Supplier Selection Research Articles

A heuristic application of systems reliability optimization in supplier selection problem of a make-to-order supply chain

A heuristic application of systems reliability optimization in supplier selection problem of a make-to-order supply chain

BIM-based search and selection of construction material suppliers: a dedicated framework and prototype

Purpose This study aims to address the key issues concerning supplier selection in traditional construction procurement by proposing an innovative, novel, state-of-the-art prototype plugin building information modelling – supplier search and ranking (BIM-SSR) and an associated conceptual framework. It enhances building information modelling (BIM) capabilities through web crawling and analytical hierarchy processes (AHP). It uses the World Wide Web to procure construction material suppliers. Design/methodology/approach Prevalent issues in traditional procurement of material suppliers have been identified through a rigorous literature review. Field experts vetted these issues. A framework has been presented to address these issues based on integrated web crawling and AHP as a multi-criteria decision-making (MCDM) method. A BIM prototype (BIM-SSR) has been developed using Python and plugged into Autodesk Revit to automate the search and evaluation of material suppliers based on precise material specifications from the BIM design. The BIM-SSR prototype is tested through a case study and validated by field professionals for its efficiency in tackling the identified issues. Findings Thirteen key issues have been identified concerning traditional construction procurement pertinent to supplier selection. Best-value procurement was encouraged by identifying supplier selection criteria such as cost, delivery time, experience, compliance with quality management standards, warranties, and claim period. The presented BIM-SSR prototype has an efficiency of 80%–95% in addressing the issues identified in this study and 97.5% effectiveness in improving the overall procurement management process. Originality/value The BIM-SSR prototype developed in this study is a novel and innovative addition to the body of knowledge that has been integrated into Autodesk Revit as a Plugin. Automation of supplier search and selection through digital technologies, including web crawling and integration of traditionally accepted MCDM methods such as AHP in BIM, is another innovation in the current study. Overall, this study presents a holistic, innovative system, from conceptual design to practical implementation and demonstration. This is one of the steps to help the traditional construction procurement process evolve into a more modern and digital procurement.

Dynamic Supplier Selection and Its Optimal Strategy Considering Full Truck Load and Fuzzy Demand using Fuzzy Expected Value-Based Programming

This article proposes a linear integer optimization model incorporating fuzzy parameters to find the optimal solution for a dynamic supplier selection problem with uncertain demand. The uncertain demand value is represented using a fuzzy variable. A fuzzy expected value-based linear optimization solver is used to address the optimization problem, to minimize the total cost under fuzzy demand values. Several computational experiments were conducted to evaluate and analyze the model. The results show that the proposed model effectively identifies the optimal suppliers for each product. Additionally, the model determines the optimal purchase volumes for each product type from the selected suppliers, leading to the minimal total expected cost.

Comprehensive Method for Supplier Selection in the Indonesian Leather Industry Considering Sustainable Development Goals Criteria

