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Research interests in machine learning (ML) and supply chain management (SCM) have yielded an enormous amount of publications during the last two decades. However, in the literature, there was no systematic examination on the research development in the discipline of ML application, in particular in SCM. Therefore, this study was carried out to present the latest research trends in the discipline by analyzing the publications between 1998/01/01 and 2018/12/31 in five major databases. The quantitative analysis of 123 shortlisted articles showed that ML applications in SCM were still in a developmental stage since there were not enough high-yielding authors to form a strong group force in the research of ML applications in SCM and their publications were still at a low level; even though 10 ML algorithms were found to be frequently used in SCM, the use of these algorithms were unevenly distributed across the SCM activities most frequently reported in the articles of the literature. The aim of this study is to provide a comprehensive view of ML applications in SCM, working as a reference for future research directions for SCM researchers and application insight for SCM practitioners.

Purpose – In this era of globalisation, as competition intensifies, providing quality products and services has become a competitive advantage and a need to ensure survival. The Six Sigma's problem-solving methodology DMAIC has been one of the several techniques used by organisations to improve the quality of their products and services. This paper aims to demonstrate the empirical application of Six Sigma and DMAIC to reduce product defects within a rubber gloves manufacturing organisation. Design/methodology/approach – The paper follows the DMAIC methodology to systematically investigate the root cause of defects and provide a solution to reduce/eliminate them. In particular, the design of experiments, hypothesis testing and two-way analysis of variance techniques were combined to statistically determine whether two key process variables, oven's temperature and conveyor's speed, had an impact on the number of defects produced, as well as to define their optimum values needed to reduce/eliminate the defects. Findings – The analysis from employing Six Sigma and DMAIC indicated that the oven's temperature and conveyor's speed influenced the amount of defective gloves produced. After optimising these two process variables, a reduction of about 50 per cent in the “leaking” gloves defect was achieved, which helped the organisation studied to reduce its defects per million opportunities from 195,095 to 83,750 and thus improve its sigma level from 2.4 to 2.9. Practical implications – This paper can be used as a guiding reference for managers and engineers to undertake specific process improvement projects, in their organisations, similar to the one presented in this paper. Originality/value – This study presents an industrial case which demonstrates how the application of Six Sigma and DMAIC can help manufacturing organisations to achieve quality improvements in their processes and thus contribute to their search for process excellence.

PurposeThe purpose of this study is to identify and prioritize the factors that can positively influence the implementation of a blockchain-based supply chain via an integrated framework. To the best of the authors' knowledge, no previous study has focused on prioritizing these factors.Design/methodology/approachFirst, this study conducts a multivocal literature review, and a total of 48 success factors (SFs) are identified and mapped into 11 categories. Second, the identified success factors and their categories are further validated by industry practitioners using a questionnaire survey approach. Finally, this study applies an analytical hierarchy process to prioritize the identified SFs and their categories and to assess their importance for successful blockchain implementation in the supply chain management process.FindingsThe “Accessibility” category has the highest importance, and the “Overall efficiency” category has the second highest rank. As far as the success factors are concerned, “Trackability” and “Traceability” are considered to be the prime success factors of a blockchain-based supply chain. The taxonomy of the categories and their success factors provide an outline for supply chain organizations to establish a strategy to implement blockchain technology.Practical implicationsThis technology can be practically applied in a sustainable supply chain. Another vital application of this blockchain technology is in banking and finance because of the blockchain's immutable data recording property.Originality/valueTo the best of the authors' knowledge, there is no previous study focused on building a taxonomic model that allows supply chain organizations to compare this paper's model with existing models and outline the necessary actions to improve supply chain activities. The questionnaire-based survey developed to validate the success factors in real-world practices and the factors' prioritization can help academic researchers and industrial practitioners to set their strategic goals accordingly.

This study explores the relationship among stakeholders, green manufacturing, and practice performance in the fashion business in China and focuses on assisting companies to enhance environmental awareness and green manufacturing practices. We collect research data by developing questionnaires for various Chinese enterprises. A five-point Likert scale is adopted to enable respondents to indicate the extent to which they agree with the items. Through tests and analyses, the questionnaire is validated as reliable, the structural equation model has a good fitting degree, and hypotheses are proved true. Specifically, corporate stakeholders have a significant positive impact on green manufacturing and practice performance, and green manufacturing has a significant positive impact on practice performance in the context of Chinese fashion businesses. Moreover, corporate stakeholders can have a positive impact on practice performance through green manufacturing. We also propose some policy implications, including implementing compulsive policies and regulations and encouraging and establishing preferential policies, such as tax concessions. Moreover, enterprises should actively strive to improve green manufacturing technology and management level to ensure the smooth implementation of green manufacturing practices. To retain sustained earnings and development, green manufacturing should be the bottom line of involved firms. We also emphasize that the importance of corporate stakeholders should be promoted in consideration of enterprises’ practice performance and future development.

