Master Thesis: Demand Forecasting

Demand forecasting informs supply-chain decisions, but greater statistical accuracy does not always translate into greater business value. This thesis at Ericsson will examine how forecasting can be assessed and managed through its overall business impact, including the effort and complexity involved. The aim is to develop a framework that helps allocate forecasting effort where it delivers the most value.

The assignment

• Develop a conceptual framework with practical applications for evaluating the business impact of demand forecasting.
• Define how forecasting methods, expert input, detail levels and review effort should vary by product–customer segment and planning horizon.
• Identify performance measures that can guide forecasting decisions for different segments and horizons.
• Determine when the cost of further accuracy improvements exceeds their business benefit.
• Potentially combine a literature review with historical forecasting and supply-chain data analysis, using approaches such as statistical analysis, forecast-error decomposition, segmentation, cost-benefit analysis, simulation and stakeholder interviews.
• Provide guidance on where forecasting effort should be increased, maintained, automated or reduced.

Who we are looking for

• An academic background in Supply Chain Management, Industrial Engineering, Operations Research, Data Science, Business Analytics, Economics or a related discipline.
• Knowledge of forecasting, statistics, optimization and data analysis is an advantage.

What we offer

• An opportunity to apply analytical skills and creative thinking to challenging business problems.
• A collaborative team environment focused on innovation and questioning established approaches.

Practical details

Location: Stockholm. Master’s thesis at Ericsson; reference number 793187.