Closing the planning intelligence gap with AI
Organizations are facing accelerating complexity and change (proliferation of products, channels, geographies, regulation, competition, technology disruptions). This leads to:
- Long tail of interconnected problems and decisions
- Dynamic conditions characterized by uncertainty
- Complexity of multiple factors affecting a decision
The implications are that operations planning and execution decisions across demand and supply areas of an organization are getting increasingly difficult. This leads to several tangible symptoms such as:
- Lost revenue growth opportunities
- High Working Capital (mostly excess inventory)
- Lower Profitability
- Dissatisfied customers, etc.
Current execution and planning systems are not designed to address this situation. Each of the existing enterprise systems from execution to ERP to planning systems are based on a traditional approach – linear, sequential, deterministic, primitive analytics/static rule, & disconnected functional-silos. They were designed to address a different environment and different set of needs. These approaches have several blind spots:
- Inability to learn evolving patterns and behavior in operations – customers, suppliers, etc.
- Ignore external signals – weather, competition, consumer sentiment, macro-economic environment, etc.
- Have a deterministic approach to insights rather than appreciation of probability
- Local/Silo based approach to enable decisions
- Inability to account for human user bias
For example, in our recent work for a leading consumer goods company we observed that the supply chain planning operation was being affected by systematic forecasting and planning bias on one end, along with localized customer demand changes and supply side delays/shocks on the other. This led to a severe inability to coordinate production, materials and inventory functions. Planners were constantly in fire-fighting and catch up mode, unable to take a step back and proactively look at the system behavior as a whole. The result was low fill rates and lost revenues.
Advances in AI (advanced machine and deep learning) can help overcome the above situation by bringing the combination of learning algorithms, internal and external data, engineering and design technology. In the subsequent series of posts, we will delve into different areas of planning (demand planning, materials planning, production planning, etc.) to talk about limitations of existing methods and systems and how Enterprise AI can close the intelligence gap.
- Introduction to MLflow for MLOps Part 3: Database Tracking, Minio Artifact Storage, and Registry
- Believe the Hype: Gartner, Noodle.ai, and Decision Intelligence
- Introduction to MLflow for MLOps Part 2: Docker Environment
- Atlas, Noodle.ai’s Machine Learning (ML) Framework Part 3: Using Recipes to Build a ML Pipeline
- Supply Chain Leaders Struggle with Unpredictability. Noodle.ai has the Cure.