A New Integrated Multi-Criteria Decision Making and Multi-Objective Programming Model for Sustainable Supplier Selection and Order Allocation

Assessing the impact of blockchain technology on the overall performance of sustainable supply chains: an analytical perspective

Supply chain control and sustainability can be significantly improved using distributed ledger technologies such as blockchain. The blockchain has the potential to facilitate responsible sourcing appropriately, compliance with weather requirements, and sustainable delivery chains. The purpose of this study is to address the hassle of managing conservatism when approaching era adoption and to explore the performance enhancements in blockchain-generated implementations. To achieve this goal, we introduce a scientific approach aimed at studying the outcomes of various factors in the adoption process in the blockchain era and verifying their impact on the overall performance of the delivery chain. Furthermore, a team of multidisciplinary professionals will establish causal relationships among these elements through a consensus-based approach. Ultimately, fuzzy reasoning tools can be used to determine the relative weights between identified factors and delivery chain performance goals. We will assemble causal representations of diagnoses using a dense scientific map model and dynamically generate scenarios for each. The study demonstrates that the integration of blockchain power generation can significantly improve the effectiveness of mineral supply chains. It uses smart contracts to promote environmental sustainability, traceability, and transparency.

A model for green-resilient supplier selection: fuzzy best-worst multi-criteria decision-making method and its applications

Supplier selection is regarded as the primary goal of supply chain management (SCM) because it affects its performance, productivity, pleasure, flexibility, and system speed in lockdown. A new method is proposed based on a multi-stage fuzzy sustainable supplier index (FSSI). Experts can use the triple bottom line (TBL) criteria to select the best supplier. In addition, the worst method is proposed based on trapezoidal membership and fuzzy membership functions, which can cover uncertainties and ambiguous environments. Because it collects the related criteria and sub-criteria and uses a direct fuzzy methodology, this research has impacted the SCM literature because it helps solve previous expert methods' computational difficulties. In addition, an ordered mean integration representation method has been implemented to prioritize the selection of the best supplier (SS) based on the sustainability performance of the best supplier, which improves the selection accuracy compared to the previous ranking method. This study can be used as a benchmark to determine which supplier is the best in sustainability. To provide the superiority and broad applicability of the proposed model, a practical case study was completed. On the other hand, the COVID-19 pandemic harms productivity, company performance, and selecting the best suppliers based on sustainability performance. The lockdown situation caused by the COVID-19 pandemic hurts company performance and management.

Sustainable supplier selection using HF-DEA-FOCUM-MABAC technique: a case study in the Auto-making industry

The assessment of sustainable supplier is very significant for supply chain management (SCM). The procedure of sustainable supplier selection (SSS) is a complex process for decision experts (DEs) due to the association of diverse qualitative and quantitative attributes. As the uncertainty is usually ensued in the SSS and hesitant fuzzy set (HFS), an extension of fuzzy set (FS) has been demonstrated as one of the effective ways to treat the uncertain information in realistic problems. The objective of this paper is to propose an integrated hesitant fuzzy–data envelopment analysis (DEA)–full consistency method (FOCUM)–multi attribute border approximation area comparison (MABAC) method called HF-DEA-FUCOM-MABAC framework to assess the multi-attribute decision-making (MADM) problems on HFSs settings. In this line, first, the efficient alternatives are chosen using the DEA method. Second, The FUCOM is used to compute the subjective weight of attributes. Third, The HF-MABAC method is presented to prioritize the alternatives in an MADM problem. In the following, a case study of SSS problem for an Auto-making company is taken to show the practicality and utility of the presented approach. Next, we present a sensitivity investigation with different attribute weights set to observe the steadiness of the presented approach. Finally, we draw attention toward a comparison between presented approach with the extant HF-FOCUM-TOPSIS model to show its advantage and potency as well.

Application of Interval Fuzzy Logic in Selecting a Sustainable Supplier on the Example of Agricultural Production

The selection of sustainable suppliers (SSS) is the first step in applying a sustainable supply chain and sustainable production. Therefore, it is necessary to select the supplier that best meets the set sustainability criteria. However, the selection of suppliers cannot be done by applying symmetric information, because the company does not have complete information, so asymmetric information should be used when selecting suppliers. Since the SSS applies three main sustainability criteria, environmental, social, and economic criteria, this decision-making problem is solved by applying multi-criteria decision-making (MCDM). In order to solve the SSS for the needs of agricultural production, interval fuzzy logic was applied in this research, and six suppliers with whom agricultural pharmacies in Semberija work were taken into consideration. The application of interval fuzzy logic was performed using the methods PIPRECIA (Pivot pairwise relative criteria importance assessment) and MABAC (Multi-Attributive Border Approximation Area Comparison). Using the PIPRECIA method, the weights of criteria and sub-criteria were determined. Results of this method showed that the most significant are economic criteria, followed by the social criteria. The ecological criteria are the least important. The supplier ranking was performed using the MABAC method. The results showed that supplier A4 best meets the sustainability criteria, while supplier A6 is the worst. These results were confirmed using other MCDM methods, followed by the sensitivity analysis. According to the attained results, agricultural producers from Semberija should buy the most products from suppliers A4, in order to better apply sustainability in production. This paper showed how to decision make when there is asymmetric information about suppliers.

Hesitant Fuzzy SWARA-Complex Proportional Assessment Approach for Sustainable Supplier Selection (HF-SWARA-COPRAS)

The selection of sustainable supplier is an extremely important for sustainable supply chain management (SSCM). The assessment process of sustainable supplier selection is a complicated task for decision experts due to involvement of several qualitative and quantitative criteria. As the uncertainty is commonly occurred in sustainable supplier selection problem and hesitant fuzzy set (HFS), an improvement of Fuzzy Set (FS), has been proved as one of the efficient and superior ways to express the uncertain information arisen in practical problems. The present study proposes a novel framework based on COPRAS (Complex Proportional Assessment) method and SWARA (Step-wise Weight Assessment Ratio Analysis) approach to evaluate and select the desirable sustainable supplier within the HFSs context. In the proposed method, an extended SWARA method is employed for determining the criteria weights based on experts’ preferences. Next, to illustrate the efficiency and practicability of the proposed methodology, an empirical case study of sustainable supplier selection problem is taken under Hesitant Fuzzy (HF) environment. Further, sensitivity analysis is performed to check the stability of the presented methodology. At last, a comparison with existing methods is conducted to verify the strength of the obtained result. The final outcomes confirm that the developed framework is more consistent and powerful than other existing approaches.

Evaluation of Process Orientation Dimensions in the Apparel Industry

The dimensions that influence the establishment of business process management (BPM) practices and the progression to higher levels of process maturity derive from exploring the dimensions of process orientation of organizations. Small and medium-sized clothing enterprises (SME’s) are characterized by various specifics that can affect the degree of process orientation adoption and the pace of transition from lower to higher levels of process maturity. According to these specifics, the acceptance of the process approach may be differently affected. For the purpose of adequate evaluation and prioritization of the most influential dimensions, a new integrated multicriteria decision-making (MCDM) model that combines classical and fuzzy theory was developed. First, the full consistency method (FUCOM) method was applied, followed by the fuzzy pivot pairwise relative criteria importance assessment (fuzzy PIPRECIA) method to obtain more accurate criteria values. Prioritization of the most influential BPM dimension contributes to highlighting the area of business that needs to be primarily strengthened by appropriate actions for successful establishment of BPM in apparel industry SMEs. Within this research, the prioritized dimension refers to human resource management in accordance with the specific aspects of business within the apparel industry.