Thinking
demand forecasting
All articles tagged with "demand forecasting".
Operational workflow improvement
Why Fortune 500 Supply Chain AI Fails at Mid-Market: The Complexity Mismatch Problem
Fifty-seven percent of supply chain leaders cite data quality as the primary barrier to AI adoption. Not model accuracy. Not cost. Data quality. Most mid-market distributors have data spread across five to seven systems with no single source of truth. You cannot train an AI model on conflicting data. The unglamorous work of master data management, integration, and governance must come first.
Operational workflow improvement
Inventory Decisions Under Uncertainty: When AI Forecasts Conflict With Safety Stock Rules
Deploying demand sensing AI without retiring legacy safety stock policies creates a hidden cost: dual everyday work. Inventory planners second-guess AI recommendations, override autonomous reorder points, and maintain manual guardrails 'just in case.' This article quantifies the cost of running both systems, explains why most deployments fail at the governance layer, and outlines the workflow clarity required to let one system own inventory decisions.
Operational workflow improvement
Predictive Orchestration: The 2026 Supply Chain AI Priority CSCOs Can't Ignore
Most supply chain directors spend Monday mornings reviewing exceptions from last week. A supplier shipment arrived late. A customer order exceeded forecast. A warehouse ran short on a fast-moving line. By the time the team decides what to do, the problem has compounded.
Platform capabilities and technical insights
Market Sensing Intelligence: Reading Weak Signals Before Competitors
Quarterly forecasts miss demand shifts that happen in days. Demand sensing uses real-time weak signals—POS data, supply disruptions, social trends—to detect changes 24-72 hours before they appear in historical patterns. For mid-market distributors managing volatile categories, this decision speed translates directly into margin capture and stockout reduction.
Operational workflow improvement
Faster Supply Chain Planning: Why Speed Beats Perfect Forecasts in 2026
Most mid-market distributors believe their slow decisions are caused by poor AI models or insufficient data. In reality, they've built latency into their system architecture. A well-trained model can generate a recommendation in milliseconds, but the decision doesn't reach the operator for 3-8 seconds—or longer—because of data pipeline delays, API call chains, and integration bottlenecks. Decision speed directly impacts margin recovery, inventory turns, and fulfilment efficiency. Yet companies continue to upgrade models while ignoring the infrastructure that determines how fast those decisions can actually execute.
WithPraxis
Fashion Distribution Intelligence: How AI Transforms Seasonal Buying Decisions
Fashion distributors write off £25,000-£45,000 in dead stock each season from three decisions made six months early: style selection, quantities, and size curves. Applied AI transforms seasonal planning by handling complex optimisation while preserving buyer expertise in trend interpretation.