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In this paper, we propose a scenario based global supply chain planning (GSCP) process considering demand uncertainty originated from various global supply chain risks. To generate the global supply chain plan, we first formulate a GSCP model. Then, we need to generate several scenarios which can represent various demand uncertainties. Lastly, a planning procedure for considering those defined scenarios is applied. Unlike the past related researches, we adopt the fuzzy set theory to represent the demand scenarios. Also, a scenario voting process is added to calculate a probability (possibility) of each scenario. An illustrative example based on a real world case is presented to show the feasibility of the proposed planning process.

Effective planning requires the participation of different functions and may be hampered by lack of integration and information quality (IQ). This paper aims to investigate the relationships among integration, uncertainty, IQ and performance, in the context of the production planning and control function. The literature lacks in-depth studies that consider these factors altogether, showing how they interact and how they contribute to improve business performance.

The authors introduce the variable of planning performance, which represents the quality of the production plans/planning process and is related to the frequency and causes of modifications to these plans. The relationships among the mentioned constructs are investigated by means of multiple case studies.

The results illustrate that integration is positively related to planning performance, and this relationship is mediated by IQ and moderated by uncertainty.

The presented analysis may help practitioners to foster interfunctional integration, better cope with uncertainty and improve information management, aiming to achieve better planning performance. The managers can choose integration and IQ improvement mechanisms that better fit to their environment/reality, using the four different cases as a benchmark. Moreover, this research contributes to the literature exploring this contingency perspective by means of in-depth case studies, considering that most of the existing research adopting this perspective is survey-based.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Due to its fast growth, cross-border e-commerce (CBEC) is becoming a popular internationalization model, especially in those destination markets with impressive e-commerce development like China. However, CBEC also brings new logistics challenges and uncertainty. This paper aims to understand how companies cope with logistics uncertainty in this field and whether the different types of uncertainty influence the risk management strategies adopted to face them.

A survey targeting online exporters to China and third-party forwarding logistics service providers (3PFLs) is conducted. A structural equation model (SEM) analysis is performed to test the possible relationship between the adopted risk management strategies and the types of uncertainty. The type, industry and size of the company, as well as the distance between the company's home country and China, are used as control variables in the study. Survey results are enriched via interviews with some of the respondents.

The risk management strategies adopted are dependent on the type of logistics uncertainty that the companies face and, to a minor extent, on the industry the company operates in. Conversely, no significant influence is exerted by other types of control factors, i.e. home country, company size or company type.

The paper investigates logistics uncertainty and risk management approaches in the novel context of CBEC. A systematic review of relevant sources of uncertainty is offered to help both scholars and practitioners understand the current complexities of CBEC. From a theoretical perspective, the paper models the investigated concepts in light of the contingency approach. From a practical perspective, results can be of interest since the list of proposed items can support risk identification and evaluation while the interviews with managers can provide insights on risk management practices.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

The purpose of this paper is to promote decision-making structures between the customer and the supplier in a highly uncertain environment. This phenomenon of demand-supply chain synchronisation includes sharing of high-quality and timely demand and supply information in order to improve the quality and speed of decision-making.

The study was carried out as an abductive case study, which started from empirical observations that did not match the prior theoretical framework. Through abductive reasoning and empirical experiments, the prior framework was extended to a new synchronisation model and tools that better accommodate the observed need.

A new co-innovation toolbox was developed to create common understanding of demand-supply chain synchronisation between the customer and the supplier. The toolbox includes Demand Visibility Point-Demand Penetration Point, Supply Visibility Point–Supply Penetration Point and Integrative Synchronisation tools.

The study extends the current models and tools of demand-supply chain synchronisation. With the new toolbox, the development needs of decision-making structures can be identified more comprehensively than with the current tools.

The developed visual toolbox helps partners create a common understanding of problems and development possibilities in demand-supply chain synchronisation in a highly uncertain environment. Common understanding is a starting point for changing decision-making structures to improve the overall performance of a demand-supply chain.

The new toolbox is both more comprehensive and more detailed than the previous tools.

Unforeseen events can disrupt the operational process and negatively impact emergency resources optimization and its supply chain. A limited number of studies have addressed risk management issues in the context of emergency supply chains, and this existing research lacks inbuilt and practical techniques that can significantly affect the reliability of risk management outcomes. Therefore, this paper aims to identify and practically analyze the specific risk factors that can most likely disrupt the normal functioning of the emergency supply chain in disaster relief operations.

This paper has used a three-step process to investigate and evaluate risk factors associated with the emergency supply chain. First, the study conducts a comprehensive literature review to identify the risk factors. Second, the research develops a questionnaire survey to validate and classify the identified risk factors. At the end of this step, the study develops a hierarchical structure. Finally, the research investigates the weighted priority of the validated risk factors using the fuzzy-analytical hierarchy process (FAHP) methodology. Experts were required to provide subjective judgments.

This paper identified and validated 28 specific risk factors prevalent in emergency supply chains. Based on their contextual meanings, the research classified these risk factors into two main categories: internal and external risk factors; four subcategories: demand, supply, infrastructural and environmental risk factors; and 11 risk types: forecast, inventory, procurement, supplier, quality, transportation, warehousing, systems, disruption, social and political risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules that can influence and support disaster relief operations, the impact of cascading disasters, limited quality of relief supplies and sanctions and constraints that can hinder stakeholder collaboration. Therefore, emergency supply chain managers should adopt appropriate strategies to mitigate these risk factors.

This study will contribute to the general knowledge of risk management in emergency supply chains. The identified risk factors and structural hierarchy taxonomic diagram will provide a comprehensive risk database for emergency supply chains.

The research findings will provide comprehensive and systemic support for respective practitioners and policymakers to obtain a firm understanding of the different risk categories and specific risk factors that can impede the effective functioning of the emergency supply chain during immediate disaster relief operations. Therefore, this will inform the need for the improvement of practices in critical aspects of the emergency supply chain through the selection of logistics and supply chain strategies that can ensure the robustness and resilience of the system.

This research uses empirical data to identify, categorize and validate risk factors in emergency supply chains. This study contributes to the theory of supply chain risk management. The study also adopts the fuzzy-AHP technique to evaluate and prioritize these risk factors to inform practitioners and policymakers of the most significant risk factors. Furthermore, this study serves as the first phase of managing risk in emergency supply chains since it motivates future studies to empirically identify, evaluate and select effective strategies that can eliminate or minimize the effects of these risk factors.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The objective of this study is to mitigate the risks of a blood shortage. The authors designed two simulation studies to identify the superior methodology that can decrease the impact of a massive national donor shortage.

The simulation designs are triggered by the COVID-19 pandemic. The first simulation examines the company’s choice of strategic partners (regionally and nationally), and the second inspects creating a national coordinated effort to organize a pooled blood inventory that would require blood centers to contribute a small percentage of their monthly donations to become a member.

The results indicate that both methods can significantly manage the risk of stockouts regardless of the availability of safety inventory in a blood center; however, although more effective in reducing the number of shortages per month, creating a national blood pool causes the shortages to be recognized earlier than desired.

The authors contribute to the literature by focusing on the potential risk of blood shortage because it directly impacts healthcare, hospitals’ costs and their ability to provide care. Though a handful of researchers have targeted the study of the blood supply chain, there is not any article that is similar to this study.

Environmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control.

A biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand.

The proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO₂ emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO₂ emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO₂ emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO₂ Emissions Control Reduction.” It can provide managers with a way to double-target conversion.

We proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.