Objective: This pivotal research aims to rank raw material suppliers at an Indonesian leather company using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It is based on Sustainable Development Goals (SDGs) criteria selected by many decision-makers through the Delphi method, with a compromise criteria weight between Methods based on Removal Effects of Criteria (MEREC) and Stepwise Weight Assessment Ratio Analysis (SWARA) II. Theoretical Framework: The global and national commitments of Sustainable Development Goals (SDGs) to improving society's welfare, including the sustainable use of resources, are not just theoretical concepts. They are practically applied in this research to guide supplier selection in the leather industry. These criteria are then weighted using objective weighting (MEREC) and subjective weighting (SWARA) II, and the results are compromised to form the basis for evaluating suppliers using TOPSIS. Method: The proposed method in this research consists of three stages. The first stage is selecting the SDGs criteria using the Delphi method, a structured communication technique involving experts. The second stage is weighting the criteria using MEREC for objective weighting and SWARA II for subjective weighting. The third stage is supplier evaluation using TOPSIS based on the weight of the compromised criteria. Results and Discussion: The ranking of leather raw material suppliers for Indonesian leather companies is Kediri, Rembang, Jombang, Cirebon, Wonogiri, Lumajang, Cianjur, and Sidoarjo. This ranking is based on the SDGs criteria. Responsible leather sourcing, eco-friendly tanning processes, water use audits, and waste management are the SDGs' criteria for selecting suppliers in the Indonesian leather industry. Responsible leather sourcing is the only sustainability criterion more important than the price criterion. This criterion directly affects the quality of the animal skin, which is why it is rejected. Research Implications: The manager should prioritize the SDGs criteria, such as the product's significance to the organization's survival over cost and other factors, which have become secondary due to high demand. Apart from rejecting and delivery factors, management also considers responsible leather sourcing. This means that good animal care impacts the reject factor, and management prioritizes this factor. The rejection criterion must be prioritized for the production process to operate efficiently. Originality/Value: It is imperative to establish a technique for managers in the Indonesian leather sector to choose sustainability suppliers incorporating Delphi, MEREC, SWARA II, and TOPSIS.

Fermatean fuzzy distance and Sugeno–Weber operators-based SPC-MARCOS approach for sustainable supplier evaluation in the healthcare supply chain

The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the ‘FF-SPC-RS-MARCOS’ framework. The developed model firstly determines the decision experts’ weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts’ opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems.

Computational analysis of the Logistics Supply Chain from a global trade perspective

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

Toward robust decision-making under multiple evaluation scenarios with a novel fuzzy ranking approach: green supplier selection study case

In the evolving field of decision-making, the continuous advancement of technologies and methodologies drives the pursuit of more reliable tools. Decision support systems (DSS) provide information to make informed choices and multi-criteria decision analysis (MCDA) methods are an important component of defining decision models. Despite their usefulness, there are still challenges in making robust decisions in dynamic environments due to the varying performance of different MCDA methods. It creates space for the development of techniques to aggregate conflicting results. This paper introduces a fuzzy ranking approach for aggregating results from multi-criteria assessments, specifically addressing the limitations of current result aggregation techniques. Unlike conventional methods, the proposed approach represents rankings as fuzzy sets, providing detailed insights into the robustness of decision problems. The study uses green supplier selection as a case study, examining the performance of the introduced approach and the robustness of its recommendations within the sustainability field. This study offers a new methodology for aggregating results from multiple evaluation scenarios, thereby enhancing decision-maker awareness and robustness. Through comparative analysis with traditional compromise solution methods, this paper highlights the limitations of current approaches and indicates the advantages of adopting fuzzy ranking aggregation. This study significantly advances the field of decision-making by enhancing the understanding of the stability of decision outcomes.

A sustainable joint economic lot size model for supplier selection under carbon emissions: A case study

Selecting suppliers strategically is a critical challenge in supply chain design, particularly in a dynamic global marketplace where maintaining competitiveness and sustainability is essential. Supplier location is a key factor that significantly impacts supply chain sustainability. Hence, this study proposes a new Sustainable Joint Economic Lot Size (S-JELS) model that integrates average annual cost and total amount of emissions associated with annual activities related to both buyer and supplier. The developed model accounts for costs and emissions from the buyer’s purchasing, inventory management, and transportation activities, as well as from the supplier’s production, packaging, and delivery processes. Additionally, the model incorporates various transportation modes, including shipping, trucking, and rail, to more accurately assess the environmental impact of different supply chain strategies. The proposed JELS model can support managers performing sustainable purchasing decisions and assess a sustainable supplier selection. The developed model is applied in a leather case study characterized by high resource intensity, significant environmental impacts, and complex global logistics to accomplish supplier selection between one domestic and one international supplier, aiming to determine the most sustainable strategy in terms of cost and emission

Total cost analysis in purchasing education: A two-step open-ended teaching case approach

