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© 2010 – 2016 eoda GmbHAndreas Wygrabek
Application fields for in
classical industrial-analytics
industry
eRum 2016Andreas WygrabekData Scientist
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Objectives
1. Differentiation of data science and classical analytics tasksin industry
2. Application scenarios for R in industry3. Reveal the potential of R in scenarios of classical analytics
What is this talk about?
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Representative applications of ...
Data Science
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
AnalyticMetrological Data Data Science
Data Science | Energy Forecast
Weather Forecast
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
AnalyticSensor Data Data Science
Data Science | Predictive Maintenance
Machine Failure Predictions
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
AnalyticCustomer Data Data Science
Data Science | Scoring
Conversion Probability
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Representative applications of ...
Classical_Analytics
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
AnalyticAcceptance Sampling Analytic
Classical Analytics | Incoming Goods Inspection
Results (Acceptance, Rejection)
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytic
Sampling Data
Analytic
Classical Analytics | Statistical Process Control
Process Charts
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytic
Sampling Data
Analytic
Classical Analytics | Design of Experiments
Cause-Effect-Relations
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
How do the applications of data science and
classical analytics in industry differ?
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Exploratory Data Analysis (Data Science Methods)
Predictive
Classification
Regression
Clusteranalysis
Modelling
PCA
Forecasts
Data-Mining
Algorithms
Statistics
Mu Sigma
alpha/beta
ProbabilitiesNormal Distribution
Confidence
Inference Statistics
DescriptiveStatistics
MeanRange
Variance
Correlation
…
…
Time Series
…
Testing
Deep Learning
Analytics and Data Science | Methodology
Analytics Data Science
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Project Management
Analytic: Traditional PM
- Solution known- Standardized Issues- Ressource estimation
Data Science: Agile
- Interative solution- Individual Issues
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Data
Analytic: Data is certain
- Data certainly containsall required informations
Data Science: Data is uncertain
- Often it is not certain thatthe data contains therequired information
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Difficulty
Analytic is complicated
- Experts will solve the issue
Data Science is complex
- No guarantee
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Location
Analytic:
Data Science:
- Quality Management- Production
- IT- Business Intelligence- Data Science Lab
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Implementation
Analytic: Choosing the right tool
Data Science: Proof of Concept
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
How capable is R to solve the issues which are
typical in industry?
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
- Incoming Goods Inspection- Sampling- AcceptanceSampling
- Statistical Process Control- spc- qcc- IQCC- qualityTools- SixSigma
- DoE- CRAN Task View: Design of Experiments (DoE) &
Analysis of Experimental Data- Most used package: agricolae
Classical_Analytics
Reporting
- knitr- shiny- interfaces to external
software
in
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Mind the gap
1. R is powerfull – all the tasks mentioned can be solved2. R is free3. R is able to report4. The need for new tools in classical analytics is growing
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
What are the reasons?
1. Qualification2. Old processes3. Established Software
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
To Do´s
1. Bring R-Developers closer to QM andproduction
2. Upskill engineers in R
3. Further package development (AQL, ISO 2859, ISO 3951)
4. Create stable R environments
© 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
@eodaGmbH
@eodaGmbH eodaGmbH
blog.eoda.de
eoda GmbHUniversitätsplatz 12
34127 Kassel - Germany
www.eoda.de/[email protected]
+49 561 202724-40
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