Webinar – Pos campaing

Make an appointment with an manufacturing analytics expert today

Learn how analytics for manufacturing can help your production line operate more efficiently, with higher quality, saving costs.

  • Manufacturing KPIs to maximize ROI
  • Improve your first pass yield
  • AI Analytics dashboard
  • Prediction of failures
  • Product quality problems
  • Root cause analysis down
    to the component level
  • Cross correlations between processes
&nbp;

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How does it work?

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What is required from you?

Step 1

Select your data

You are welcome to select any type of historical testing data
(AOI, end of line, functional testing and so on..).

Data Security

QualityLine is certified for ISO-27001 for data security.  ISO Certificate QualityLine

  • Your data will be encrypted and will never be shared with anyone.
  • You will set a user name and a password for secured access and data upload.
  • If NDA is required, please send your NDA form for us to review and sign to:
    info@quality-line.com

Step 2

Testing data and Repair / Rework data

  • If the testing data is saved as test log files then please save all to zip.
  • If the testing data is saved in database format then please send us a dump.
  • Repair/Rework data (in any format) of units that failed during
    the testing processes you upload and within the same period of time
    (units that were found to be defective in testing).

Step 3


  

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Tips for best analytics results

we recommend that your data will include:

Data for Testing Analytics

  • Serial Number (SN) of each unit
  • Product Part Number
  • Testing station Name/number
  • Operator Name (if exist)
  • Timestamp (time/date)
  • Status of the testing unit (PASS/FAIL)
  • Process Name (if exist)
  • Parameter name (the names of each tested parameter within each test session)
  • Lower Limit of each test
  • Higher Limit of each test
  • Result of each test
  • Test Status – the result of each test – pass/fail (if exist)
  • Test Duration (if exist)
  • Measuring Units (if exist)

Data for Repairs Analytics

  • Serial Number (SN) of each unit
  • Product Part Number
  • Technician name or number  (if exist)
  • Failed parameter of symptom found in the testing
  • The faulty component or material found defective during repair (component number and also reference designator)
  • The failure code that was found by the technician (broken component, cold soldering…)
  • Text description of the problem found (if exists)
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    Unlock the value of your data
    Global end-to-end control

    Automated and fast integration of any manufacturing data source

    QualityLine experts have experience with more than 1500 production lines around the world and more than 30 years in the manufacturing industry.

    We want to use our knowledge and experience to help you improve your manufacturing efficiency and product quality.

    Our machine learning and artificial intelligence technology will make this possible.

    tips_icon

    Tips for best analytics results

    we recommend that your data will include:

    Data for Testing Analytics

    • Serial Number (SN) of each unit
    • Product Part Number
    • Testing station Name/number
    • Operator Name (if exist)
    • Timestamp (time/date)
    • Status of the testing unit (PASS/FAIL)
    • Process Name (if exist)
    • Parameter name (the names of each tested parameter within each test session)
    • Lower Limit of each test
    • Higher Limit of each test
    • Result of each test
    • Test Status – the result of each test – pass/fail (if exist)
    • Test Duration (if exist)
    • Measuring Units (if exist)

    Data for Repairs Analytics

    • Serial Number (SN) of each unit
    • Product Part Number
    • Technician name or number  (if exist)
    • Failed parameter of symptom found in the testing
    • The faulty component or material found defective during repair (component number and also reference designator)
    • The failure code that was found by the technician (broken component, cold soldering…)
    • Text description of the problem found (if exists)