Machinery manufacturer achieves 39% better lead time predictions with AI
A global manufacturer needed better predictions for delivery times and costs across its supply chain. AI replaced slow manual estimates and turned procurement from reactive to strategic.
The challenge
The global manufacturer struggled with predicting lead times and costs accurately - less than half of non-stockable items arrived on time and errors multiplied across thousands of suppliers.
Late deliveries, frustrated customers
Less than half of non-stockable items arrived on time. Customers were frustrated, inventories grew unnecessarily and costs spiraled.
Manual estimates didn't scale
Estimating supplier lead times and costs manually was slow and often inaccurate. With over 5,000 active suppliers worldwide, errors were unavoidable - and every wrong estimate showed up as a delay, extra cost or lost order.
AI needed business impact, not experiments
The company knew AI could be the answer - but where to start, which use cases mattered and how to make sure the investment paid off? They needed a partner who understands both business and technology.
What we built
A five-week pre-study mapped the bottlenecks, and within six months we put several AI solutions into production - bridging executive vision and technical delivery.
- 01Diagnosis
Pre-study and use case discovery
In a five-week pre-study we mapped the entire service supply chain: where the biggest bottlenecks sat and where data unlocked smarter solutions. Dozens of opportunities were identified - we picked the most impactful.
- 02Machine learning
Supplier lead time model
A machine learning model analyzes historical data and predicts when goods will actually arrive - not what the supplier promises. The model considers supplier, product, season and dozens of other factors.
- 03AI
Purchasing cost predictions
AI estimates probable purchase prices using over 160 variables. Item cost is known immediately, without sending a request for quote to the supplier.
- 04Production
Customer delivery promises
We unified suppliers, warehouses and logistics into one view of when the customer will receive the product. No more guesses - data-driven promises.
- 05Adoption
Bridge between vision and execution
Across every solution we acted as the bridge between executive vision and technical execution - AI became part of daily operations rather than a stand-alone experiment.
Results
A dramatic shift in the supply chain: more accurate predictions, better inventory turnover and procurement moving from reactive to strategic.
Fewer rush orders, better inventory turnover and happier customers - the machine learning model predicts real arrival times.
Manual estimates were off by EUR 820 per item on average. The AI is off by just EUR 45.
For externally sourced items the improvement reached 23%.
Decisions are based on data, not guesswork. Teams can focus on customers and development while AI handles routine predictions.
Beyond the numbers
- Over 5,000 active suppliers managed under a single model
- AI as part of daily operations - a production solution, not an experiment
