If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Structured Abstract:Purpose: As traditional supply chains are increasingly becoming intelligent with more objects embedded with sensors and better communication, intelligent decision-making and automation capabilities, the new smart supply chain presents unprecedented opportunities for achieving cost reduction and enhancing efficiency improvement. The purpose of the paper is to study and explore the currents status and remaining issues of smart supply chain management (SSCM) .Design/methodology/approach: A literature review is conducted to synthesize the earlier work in this area, and to conceptualize and discuss the smart supply chain characteristics. Further, we formulate and investigate the five key research topics including information management, IT, process automation, advanced analytics, and supply chain integration.Findings: Studies in those aforementioned subject fields are reviewed, categorized, and analysed based on the review questions defined in the study. It is notable that while the topics of converging atoms with digits are increasingly attracting attention from researchers and practitioners alike, there are many more interesting research questions needing to be addressed.Originality/value: The paper provides original and relevant guidance for SCM researchers and practitioners on developing smart supply chains.

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"Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.

T he mixed-channel model is becoming increasingly popular in the marketplace. In this model, a firm selling through the traditional supply chain of wholesaler and retailer opens a direct channel to the customer through Internet sales. Because both channels have their respective advantages, the manufacturer is attracted to this business model. However, it also leads to channel conflict, with the retailer feeling threatened by direct competition. One way of eliminating the possibility of this channel conflict, where the retailer is allowed to add value to the product to differentiate its offering to the customers, is proposed in this paper. The retailer is also given full authority to make pricing decisions. This paper presents a model, under this scenario, of obtaining optimum pricing decisions by both parties, the amount of value added by the retailer, and the manufacturer's wholesale price to the retailer. Our model incorporates information asymmetry, where the manufacturer has incomplete information about the retailer's cost of adding value. We obtain closed-form contracts with incomplete information and compare them with those with complete channel coordination. We also develop a number of managerial guidelines and identify future research topics.

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This paper examines the incentives of a manufacturer and a retailer to share their demand forecasts. The demand at the retailer is a linearly decreasing function of price. The manufacturer sets the wholesale price first, and the retailer sets the retail price after observing the wholesale price. Both players set their prices based on their forecasts of demand. In the make‐to‐order scenario, the manufacturer sets the production quantity after observing the actual demand; in the make‐to‐stock scenario, the manufacturer sets the production quantity before the demand is realized. In the make‐to‐order scenario, we show that sharing the forecast unconditionally by the retailer with the manufacturer benefits the manufacturer but hurts the retailer. We also demonstrate that a side payment contract cannot induce Pareto‐optimal information sharing equilibrium, but a discount based wholesale price contract can. The social welfare as well as consumer surplus is higher under the discount contract, compared with under no information sharing. In the make‐to‐stock scenario, the manufacturer realizes additional benefits in the form of savings in inventory holding and shortage costs when forecasts are shared. If the savings from inventory holding and shortage costs because of information sharing are sufficiently high, then a side payment contract that induces Pareto‐optimal information sharing is feasible in the make‐to‐stock scenario. We also provide additional managerial insights with the help of a computational study.

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In the Industry 4.0 era, automation and data analytics emerge as the major forces to enhance efficiency in operations management (OM). Disruptive technologies, such as artificial intelligence, robotics, blockchain, 3D printing, 5G, Internet‐of‐Thing, digital twins, and augmented reality, are widely applied. They potentially will bring a radical change to real world operations. In this study, we first explore several major disruptive technologies, examine the corresponding OM studies, and highlight their current applications in the industry. Then, we discuss the pros and cons associated with the use of these technologies and uncover the potential human–machine conflicting areas. After that, we propose measures which may be able to achieve human–machine reconciles in the coming Industry 5.0 era. A concept of “sustainable social welfare” which includes worker welfare, privacy, etc. is proposed and the roles played by policy makers are also discussed. Finally, a future research agenda, which covers topics in both the Industry 4.0 and Industry 5.0 eras, is established.