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AI & BI use case · Business Intelligence

Raw-material requirements planning

Predict demand precisely and control procurement.

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What it's about

Raw-material requirements planning combines production plans, inventory levels, lead times and market prices, identifying shortages and overstock early.

  • Lower inventory
  • Cost reduction
  • Better planning
Business case & ROI
−25 %

overstock

−30 %

stockouts

4–6 Mon.

payback

+12 %

forecast accuracy

Calculated from reduced inventory costs, fewer production interruptions and better procurement terms.

How we do it
  1. 01

    Data integration

    Consolidate data from ERP, MES and further sources for Raw-material requirements planning.

  2. 02

    Data quality

    Clean, harmonise and validate data for plausibility.

  3. 03

    Model development

    Train and validate the Raw-material requirements planning model on historical data.

  4. 04

    Pilot

    Pilot Raw-material requirements planning in one area and collect feedback.

  5. 05

    Rollout

    Scale the solution and integrate it into operational processes.

5

Steps

6

Data sources

4

Stakeholders

From first data access to production – every step delivers a tangible interim result.

Data typically needed

ERP data

Master and transaction data from ERP.

Inventory data

Stock levels, movements and locations.

Supplier data

Lead times, certificates, prices and ratings.

Production data

Orders, quantities and feedback.

Sales data

Orders, revenue, channels and customer feedback.

Supply-chain data

Inventory, transport and risk indicators.

Stakeholders

  • Purchasing

    Improves negotiations and supplier selection.

  • Production manager

    Uses insights directly in daily operations.

  • Controlling

    Quantifies effects and supports budgeting.

  • Logistics

    Optimises transport, warehousing and deliveries.

Typical business value
01

Lower inventory

02

Cost reduction

03

Better planning

04

Higher efficiency

05

Higher margins

The data platform advantage

With a solid data foundation this use case gets faster, cheaper and far more stable.

Central data platform

Real-time data integration

Scalable analytics pipelines

Reusable data products

Build a data platform →
Synergies & positive side effects

Inventory optimisation

Forecasts drive inventory and procurement.

Supplier scoring

Quality and delivery data feed scoring.

Demand forecasting

Data flow enables more precise forecasts.

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