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In this discussion paper, we aim to examine the key issues of logistics management in the Industry 4.0 era by addressing various major challenges. To be specific, we first establish an amended definition of logistics management, highlighting the new elements of cash flow, in both the cyber and physical worlds. We then review the highly relevant selected literature and propose a framework for future logistics management. Our proposed framework includes five critical areas, namely Industry 5.0, Joint-venture, Legal-concerns, Risk management, Automation and artificial intelligence (AI), and hence we call it the 'IJLRA' framework. For each area, we identify the major challenges that deserve further analyses. We believe that with proper logistics management under the IJLRA framework, organisations in the supply chain can achieve the crucial goals of logistics – achieving the right time, right place, right quality, and everything right – to satisfy customer needs. Finally, we also propose a future research agenda for logistics management. [ABSTRACT FROM AUTHOR]

Capital constraints are one of the major challenges for the supplier to invest in activities to enhance environmental and social responsibility (ESR) in production. Responsible buyer direct financing has emerged as an important solution to motivate supplier ESR by pegging the interest rate to the supplier's ESR level. Motivated by real practices, we develop game‐theoretical models to explore the value of conducting blockchain‐assisted due diligence to inspect the borrowing supplier's ESR qualification in a two‐tier supply chain structure where the downstream buyer directly finances the capital‐constrained supplier. Conventional wisdom tells us that driving greater supplier transparency (e.g., conducting blockchain‐assisted due diligence) will motivate the supplier to strengthen environmental and social compliance. However, we identify the necessary and sufficient "backfiring condition" under which the blockchain‐assisted due diligence may "backfire" by reducing the supplier's ESR efforts. The supplier devotes more ESR efforts if and only if the due diligence cost is sufficiently high. Our result implies that the heavy cost of adopting blockchain technology is not necessarily bad from the responsibility perspective as long as the backfiring condition does not hold. Moreover, we identify the Pareto zone in which conducting blockchain‐assisted due diligence could benefit both supply chain members. We extend our model by examining the impact of a shift from profit orientation to social welfare orientation. Our findings provide managerial insights into the implementation of blockchain‐assisted due diligence to enhance supplier ESR and improve supply chain members' profits. [ABSTRACT FROM AUTHOR]

E-commerce platforms are digital marketplaces for selling goods and services online. Operational research (OR) models support business model innovation in e-commerce platforms. This issue covers eight papers that utilize OR models to examine business model innovation in e-commerce platforms. They all introduce new research problems and concepts, develop novel and rigorous OR methodologies to tackle them, and present innovative OR applications according to the real problems in e-commerce platforms. Based on the eight papers' discussions, we propose future research directions. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the research trends in fashion industry during the coronavirus disease 2019 (COVID-19) pandemic. Besides, it also provides an overview on the new marketing and operational strategies, and reveals the corresponding business challenges of a footwear enterprise in Hong Kong during the COVID-19 pandemic. Design/methodology/approach: A comprehensive literature review is first conducted to identify the research trends in fashion industry during the COVID-19 pandemic. A qualitative exploratory case study is then used to illustrate how a footwear enterprise has coped with the COVID-19 pandemic. Findings: The case study has showed that omni-channel retailing, collaboration with e-tailers, quick response system and mixed production strategy are adopted in the targeted case during the COVID-19 pandemic. Besides, the targeted case has also faced the challenges in the areas of sales, customer relationship management, and demand forecasting and inventory planning during the COVID-19 pandemic. Originality/value: This study provides managerial insights on the real practices used to deal with the COVID-19 pandemic and proposes various academic future research directions in fashion industry based on the real-world observations. [ABSTRACT FROM AUTHOR]

Quick response strategy (QRS) has been widely adopted in a supply chain where members collect timely market information for better forecasting, and then respond promptly to the market changes by adjusting initial inventory decision. After adopting the QRS, sustainability issues such as greenhouse gas emission and energy waste may be more serious as production lead time is shorter. In this study, due to this dilemma, we develop a two-stage quick response supply chain with cleaner technology, where the manufacturer determines the cleaner technology investment and afterwards the retailer decides the ordering quantity. Based on Bayesian theory, we depict an information updating process for the QRS with cleaner technology. First, we find that the inventory service level significantly affects both manufacturer’s and retailer’s performance under the QRS with cleaner technology. Moreover, our analytical results indicate that the performance of centralised supply chain system is always better than the decentralised one. As a result, we propose two supply chain contracts, minimum ordering quantity (MOQ) and MOQ with buyback (MOQ-BB) to achieve supply chain coordination. Comparing with the MOQ, the MOQ-BB is more flexible to allow better allocation of the ‘additional’ expected profit between the channel members through the buyback price negotiation. [ABSTRACT FROM AUTHOR]

