An Integer Linear Program for Integrated Supplier Selection: A Sustainable Flexible Framework

Flexible Fuzzy Goal Programming Approach in Optimal Mix of Power Generation for Socio-Economic Sustainability: A Case Study

The demand for cost-efficient and clean power energy cannot be overemphasised, especially in a developing nation like India. COVID-19 has adversely affected many nations, power sector inclusive, and resiliency is imperative via flexible and sustainable power generation sources. Renewable energy sources are the primary focus of electricity production in the world. This study examined and assessed the optimal cost system of electricity generation for the socio-economic sustainability of India. A sustainable and flexible electricity generation model is developed using the concept of flexible fuzzy goal programming. This study is carried out with the aim of achieving the government’s intended nationally determined contribution goals of reducing emission levels, increasing the capacity of renewable sources and the must-run status of hydro and nuclear, and technical and financial parameters. The result shows an optimal cost solution and flexibility in how increased electricity demand would be achieved and sustained via shifting to renewable sources such as solar, wind and hydro.

A stochastic fuzzy multi-criteria group decision-making for sustainable vendor selection in Indian petroleum refining sector

Purpose

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).

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

A systematic literature review of quantitative models for sustainable supply chain management

Supply chain management is the basis for the execution of operations, being considered as the core of the business function in the 21st century. On the other hand, at present, factors such as the reduction of natural resources, the search for competitive advantages, government laws and global agreements, have generated a greater interest in the sustainable development, which, in order to achieve it, industries need to rethink and plan their supply chain considering a path of sustainability. So sustainable supply chain management emerges as a means to integrate stakeholders' concern for profit and cost reduction with environmental and social requirements, attracting significant interest among managers, researchers and practitioners. The main objective of this study is to provide a synthesis of the key elements of the quantitative model offerings that use sustainability indicators in the design and management of forward supply chains. To achieve this objective, we developed a systematic literature review that includes seventy articles published during the last decade in peer-reviewed journals in English language. In addition a 4 W's analysis (When, Who, What, and Where) is applied and three structural dimensions are defined and grouped by categories: Supply chain management, modeling and sustainability. As part of the results we evidenced a continuous growth in the scientific production of this type of articles, with a predominance of deterministic mathematical programming models with an environmental economic perspective. Finally, we identified research gaps, highlighting the lack of integral inclusion of a life cycle analysis in the design of supply chain networks.

Effect of SCF on automobile industry performance during sales promotional schemes

Purpose

In today's competitive market subject to various uncertainties, supply chain flexibility (SCF) arose as a potential weapon to enhance the firm's performance. This paper model the effect of SCF on automobile industry performance during sales promotional schemes (SPS), which has turned out to be one of the most important marketing tools.

Design/methodology/approach

In view of the literature and the expert opinion taken from an automobile OEM, the SCFs and performance measures pertaining to the SPS environment have been identified. For the purpose of ranking the SCFs with respect to the performance measures, the Interpretive Ranking Process (IRP) has been applied using the direct contact method as a means for establishing the contextual relationships between SCFs and performance measures and to draw the interpreting reasons behind them.

Findings

The findings of this study along with the validity and stability of the results assessed through the system graphs and sensitivity analysis demonstrate that flexibility at the procurement end followed by the organizational end has the highest impact on the performance of the company during SPS.

Originality/value

Analysis of SCFs in regard to performance measures during SPS helps improve supply chain performance and offer valuable insights to the practicing managers in decision-making. This study augments the flexibility literature, by clubbing the two independent research streams, SCF and SPS.

Dynamic supplier selection through optimal ranking under two-echelon system

Purpose

With the increasing competition among the industries, they remain under pressure as how to select the best set of suppliers for the competitive edge. Often, it has been challenging to develop an effective set of suppliers due to varied and asymmetric mode of criteria. The purpose of this paper is to develop a responsive chain under original equipment manufacturer (OEM).

Design/methodology/approach

This study proposes a responsive chain under a two-echelon system (TES) of OEM, which needs to collaborate with a set of suppliers at each echelon through an integrated methodology of AHP and TOPSIS. According to the OEM’s criteria, demands and suppliers’ capacity vary with time, therefore they are not static for a longer period. Hence, supplier selection (SS) problem possesses dynamicity in real practice. For this, MILP is used for finding optimal order quantities based on the optimal ranking at each echelon in the multi-period scenario. Subsequently, sensitivity analysis (SA) is conducted through Taguchi method of parameter design (TMPD) to achieve an optimal ranking in the TES.

