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Forecasting demand of emergency supplies under major epidemics plays a vital role in improving rescue efficiency. Few studies have combined intuitionistic fuzzy set with grey-Markov method and applied it to the prediction of emergency supplies demand. Therefore, this article aims to establish a novel method for emergency supplies demand forecasting under major epidemics.

Emergency supplies demand is correlated with the number of infected cases in need of relief services. First, a novel method called the Intuitionistic Fuzzy TPGM(1,1)-Markov Method (IFTPGMM) is proposed, and it is utilized for the purpose of forecasting the number of people. Then, the prediction of demand for emergency supplies is calculated using a method based on the safety inventory theory, according to numbers predicted by IFTPGMM. Finally, to demonstrate the effectiveness of the proposed method, a comparative analysis is conducted between IFTPGMM and four other methods.

The results show that IFTPGMM demonstrates superior predictive performance compared to four other methods. The integration of the grey method and intuitionistic fuzzy set has been shown to effectively handle uncertain information and enhance the accuracy of predictions.

The main contribution of this article is to propose a novel method for emergency supplies demand forecasting under major epidemics. The benefits of utilizing the grey method for handling small sample sizes and intuitionistic fuzzy set for handling uncertain information are considered in this proposed method. This method not only enhances existing grey method but also expands the methodologies used for forecasting demand for emergency supplies.

1. An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.

2. The safety inventory theory is combined with IFTPGMM to construct a prediction method.

3. Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.

The safety inventory theory is combined with IFTPGMM to construct a prediction method.

Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

Integrating demand with supply has always been an evolving research area, which is affected by different factors including, but not limited to, sustainability, changing consumer demands and emerging supply chain practices. However, most of the research targeted developed countries due to better data access and enhanced information visibility. The research on supply and demand integration in Saudi Arabia is still in its infancy. This study explores this gap by providing a comprehensive analysis of demand and supply chain integration in the context of sustainability. This is done by first creating a developing countries-based research framework and then validating it through surveying 162 Saudi companies operating in multiple sectors like food, fashion and retail. Results suggest that there is more emphasis on supply-side factors relative to demand-side factors in the context of integration. Demand side factors only marginally impact waste reduction efforts. Similarly, the food sector is most sensitive to integration efforts attaching high importance to sustainability and efficiency initiatives. These results can be a useful guide for managers and policymakers who are planning and executing at the interface of demand and supply chains in developing countries.

Cross-sector analysis by survey questionnaire of 162. Descriptive statistical analyses were performed on the data for the study participants. Continuous variables were summarised using mean and standard deviation (SD), median and IQR; and proportions were used for nominal and ordinal variables. A total score measuring perception of employees towards supply–demand integration was analysed and compared by company-related factors (sector/industry, length of service in firm (years) and the number of employees). Comparisons were made using the chi-square test or ANOVA. A linear regression model was utilised to examine the independent effect of supply and demand side factors on employees' perception of integration. The model controlled for the sector, length of service and number of employees. Statistical significance was considered at p < 0.05.

The linear regression results showed that supply items significantly predicted integration. That is, higher supply–demand integration scores were significantly related to indicators for using supply variables in supply chain management (SCM) improves operational efficiency (OE), integrating supply variables at the execution stage reduces overall waste and including at least one supply-side variable in supply planning (p < 0.05). The indicator for integrating demand variables at the execution stage reduces overall waste was only marginally related to an increased integration scores (p = 0.089). Lastly, a significantly better perception of supply–demand integration was reported by employees in the food sector compared to those in the fashion sector (p = 0.018). Length of service or number of employees in the firm was not related to perception towards supply–demand integration.

Though accessing 162 companies in the Saudi market is a significant sample size given the fact that they are quite conservative in sharing supply and demand information. However, for better generalisation and richer insights, more sectors like manufacturing, oil and gas and hospitality should be added in future. Cross-country analysis from developing countries can be another interesting future direction.

This empirical paper draws its strength from the scale and depth of empirical data collected from Saudi companies belonging to multiple sectors to better understand demand and supply chain integration, especially from sustainability and waste reduction. This is true in the era of the pandemic and its associated risks for both upstream and downstream. The developing countries' perspective is important as not enough is written in the literature given the difficulties in accessing the information and subsequent analysis. The insights are bound to be unique as a consumer in these countries is undergoing significant socioeconomic changes affecting the supply chain of the companies.

Drawing on absorptive capacity theory, this study explores the impacts of supply visibility and demand visibility on product innovation (i.e. exploratory and exploitative innovation), and it examines how supplier integration, customer integration and internal integration mediate these impacts.

The authors employ empirical survey data from 200 Chinese manufacturers and use structural equation modeling to test the proposed relationships.

