Data-Driven Sales and Operations Planning: Development of a Design Method for Integrating Business Analytics

In today's globalized business environment, marked by growing market uncertainties and increasing supply chain complexity, Sales and Operations Planning (S&OP) has emerged as a critical practice to balance demand and supply. As industries evolve in volatile and competitive landscapes, the rapid advancements of business analytics and its integration into S&OP gains prominence; they offer promising avenues to leverage data-driven insights that enable agile decision-making and swift adaptation to market fluctuations. Yet, despite its widespread adoption, the integration of business analytics into S&OP remains insufficiently understood, exposing a gap between theory and practical application. This thesis explores the gap at the intersection of S&OP and business analytics. It highlights the potential of integrating business analytics capabilities into S&OP frameworks through a socio-technical approach that addresses both technological and organizational challenges. It provides a comprehensive S&OP taxonomy, a performance framework, and a dedicated Business Analytics-S&OP Framework, culminating in a systematic method for designing and implementing a data-driven S&OP process that aligns capabilities, business objectives, processes, and analytics applications. Demonstrated through a series of case studies, these findings offer actionable insights for transforming supply chain management in today's dynamic market.