An inventory stacking decision assigns positions to items which are stacked vertically, such as containers in container terminals and steel plates in steel plants. The performance of stacking decisions is greatly affected by the arrival and departure information on items. We study an inventory stacking problem with partial information based on industrial observation in a steel plant. On the inbound side, we investigate three levels of information on future arriving items and their effect on performance. On the outbound side, we study the impact of the retrieval sequence, which is often random. We develop models incorporating different availabilities of information and determine stacking strategies. The study shows that the stacking strategy and stacking performance depend highly on information quality and space utilisation. Especially, when the space utilisation is high, low-quality information deteriorates the performance and such information should be ignored. This contradicts the general belief that more information should bring better performance. The study further proposes a time window allocation approach to reduce the uncertainty in retrieval, and it is effective in improving stacking performance.

The rapid development of the supply chain of the Internet of Things (IoT) industry may trigger financial risk contagion among IoT manufacturers. This article collects data on listed IoT companies in the Chinese market from 2010 to 2019 and explores the development of the environment for the IoT industry. Two dynamic time wrapping (DTW) networks are created to analyze the topological structures of the IoT industry environment. Both the standard DTW and strongly connected DTW networks are revolutionary in terms of their interconnectedness in the IoT industry. We found that the level of clustering and transitivity of the network continued to decline between 2010 and 2019; i.e., the efficiency of financial risk contagion on IoT networks was significantly reduced. This article contributes to the literature in two aspects. First, it reveals that Chinas IoT industry is increasingly competitive; financial risks have become more difficult to transfer. The IoT industry has become more robust and exhibits a lower likelihood of financial risk contagion. Second, the article provides empirical evidence for the theory of coevolution, showing that risk contagion ability in an industry setting is decreasing with the development of individual firms.

PurposeUsing social network theory (SNT), this study empirically examines the impact of digital supply chain announcements disclosing corporate social responsibility (CSR) information on stock market value.Design/methodology/approachBased on 172 digital supply chain announcements disclosing CSR information from Chinese A-share listed companies, this study uses event study method to test the hypotheses.FindingsFirst, digital supply chain announcements disclosing CSR information generate positive and significant market reactions, which is timely. Second, strategic CSR and value-based CSR disclosed in digital supply chain announcements have a more positive impact on stock market, however there is no significant difference when the CSR orientation is either towards internal or external stakeholders. Third, in terms of digital supply chain network characteristics, announcements reflecting higher relationship embeddedness and higher digital breadth and depth lead to more positive increases of stock value.Originality/valueFirst, the authors consider the value of CSR information in digital supply chain announcements, using an event study approach to fill the gap in the related area. This study is the first examination of the joint impact of digital supply chain and CSR on market reactions. Second, compared to the previous studies on the single dimension of digital supply chain technology application, the authors innovatively consider supply chain network relationship and network structure based on social network theory and integrate several factors that may affect the market reaction. This study improves the understanding of the mechanism between digital supply chain announcements disclosing CSR information and stock market, and informs future research.

Remanufacturing has been studied extensively from an economic modelling perspective. However, the number of studies focusing on models that consider the implementation difficulties in industry, such as data availability and model transparency, is low in comparison. This paper aims to identify how an initiation of remanufacturing can be economically assessed with models particularly suitable for implementation in industry. These models should contribute to spreading the know-how about initiating remanufacturing to transit industries towards more circular resource flows. To target the aim, a systematic literature review was conducted. The search resulted in a total of 964 papers published through August 2021. The relevant papers were categorised into a two-dimensional framework indicating the level of computer support needed to handle the model and the level of requirements to use the model in terms of data requirements, both of which are considered essential in the industrial implementation of the models. The review revealed a further need for research on assessing remanufacturing initiation profitability, especially on economic assessment models designed to assist industries without previous remanufacturing knowledge in such an initiation.

We introduce an original concept of the backroom effect for perishable products when the deterioration rate in a backroom is lower than that on retail shelves. With IoT-generated real-time information about the perishable products, this phenomenon has a significant impact on joint shelf-space and inventory decisions. We define the deterioration rate gap, formulate the perceived on-shelf product freshness, and describe the freshness-dependent demand distribution, with continuous backroom-shelf replenishment. Assuming that demand depends on both perceived freshness and shelf level, we develop a decision-making model that simultaneously determines the inventory replenishment policy and the shelf space allocation for multiple items. To facilitate the solution process, we propose a hybrid solution approach by combining genetic algorithm (GA) and variable neighbourhood search (VNS). The results provide a prioritised inventory policy for item selection that incorporates the deterioration improvement. The results of the performance analysis show that a policy considering the backroom effect achieves increased profit when the backroom/shelf deterioration gap increases. The optimal solutions for the problems with large backroom/shelf gap also show that the practitioner should increase the ordering quantity, which is contradictory to the outcome of traditional models.

Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations.

We construct portfolio strategies consisting of different stocks from four main energy market sectors, including oil and gas, oil and gas related equipment and services, multiline utilities and renewable energy. To construct portfolio strategies, we first forecast assets returns by using multivariate copula models. These forecasting frameworks enable us to undertake both symmetric and asymmetric tail connectedness in simulating from the joint distribution. Second, we applied four major risk measures including volatility, mean absolute deviation, conditional value-at-risk and conditional drawdown-at-Risk. Our findings indicate that the consideration of homogeneity of oil and gas sector and oil and gas related equipment and services sector, together with the heterogeneity of multiline utilities sector and renewable energy sector should lead to information decoupling among these sectors, thereby providing portfolio diversification. The mixed copula model results in better out-of-sample economic performance, indicating the advantage obtained from modelling both symmetric and asymmetric tail dependence. Our analysis of the portfolio weights, among the energy market sectors, shows that for optimal portfolios, multiline utilities and renewable energy sectors constitute higher portion of the invested assets. The study results provide an encouraging guideline for developing renewable energy sector from the perspective of financial market.

PurposeThis study explores the impact of carbon neutrality policies on Chinese stock market from a supply chain perspective. Specifically, the carbon policy refers to the Action Plan for Carbon Dioxide Peaking Before 2030 (the Plan) in China.Design/methodology/approachThis paper is based on the resource dependence theory (RDT) and applies the event study methodology to explore the impact. It uses the cross-sectional regression model to reveal the moderating effect of supply chain characteristics on the stock market reaction with a data set of 354 listed companies in A-shares (excluding ChiNext and SME board).FindingsThe promulgation of the Plan shows a significant negative stock market reaction. Customer concentration, out-degree centrality and smart supply chains (SSCs) have a significant negative moderating effect. In-degree centrality and supplier concentration have a significant positive moderating effect. Furthermore, the conclusions concerning out-degree centrality, supplier concentration and SSCs are counterintuitive.Practical implicationsFor policymakers, the study results provide a reference for evaluating the carbon neutrality policy. For managers, this study provides theoretical support for strategically adjusting and designing supply chain structures in the context of advocating peak carbon dioxide emissions and carbon neutrality.Originality/valueThis paper is the first attempt that includes the supply chain structure factors into the impact of carbon neutrality policies on the stock market.