Global supply chains remain fragile, and raw materials are more expensive than ever. Manufacturers in metals and plastics industries—from aluminium extruders to plastic injection moulders—must juggle hundreds of materials with unique properties (hardness, thickness, density). Holding too much stock ties up working capital and warehouse space; holding too little causes production stoppages and lost sales. Simply relying on spreadsheets and intuition is no longer viable.
Recent research underscores the scale of the challenge. PwC finds that improving demandsensing accuracy by 15 % can drive a 10 % reduction in stockouts and a 5 % increase in ontime deliveries. Moreover, PwC found that companies using demand planning and forecasting to optimize production schedules achieved a 30% reduction in downtime. Last but not least, according to KPMG, businesses that share demand forecasts with their suppliers achieve a 15% reduction in lead times. Within the same spirit, Gartner reported that companies integrating demand planning and forecasting processes with their suppliers saw a 30% improvement in on-time deliveries.
Accurately forecasting future demand allows businesses to make smarter decisions about inventory levels. This capability helps prevent overstocking, which often leads to higher holding costs. In fact, PwC research shows that holding costs can represent 20–25% of a product’s value each year. On the other hand, effective demand planning and forecasting reduce the risk of stockouts, which can cause lost sales and customer dissatisfaction. According to Forrester Research, 15% of customers would not return to a business after experiencing a stockout.
McKinsey shows that companies using AI for demand forecasting can reduce inventory levels by 20–30 % and cut forecast errors by 20–50 %, translating into up to 65 % fewer lost sales or product unavailability. These findings illustrate how closely accurate forecasting is tied to material availability and cash flow. Deloitte research shows that demand planning and forecasting in the supply chain improves forecast reliability by 10-20% ; plus, reducing inventory costs by up to 20% and increasing supply chain efficiency by 15%.
From Data Overload to AI-Driven Decisions
Artificial intelligence unlocks value in three critical areas of inventory management:
- Demand forecasting with machine learning. Traditional forecasting tools extrapolate from historical demand, ignoring real time signals from markets and supply chains. AI algorithms ingest multiple data streams—sales history, economic indicators, weather, raw material prices—and detect nonlinear patterns humans miss. Machine learning (ML) models have revolutionized how businesses analyze and forecast production needs. Unlike traditional approaches, they adapt to new data in real time, enabling forecasts to adjust to shifting market conditions. According to Deloitte, ML models improve forecast accuracy by 30–50% compared to conventional methods—helping companies optimize supply chains and respond more effectively to market changes. McKinsey also agrees, reporting that companies leveraging AI for supply and demand planning achieved a 50% reduction in forecasting errors. In addition, McKinsey reports that companies adopting these models reduce forecast errors by 20–50 % and avoid lost sales by up to 65 %. Better forecasting means having the right materials when needed and avoiding costly safety stock cushions.
- Inventory optimization and dynamic safety stock. McKinsey notes that AI driven planning can lower inventory levels by 20–30 %. By segmenting demand into stable, seasonal and sporadic patterns, AI models adjust safety stocks automatically. The result: lower capital tied up in slow moving items and higher availability of critical parts. Supply chain executives also gain clarity on the true cost of stockouts versus carrying excess, enabling smarter procurement decisions.
- Multiechelon inventory optimization (MEIO). Most businesses manage each warehouse and factory’s inventory independently. MEIO looks at the supply chain holistically, placing stock where it delivers the most value. PwC notes that by combining real-time demand sensing with MEIO, companies achieve a 15 % boost in forecasting accuracy, 10 % fewer stockouts, and 5 % improvement in ontime deliveries. This approach synchronizes procurement, production and distribution, ensuring material flows match actual demand.
Epicor Kinetic in Action: Inventory Planning & Optimization (IP&O)
Epicor Kinetic brings these concepts to life through a comprehensive Inventory Planning & Optimization (IP&O) suite that integrates seamlessly with the ERP. The solution provides:
- AI forecasting with adaptability to seasonality and external factors.
- Automated replenishment linked to suppliers and warehouses.
- Multi-level coordination across warehouses, factories, and distribution points.
- Dynamic dashboards showing real-time forecast accuracy, stockouts, on-time delivery, and inventory coverage.
On top of the standard functionality, ATC can also implement customized dashboards (without additional modules), leveraging ERP/MES data to display measurable results per material code, product category, or supplier. This gives decision-makers granular visibility and actionable insights, turning supply chain data into real business outcomes.
The Business Impact: IDC’s Findings
What happens when manufacturers implement these technologies on a modern ERP platform? A recent IDC study interviewed companies running Epicor Kinetic and found that they realize benefits worth an average of US $2.90 million per organization annually. Key metrics from the report include:
- 373 % three year ROI and nine months to payback.
- 14.2 % higher revenue and a 2.6point increase in gross margin.
- 34 % higher order volume and 39 % more orders delivered on time (94 % ontime delivery).
- 39 % fewer manufacturing errors, 12 % higher equipment use rate, and a 90 % increase in inventory automation.
These results stem from higher throughput, greater operational continuity, inventory automation and the ability to win more business. In other words, AI driven forecasting, MEIO and automation do not just save money; they help companies grow.
Conclusion
Smart inventory management is no longer optional. By combining AI driven forecasting, dynamic safety stock optimization and MEIO, manufacturers can cut forecast errors, reduce stockouts, free up working capital and improve on time delivery. The IDC study confirms that these capabilities, when deployed on Epicor Kinetic, deliver substantial ROI, higher revenues and fewer errors.
Let AI turn your supply chain challenges into competitive advantages.
