Master Thesis: AI-Driven SOP Scenarios

Sales and Operations Planning (S&OP) connects demand, supply, production and financial goals, but disconnected data and manual spreadsheets can slow down decisions. This thesis at Ericsson investigates how an AI-driven platform could integrate data, simulate responses to changing conditions and prioritize actions. The aim is to support planners while assessing improvements in planning effort, delivery performance, inventory and supply-chain costs.

The assignment

• Develop a conceptual, practically applicable AI platform for automated S&OP scenario planning.
• Design an architecture for near-real-time integration of operational and financial data, covering data models, pipelines, master-data governance and connections to enterprise systems.
• Investigate how demand and supply changes can be detected and translated into automatically generated and evaluated production scenarios.
• Develop approaches to ranking actions involving capacity, inventory, sourcing, production schedules, demand allocation and delivery, accounting for operational and financial trade-offs.
• Adapt planning capabilities to different time horizons, product lifecycles, supply-chain tiers and product-location or product-plant combinations.
• Explore suitable research methods, potentially including literature review, historical data analysis and stakeholder interviews or workshops, alongside forecasting, simulation, optimization or machine learning.
• Produce a framework identifying where AI can support automation, standardization, differentiation, simplification or transformation of S&OP.

Who we are looking for

• A background in Supply Chain Management, Industrial Engineering, Operations Research, Data Science, Business Analytics, Computer Science, Economics or a related discipline.
• Knowledge of S&OP, demand and production planning, inventory management, forecasting, optimization, simulation, machine learning, data engineering or system design is beneficial.
• Experience with statistical and predictive modeling, time-series analysis, feature engineering, exploratory analysis, causal inference, clustering or classification is advantageous.
• Skills in analytical tools and languages such as Python, R and SQL are an advantage.

What we offer

• An opportunity to apply technical skills and creative thinking to challenging problems.
• A collaborative team environment with innovators working on new solutions.

Practical details

Location: Stockholm. Master's thesis at Ericsson. Reference number: 793186.