The results show that supply visibility is positively related to supplier integration and internal integration and that demand visibility is positively related to customer integration. Furthermore, only customer integration and internal integration positively relate to exploratory and exploitative innovation.

First, this study emphasizes that supply visibility and demand visibility are important sources of a firm's innovation performance and that supply chain integration increases focal firms' capability of exploiting information and facilitates product innovation. Second, the study shows that supply visibility and demand visibility have distinct effects on three dimensions of supply chain integration and exploratory and exploitative innovation. The study also provides significant managerial guidelines for effectively leveraging supply chain visibility and integration in the promotion of product innovation.

Drawing on information processing theory, the linkage between buffering and bridging and the ability on the part of procurement to resolve demand–supply imbalances is investigated, as well as contexts in which these strategies may be particularly useful or detrimental. Buffering may be achieved through demand change or redundancy, while bridging may be achieved by the means of collaboration or monitoring.

This study employs a hierarchical regression analysis of a survey of 150 Finnish and Swedish procurement and sales and operations planning professionals, each responding from the perspective of their own area of supply responsibility.

Both the demand change and redundancy varieties of buffering are associated with procurement's ability to resolve demand–supply imbalances without delivery disruptions, but not with cost-efficient resolution. Bridging is associated with the cost-efficient resolution of imbalances: while collaboration offers benefits, monitoring seems to make things worse. Dynamism diminishes, while the co-management of procurement in S&OP improves procurement's ability to resolve demand–supply imbalances. The most potent strategy for tackling problematic contexts appears to be buffering via demand change.

The results highlight the importance of procurement in the S&OP process and suggest tactical measures that can be taken to resolve and reduce the effects of supply and demand imbalances.

The results contribute to the procurement and S&OP literature by increasing knowledge regarding the role and integration of procurement to the crucial process of balancing demand and supply operations.

Houses are durable, so an imbalance between demand and supply occurs after time has passed since initial construction. The purpose of this study is to quantify the extent of this imbalance for existing houses, focusing on the heterogeneity across property segments.

This study uses a unique data set on the “inquiry volume” that each property received from an online real estate portal to measure the volume of demand in relation to supply. Simple regressions are conducted in the resale condominium market across the Tokyo metropolitan area.

The inquiry volume successfully tracked a recent expected trend in which demand relative to supply is stronger for condominiums in reasonably priced areas, condominiums in convenient, accessible locations, condominiums built within the last 20 years and compact and spacious units. This study also confirms that these trends cannot be captured through heterogeneity in price levels, which has been widely used in previous studies on measuring housing preferences.

As an indicator of conditions in the housing market, the property-level inquiry volume has strong potential to provide useful information for supply strategies and for the sustainable use of existing housing stocks.

The originality of this paper is the use of information on the buyer side, which is typically unobservable.

The increasing prevalence of a wide range of infectious diseases, as well as the underwhelming results of vaccination rates that may be traced back to problems with vaccine procurement and distribution, have brought to the fore the importance of vaccine supply chain (VSC) management in recent years. VSC is the cornerstone of effective vaccination; hence, it is crucial to enhance its performance, particularly in low- and middle-income countries where immunization rates are not satisfactory.

In this paper, the authors focus on VSC performance improvement of India by proposing supply contracts under demand uncertainty. The authors propose three contracts – wholesale price (WSP), cost sharing (CS) and incentive mechanism (IM) for the government-operated immunization program of India.

The authors' findings indicate that IM is capable of coordinating the supply chain, whereas the other two contracts are inefficient for the government. To validate the model, it is applied to a real-world scenario of coronavirus disease 2019 (COVID-19) in India, and the findings show that an IM contract improves the overall efficiency of the system by 23.72%.

Previous studies focused mainly on the influenza VSC industry within developed nations. Nonetheless, there exists a dearth of literature pertaining to the examination of supply contracts and their feasibility for immunization programs that are administered by the government and aimed at optimizing societal benefits. The authors' findings can be beneficial to the immunization program of India to optimize their VSC cost.

This study aims to examine the value of the call option contract in hedging the risks in the supply chain. The decentralized supply chain without call option contract is first studied as the criterion model for evaluations. This paper addresses several questions: What will be the optimal manufacturer’s production quantity, retailer’s ordering and pricing policies in the presence of random demand and random yield by applying the downconversion approach? How will the call option contract influence the optimal decisions for the members of the supply chain? Can the risk from randomness be divided among the members in the supply chain through the call option contract?

