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AI & BI use case · Data platform

Supply-chain transparency

Track suppliers, inventory and transport seamlessly.

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What it's about

Supply-chain transparency links inventory, production and logistics data along the value chain, making risks and bottlenecks visible early.

  • More transparency
  • Better planning
  • Cost reduction
Business case & ROI
−20 %

delivery delays

−15 %

safety stock

6 Mon.

payback

100 %

transparency

Calculated from reduced inventory costs, fewer production interruptions and less rush-order effort.

How we do it
  1. 01

    Data integration

    Consolidate data from ERP, MES and further sources for Supply-chain transparency.

  2. 02

    Data quality

    Clean, harmonise and validate data for plausibility.

  3. 03

    Model development

    Train and validate the Supply-chain transparency model on historical data.

  4. 04

    Pilot

    Pilot Supply-chain transparency 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

Supply-chain data

Inventory, transport and risk indicators.

Supplier data

Lead times, certificates, prices and ratings.

Inventory data

Stock levels, movements and locations.

Logistics data

Transport, delivery dates and routes.

ERP data

Master and transaction data from ERP.

Order data

Customer orders, line items and dates.

Stakeholders

  • Purchasing

    Improves negotiations and supplier selection.

  • Logistics

    Optimises transport, warehousing and deliveries.

  • Production manager

    Uses insights directly in daily operations.

  • IT

    Builds on a scalable and secure data infrastructure.

Typical business value
01

More transparency

02

Better planning

03

Cost reduction

04

Lower risk

05

Higher efficiency

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

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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.

More use cases

Lakehouse Architecture

A central lakehouse replaces the patchwork of data warehouse, data lake and Excel exports.

Data Integration from ERP & MES

Automatically merge ERP and MES data instead of exporting and reconciling manually.

Streaming & Real-Time Data

Instead of daily batch runs, relevant metrics are available within seconds.