Preordering is an emerging business model in luxury fashion, which provides consumers the opportunity to preorder new collection products immediately after fashion runway shows and helps retailers collect preordering information for updating demand forecast. Based on our observations of industrial practices, we establish stylized analytical models for a supply chain consisting of a luxury brand (supplier) and a retailer. In addition, we explore the consequences of preordering for the supply chain, its members, and conspicuous consumers. Results show that preordering information incentivizes the retailer to increase product scarcity and leverage consumers' conspicuous consumption to charge a premium retail price, which consequently allows the supplier to increase the wholesale price. However, preordering information updating may have negative effects on the retailer due to the increased wholesale price, but this case always benefits the supplier. We recognize that preordering has two effects, a service‐extending effect and a precision‐improving effect, which positively and negatively affect consumers, respectively. Thus, retailers offering the preorder services should provide consumers with incentives (e.g., gifts or coupons) to achieve an all‐win scenario with preordering information updating. This finding is consistent with widely observed industrial practices in e‐commerce. In addition, our numerical studies interestingly show that all supply chain members can benefit from an increase in the level of conspicuous consumption in the market. [ABSTRACT FROM AUTHOR]

Data mining has long been applied in information extraction for a wide range of applications such as customer relationship management in marketing. In the retailing industry, this technique is used to extract the consumers buying behaviour when customers frequently purchase similar products together; in warehousing, it is also beneficial to store these correlated products nearby so as to reduce the order picking operating time and cost. In this paper, we present a data mining-based algorithm for storage location assignment of piece picking items in a randomised picker-to-parts warehouse by extracting and analysing the association relationships between different products in customer orders. The algorithm aims at minimising the total travel distances for both put-away and order picking operations. Extensive computational experiments based on synthetic data that simulates the operations of a computer and networking products spare parts warehouse in Hong Kong have been conducted to test the effectiveness and applicability of the proposed algorithm. Results show that our proposed algorithm is more efficient than the closest open location and purely dedicated storage allocation systems in minimising the total travel distances. The proposed storage allocation algorithm is further evaluated with experiments simulating larger scale warehouse operations. Similar results on the performance comparison among the three storage approaches are observed. It supports the proposed storage allocation algorithm and is applicable to improve the warehousing operation efficiency if items have strong association among each other. [ABSTRACT FROM AUTHOR]

Fast fashion is a timely, influential and well observed business strategy in the fashion retail industry. An effective fast fashion supply chain relies on quick and competent forecasts of highly volatile demand that involves multiple stock keeping units. However, there are multiple sources of uncertainty, such as market situation and rapid changes of the fashion trends, which makes demand forecasting more challenging. Therefore, it is crucial for the fast fashion companies to carefully select the right forecasting models to thrive and to succeed in this ever changing business environment. In this study, we first review a selected set of computational models which can be applied for fast fashion demand forecasting. We then perform a real sale data based computation analysis and discuss the strengths and weaknesses of these versatile models. Finally, we conduct a survey to learn about the perceived importance of different demand forecasting systems' features from the fashion industry. Finally, we rank the fast fashion demand forecasting systems using the AHP analysis and supplement with important insights on the preferences on the demand forecasting systems of different groups of fashion industry experts and supply chain practitioners. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) is an important tool for enhancing the performance of inventory management. Motivated by our real-world observations in local hospitals, we study the case where a hospital, which is using a bar-coding system, considers switching to an RFID system because the RFID technology can potentially enhance the hospital’s inventory management under the quick response system (QRS). In order to examine the value of RFID, we develop a formal analytical Bayesian model for the information updating process. We derive the expected value of information of the RFID system, and reveal the conditions in which the RFID system outperforms the bar-coding system. In addition, we evaluate the impacts of the QRS toward the expected profit and level of risk of the hospital, the supplier, and the whole supply chain (SC) system. We further propose two policies to help achieve SC coordination. Numerical analyses are reported and important insights are generated. [ABSTRACT FROM PUBLISHER]