This paper considers a two-level decentralized supply chain under random yield and random demand in which the manufacturer takes advantage of the downconversion approach with two scenarios, with and without option contract. To the best of the authors’ knowledge, no article or study uses the downconversion approach in a supply chain regarding random yield and random demand. Furthermore, the paper considers pricing with option contract in the supply chain, which makes this article stands out significantly from other articles in the literature.

This study shows that the downconversion approach would reduce the risk caused by the random yield, which appears to be the appropriate method for the environmental goal of the supply chains. Moreover, adopting a call option contract can increase flexibility and mitigate risks, resulting in more expected members’ profits.

To simplify the model, the authors assume one manufacturer and one retailer, so extending the model to consider multiple retailers instead of one retailer and inventory sharing between them would be interesting. Considering the option and exercise prices as decision variables would be important future research topics. Put option and bidirectional option contracts could be investigated in the future. Another extension is modeling asymmetry of information in supply chain.

This paper provides managerial insights on dealing with both demand and yield risks in a manufacturer–retailer supply chain. The manufacturer has a random yield production and produces two types of vertical products: low-end and high-end. To reduce waste caused by the random yield, the manufacturer uses a downconversion approach in which low-end products are made by converting the defective high-end products. The manufacturer purchased a shortage of high-end products from the secondary market (i.e. emergency sourcing). High-end products are sold through the retailer, and low-end products are sold directly by the manufacturer. The customer demand for high-end products in the end market is random and depends on the selling price, and the customer demand for the low-end products in the secondary market is independent and random. The retailer contracts the manufacturer with the call option to obtain high-end products to meet a random demand; in fact, by using the call option contract, the authors try to balance the risks between two members. Two scenarios of with and without call option contract are proposed. After the high-end product demand is observed, the retailer would exercise the option order quantity in the call option contract scenario and then place an instant order with the manufacturer if necessary. In each scenario, the manufacturer and the retailer make their decisions simultaneously (static game) to determine the retailer’s optimal ordering and pricing policies and the optimal production quantity of the manufacturer (Nash equilibrium) by maximizing their expected profits. Finally, the impact of the model parameters on the supply chain is expressed through numerical examples. The numerical analysis shows that the call option contract provides greater profit than the wholesale price contract.

This study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).

A five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.

The simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.

A two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.

The proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.

Despite the importance of demand forecasting in retail industry, its influence on supply chain agility has not been sufficiently examined. From a total information technology (IT) capability perspective, the purpose of this paper is to examine the antecedent of supply chain agility through retail demand forecasting.

Combining the literature reviews, the quantitative method of algorithm analysis was targeted at, and the firm data were processed on MATLAB.

This paper summarizes IT dimensions of demand forecasting in retail industry and distinguishes the relationship of supply chain agility and demand forecasting from an IT capability view.

Managers can derive a better understanding and measurement of operating activities that appropriately balance among supply chain agility, IT capability and demand forecast practice. Demand forecasting should be integrated into the firm operations to determine the agility level of supply chain in marketplace.

This paper constructs new theoretical grounds for research into the relationship of demand forecasting-supply chain agility and provides an empirical assessment of the essential components for the means to prioritize IT-supply chain.

This paper proposes a two-level supply chain including suppliers and manufacturers. The purpose of this paper is to design a resilient fuzzy risk-averse supply portfolio selection approach with lead-time sensitive manufacturers under partial and complete supply facility disruption in addition to the operational risk of imprecise demand to minimize the mean-risk costs. This problem is analyzed for a risk-averse decision maker, and the authors use the conditional value-at-risk (CVaR) as a risk measure, which has particular applications in financial engineering.

The methodology of the current research includes two phases of conceptual model and mathematical model. In the conceptual model phase, a new supply portfolio selection problem is presented under disruption and operational risks for lead-time sensitive manufacturers and considers resilience strategies for risk-averse decision makers. In the mathematical model phase, the stages of risk-averse two-stage fuzzy-stochastic programming model are formulated according to the above conceptual model, which minimizes the mean-CVaR costs.

In this paper, several computational experiments were conducted with sensitivity analysis by GAMS (General algebraic modeling system) software to determine the efficiency and significance of the developed model. Results show that the sensitivity of manufacturers to the lead time as well as the occurrence of disruption and operational risks, significantly affect the structure of the supply portfolio selection; hence, manufacturers should be taken into account in the design of this problem.

The study proposes a new two-stage fuzzy-stochastic scenario-based mathematical programming model for the resilient supply portfolio selection for risk-averse decision-makers under disruption and operational risks. This model assumes that the manufacturers are sensitive to lead time, so the demand of manufacturers depends on the suppliers who provide them with services. To manage risks, this model also considers proactive (supplier fortification, pre-positioned emergency inventory) and reactive (revision of allocation decisions) resilience strategies.