A reference model for data-driven sales planning: Development of the model's framework and functionality - Daniel Büttner; Anne Antonia Scheidler; Markus Rabe (Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 31, 2021)
Purpose: Having accurate forecasts of future sales is mandatory for planning Supply Chains and providing the right distribution task resources. The usage of data in forecasting models enables precise planning and supports the company's competitiveness. This research shows a reference model framework that helps to establish data-driven sales planning in producing companies. Methodology: The presented framework is derived from theoretical and practical challenges in a company where data-driven sales planning is not accomplished. The scope of the study originates from an industry project, and the developed framework forms the foundation for further research. Findings: Data-driven sales planning is neither clearly defined nor the industry's norm, though data-driven methods exist for decades. The lack of methodical knowledge, incomplete data, and company characteristics cause diverse sales planning challenges. The research shows the requirements for integrating and advancing data-driven sales planning in companies. Originality: This study clarifies the role of data-driven sales planning, identifies theoretical and practical challenges, derives requirements for the reference model and its functionality to support the establishment and advancement of data-driven sales planning in companies. The reference model aims for a comprehensive approach to counteract the mentioned challenges and guides the development of company-specific sales planning procedures.
urn:nbn:de:gbv:830-882.0161744; hdl:10419/249608; https://econpapers.repec.org/RePEc:zbw:hiclpr:31
Keywords: 650; Business Analytics