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

2

Agenda

3

Scope and goals of the research project Pro Mondi

Assembly oriented data model

(product and process view)

Identification and

representation

of process-relevant

product properties

Detection and description

of 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

4

Assembly time is the basis for various processes

Time

data

Benchmarking

Work system

design

Production

control

Performance

assessment

Capacity

planning

Cost

calculation

Human resource

planningScheduling

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

5

Two results of the research project Pro Mondi

5

Time agreement

Predetermined motion time systemsSimulation (calculation)

Registering by devices Inquiry

Self-recordingTime and motion study

Work sampling study

Standard data building blocks

Comparative estimating

MTM-AnalysisMTM-ProKon

ProductionProduction planning

Product development

ProWiZei Knowledge

discovery for

prospective

assembly

time

prediction

ATPAssembly

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

6

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

of 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.

Pro

WiZ

ei

Morp

holo

gy Determination Pre-Processing Application

Data management

Rapid

Min

er

7

Realization of KDID and some prediction results

Data Mining method RapidMiner operatorRelative

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.

8

Continuity of time data determination along the product

emergence process (PEP)

design phasedesign

freezedevelopment phase

desig

ner

industr

ialengin

eer,

assem

bly

pla

nner

process planningdevelopment

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

9

Mapping of product and process data

B-building-block(s)

(joining processes)

V-building-block(s)

(connection processes)

+

basic assembly time for component A

+

+

pro

du

ct-

orie

nte

dtim

e-d

ata

str

uctu

re

…

hie

rarc

hic

alp

rod

uctstr

uctu

re

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 dataproduct data

Source: Personal research work by R. Kretschmer

10

Concept for assembly time prediction

current data (model use)data based on the past

(model creation based on instances group)

process steps process dataproduct data

pro

du

ctd

ata

pro

ce

ss

da

tacomponents 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) 11

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 toProKondigital

assembly time prediction (ATP) for achanged computer-aided design (k=4)

assembly time prediction (ATP) for a newcomputer-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.

12

Conclusion

13

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

olga.erohin@miele.com

http://www.miele.com

Thank you very much for your kind attention!

For further information please visit:

www.pro-mondi.de