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Build a strong data supply! But based on internal data demand

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- Gergely Szertics professional - Digitalcompass -

Most companies have some kind of centralised Enterprise Resource Planning (ERP) system upgraded (or downgraded) with a regiment of excels. So companies can process data, can elaborate the rules and processes to use connections within an integrated system. But they often struggle with the amount of work that is needed for data collection and the quality of the data. Sometimes they dream about projects of investing in putting sensors to every corner or make people administer all details, but it seems to be too expensive. We are going to cover the principles of rational data collection based on things that are missing for good decision making instead of based on what could be collected, and the typically neglected sources of data.

IVSZ | EuDEco project
Data Economy Conference
Budapest, 2018. 01. 31.

Published in: Data & Analytics
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Build a strong data supply! But based on internal data demand

  1. 1. Build a strong data supply! But based on internal data demand!
  2. 2. Layers of a data driven company Data driven decision making Data processing – integration and transforming Data collection layer
  3. 3. Identifying what data we need Manual data collection Data collected by machinery Data processed Data used
  4. 4. Identifying what data we need Data that can be collected Data used Processable data with current IT Data that could be used Processable data with IT development Extended collection of data with current machinery We need LEVERS for decision making Data processed
  5. 5. General purpose: improving OEE OEE = Good count Planned production time / Ideal cycle time Availability Performance Qualityx x CONSISTENT DATA Collected, can be analysed CAUSES Aggregated, validated ACTION LIST • Focused • Prioritised • Result oriented Interventions Overall Equipment Effectiveness
  6. 6. Semi-finished products Steel Coveringmaterial Adding covering Paint mixing BURN Paint Mixing covering Finishedproducts Paint1 Paint load Quality measure Quality measure Office Office PLC PLC PLC Paint2 Press
  7. 7. Level 1. Reducing manual data input labour Semi-finished products Steel Coveringmaterial Adding coveri ng Paint mixing BURN Paint Mixin g coveri ng Finishedproducts Paint1 Paint load Quality measur e Quality measur e Office Office PLC PLC PLC Paint2 Press Connecting PLC logs to Excels More reliable data Less manual data entry
  8. 8. Level 2. Causes of downtime and waste Semi-finished products Steel Coveringmaterial Adding coveri ng Paint mixing BURN Paint Mixin g coveri ng Finishedproducts Paint1 Paint load Quality measur e Quality measur e Office Office PLC PLC PLC Paint2 Press • New MES with tablets • Elaborating downtime and waste causes • Automated OEE aggregation • Automated cause analysis • Actions on OEE drivers
  9. 9. Level 3. Digitizing all inputs Semi-finished products Steel Coveringmaterial Adding coveri ng Paint mixing BURN Paint Mixin g coveri ng Finishedproducts Paint1 Paint load Quality measur e Quality measur e Office Office PLC PLC PLC Paint2 Press • New input fields for material qualities • Digitizing all machinery settings • Automated recepy suggestion based on stat data • Faster change time
  10. 10. Level 4. Incorporating quality loop Semi-finished products Steel Coveringmaterial Adding coveri ng Paint mixing BURN Paint Mixin g coveri ng Finishedproducts Paint1 Paint load Quality measur e Quality measur e Office Office PLC PLC PLC Paint2 Press • New input fields and terminals for quality (15’) • New modul for statistical feedback loop • Faster search for complaint handling • SPC – continuous adjustment based on quality trends
  11. 11. Level 5. Automating visual control Semi-finished products Steel Coveringmaterial Adding coveri ng Paint mixing BURN Paint Mixin g coveri ng Finishedproducts Paint1 Paint load Quality measur e Quality measur e Office Office PLC PLC PLC Paint2 Press • Installing camera with machine learning • Building auto waste- extracting system • Work of visual controller spared • Increased quality, reduced complaint handling costs
  12. 12. Start anywhere, but close the loop! Business KPIs Technology Data

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