A grey-based decision-making approach to the supplier selection problem in a steelmaking company

PurposeThe purpose of this paper is to propose a site selection method using grey system theory for a desalination plant in Libya.Design/methodology/approachIn order to tackle incompleteness and imprecision of human’s judgments, grey numbers were used. This work uses a grey-based approach to represent decision makers’ comparison judgments and extent analysis method to select the best site. Therefore, a real case study of a selection problem of a site selection of desalination plant in Libya was used to illustrate the proposed approach.FindingsSite selection in a desalination plant can be one of the most important decisions in planning a desalination project. The decision affects both the project cost and potentially the project schedule. Based on the results of grey model, a clear order of these sites and the degree of preference are obtained. This paper presents a way to improve a site selection by using a grey model, especially in a complex environment like Libya.Originality/valueTo the best knowledge of the authors, there is no literature for site selection using grey system theory in a desalination plant in Libya. This attempt may well enhance and facilitate the decision-making process of the best site in the country involved in this research.

In order to optimise a supply chain, the effective management of suppliers plays a key role. Firms seeking competitive advantage are participating in cooperative supply chain arrangements, such as strategic partnerships, which combine their individual strengths and unique resources. Supplier selection is a multi-criteria decision making problem which includes both qualitative and quantitative factors. In the present scenario environmental criteria should also be given due importance, this further increases the challenge of supplier selection. Generally, decision makers' judgments are often uncertain and cannot be estimated by an exact numerical value. The grey systems theory is a powerful tool to deal with uncertain information. This paper presents a structured model for evaluation and selection of strategic supplier. Stepwise Weight Assessment Ratio Analysis (SWARA) is applied to decision making in order to calculate the relative importance of the criteria. The COmplex PRoportional ASsessment of alternatives with grey relations (COPRAS-G) method is used for supplier evaluation and selection of strategic supplier. A case study is presented to demonstrate the applicability of the proposed model to select a strategic green supplier.

The fundamental objective of supply chain management (SCM) is to integrate various suppliers to satisfy market demand. Supplier evaluation and selection is very important for establishing an effective supply chain. As a matter of fact, supplier selection consists of both qualitative and quantitative criteria, so it is considered as a multi criteria decision making (MCDM) problem. Under incomplete or uncertain information, the fuzzy set theory allows us make decisions with approximate reasoning. In order to overcome the uncertainty which is constituted by vague situations in supplier selection, we utilize the “extension of the PROMETHEE method in a fuzzy environment” (F-PROMETHEE). In this chapter, multi criteria supplier selection based on a fuzzy PROMETHEE method with an application to supplier selection decision problem is conducted. The main advantages of the methodology are the user friendliness coming from the linguistic evaluations, and the consideration of the vagueness or fuzziness inherent to the decision making environment. Hence, the method can be an efficient and effective methodology to be used by decision makers on supply chains. The proposed methodology can also be applied to any other selection problem.

