Examples of data, AI and automation projects applied to concrete operational challenges.
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Procurement teams lacked early visibility on future component needs, creating last-minute orders, stock risks and production delays.
Predictive planning model using BOMs, ERP data, stock levels and historical consumption to anticipate purchasing needs before shortages appear.
Engineering teams needed to translate technical data from isometric drawings and CAD files into production systems, creating delays and avoidable interpretation errors.
Automated translation of CAD and isometric drawing data into production-ready structures, with validation before release.
Supplier invoice processing relied on manual checks between invoices, purchase orders and inventory receipts, increasing the risk of errors and uncontrolled payments.
Automated three-way matching workflow comparing invoice data, purchase orders and received inventory before approval.
Static production planning did not reflect real-time machine constraints, creating avoidable throughput losses.
Capacity scheduling engine using shop-floor signals to support dynamic workload balancing.
Sales teams needed to create customer orders from existing ERP quotes through a complex, multi-step process that slowed down confirmation.
Simple outsourced interface retrieving the linked ERP quote and simulating the full order creation workflow before submission.
Leadership lacked a consolidated view of operational and financial performance across multiple sites.
Management cockpit aggregating ERP, MES and CRM data into a unified performance view.
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