Application of Data Mining for Prospective Assembly Time - - PowerPoint PPT Presentation

IDS 2017 Industrial Data Science Conference

Application of Data Mining for Prospective Assembly Time Determination

Dortmund / 05.09.2017, Dr. Olga Erohin and Ralf Kretschmer

 Miele Group and Business Unit Professional  Research Project „Pro Mondi“ and time data management  Knowledge discovery for prospective assembly time prediction  Assembly time prediction in the product development phase  Conclusion

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Agenda

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Scope and goals of the research project Pro Mondi

Assembly oriented data model (product and process view)

Identification and representation

• f process-relevant

product properties Detection and description

• f product-characteristic

process pattern

Mapping of product and process structure

Functional BOM

Product planning

Engineering BOM

Product development

Manufacturing BOM Manufacturing BOM

Production Product emergence Manufacturing

Cross-domain knowledge BOM=Bill of Materials

Process planning Research project (2012-2015): „Prospective determination of assembly work content in digital factory (Pro Mondi)“

Source: Research project “Pro Mondi”, http://www.pro-mondi.de/

• 15-70% of production time is assembly time
• Manual assembly is a wide-spread assembly method

for multi-variant products

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Assembly time is the basis for various processes

Time data

Benchmarking Work system design Production control Performance assessment Capacity planning Cost calculation Human resource planning Scheduling Invest planning Value stream design

Sources: B. Lotter: Einführung. In: B. Lotter, H.-P. Wiendahl (Hrsg.): Montage in der industriellen Produktion – Ein Handbuch für die Praxis. Springer Verlag 2012.

• J. Deuse, F. Busch: Zeitwirtschaft in der Montage. In: B. Lotter, H.-P. Wiendahl (Hrsg.): Montage in der industriellen Produktion – Ein Handbuch für die Praxis. Springer Verlag 2012

Miele & Cie. KG.

• Time-related data are applied in manifold areas
• Time data management (e.g. time studies) is an

essential task field of Industrial Engineering

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Two results of the research project Pro Mondi

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Time agreement Predetermined motion time systems Simulation (calculation) Registering by devices Inquiry Self-recording Time and motion study Work sampling study Standard data building blocks Comparative estimating MTM-Analysis MTM-ProKon

Production Production planning Product development ProWiZei Knowledge discovery for prospective assembly time prediction ATP Assembly time prediction in the product development phase

Sources: O. Erohin, J. Schallow, J. Deuse, R. Klinkenberg: Application of data mining to predict assembly time in early phases of product emergence. CIE43 Proceedings, 2013. Miele & Cie. KG

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KDID in context of time data management

Milestones Iterative steps TDM: Time Data Management KDID: Knowledge Discovery in Industrial Databases

Preparation Data Mining Realization

Define the goals of knowledge discovery in time data management Explore the processes

• f time data management

Explore relevant IT systems and TDM-data connections

1.1 1.2 1.3

Create an IT prototype Integrate KDID into planning processes and IT systems

3.1 3.2

Visualize and interpret the results Create and apply data mining models Descriptive and explorative analysis of data Build a raw data matrix of TDM-data M2

2.4 2.1 2.2 2.5

Preprocessing of raw data matrix of TDM-data

2.3

M1

Source: O. Erohin: Wissensgewinnung durch Datenanalyse zur prospektiven Zeitermittlung. Shaker Verlag 2017.

ProWiZei Morphology

Determination Pre-Processing Application Data management

RapidMiner

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Realization of KDID and some prediction results

Data Mining method RapidMiner operator Relative error Linear regression Linear Regression 18,2% Local polynomial regression Local Polynomial Regression 33,1% Regression model tree W-M5P 33,4% Regression tree W-RepTree 29,5% Support vector regression LibSVM Support Vector Machine 17,1%

Source: O. Erohin: Wissensgewinnung durch Datenanalyse zur prospektiven Zeitermittlung. Shaker Verlag 2017.

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Continuity of time data determination along the product emergence process (PEP)

design phase design freeze development phase

designer industrial engineer, assembly planner

process planning development

iteration loop iteration loop iteration loop

PEP time type process step software Data class approximate time production-oriented design (assessment of assembly fairness) target time analysis of predetermined motion time systems prediction time prospective assembly time prediction product data process data

Source: Personal research work by R. Kretschmer

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Mapping of product and process data

B-building-block(s) (joining processes) V-building-block(s) (connection processes)

+

basic assembly time for component A

+ +

product-oriented time-data structure

…

hierarchical product structure

product component group 1 component A component B component C component group 2 component D component E …

process data

(industrial engineering, assembly planning, …)

product data

(CAD, PDM, …) assignment via ID process data product data

Source: Personal research work by R. Kretschmer

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Concept for assembly time prediction

current data (model use) data based on the past (model creation based on instances group) process steps process data product data product data process data components 1-n new component assembly times 1-n n n

B-building blocks (joining processes)

n n

V-building blocks (connection processes)

Connection process type(s) + number identification of k- nearest-neighbours predicted assembly time for a new component n n

prediction B-building block(s) (joining processes) prediction V-building block(s) (connection processes)

+

mapping via ID

Source: Personal research work by R. Kretschmer

 Assembly-time prediction compared to system of predetermined time (MTM-TiCon)

and other established time-determination systems

 Conclusion:  Best results for further development within the component family  Widely varying quality of results for complete new design and/or missing component

family (=> small data amount for comparable components related to the past)

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Evaluation: Validation and assessment of results

Deviation to system of predetermined time (relative error)

Source: Personal research work by R. Kretschmer

0,0% 5,0% 10,0% 15,0% 20,0% 25,0% 30,0% front panel A front panel B front panel C front panel D

approximate time according to ProKondigital assembly time prediction (ATP) for a changed computer-aided design (k=4) assembly time prediction (ATP) for a new computer-aided design (k=4) actual time recording according to REFA

 Integration of data mining for prospective determination of assembly time leads to

essential added value for planning and decision-making

 … and supports the idea of simultaneous engineering to reduce the product

emergence time.

 Current portfolio of methods for time determination can be successfully extended

by new data mining methods.

 Fundamental factors of success are  Integration of specific know-how of the application area (especially at the beginning of

knowledge discovery).

 Overcoming the challenges of “historically evolved” IT infrastructures.

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Conclusion

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Ralf Kretschmer Director Segment Professional Laundry Technology Lehrte Miele & Cie. KG Industriestraße 3, 31275 Lehrte ralf.kretschmer@miele.com http://www.miele.com

• Dr. Olga Erohin

Director Corporate Development Professional Technology Miele & Cie. KG Mielestraße 2, 33611 Bielefeld

• lga.erohin@miele.com

http://www.miele.com

Thank you very much for your kind attention!

For further information please visit: www.pro-mondi.de