Embedding Scenario Analysis into S&OP

Sammanfattning

This thesis has researched how analytical tools can be used to assist in balancing supply and demand at the global telecommunications company Ericsson. The project stems from a need to manage demand uncertainty in Sales and Operations Planning in order to make scenario analysis both standardized and data-driven. Consequently, the purpose was to develop a practical scenario planning framework and tool for modeling uncertainty to support rational decision-making. To maintain a reasonable scope, the thesis was limited to study capacity planning and a single production facility. Given this purpose, and in accordance with the Operations Research Modeling Approach, a literature review, open-ended interviews, observations, and benchmarking were carried out to map the current state and establish the modeling requirements. Thereafter, two models were developed: a linear programming model for cost-optimal capacity planning under a known demand change, and a Monte Carlo simulation that stress-tests production configurations across a range of uncertain demand realizations. The models quantify delays and investment requirements by providing cost breakdowns, utilization heatmaps, and service level distributions across demand scenarios. The findings in this thesis demonstrate that linear programming and Monte Carlo simulation can together operationalize scenario planning by standardizing and quantifying decision alternatives. Furthermore, a five-stage scenario planning process map is proposed to formalize and integrate the tools into the S\&OP cycle.