Total Cost Analysis, TCA, is an approach to handle all relevant costs in decisions concerning a choice between alternatives. Although TCA is considered important within Purchasing and SCM, there is little guidance on how to perform such analyses in a structured way. This is challenging to total cost analysts, but also to teachers wanting their students to learn TCA, especially since there is also a lack of guidance in literature on how to promote students' learning of TCA.Based on experiences from a teaching case on supplier selection used in master's level education for several years, this paper presents a two-step teaching case approach which can serve as inspiration for teaching and learning TCA in a way that promotes students to be critical and engaged learners. In the two-step procedure, students first define which cost factors are relevant for the specific situation and which data they need to perform the TCA. Second, provided with requested data, they perform the TCA and evaluate their solution's robustness.The proposed two-step model is novel, thereby adding to the case teaching literature. Moreover, the study adds to the scarce amount of research on purchasing education, as well as to the limited amount of literature on teaching and learning total cost analysis in general.

Optimizing Supplier Selection using Fuzzy MCDM

This study explores the supplier selection problem within technology manufacturing by employing the Fuzzy Analytic Hierarchy Process (FAHP). Data was collected from a technology manufacturing company, and expert evaluations were sought from professionals with over 10 years of experience in the industry. The criteria considered for supplier selection included profitability of the supplier (C1), relationship closeness (C2), technological capability (C3), conformance quality (C4), and conflict (C5). The FAHP results indicated that relationship closeness (C2) ranked highest among the criteria, highlighting its significance in the supplier selection process, while conflict (C5) received the lowest ranking. These findings emphasize the importance of nurturing strong relationships with suppliers and vendors to enhance overall supply chain performance. The proposed FAHP model effectively addresses the complexities and uncertainties inherent in supplier evaluation, offering a robust decision-making tool tailored to the specific needs of technology manufacturing companies

A New Vendor Managed Inventory for Perishable Products Considering Supplier Selection

A New Vendor Managed Inventory for Perishable Products Considering Supplier Selection

Revolutionizing Procurement: How AI & Data Analytics Driving Cost Effective Solutions in EPC

This paper, therefore, discusses the revolution brought by AI and Data Analytics in the procurement processes of EPC (Engineering, Procurement and Construction) and how each of the challenges, including cost control, enhanced efficiency and procurement downtime, could be solved using the two technologies. When the use of the procurement models is applied, the following benefits will be realized: supplier selection is facilitated using artificial intelligence, thereby automating the entire process; there is increased accuracy of costs compared to manual estimation, therefore reducing bias. The use of AI will allow better historical and real-time data analysis for procurement needs forecast, supplier evaluation and material cost prediction. Furthermore, machine learning algorithms can automate adjustments depending on real-time market variability, such for instance, a change in the price of the materials or a disruption in the supply chain, as well as execution flexibility regarding the project needs. Data Analytics also adds more weightage to procurement efficiency because it gives detailed analysis of the past and present procurement data. By utilizing state-of-the-art analytical frameworks, top managements are allowed to make powerful supplier analyses in relation to reliability, lead time, and cost of contracts prior to negotiations on contract supplier contracts and before contracting risks. What this study aims to do is to highlight real-life implementations of these technologies and how EPC firms can capitalize on these technologies to drive procurement efficiency, cost reduction, project delivery, and supplier relationship all for efficient project delivery. This paper thus provides a detailed discussion on the MI and TI of implementing AI and DA within procurement in the EPC sector via case studies and examples.

Analytical Hierarchy Process Method as a Solution to Fiber Optic Cable Supplier Quality

PT Prima Akses Solusi Global faces challenges in selecting fiber optic cable suppliers due to a subjective evaluation process focused mainly on price and product quality. Other factors, such as delivery, service, and payment, are also crucial. To improve effectiveness, this study uses the Analytical Hierarchy Process (AHP) for a more objective approach. AHP evaluates criteria like delivery, quality, service, price, and payment. Data from questionnaires is processed using pairwise comparison weighting. The results indicate that delivery and quality are top priorities, with PT Fiberhome ranking highest (0.417), followed by PT Voksel (0.323), and PT Furukawa (0.260). The AHP method effectively enhances decision-making accuracy in supplier selection. This study recommends ongoing AHP implementation, although it is limited to fiber optic cable supplier evaluation at PT Prima Akses Solusi Global.

