In today’s fast-paced global economy coupled with the availability of mobile internet and social networks, several business models have been disrupted. This disruption brings a whole list of opportunities and challenges for organizations and the domain of supply chain management. Given big data availability, data analytics is needed to convert data into meaningful information, which plays an important role in supply chain management. One of the disruptive data analytics techniques that are predicted to impact growth, employment, and inequality in the market is automation of knowledge work, better known as machine learning. In this paper, we focused on comprehensively overviewing machine learning applications in demand forecasting and underlying its potential role in improving the supply chain efficiency. A total of 1870 papers were retrieved from Scopus and Web of Science databases based on our string query related to machine learning. A reduced total of 79 papers focusing on demand forecasting were comprehensively reviewed and used for the analysis in this study. The result showed that neural networks, artificial neural networks, support vector regression, and support vector machine were among the most widely used algorithms in demand forecasting with 27%, 22%, 18%, and 10%, respectively. This accounted for 77% of the total reviewed articles. Most of the machine learning application (65%) was applied in the industry sector, and a limited number of articles (5%) discussed the agriculture sector. This paper's practical implication is in exposing the current machine learning issues in the industry to help stakeholders and decision-makers better plan transformation actions.

Culture of Quality (COQ) is regarded as an important component of total quality management (TQM) however this is a relatively new emerging theme, compared to other concepts in the quality management domain. As a result, literature resources on this topic are relatively scarce and there is a lack of empirical validation of the COQ framework. This study therefore attempts to fill this research gap and aims to empirically investigate the dimensions of the COQ and their impact on organisational and employee performance. The study also explores the interrelationship between each dimension of COQ. A set of hypotheses are proposed and empirically tested based on the 120 survey responses mostly from the Asian region. The survey data was analysed using SPSS through descriptive analysis, reliability analysis, correlation analysis and regression analysis. Findings show that COQ dimensions; leadership emphasis, message credibility, peer involvement and employee ownership encourage better employee performance. The study further suggests that organisations should work on ensuring supervision from top to bottom, accelerating information flow, creating autonomous working environment and getting staff involved in strategic management. In addition, findings show that COQ factors also interact with each other in varying degrees. The study therefore addresses an important research gap by empirically investigating the COQ dimensions and suggesting that from an employee perspective, organizational performance can be accelerated through quality culture management.

This paper presents an integrated supply chain inventory model for deteriorating items with an imperfect quality considering its environmental impact, particularly the supply chain carbon footprint. An imperfect production system produces a certain number of defective items. Therefore, in our model, the manufacturer conducts a 100% quality check to prevent the delivery of defective items. A third-party logistics (3PL) company supports the logistics between the manufacturer and the buyer, by transporting the products from the manufacturer to a warehouse and then delivering the products in a smaller quantity to the buyer. The proposed solution procedure determines the number of deliveries per cycle, delivery interval, and delivery quantity between the 3PL and the buyer simultaneously. It also determines the production quantity of the manufacturer and the delivery quantity from the manufacturer to the 3PL. The objective is to minimize the expected total cost and to reduce total carbon emissions.

The aim of this research study is to look for possible re-search opportunities to applying blockchain technology in supply chain management and logistics. In addition, accom-panying challenges to utilizing blockchain in supply chain management along with possible solutions are also provided. To fulfil the study objective, both theoretical and empirical approaches are adopted for this study. With respect to theo-retical approach, relevant literature on blockchain was re-viewed considering both technical and economic aspects, its architecture and implementation challenges. The empirical part of the research was conducted by studying three case companies operating in the domains of wood construction, consulting and regional development, and technology. Three case companies were analysed with respect to the application of blockchain in their supply chain operations. From the study outcomes, it was noticed that blockchain technology can be utilized successfully in supply chain management in vari-ous business domains in order to provide, for instance, better services and transparency.

The frequency of fire disasters is fortunately low, but planning the logistics and related routings for rescue, relief, and rehabilitations operations are major issues in launching any humanitarian assistance. In addition to the logistics planning issues, the reasons for the occurrence of the fire disasters should also be determined so that measures may be put in place to prevent future disasters as well as any potential consequences that may occur after the fire is extinguished. Most cities have efficient fire departments quipped with resources and fire brigades to initiate immediate measures to control the fire and to start the rescue operation. Moreover, most governments take steps to provide relief and rehabilitation assistance to the affected population. Rescue, relief, and rehabilitation steps in traffic-congested cities, especially in heavily populated areas with many businesses and markets, are highly challenging. This research proposes a mathematical modeling-based approach for planning the transportation of relief and rescue resources; conducting relief and rescue operations; and outlining measures to prevent future recurrences. The model will be illustrated using the chemical explosion-fed fire which occurred on February 20, 2019 in the old part of Dhaka in Bangladesh.