Findings

This study suggests optimal criteria’s weight, percentage contribution, and flexibility for the suppliers and manufacturers involving through maximum demand strategy at each echelon of OEM. It also provides robust group of suppliers and manufacturers in the TES through optimal ranking and simultaneously in the order allocations. Furthermore, it restricts the number of suppliers and manufactures at each echelon through proposed methodology to obtain the solution in a very short running time.

Originality/value

To validate this model, a real data set for the case of chain conveyor company is used. This adopted methodology can suggest the organization that how the approach should be implemented.

Fuzzy Multi-Criteria Decision-Making Model for Supplier Evaluation and Selection in a Wind Power Plant Project

In order to meet ambitious growth targets in the medium term, Vietnam must continue exploiting traditional energy sources. In the longer term, Vietnam has to develop a strategy and roadmap for the development of new energy sources. In these new energy sources, wind energy has emerged as a viable option. Given the geographic conditions of a locality with a long coastline and high winds that are fairly distributed all year, many wind-power plants are being built in Vietnam. One of the most important pieces of equipment in a wind-power plant is the wind turbine. The wind turbine suppliers’ selection is a complex and multicriteria decision-making (MCDM) process that can reduce the costs of procuring equipment and aid in receiving products on time. Many studies have applied the MCDM model to various fields of science and engineering. One of the fields that the MCDM approaches have been applied to is the supplier selection problem. Supplier selection is an important issue of the MCDM model. Especially in a renewable energy project, decision-makers have to evaluate both natural and society factors. Although some researchers have reviewed the applications of the MCDM model in wind turbine supplier selection, limited work has focused on this problem in a fuzzy environment. Therefore, in this work, the authors propose a fuzzy MCDM model for the wind turbine supplier selection process under fuzzy environment conditions. In the first step, all factors for wind turbine supplier selection are identified by supply chain operations reference (SCOR) metrics and the results from a review of the literature. A fuzzy analytic network process (FANP) model is applied for determining the weight of all the criteria in the second stage, and the technique for order preference by similarity to an ideal solution (TOPSIS) model is used to rank all the potential suppliers in the final stage. As a result, Decision-Making Unit 010 (DMU010) becomes an optimal option for the wind turbine supplier selection processes. The contribution of this research is to develop new hybrid fuzzy MCDM approaches for wind turbine supplier selections. Furthermore, this work presents useful guidelines for wind turbines as well as provides a guideline for supplier selection in other industries.

Interpretive multi-criteria valuation of flexibility initiatives on direct value chain

Purpose

Activities on direct value chain directly add value to the products/services delivered to the customers. This value addition can be further enhanced by taking up flexibility initiatives on these direct value chain activities. The purpose of this paper is to identify flexibility initiatives on the direct value chain and carry out their valuation.

Design/methodology/approach

Since different value addition criteria (i.e. benefits and costs) may be both tangible and intangible, an interpretive valuation based on multi-criteria framework would be desirable. The paper deploys the efficient interpretive ranking process (IRP) for this purpose. It uses total interpretive structural modeling (TISM) to derive weights of criteria based on their respective driving power. The paper also makes a methodological contribution to assess transitive dominance in IRP based on transitivity check as used in the modified TISM process.

Findings

The paper uses a multi-criteria valuation to examine the proposition that the flexibility initiatives will add a higher value as we go downstream in the value chain. The flexibility initiatives linked with marketing such as product upgradation and switching, customized services and dynamic pricing are found to be most value adding in character.

Research limitations/implications

The main limitation of this study is that it is generic in nature and need to be replicated in multiple case situations. The methodology proposed can be utilized for specific case analysis for flexibility valuation on the direct value chain.

Practical implications

The practitioners may be able to use the proposed method of TISM–IRP with improvements in real-life applications. The proposed multi-criteria valuation is interpretive in nature and can be utilized by practicing managers in group settings even if they do not have complete data.

Originality/value

The strategy and operations researchers will find promise in the methodology to investigate and prioritize the initiatives planned to enhance flexibility in different value chain activities. The researchers on multi-criteria decision making will find the methodological enhancement proposed in terms of graphically deriving the transitive dominance to be useful in other applications as well.

Modeling big data enablers for operations and supply chain management

Purpose

The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM).

Design/methodology/approach

Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established.

Findings

The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM.

Research limitations/implications

The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community.

Practical implications

The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM.

Originality/value

MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.