PurposeWith the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.Design/methodology/approachThis research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.FindingsThe results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.Research limitations/implicationsThe data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.Originality/valuePrior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

Prior studies are lacking which address permutation flow shop scheduling problems and hybrid flow shop scheduling problems together to help firms find the optimized scheduling strategy. The permutation flow shop scheduling problem and hybrid flow shop scheduling problems are important production scheduling types, which widely exist in industrial production fields. This study aimed to acquire the best scheduling strategy for making production plans. An improved fireworks algorithm is proposed to minimize the makespan in the proposed strategies. The proposed improved fireworks algorithm is compared with the fireworks algorithm, and the improvement strategies include the following: (1) A nonlinear radius is introduced and the minimum explosion amplitude is checked to avoid the waste of optimal fireworks; (2) The original Gaussian mutation operator is replaced by a hybrid operator that combines Cauchy and Gaussian mutation to improve the search ability; and (3) An elite group selection strategy is adopted to reduce the computing costs. Two instances from the permutation flow shop scheduling problem and hybrid flow shop scheduling problems were used to evaluate the improved fireworks algorithm’s performance, and the computational results demonstrate the improved fireworks algorithm’s superiority.

Purpose This paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions. Design/methodology/approach This study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case. Findings The results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning. Research limitations/implications There are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions. Originality/value Prior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.

Machine learning (ML) algorithms, such as neural networks, random forest, and more recent deep learning, are illustrating their utility for waste recycling. The increasing computational power of ML makes waste generation prediction, even at municipal level, possible with satisfying accuracy. ML is so critical and efficient and yet it is severely under-researched in recycling business. Also, the ML application in the recycling business is still a niche area judged by the limitations in its literature sources, the research domains, the ML algorithms’ use and benefits involved or reported in the literature. To unlock the value of ML in recycling business, this paper reviewed 51 related articles systematically and presents the current obstacles and future directions in applying ML to waste recycling industries.

PurposeThis paper studies low-carbon vehicle routing problem (VRP) for cold chain logistics with the consideration of the complexity of the road network and the time-varying traffic conditions, and then a low-carbon cold chain logistics routing optimization model was proposed. The purpose of this paper is to minimize the carbon emission and distribution cost, which includes vehicle operation cost, product freshness cost, quality loss cost, penalty cost and transportation cost.Design/methodology/approachThis study proposed a mathematical optimization model, considering the distribution cost and carbon emission. The improved Nondominated Sorting Genetic Algorithm II algorithm was used to solve the model to obtain the Pareto frontal solution set.FindingsThe result of this study showed that this model can more accurately assess distribution costs and carbon emissions than those do not take real-time traffic conditions in the actual road network into account and provided guidance for cold chain logistics companies to choose a distribution strategy and for the government to develop a carbon tax.Research limitations/implicationsThere are some limitations in the proposed model. This study assumes that there are only one distribution and a single type of vehicle.Originality/valueExisting research on low-carbon VRP for cold chain logistics ignores the complexity of the road network and the time-varying traffic conditions, resulting in nonmeaningful planned distribution routes and furthermore low carbon cannot be discussed. This study takes the complexity of the road network and the time-varying traffic conditions into account, describing the distribution costs and carbon emissions accurately and providing the necessary prerequisites for achieving low carbon.

PurposeThis paper aims to provide a comprehensive understanding of the supply chain learning (SCL)–performance relationship based on the existing empirical evidence.Design/methodology/approachWe sampled 54 empirical studies on the SCL–performance relationship. We proposed a conceptual research framework and adopted a meta-analytical approach to analyse the SCL–performance relationship.FindingsThe results of the meta-analysis confirm the positive effects of SCL on the performance of both firms and supply chains. In addition, building on the knowledge-based view, we found that learning from customers has a stronger positive effect on performance than does learning from suppliers, while joint learning has a stronger positive effect on performance than does absorptive learning. Business knowledge had a greater effect on performance than did general knowledge, process knowledge or technical knowledge, while explicit knowledge had a stronger effect than tacit knowledge. Moreover, the SCL–performance relationship is moderated by performance measure and industry type but not by regional economic development, highlighting the broad applicability of SCL.Originality/valueThis study is the first meta-analysis on the SCL–performance relationship. It differentiates between learning from customers and learning from suppliers, examines a more comprehensive list of performance measures and tests five moderators to the main effect, significantly contributing to the SCL literature.