Future trends and challenges in sales and operations planning (S&OP): A systematic literature review
محورهای موضوعی : Operations Managementhassan babaei 1 , Hassan Mehrmanesh 2 , حسین معین زاد 3
1 - گروه مدیریت صنعتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
2 - گروه مدیریت صنعتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
3 - دانشگاه آزاد اسلامی - دانشگاه آزاد اسلامی واحد تهران مرکزی - دانشکده مدیریت و حسابداری
کلید واژه: Sales and Operations Planning, Supply Chain Management, Machine Learning, Artificial Intelligence, Sustainability.,
چکیده مقاله :
Effective supply chain management (SCM) enhances organizational performance by optimizing resource allocation, reducing costs, and increasing customer satisfaction through streamlined operations and cross-functional collaboration. This leads to improved inventory management, higher service levels, and a competitive edge. Sales and operations planning (S&OP) plays a vital role in aligning demand forecasts with supply capabilities, fostering visibility and proactive decision-making. This minimizes inventory costs and improves responsiveness to market changes, supporting strategic goals and long-term success.
Despite its importance, a gap remains in systematic literature reviews that categorize trends and challenges in S&OP. Addressing this gap aids supply chain managers in identifying and understanding current challenges and trends, facilitating informed decision-making.
This study conducted a comprehensive systematic literature review, examining 295 studies and selecting 66 relevant articles published between 2012 and 2023 using screening methods coupled with TOPSIS and ANP techniques. The results reveal that most studies focus on optimization models for S&OP, employing optimization techniques, simulation, heuristic methods, artificial intelligence, machine learning, statistical approaches, and qualitative models.
The research identified key S&OP planning issues and various models for addressing them. It also highlights emerging trends, such as the increasing use of machine learning and artificial intelligence to improve demand forecasting and decision support systems. Additionally, the growing focus on sustainability in supply chains, including reducing carbon emissions and minimizing waste, is being integrated into S&OP models. However, challenges persist, including dependence on accurate and reliable data, data quality issues, and organizational resistance to change. The complexity of S&OP processes also presents obstacles.
This review provides insights into S&OP models, trends, and challenges, and offers future research directions, emphasizing AI integration, sustainability, and hybrid modeling approaches. Addressing these challenges can enhance alignment between sales, production, and inventory, ultimately improving business performance.
Effective supply chain management (SCM) enhances organizational performance by optimizing resource allocation, reducing costs, and increasing customer satisfaction through streamlined operations and cross-functional collaboration. This leads to improved inventory management, higher service levels, and a competitive edge. Sales and operations planning (S&OP) plays a vital role in aligning demand forecasts with supply capabilities, fostering visibility and proactive decision-making. This minimizes inventory costs and improves responsiveness to market changes, supporting strategic goals and long-term success.
Despite its importance, a gap remains in systematic literature reviews that categorize trends and challenges in S&OP. Addressing this gap aids supply chain managers in identifying and understanding current challenges and trends, facilitating informed decision-making.
This study conducted a comprehensive systematic literature review, examining 295 studies and selecting 66 relevant articles published between 2012 and 2023 using screening methods coupled with TOPSIS and ANP techniques. The results reveal that most studies focus on optimization models for S&OP, employing optimization techniques, simulation, heuristic methods, artificial intelligence, machine learning, statistical approaches, and qualitative models.
The research identified key S&OP planning issues and various models for addressing them. It also highlights emerging trends, such as the increasing use of machine learning and artificial intelligence to improve demand forecasting and decision support systems. Additionally, the growing focus on sustainability in supply chains, including reducing carbon emissions and minimizing waste, is being integrated into S&OP models. However, challenges persist, including dependence on accurate and reliable data, data quality issues, and organizational resistance to change. The complexity of S&OP processes also presents obstacles.
This review provides insights into S&OP models, trends, and challenges, and offers future research directions, emphasizing AI integration, sustainability, and hybrid modeling approaches. Addressing these challenges can enhance alignment between sales, production, and inventory, ultimately improving business performance.
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