AI-Driven Supply Chain Optimisation for Manufacturing
Deploy AI across demand forecasting, inventory optimisation, and supplier management to reduce inventory costs by 20% while improving service levels.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Demand planning uses spreadsheet-based statistical models with limited accuracy (MAPE 25-35%). Safety stock levels are set conservatively, tying up 30-40% excess inventory. Supplier performance is tracked reactively. Stockouts still occur 3-5% of the time, causing production delays and lost revenue.
After
AI demand forecasting achieves 15-20% MAPE improvement by incorporating external signals (economic indicators, weather, social media trends). Dynamic inventory optimisation adjusts safety stock in real-time based on demand volatility and lead time variation. AI monitors supplier risk and recommends proactive actions.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Baseline Current Performance
2 weeksMeasure current demand forecast accuracy (MAPE, bias), inventory metrics (turns, days of supply, excess/obsolete), service levels (fill rate, OTIF), and supplier performance (on-time delivery, quality). These become the benchmarks for AI improvement.
Build AI Demand Forecasting
6 weeksDevelop ML demand models that incorporate: historical sales, promotional calendars, pricing, weather, economic indicators, and competitor signals. Use ensemble methods combining statistical and ML approaches. Implement at SKU-location level for granular planning.
Implement Inventory Optimisation
4 weeksReplace static safety stock rules with dynamic, AI-calculated levels. Factor in demand variability, lead time uncertainty, service level targets, and cost of stockout vs. holding. Build automated replenishment triggers connected to ERP.
Add Supplier Intelligence
4 weeksDeploy AI to monitor supplier risk: financial health, geopolitical exposure, quality trends, and delivery reliability. Build early warning system for supply disruptions. Generate recommendations for order reallocation and alternative sourcing.
Optimise & Scale
3 weeks + ongoingContinuously tune demand models with new data. Expand from pilot SKUs/locations to full product portfolio. Integrate with S&OP (Sales & Operations Planning) process. Build executive dashboards showing AI-driven supply chain KPIs.
Tools Required
Expected Outcomes
Improve demand forecast accuracy by 15-20% (MAPE reduction)
Reduce inventory holding costs by 15-25%
Decrease stockout rate from 3-5% to under 1%
Identify supply disruption risks 4-8 weeks earlier
Free up $1-5M in working capital (for mid-size manufacturer)
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
For new products, AI uses analogous product patterns, market research data, and early sales signals to build initial forecasts. Models improve rapidly as actual sales data accumulates. This "cold start" approach typically outperforms traditional methods (which often rely on team judgment alone) within 2-3 months of launch.
Yes. Modern AI supply chain platforms integrate with all major ERP systems (SAP, Oracle, Microsoft Dynamics, etc.) via standard APIs and data connectors. The integration typically takes 2-4 weeks and can be done without disrupting ongoing ERP operations.
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