Supplier evaluation and selection are key components in the supply chain because supplier performance directly affects the supply chain's efficiency. Therefore, companies should think strategically when they need to select their suppliers. Thus, selecting and evaluating new suppliers is essential in shaping the supply chain's smoothness and efficiency. Selecting suppliers is a complex issue as it involves many factors and decisions to be considered and needs to be assessed with an auditing process. However, a supplier audit is an expensive way to evaluate supplier capability. This research aims to propose a supplier selection model for a corrugated carton manufacturing company. The SOCCER model, developed by Steve Rogers, was used as the supplier selection criteria. Analytical Hierarchy Process (AHP) was used in the supplier selection. A face-to-face interview method was used in collecting data. The results show that the cost structure is the ultimate concern on supplier selection which bears 44.2% of the SOCCER model, followed by operational capability (23%), customer approach (13.5%), economic performance (8.3%), strategic direction (6.9%), and lastly research and development (4.1%). The percentages inform the company how much attention they need to pay when evaluating and selecting a new supplier. ABSTRAK: Penilaian dan pemilihan pembekal adalah kunci utama dalam rantaian bekalan kerana prestasi pembekal secara langsung melibatkan kecekapan rantaian bekalan. Oleh itu, syarikat perlu memikirkan secara strategik apabila ingin memilih pembekal. Dengan demikian, pemilihan dan penilaian pembekal baru adalah penting dalam pembentukan kelancaran rantaian bekalan dan kecekapan. Pemilihan pembekal adalah isu kompleks kerana ianya melibatkan banyak faktor dan keputusan perlu difikirkan dan perlu dinilai bersama proses audit. Namun, audit pembekal adalah mahal bagi menilai kemampuan pembekal. Kajian ini mencadangkan model pemilihan pembekal bagi syarikat pembekal kotak karton. Model SOCCER dicipta oleh Steve Rogers, telah digunakan sebagai kritia pemilihan pembekal. Proses Hirarki Analitikal (AHP) digunakan dalam pemilihan pembekal. Kaedah temuduga bersemuka digunakan dalam pengumpulan data. Dapatan kajian menunjukkan struktur harga adalah kehendak utama dalam pemilihan pembekal iaitu 44.2% daripada model SOCCER, diikuti kemampuan operasi (23%), pendekatan pelanggan (13.5%), prestasi ekonomi (8.3%), misi strategik (6.9 %), dan akhirnya penyelidikan dan pembangunan (4.1%). Peratusan ini berguna untuk syarikat dalam memberi keutamaan dalam penilaian dan pemilihan pembekal baru. ABSTRAK: Supplier evaluation and selection are key components in the supply chain because supplier performance directly affects the supply chain's efficiency. Therefore, companies should think strategically when they need to select their suppliers. Thus, selecting and evaluating new suppliers is essential in shaping the supply chain's smoothness and efficiency. Selecting suppliers is a complex issue as it involves many factors and decisions to be considered and needs to be assessed with an auditing process. However, a supplier audit is an expensive way to evaluate supplier capability. This research aims to propose a supplier selection model for a corrugated carton manufacturing company. The SOCCER model, developed by Steve Rogers, was used as the supplier selection criteria. Analytical Hierarchy Process (AHP) was used in the supplier selection. A face-to-face interview method was used in collecting data. The results show that the cost structure is the ultimate concern on supplier selection which bears 44.2% of the SOCCER model, followed by operational capability (23%), customer approach (13.5%), economic performance (8.3%), strategic direction (6.9 %), and finally research and development (4.1%). The percentages inform the company how much attention they need to pay when evaluating and selecting a new supplier.

Group multi-criteria supplier selection using combined grey systems theory and uncertainty theory

Supplier selection is an important issue in supply chain management (SCM) which, as with most dimensions of business, has been strongly impacted by the COVID-19 pandemic. Previous research on the clothing and textiles (C&T) industry has overlooked efforts to provide a regular reference method for addressing the problem of supplier selection. This study discusses the COVID-19 pandemic’s impact on, and the relative importance of, supplier selection implementation. This study applied fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multisegment goal programming (MSGP) method to the problem of supplier selection and combined these two methods to propose a novel multicriteria decision-making (MCDM) method to be used by C&T companies. It proposes a simple method to provide decision-makers (DMs) with guidelines for supplier selection considering existing constraints on business resources. The advantage of this method is the incorporation of both qualitative and quantitative criteria (e.g., both tangible and intangible resources), which allows DMs to set multisegment aspiration levels (MSALs) for supplier selection. A case study of a C&T manufacturer’s application of the model is also presented.

The Fuzzy ART algorithm: A categorization method for supplier evaluation and selection

The requirement of large sample size and the strong subject knowledge to build suitable membership function restrict the applicability of probability and fuzzy theories in supplier selection problem. Due to this fact, the combination of grey system theory and uncertainty theory-based approach is applied for supplier selection problem, which requires neither any exact probability distribution nor membership function. The proposed approach is able to consider stochastic and recognitive uncertainties associated with supplier selection decision in supply chain management. This chapter develops framework for reducing the risks associated with suppliers. The novelty of this research is to consider proper uncertainty approaches in different stages of proposed framework instead of solving the whole selection problem using the same uncertainty theory or grey system theory. The proposed framework is based on three stages: identification of supplier selection criteria, defining weights to goals and ratings to supplier attributes as grey linguistic variables, and supplier selection and order allocation using uncertain-goal programming considering uncertain demand and lead time. The proposed model will help the practitioners to effectively evaluate and select suitable set of suppliers and optimal order allocation.

