Demand Planning Process Optimization: Challenges, Benefits, and Improvement potentials within Judgment Forecast Adjustments
Reliable forecasting is essential for companies. When forecasting systems generate statistical forecasts, demand planners review and judgmentally adjust them to improve their accuracy. In this thesis, demand planners’ perspectives were thematically and statistically analyzed to explore demand planning process optimization.
In supply chain management, demand planning is an important process for balancing the customer demand with the companies’ supply capabilities. This process involves strategic and operational steps conducted with different approaches.
One of such approaches is judgment adjusted forecasting where data are processed by planning systems to generate system forecasts. Then, these system forecasts are reviewed by system operators called demand planners, and sometimes adjusted based on judgment and experience.
For those demand planners, the task of interacting with the system to conduct adjustments can be challenging, especially when there are many forecasts for many sold components that should be reviewed.
This thesis is a case study in a large Swedish manufacturer where data were collected from interviews, observations, and company documents. Demand planners’ perspectives were thematically analyzed using software qualitative coding for identification of themes relevant to process optimization. These themes included challenges, benefits, and improvement potentials.
A statistical analysis utilizing full factorial Design of Experiment supported the usefulness and relevance of these themes for process optimization. It was found that demand planners’ perspectives can yield active effects on reducing needless demand planning alerts, hence allowing for higher quality forecast adjustments.
This thesis retains promising implications to sustainable supply chain management by exploring an important forecasting approach. Sustainable developments could be supported by such implications including minimized costs and elevated service levels (economic sustainability), enhanced operational and logistics planning (environmental Sustainability), and taking workers’ perspectives into consideration to improve their work task and serve customer needs better (social sustainability).
