While adopting VMI, selecting and prioritising different adoption issues are important tasks, and owing to the fact that the analytical hierarchical process (AHP) is a structural technique for dealing with complex decisions, it provides a comprehensive and rational framework for structuring a decision problem, for representing and quantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions. The purpose of this paper is to present an AHP-based framework for the adoption of VMI practices. In this study, a group of academic and supply-chain managers were consulted for the purpose of identifying the important VMI adoption issues. The proposed framework provides quantification for subjective evaluations of adoption variables, and it may also serve as a benchmarking tool for supply-chain managers while making strategic decisions related to VMI. The developed framework shows adoption issues at different levels in the hierarchy and helps to prioritise the VMI adoption issues by making pairwise comparisons between them.

An analysis of the general benefits of a centralised VMI system based on the EOQ model

Within a vendor-managed inventory (VMI) agreement, the upstream supply chain member (the vendor) takes responsibility for managing the inventory of the downstream member (the customer) within specific levels previously agreed upon without the need of orders from the customer side to be placed. Therefore, the vendor can focus on optimising production efficiency and capacity planning, while the customer has to improve forecast accuracy. This paper analyses the benefits a VMI agreement could bring for a one-supplier, multiple-customer case through analysing two cases: a supply chain managed in a traditional manner and VMI when both the vendor and the customers belong to the same organisation. The analysis is based on the economic ordering quantity (EOQ) formula and its related total cost, and the novelty is captured by evaluating one vendor, multiple buyers, and multiple product situations. The modelling is done so as to capture the needs and factors which occur within the pharmaceutical industry and a numerical application will be executed with data from one of the main leaders within the pharmaceutical field.

A hybrid normalised multi criteria decision making for the vendor selection in a supply chain model

This paper aims to develop an effective and efficient hybrid normalised multi criteria decision making model for evaluating and selecting the vendor using an Analytical Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (FAHP) and an integrated approach of Grey Relational Analysis (GRA) in a Supply Chain Model (SCM). The first part of the model deals with the selection of vendor using AHP and FAHP. The second part of the model deals with a hybrid approach of AHP along with GRA and FAHP along with GRA. This paper demonstrates how the model can help in solving such decisions in practice. The effectiveness of the hybrid model is illustrated using a case study taken in paper manufacturing industry located in southern part of India and validated the results of hybrid model using the result obtained from AHP and FAHP. The proposed model helps the industry to effectively select the vendor.

Multicriteria group decision making for the third party reverse logistics service provider in the supply chain model using fuzzy TOPSIS for transportation services

Generally Supply Chain Management (SCM) research focuses on the forward movement of materials from the suppliers to the end consumer. However, the reverse flow of products from consumers to upstream businesses has not received much interest (Rogers and Tibben-Lembke, 2001; Simchi-Levi et al., 2003). Due to the revolution of green manufacturing aspect in the globalisation market, the reverse logistics concepts have become an important entity and play a pivotal role in a companys strategic decision. In real time situation, the collection of the returned products from the customer to manufacturer is complex and time consuming task. The separate logistics system has to be developed in order to inspect and maintain the collected returned products in the down stream management, which does not include disposition management, administration time and cost of converting unproductive returns into productive assets. This gives rise to the situation for outsourcing. Outsourcing has recently given many third party providers with a unique opportunity to enter the reverse logistics market. This paper proposes a structured, Multi Criteria Decision Making (MCDM) model for evaluating and selecting the best third Party Reverse Logistics Provider (3PRLP) using the fuzzy-technique for order preference by similarity to ideal solution (Fuzzy-TOPSIS).

Effect of forecasting on the multi-echelon distribution inventory supply chain cost using neural network, genetic algorithm and particle swarm optimisation

Supply Chain Management (SCM) is an emerging field that has commanded attention and support from the industrial community. Forecasting activities are widely performed in various areas of supply chains for predicting important SCM measurements such as demand volume in order management, capacity usage in production management, traffic costs in transportation management and so on. Firstly, the demand is forecast using Neural Networks (NN) and different forecasting methods. The Multi-Echelon Distribution Inventory Supply Chain Model (MEDISCM) is formulated using Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). This paper considers the impact of forecasting methods on the total cost of the multi-echelon distribution inventory supply chain. In this paper, the proposed model is validated by considering the case study in a tyre industry located in southern part of India. To solve the iterative procedure involved, the algorithm involved in GA and PSO model is coded in C++.

Two-echelon distribution-inventory supply chain model for the bread industry using genetic algorithm

The core issue in achieving a focused approach for the improvement of supply chain distribution inventory management is the optimisation of the inventory level adopted by various supply chain entities. This paper presents an approach of optimising the inventory level for a two-echelon supply chain by considering the distribution costs and various production related costs in meeting the customer demand. This methodology is devised for a single product based on the assumption that the total demand of customers for all periods should be less than the limited production capacity and inventory level of plant and warehouse. The focus is to determine the optimal production rate and cost of the plant, the optimal inventory level of the plant and warehouse and the distribution costs for two-echelon in each period by optimising the entire supply chain using genetic algorithm based distribution inventory model (GADIM).