Purpose – The purpose of this paper is to study a novel grey possibility degree approach, which is combined with multi-attribute decision making (MADM) and applied MADM model for solving supplier selection problem under uncertainty information. Design/methodology/approach – The supplier selection problem is a typical MADM problem, in which information of a series of indexes should be aggregated. However, it is relatively easy for decision makers to define information in uncertainty, sometimes as a grey number, rather than a precise number. By transforming linguistic scale of rating supplier selection attributes into interval grey numbers, a novel grey MADM method is developed. Steps of proposed model were provided, and a novel grey possibility degree approach was proposed. Finally, a numerical example of supplier selection is utilized to demonstrate the proposed approach. Findings – The results show that the proposed approach could solve the uncertainty decision-making problem. A numerical example of supplier selection is utilized to demonstrate the proposed approach. The results show that the proposed method is useful to aggregate decision makers’ information so as to select the potential supplier. Practical implications – The approach constructed in the paper can be used to solving uncertainty decision-making problems that the certain value of the decision information could not collect while the interval value set could be defined. Obviously it can be utilized for other MADM problem. Originality/value – The paper succeeded in redefining interval grey number, constructing a novel interval grey number based MADM approach and providing the solution of the proposed approach. It is very useful to solving system forecasting problem and it contributed undoubtedly to improve grey decision-making models.

As global supply chains become more developed and complicated, supplier quality has become increasingly influential on the competitiveness of businesses during the Covid-19 pandemic. Consequently, supplier selection is an increasingly important process for any business around the globe. Choosing a supplier is a complex decision that can result in lower procurement costs and increased profits without increasing the cost or lowering the quality of the product. However, these decision-making problems can be complicated in cases with multiple potential suppliers. Vietnam's textile and garment industry, for example, has made rapid progress in recent years but is still facing great difficulties as the supply of raw materials and machinery depends heavily on foreign countries. Therefore, it is extremely important for textile and garment manufacturing companies in Vietnam to implement an effective supplier evaluation and selection process. While multicriteria decision-making models are frequently employed to assist with supplier evaluation and selection problems, few of these models consider the problem under the condition of a fuzzy decision-making environment. The aim of this paper is to create a hybrid MCDM model using the Fuzzy Analytical Hierarchy Process (FAHP) model and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to assist the supplier selection process in the garment industry in a fuzzy decision-making environment. In this study, the FAHP method is used to evaluate the performance and the weight of each criterion. TOPSIS is then used to rank all potential suppliers. The proposed model is then applied to a real-world case study to demonstrate both the process of calculation as well as its real-world applicability. The results from the case study provide empirical evidence that the model is feasible. The proposed approach can also be used in combination with other MCDM models to better support decision makers and can be modified to be applied in similar supplier selection processes for different industries.

Supplier evaluation and selection is an important issue in the supply chain management. Many different factors would have effect on the performance of suppliers, therefore supplier evaluation and selection is a typical multi-criteria decision making problem. Data Envelopment Analysis (DEA) is an effective methodology of evaluating the relative efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. DEA has been widely used in the supplier evaluation and selection problem. However, traditional literatures mostly concentrated on the supplier evaluation and selection without consideration of the decision maker's preference. In this paper, we propose a weight restricted DEA model for the supplier evaluation and selection problem in which two kinds of decision maker's preferences can be reflected by introducing different kinds of weight restrictions. Numerical example is provided to examine the validity and effectiveness of our proposed model. KEYWORD: data envelopment analysis; supplier evaluation; supplier selection; weight restriction

The impact of sustainability on supplier selection: A behavioural study

With a growing global awareness of environmental protection, green production has become an important issue for almost all manufacturers. Supplier evaluation and selection has been a critical strategic concern for long time. A green supplier selection system is therefore necessary to determine the suitability of suppliers to cooperate with the company. Researchers have developed many decision-making (DM) models to handle the green supplier evaluation and selection problem. In this paper, we conducted a comprehensive analysis of ten-literature review on DM models for solving Green Supplier Evaluation and Selection (GSES) problem. The review is focused entirely on 1098 research articles published in academic journals between 1990 and 2020 and 271 DM models divided into 170 single models and 101 combined models were reviewed, our study focused just on the single models. The analysis of the ten review show that the Analytic hierarchy process (AHP) model is the prominent DM technique (160 articles are used them) followed by Data envelopment analysis (DEA), which 122 papers apply them to achieve this goal and 101 researches which utilized Technique for order performance by similarity to ideal solution (TOPSIS) model. As a result, the AHP, DEA and TOPSIS are the most popular DM models used to solve the green supplier evaluation and selection problem.

On the background of global warming, energy shortages and environmental pollution, people pay more attention to the low-carbon economy.In this paper, the problem of green supplier selection based on low-carbon economy is studied.Concretely, an evaluation index system for green supplier selection under the low-carbon economy is presented, and then a multi-attribute decision model of green supplier selection is established based on the method of analytic hierarchy process (AHP).This model provides decision reference for the department of supply chain management.

Purpose This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature. Design/methodology/approach This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations. Findings The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service. Research limitations/implications The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties. Practical implications The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly. Social implications The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains. Originality/value Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.