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Due to uncertainty in supply chains caused by the coronavirus disease 2019 (COVID-19), organizations are adjusting their supply chain design to address challenges faced during the pandemic. To safeguard their operations against disruption in order quantities, supply chain members have been looking for alternate suppliers. This paper considers a two-level supply chain consisting of a manufacturer and two suppliers of a certain type of components required for the production of a finished product. The primary supplier (supplier A) is unreliable, in the sense that the quantity delivered is usually less than the ordered quantity. The proportion of the ordered quantity delivered by supplier A is a random variable with a known probability distribution. The secondary supplier (supplier B) always delivers the order in its entirety at a higher cost and can respond instantaneously. In order for supplier B to respond instantaneously, the manufacturer is required to reserve a certain quantity at an additional cost. Once the quantity received from the main supplier is observed, the manufacturer may place an order not exceeding the reserved quantity.

A mathematical model describing the production/inventory situation of the supply chain is formulated. The model allows the determination of the manufacturer's optimal ordering policy.

An expression for the expected total cost per unit time function is derived. The optimal solution is determined by solving a system of nonlinear equations obtained by minimizing the expected total cost function.

The proposed model can be used by supply chain managers aiming at identifying various ways of handling the uncertainty in the flow of supplies across the chain.

This proposed model addresses a gap in the production/inventory literature.

The purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered.

A mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy.

This study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided.

This study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors.

This study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.

This paper aims to estimate a statistical model of the country risk determination as represented by the country price earnings ratio (PE) to identify potentially mispriced countries. It uses the gross domestic product (GDP) growth rate and a dummy indicator for market-related events (i.e. financial crises), both approximating the business cycle. The model is used to compare a major Asian country’s (i.e. Japan) risk with Western countries’ risk.

The model used finance variables such as the systemic, non-diversifiable, risk and foreign direct investments to characterize any country risk. A random effects model with panel data estimated the effects of macroeconomic and financial variables on PE. The simultaneity problem was checked using two stage least squares and some lagged independent variables.

The results explained to investors the country risk contributing factors: PE was positively correlated with variables that may increase dividends and market risk premia similar to GDP growth rates and total risk and negatively correlated with variables that increase market risk, namely, nominal risk-free interest rates and financial crises. Japan’s PE seemed to exceed most of the Western countries considered here, implying lower risks, lower interest rates and higher growth in the major Asian country Japan.

This paper focuses on the effectiveness of country risk measures in predicting periods of intense instability, similar to financial crises. This study contributes a model to measure market risk premium, using PE (or inversely, the earnings yield) as a proxy variable. Investors can use this risk measure in picking less risky stocks to include in their portfolio, calling for liberalizing Asian countries’ financial markets to improve their stock market capitalization.

This study investigates a production process that requires N kinds of components for the production of a finished product. The producer orders the various kinds of components from different suppliers and receives the orders in lots at the beginning of each production cycle. Similar to situations often encountered in real life, the lead times are random variables with known probability distributions so that a production cycle starts whenever all N kinds of components become available. Each of the lots received at the start of a production run contains both perfect and imperfect quality components. Once all N kinds of components become available, the producer initiates a screening process to detect the imperfect components. The production of the finished product uses only perfect quality components. The imperfect components are removed from inventory whenever the screening process is completed. The percentage of components of perfect quality present in each lot is a random variable with a known probability distribution.

This production process is described and modeled mathematically and the optimal production/ordering policy is derived based on the mathematical model.

The formulated mathematical model resulted in the determination of the optimal policy consisting of the optimal number of finished items ordered to be produce during each production run, the number of components ordered from each supplier, and the reorder point. The derived closed form expression for the optimal lot size depends on the minimum of the number of perfect quality components in a lot, whereas the reorder point is determined based on the maximum lead time.

The modeling approach and results of this study provide practical implications that may be beneficial to both production and supply chain managers as well as researchers.

This modeling approach that incorporates decision-making related to the logistics of acquiring the components and accounts for the probabilistic nature of the lead times and quality of components addresses a gap in the logistics/production literature.

Facility location and re-location decisions are critical managerial decisions in modern supply chains. Such decisions are difficult in this environment as managers encounter uncertainty and risks. The study investigates establishing or moving distribution facilities in the global supply chain by considering costs, fulfilment, trade uncertainties, risks under environmental trade-offs and disruptive technologies.

This paper combines the possibilities and probabilistic scenarios for a supply chain network by proposing the novel Robust Optimisation and Mixed Integer Linear Programming (ROMILP) method developed under the potential uncertainty of demand while considering the costs associated with a four-tier supply chain network. ROMILP has been solved in a real-time logistics environment by applying a case study approach.

The solution is obtained using an exact solution approach and provides optimality in all tested market scenarios along the proposed global logistics corridor. A sensitivity analysis examines potential facility location scenarios in a global supply chain context.

Logistics managers can apply the ROMILP model to test the cost-benefit trade-offs against their facility location and relocation decisions while operating under uncertainty. Future research is proposed to extend the literature by applying data from the OBOR logistics corridor.