This study develops a global production–shipping planning model that incorporates a decision on transportation mode after considering the cost function of different transportation modes from the shipper’s point of view. We propose tramp shipping, liner shipping, and a mixed mode to select the optimal shipping mode, considering transshipment and consolidation to exploit economies of scale under a given network. We present mathematical formulations of tramp, liner and mixed modes and apply a piecewise approximation technique with mixed-linear integer programming to linearly approximate the concave minimization problem and efficiently solve it using an off-the-shelf solver. In order to verify the effectiveness of the proposed modes, the three modes are compared and discussed in numerical examples. The advantages and disadvantage of the three modes are discussed under different situations. The result is a decision aided system to support production–shipping planning in selecting the optimal transportation mode.

This study deals with the green supply chain evaluation for organic products. The authors present the development of the market for such products and the latest trends in world markets in connection with the creation and implementation of a new business strategy relating to the implementation of environmental aspects. The aim of the discussion is to indicate the theoretical basis for the construction of the green supply chain, with particular emphasis on the specificity of organic products. Theoretical considerations are accompanied by the results of empirical research, which indicate in which areas and in the scope of implementation of which management tools there are significant differences between enterprises and what may be the reasons for this. The novelty and value of this study lie in the reference to the principles and elements of the green chain to ecological products and the indication why a holistic approach to environmental protection should be promoted (i.e., production and the whole supply chain promoting the principles of sustainable development). The analysis of literature, methods of descriptive and mathematical statistics and ANOVA (analysis of variance) have been used in this study.

This research studies the effects of preventive maintenance and replacement activities on operational costs, overall reliability, and availability of a multi-tasking manufacturing machine. A multi-objective optimization model to find Pareto-optimal preventive maintenance and replacement schedules for a repairable multi-component machine with increasing failure rate is developed. The planning horizon is divided into equally-sized periods in which minimal repairs, full replacement, or do-nothing actions can be performed on each machine’s component. The machine reliability for preventive maintenance aspects, its availability for production purposes, and total operational costs for both preventive maintenance and manufacturing planning are formulated as the objective functions and the multi-objective model is solved using a simulation-based optimization algorithm in real case study. It is shown that the developed mathematical models and the solution method can effectively generate Pareto-optimal preventive maintenance schedules that can be integrated into aggregate production plans.

The purpose of this study is to identify key retail store attributes that impact customer satisfaction. The conceptual model is developed with the help of extant literature and expert opinion. The study analyzes the relationship among the store attributes, customer satisfaction, advocacy, and repurchase intention to provide operational insights to retail store managers concerning key store attributes. In particular, jewellery stores in India are selected for this research investigation. The data from jewellery stores and customers located in the Gwalior district is collected using the convenience sampling. The partial least squares-structural equation modeling (PLS-SEM) method is used for model validation and data analysis. The results reveal that the store-related attributes, product-related attributes, and service quality-related attributes have a positive influence over customer satisfaction. In these three attribute categories, the most sought factors by consumers were store layout & ambience, product durability, and overall assistance, respectively. Further, the customer satisfaction is found to affect advocacy and repurchase intention. The study suggests that jewellery store managers should focus on managing the select store attributes identified in this study to improve customer satisfaction, advocacy, and repurchase intentions. Though the study is conducted in the context of Indian jewellery stores, the insights are equally useful and explorable in other retail stores situated at diverse locations.

Despite there are some arguments about blockchain, it has been highlighted as an important distributed secure technology in the 21st century. It is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value. Dobrovnik et al. (2018) suggest that blockchain is a revolutionising technology that would change industries at an international level, add values to firms and supply chain networks, improve commerce, and drive economy. Although blockchain has attracted attentions, very few blockchain studies have been focussed on supply chain integration and collaboration areas. This study illustrates the possibilities of applying blockchain technology in the coordination of activities for effective and efficient supply chain management. The study takes a closer look at the use of blockchain in supply chains beyond cryptocurrency, payment, and finance via the use of smart contract and consensus algorithm (i.e. imposing constraints). The key attributes of blockchain are discussed and potential questions were identified in New Zealand. The expected outcome of this study will advance the understanding of the blockchain and supply chain literature, besides inspire both researchers and practitioners to consider the use of blockchain in different context-aware future studies.