A Comprehensive Approach for Selecting Third-Party Logistics Providers

The finest third-party logistics (3PL) supplier is a vital decision for businesses trying to streamline their supply chain operations. In order to free up time for businesses to concentrate on their core skills, third-party logistics providers offer a variety of services, such as distribution management, warehousing, and transportation. However, selecting the best 3PL supplier necessitates a rigorous assessment of variables including sector expertise, technical prowess, service quality, scalability, and cost-effectiveness. This introduction discusses the significance of choosing the best 3PL supplier and emphasizes important factors to take into account when making a choice. It is essential since there is a chance that third-party logistics (3PL) provider selection study may boost the supply chain's effectiveness and competitiveness. Firms may make selections that are tailored to their unique requirements by recognizing and assessing the elements that affect the choosing process. Proper 3PL selection may boost market response, save costs, expand the capacity of operations, and improve customer happiness. As a result, research in this field helps to create best practices and strategies that enhance supply chain management and promote entrepreneurship. A multi-criteria decision-making approach called The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank and choose the best option from a list of options. To determine how closely each alternative resembles the ideal answer, TOPSIS takes into account both positive and negative features. Based on the greatest distance from the inverse idealistic and the lowest distance to the positive ideal, the outcome score is calculated. This approach facilitates decision-making processes in a variety of contexts, such as project prioritization, supplier selection, and investment appraisal. Alternative parameters taken as 3PL-1, 3PL-2, 3PL-3, 3PL-4, 3PL-5. Evaluation parameters taken as Delivery Service (%), Quality of the Customer Experience (Quos), Coverage of the EU Territories (%), Flexibility, and Cost (EUR/km). From the result it is seen that 3PL-2 secures the first rank which is acquired by the TOPSIS method. To secure the first place on the table consistency will be the key factor which was maintained by 3PL-2

Influence of Tendering Process on Performance of County Governments in Kenya

Procurement is essential to every organization, serving as a platform for all stakeholders to come together and discuss their needs and goals related to purchasing. The study aimed to explore factors influencing the tendering processes of County Governments in Kenya, focusing specifically on Siaya County Government. The study was guided by three objectives, to examine supplier selection criteria on performance of county Governments in Kenya, to establish the influence of Ethical Practices in Tendering processes on performance of county Governments in Kenya, to investigate the influence of automation processes of Tendering processes on performance of county Governments in Kenya, and to establish moderating effect of government policy on Public procurement And Disposal Act on performance of County Governments in Kenya. The research utilized a Descriptive Research Design approach to gather pertinent information. The study involved Census Sampling in which all 74 participants from different management levels and ad hoc committee members within the department. Questionnaires were used as the main data collection instruments. Data was gathered through the distribution of questionnaires to the chosen participants by means of pick and drop. respondents of 66% were received and were completed accurately, demonstrating an adequate response rate. The statements of the problem mainly focused on major issues facing the tendering procedures and analyzes their influence on organizational performance. The study findings show that Supplier selection criteria Mean of 4.002 and standard Deviation of 0.1428 the highest score amongst the key performance indicators was quality with mean of 4.29 and standard deviation of 0.097 showing highly significant to supplier selection criteria. Ethical practices in Tendering Mean of 4.012 Standard deviation of 0.097, of highest score in key performance indicators was Integrity with mean of 4.468 and standard deviation of 0.097. its shows that highly significant to Ethical practices, Automation of Tendering mean of 4.093 standard deviation of 0.148 highest amongst the key performance indicators was E informing with mean 4.224 and standard deviation 0. 147 shows highly significant to Automation. Moderating variable Mean of 4.094 standard deviation of 0.136 highest score among st key performance indicators of quality 4.306 standard deviation of 0.114 shows highly significant to Moderating variable and all the variables were critical to the performance of County Governments in Kenya. The study recommends that there is need to enormously implement procurement policies which encourage tendering, in order to enhance performance

