This page (revision-12) was last changed on 12-Jul-2022 15:49 by Jonas Umgelter

This page was created on 07-Jun-2022 18:08 by Jonas Umgelter

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Version Date Modified Size Author Changes ... Change note
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10 12-Jul-2022 15:39 1 KB Jonas Umgelter to previous | to last
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8 12-Jul-2022 14:46 939 bytes Jonas Umgelter to previous | to last
7 13-Jun-2022 16:40 851 bytes Jonas Umgelter to previous | to last
6 13-Jun-2022 16:39 854 bytes Jonas Umgelter to previous | to last
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2 07-Jun-2022 19:15 313 bytes Jonas Umgelter to previous | to last
1 07-Jun-2022 18:08 162 bytes Jonas Umgelter to last

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At line 1 changed one line
!!%%text-primary Making use of your process data
!!%%(color: #003399; font-size: 24px;) Making use of your process data
At line 8 changed 2 lines
!!%%text-primary You are not sure if your current process data is ready to use?
-> link to [Data Validation]
!!%%(color: #003399; font-size: 24px;) You are not sure if your current process data is ready to use?
Having high quality data has an enormous potential in improving the data flows within the processes of your company, and is vital in analyzing your data and interpret the results. Therefore, the data quality needs to be checked.
At line 11 changed one line
!!%%text-primary Your process data is valid but product with recyclate is weak?
\\%%(color: #F1717F)The [Data Validation] Tool can help you with that!
At line 13 changed one line
-> link to [Data Analytics]
!!%%(color: #003399; font-size: 24px;) Your process data is valid but product with recyclate shows faults?
Due to instable processes caused by temperature, pressure or other changes in the
process, the quality outcome of extrusion or recycling machines can be lower
than expected. With help of the Process Quality Analytics tool, errors in the
data found during processing certain materials can be spotted automatically.
Such errors can be hard for the naked eye to spot.
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!!%%text-primary (Problem which EPA solves)
\\%%(color: #F1717F)The [Data Analytics Tool |Data Analytics] can help you with that!
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-> link to [Exploratory Pattern Analytics]
!!%%(color: #003399; font-size: 24px;) Do you want to understande and predict faults from process data?
Despite leading to a loss of output, and therefore being potentially very costly to the business, faults are still common in many production processes. Analysing process data to identify simple rules (patterns) between some parameters that are predictive for a certain target parameter can help you prevent faults before they occure.
At line 25 added 2 lines
\\%%(color: #F1717F)The [Exploratory Pattern Analytics] can help you with that!