Product Portfolio Optimization

Sales, cost, and order overlap data identify optimal product clusters for portfolio pruning and investment

Business impact

• Product development cycle time — Accelerates innovation by focusing resources on high-potential product combinations
• Innovation rate — Enhances innovation by reallocating investment to promising product variants
• Computational accuracy — Improves decision quality by accurately solving complex portfolio optimization problems
• Computational reliability — Ensures consistent optimization results through robust quantum and AI algorithms

Data requirements

• Sales and revenue data (Numeric) — Used to evaluate product performance and customer demand patterns
• Product cost and manufacturing data (Structured) — Supports cost-benefit analysis for portfolio decisions
• Customer order overlap and purchase behavior (Structured) — Identifies product clusters and substitution effects
• Research and development data (Text) — Informs innovation potential and material combinations

AI methods and techniques

• Predictive AI — Forecasts product demand and financial impact of portfolio changes
• Generative AI — Proposes new product combinations and innovation opportunities
• Agentic AI — Automates scenario-based portfolio planning and decision-making

AI models and model families

GPT-4o, Claude, Llama

Industries

Real-world evidence

1 documented case study on record.

Companies using this: DATEV.