No abstract

Purpose – Third-party logistics (3PL) plays a main role in supply chain management and, as a result, has experienced remarkable growth. The demand for 3PL providers has become a main approach for companies to offer better customer service, reduce costs, and gain competitive advantage. This paper identifies important criteria for 3PL provider selection and evaluation, and the purpose of this paper is to select 3PL providers from the viewpoint of firms which were already outsourcing their logistics services. Design/methodology/approach – This study utilized the grey decision-making trial and evaluation laboratory (DEMATEL) method to develop 3PL provider selection criteria. Because human judgments are vague and complicated to depict by accurate numerical values, the grey system theory is used to handle this problem. Findings – The findings revealed the structure and interrelationships between criteria and identified the main criteria for 3PL provider selection. The most important criteria for 3PL provider selection are on time delivery performance, technological capability, financial stability, human resource policies, service quality, and customer service, respectively. Practical implications – The paper’s results help managers of automotive industries, particularly in developing countries, to outsource logistics activities to 3PL providers effectively and to create a significant competitive advantage. Originality/value – The main contributions of this paper are twofold. First, this paper proposes an integrated grey DEMATEL method to consider interdependent relationships among the 3PL provider selection criteria. Second, this study is one of the first studies to consider 3PL provider selection in a developing country like Iran.

Environmental issues have been worldwide matters of concern especially in the recent decade and have made many firms implement end-of-life strategies such as remanufacturing. In prior studies, the supply side of remanufacturing supply chain has been vastly brought into focus compared to the demand side. Motivational factors that encourage consumers to purchase remanufactured products are getting firms attentions in developing effective marketing strategies to assist them being more productive in the current competitive market. However, consumer acceptance of remanufactured products has been regarded as one of the main reasons why remanufacturing has remained a majorly untapped opportunity for improving supply chain productivity. This study aims at exploring the major motivational factors for buying a remanufactured bike based on the consumers' and experts' opinions. Firstly, twelve motivations identified by scrutinising the literature. Secondly, single valued trapezoidal neutrosophic numbers (SVTNN) and trapezoidal neutrosophic weighted arithmetic averaging (TNWAA) operator were employed to obtain seven significant motivations using the survey data collected from potential customers. This method is applied owing to its capability in capturing the uncertainty of consumers' subjective judgements. Thirdly, the resulted seven motivations are prioritised in accordance with the experts' judgements utilising a proposed modified fuzzy Delphi (FD) method. Ultimately, the most significant motivation to purchase a remanufactured bike identified as quality that suggests quality is the major factor affecting purchase decision of a remanufactured bike. It indicates remanufacturers should focus on quality and attempt to improve the quality of products to gain more competitive advantage. The other six factors that should be stressed by remanufacturer's marketing strategies are prioritised as warranty, price, information provision, remanufacturer's reputation, value-added services and retailer's reputation respectively.

Konstantinos (2021) Analyzing blockchain adoption barriers in manufacturing supply chains by the neutrosophic analytic hierarchy process. Annals of Operations Research.

The best-worst method (BWM) is a multiple criteria decision-making (MCDM) method for evaluating a set of alternatives based on a set of decision criteria where two vectors of pairwise comparisons are used to calculate the importance weight of decision criteria. The BWM is an efficient and mathematically sound method used to solve a wide range of MCDM problems by reducing the number of pairwise comparisons and identifying the inconsistencies derived from the comparison process. In spite of its simplicity and efficiency, the BWM does not consider the decision-makers' (DMs') confidence in their pairwise comparisons. We propose a neutrosophic enhancement to the original BWM by introducing two new parameters as the DMs' confidence in the best-to-others preferences and the DMs' confidence in the others-to-worst preferences. We present two real-world cases to illustrate the applicability of the proposed neutrosophic enhanced BWM (NE-BWM) by considering confidence rating levels of the DMs.