Data2Value Initiative
Executive Summaries & Use Cases
Frank Kienle
"Supply chain analytics can support, improve or automate any decision within supply chain management processes"
As businesses expand and supply chain structures grow in complexity, the need for enhanced visibility and decision support becomes imperative.
Analytics transforms raw data into actionable insights across the entire supply chain - from sourcing raw materials to delivering finished products to customers.
Supplier selection, performance, compliance, risk management
Manufacturing processes, cost optimization, predictive maintenance
Forecasting, inventory optimization, S&OP
Customer satisfaction, logistics, network design
| Level | Purchasing | Production | Distribution | Sales |
|---|---|---|---|---|
| Strategic (Long-term) |
Supply Network Planning | Supply Network Planning | Supply Network Planning | Demand Planning |
| Tactical (Mid-term) |
Materials Requirements Plan | Production Planning | Distribution Planning | Demand Planning |
| Operational (Short-term) |
Materials Requirements Plan | Detailed Scheduling | Transport Scheduling | Available-to-Promise |
Interpretation of historical data
"What happened?"
Predicting future event probability
"What will happen?"
Suggesting decision options
"What should we do?"
Time-series forecasting (ARIMA, Prophet) • Machine Learning (Random Forests, XGBoost) • Deep Learning (LSTM, RNN) • Optimization (Linear Programming, MILP) • Simulation • Process Mining
"Analytics translators perform some of the most essential functions for integrating analytics capabilities in a company. They define business problems that analytics can help solve, guide technical teams in the creation of analytics-driven solutions, and embed solutions into business operations."
Management, problem solving, sales framework, product management
Process modeling, software design, enterprise architecture
Statistics, data management, decision making, AI/ML
Choose a domain to dive into the use cases