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Introduction
Customer satisfaction and reliability are important elements in every aspect of business [1]. Demand planning examined as a part of the broader Sales and Operations Planning (S&OP) process is a vital part of the supply chain because it enables companies to satisfy consumer needs with sufficient inventory levels while keeping costs in check [2, 3]. Organizations may also improve customer satisfaction by including consumers in the process and considering their opinions [4]. However, while demand planning is a crucial component of every business strategy, it has faced challenges for its efficiency in empowering businesses in foreseeing and planning for future demand [5, 6]. This is due to reasons like the methods and systems that store the necessary information may not have all the features, which might make the job of demand planners challenging [7]. Planning systems may become the next problem impeding the S&OP process improvement after it has been successfully deployed in a corporation [8]. Demand planning methods usually must adapt to the limitations of information systems [9, 10]. Demand planning is challenging because of market uncertainty, but Artificial Intelligence (AI) can make it more effective [11]. These services are provided by a variety of solutions available on the market, but AI may be superior to them because it allows for better prediction at scale without bias or human mistakes. By forecasting future requests based on historical data and existing trends, AI can assist with demand planning [12, 13].
Moreover, today’s dynamic business environment necessitates agile strategies to approach the complexities of supply chain [14], rapid market demand shifts [15] and environmental sustainability requirements [16]. Companies and their supply chain stakeholders have to face diverse product portfolios and geographically dispersed operations, which require sophisticated forecasting methods to remain competitive [17]. Emerging technological trends, such as AI-driven analytics for rapid product development [18], distributed ledger technologies for supply chain configuration [19] and effective production and inventory planning [20] hold the potential to strengthen supply chain resilience. Inaccurate forecasts lead to misaligned inventory levels and result in escalation of warehouse costs and customer dissatisfaction [8]. Consequently, improving the forecasting capabilities is inevitable for reducing risks and supporting the proper resource allocation and sustaining a competitive edge [3]. By integrating emerging technologies and more effective customer...