Pre-AI vs. AI-Powered Demand and Supply Planning
Leads to inefficiencies and missed opportunities.
Adds complexity and increases the risk of human error.
Decisions are made based on what did happen rather than what will happen in the future.
Emphasis on forecast numbers leads to replacing data-driven models with subjective guesses.
Results in multiple versions of the "truth" within the organization.
Complexity continues to grow
Systems, teams, and data sit in silos
Uncertainty is difficult to manage
The workforce has changed…and there’s no going back
Benefit: Combines multiple models to improve prediction accuracy and minimize errors.
Use: Enhances demand forecasting and inventory management by reducing stockouts and overstock.
Benefit: AI breaks down demand into trend, seasonality, and fluctuations for deeper insights.
Use: Helps planners adjust strategies based on trends and shifts, improving supply chain efficiency.
Benefit: Identifies unusual data points to prevent errors in forecasts.
Use: Avoids poor decisions caused by atypical events, like supply disruptions or demand spikes.
Benefit: Groups similar data points to discover patterns without prior labeling.
Use: Aids in segmenting products, customers, or regions for more targeted supply chain strategies.
Benefit: Analyzes consumption data for more accurate, real-time demand predictions.
Use: Aligns supply with actual product usage, crucial for industries focused on consumption.
Benefit: Offers real-time support and continuous learning for dynamic environments.
Training: AI adapts to new data, enhancing team skills.
Portfolio Management: Optimizes product prioritization based on performance trends.
Generative AI: Simulates scenarios to improve demand planning and risk management.