This study is the first to examine sustainable dimensions along the global logistics corridor and investigate the global container traffic perspective. The study also adds value to the Middle East logistics corridor regarding facility location decisions.

Unprecedented endeavors have been made to take autonomous trucks to the open road. This study aims to provide relevant information on autonomous truck technology and to help logistics managers gain insight into assessing optimal shipment sizes for autonomous trucks.

Empirical data of estimated autonomous truck costs are collected to help revise classic, conceptual models of assessing optimal shipment sizes. Numerical experiments are conducted to illustrate the optimal shipment size when varying the autonomous truck technology cost and transportation lead time reduction.

Autonomous truck technology can cost as much as 70% of the price of a truck. Logistics managers using classic models that disregard the additional cost could underestimate the optimal shipment size for autonomous trucks. This study also predicts the possibility of inventory centralization in the supply chain network.

The findings are based on information collected from trade articles and academic journals in the domain of logistics management. Other technical or engineering discussions on autonomous trucks are not included in the literature review.

Logistics managers must consider the latest cost information when deciding on shipment sizes of road freight for autonomous trucks. When the economies of scale in autonomous technology prevail, the classic economic order quantity solution might again suffice as a good approximation for optimal shipment size.

This study shows that some models in the literature might no longer be applicable after the introduction of autonomous trucks. We also develop a new cost expression that is a function of the lead time reduction by adopting autonomous trucks.

The objective of this study is to investigate the role of blockchain in reducing the impact of barriers to humanitarian supply chain management (HSCM) using a list of blockchain benefits.

A decision aid was used to explore the suitability of blockchain in humanitarian supply chains. To achieve that, first, a list of barriers to HSCM was identified. Then, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF–DEMATEL) method was utilized to determine the relationships and the level of interdependencies among the criteria. Finally, the intuitionistic fuzzyanalytic network process (IF–ANP) technique was employed, as it successfully handles dependencies among the criteria.

The findings of this study suggest that interorganizational barriers are the most suitable ones, the impacts of which blockchain may alleviate. This study further suggests that trust turned out to be the most significant benefit criterion for the analysis.

The readers should construe the findings of this study with caution since it was carried out using the data collected from the experts of a particular country. Moreover, the proposed decision aid contemplates a limited set of criteria to assess a possible role of blockchain in overcoming the barriers to HSCM.

The findings of this study can assist humanitarian supply chain managers to make more judicious assessments on whether they implement the blockchain in humanitarian supply chain operations. Specifically, this research may help decision makers to identify the certain barriers, the impact of which may be reduced by using the blockchain. The findings of this research will also help various decision makers make more rational decisions and allocate their resources more effectively.

To the best of authors’ knowledge, no single study exists to investigate the role of blockchain in reducing the impact of barriers to HSCM using an intuitionistic fuzzy multi-criteria decision-making approach.

With various challenges in the digital era, stakeholders are expressing growing interests in understanding the impact of emerging and disruptive technologies on freight transportation. This paper provides a systematic literature review of the current state of affairs as well as future trends and aims to support stakeholders' decision-making in logistics management in the era of disruptive technologies.

Several recent and representative articles from academic, industrial and governmental perspectives were investigated to set the scene for this research and to serve as a baseline for electing nine emerging technologies, which were then used to conduct a systematic literature review covering the literature within the area during the past twelve years.

3D printing, artificial intelligence, automated robots, autonomous vehicles, big data analytics, blockchain, drones, electric vehicles and the Internet of Things were identified as the emerging technologies. The current state of existing research and potential future opportunities were analyzed.

Since the potential literature body is almost impossible to fully cover, a tradeoff between the number of emerging technologies and the related literature reviewed has been performed. However, the paper provides a novel approach to select the emerging and disruptive technologies and a systematic literature review to fill the identified research gap in the related literature.

The research support various stakeholders to better capture the current status of and the future opportunities in freight transportation and gain a clearer understanding of the disruptive technologies as well as to guide them in how to deploy these initiatives in future decision-making.

By providing a systematic literature review on the trends, themes and research opportunities in the era of disruptive technologies, the papers bring about broad and comprehensive review on the impact of disruptive technologies on logistics and transportation as well as opportunities to support management decision support in the logistics industry.

This paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply chain plays within these relationships.

This study uses data collected through an online survey distributed to managers of 378 Italian firms that have adopted circular economy principles. The data are processed using multiple regression analysis.

The results indicate that the three categories of circular economy practices investigated – namely circular economy supply chain management design, circular economy supply chain relationship management and circular economy HR management – play a crucial role in enhancing firm performance from a circular economy perspective. A big-data-driven supply chain acts as a moderator of the relationship between circular economy HR management and firm performance for a circular economy supply chain.

This study makes a number of original contributions to research on circular economy practices in a big-data-driven supply chain and provides useful insights for practitioners. First, it answers the call to capture digital transformation trends and to extend research on sustainability in supply chain management. Second, it enhances the literature by investigating the relationships between three different kinds of circular economy supply chain practices and firm performance. Finally, it clarifies the moderating role of big data in making decisions and implementing circular supply chain solutions to achieve better environmental, social and economic benefits.