Enhancing Economic, Resilient, and Sustainable Outcomes Through Supplier Selection and Order Allocation in the Food Manufacturing Industry: A Hybrid Delphi-FAHP-FMOP Method

In the food manufacturing industry, which is critical to national economies, there is a growing imperative to meet heightened safety, quality, and environmental standards, particularly in the face of supply chain disruptions. This study addresses the gap in literature by integrating sustainable and resilient supply chain theories with risk management and low-carbon principles into a supplier selection framework. Utilizing the Delphi method, fuzzy analytic hierarchy process (FAHP), and fuzzy multi-objective programming (FMOP), we develop a decision-making model specifically calibrated for the food sector. Initially, the study establishes a comprehensive criteria system encompassing quality, cost, delivery, low-carbon, and risk management through a literature review and expert consultation. Subsequently, FAHP is employed to determine the relative importance of each criterion in supplier selection. Furthermore, FMOP is utilized to develop a decision-making model for optimizing supplier selection and order allocation. Validated through a numerical study based on a Chinese food manufacturer, the framework presents a practical tool for food manufacturers, ensuring supply chain stability while aligning with sustainability objectives. This research refines decision making and strengthens the competitive stance of food manufacturers, significantly propelling the industry’s green transformation.

Sustainable supplier selection among supermarket’s fresh fruits and vegetable supply chains based on circular practices in India

Sustainable supplier selection among supermarket’s fresh fruits and vegetable supply chains based on circular practices in India

Leveraging Machine Learning for Advancing Circular Supply Chains: A Systematic Literature Review

Background: Circular supply chains (CSCs) aim to minimize waste, extend product lifecycles, and optimize resource efficiency, aligning with the growing demand for sustainable practices. Machine learning (ML) can potentially enhance CSCs by improving resource management, optimizing processes, and addressing complexities inherent in CSCs. ML can be a powerful tool to support CSC operations by offering data-driven insights and enhancing decision-making capabilities. Methods: This paper conducts a systematic literature review, analyzing 66 relevant studies to examine the role of ML across various stages of CSCs, from supply and manufacturing to waste management. Results: The findings reveal that ML contributes significantly to CSC performance, improving supplier selection, operational optimization, and waste reduction. ML-driven approaches in manufacturing, consumer behavior forecasting, logistics, and waste management enable companies to optimize resources and minimize waste. Integrating ML with emerging technologies such as IoT, blockchain, and computer vision further enhances CSC operations, fostering transparency and automation. Conclusions: ML applications in CSCs align with broader sustainability goals, contributing to environmental, social, and economic sustainability. The review identifies opportunities for future research, such as the development of real-world case studies further to enhance the effects of ML on CSC efficiency.

Similarity Measures of Probabilistic Interval Preference Ordering Sets and Their Applications in Decision-Making

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics.

Optimizing purchasing procedures for procurement at serenity spa and salon of Hotel LMB

This study investigates the purchasing procedures for the procurement of goods required by Serenity Spa and Salon at Hotel LMB. The research aims to identify inefficiencies and suggest improvements for a more streamlined process. Data was collected through a combination of qualitative interviews, observation, and document study with key personnel and quantitative analysis of procurement records over the past year. The findings highlight several areas where current practices fall short, particularly in supplier selection and inventory management. Recommendations include adopting a more rigorous supplier evaluation framework and implementing an automated inventory tracking system. These changes are expected to enhance operational efficiency, reduce costs, and ensure timely availability of high-quality products. The study concludes with a discussion of the potential impact on overall service quality at Serenity Spa and Salon.