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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.

The purpose of this study is to examine the impacts of different forecasting methods (judgmental, quantitative and mixed forecasting) on firms' supply chains and competitive performance.

Working with three groups of manufacturing companies, we explore the consequences of judgmental, quantitative and mixed forecasting methods on firms' competitive performance in supply chains. The validity of constructs and path relationships was examined using structural equation modeling (SEM).

Our findings indicate that supply chain efficiency influences both cost reduction and customer satisfaction. In addition, the three dimensions of supply chain performance are shown to be direct antecedents of competitive performance. Our empirical results reveal that although all studied forecasting methods meaningfully influence supply chain performance, the mixed method, compared to the other two methods, has greater capabilities to enhance supply chain performance.

This research provides originality and insight into supply chain practices through forecasting methods to improve competitive performance.

Supply chain management is built on the principles of partnerships and the development and use of the connections that exist between the links of the chain to provide information that will increase the efficiency of all members in the chain. Success stories abound describing lower costs, shorter lead times and increased customer service. Collaborative forecasting applies supply chain management concepts to the forecasting function and uses available information and technology to force a shift from independent, forecasted demand to dependent, known demand. Eventually, the future of forecasting may evolve to the point where forecasting is not even necessary. Demand information will be supplied completely by supply chain partners and the need to predict demand will be eliminated.

Forecasting is a vital part of hospitality operations because it allows businesses to make imperative decisions, such as pricing, promotions, distribution, scheduling, and arranging facilities, based on the predicted demand and supply. This chapter covers three main concepts related to forecasting: it provides an understanding of hospitality demand and supply, it introduces several forecasting methods for practical application, and it explains yield management as a function of forecasting. In the first section, characteristics of hospitality demand and supply are described and several techniques for managing demand and supply are addressed. In the second section, several forecasting methods for practical application are explored. In the third section, yield management is covered. Additionally, examples of yield management applications from airlines, hotels, and restaurants are presented.

The purpose of this paper is to test the effectiveness of fuzzy time series (FTS) forecasting system in a supply chain experiencing disruptions and also to examine the changes in performance as the authors move across different tiers.

A discrete event simulation based on the popular beer game model is used for these tests. A popular ordering management system is used to emulate the behavior of the system when the game is played with human players.

FTS is tested against some other well-known forecasting systems and it proves to be the best of the lot. It is also shown that it is better to go for higher order FTS for higher tiers, to match auto regressive integrated moving average.

This study fills an important research gap by proving that FTS forecasting system is the best for a supply chain during disruption scenarios. This is important because the forecasting performance deteriorates significantly and the effect is more pronounced in the upstream tiers because of bullwhip effect.

Having a system which works best in all scenarios and also across the tiers in a chain simplifies things for the practitioners. The costs related to acquiring and training comes down significantly.

This study contributes by suggesting a forecasting system which works best for all the tiers and also for every scenario tested and simultaneously significantly improves on the previous studies available in this area.

Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy.

A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry.

The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments.

So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.

Sales forecasting and collaboration are two business phenomena that have independently been recognized as contributing to improved organizational performance. The present research employs case study methodology to explore the synergies to be gained from combining the two processes. Depth interviews were conducted with executives at three firms currently engaged in collaborative forecasting with supply chain partners. Results revealed unique approaches to collaborative forecasting that circumvent the inhibitors of collaborative planning, forecasting, and replenishment adoption, and yield substantial improvement in company and supply chain performance including increased responsiveness, product availability assurance, optimized inventory and associated costs, and increased revenues and earnings. Seven guidelines to implementing interfirm collaborative forecasting are presented.

The purpose of this research paper is to present a conceptual model for collaborative forecasting management (CFM) developed within a European project's context.

This research follows a constructivist approach. After analysing different frameworks relating to such a thematic, and once their main weaknesses were identified, it was decided to develop the CFM framework that provides collaborative enterprises with a simple, efficient, robust and useful framework.

The paper describes a CFM model, which finds a place in the extended collaborative supply chain context, where several supply chains collaborate to deliver a product or service pack to final customers, thus forming the so‐called meta‐value chain, creating an extended value proposition.

Even though this proposal has been tested in one of the project's pilots, obtaining good results in terms of achieved and potentially achievable advantages, it should be tested further by implementing it in other collaborative organisations with the main objective of enriching and extending it to other sectors.

The CFM proposal came up as the result of applying innovative ideas between collaborative organisations supported by e‐collaboration practices, creating new paths for other disciplines.

The purpose of this paper is to empirically examine the impact of internal and external collaborative forecasting and planning on logistics and production performance.

To measure the degree of collaborative forecasting and planning, the concept of collaboration is categorized into three dimensions: sharing resources, collaborative process operation, and collaborative process improvement. Based on these dimensions, a survey of Japanese manufacturers was conducted and the analytical model is proposed to examine using structural equation modeling.

There are positive relationships between internal and external collaborative forecasting and planning. Upstream and downstream collaborative forecasting and planning are also positively related. Internal collaborative forecasting and planning has a positive effect on relative logistics and production performance. External collaborative forecasting and planning does not have a significant effect on relative logistics and production performance.

This study does not clarify how firms can achieve the improvement of forecasting and planning process. Future research should investigate the mechanism of process improvement in supply chain.

Not only sharing resources and collaborative process operation but also collaborative process improvement play a crucial role in gaining sustainable competitive advantage in logistics and production.

This study focuses on the forecasting and planning process in supply chain and proposes new dimensions measuring the degree of collaborative forecasting and planning. By focusing on the process and using the dimensions, the relationship between supply chain collaboration and performance are discussed concretely.

Supply chains must rebuild for resilience to respond to challenges posed by systemwide disruptions. Unlike past disruptions that were narrow in impact and short-term in duration, the Covid pandemic presented a systemic disruption and revealed shortcomings in responses. This study outlines an approach to rebuilding supply chains for resilience, integrating innovation in areas critical to supply chain management.

The study is based on extensive debates among the authors and their peers. The authors focus on three areas deemed fundamental to supply chain resilience: (1) forecasting, the starting point of supply chain planning, (2) the practices of supply chain risk management and (3) product design, the starting point of supply chain design. The authors’ debated and pooled their viewpoints to outline key changes to these areas in response to systemwide disruptions, supported by a narrative literature review of the evolving research, to identify research opportunities.

All three areas have evolved in response to the changed perspective on supply chain risk instigated by the pandemic and resulting in systemwide disruptions. Forecasting, or prediction generally, is evolving from statistical and time-series methods to human-augmented forecasting supplemented with visual analytics. Risk management has transitioned from enterprise to supply chain risk management to tackling systemic risk. Finally, product design principles have evolved from design-for-manufacturability to design-for-adaptability. All three approaches must work together.

The authors outline the evolution in research directions for forecasting, risk management and product design and present innovative research opportunities for building supply chain resilience against systemwide disruptions.