“industrial data science – technical, organizational and human … · 2 days ago ·...

“Industrial Data Science –

Technical, Organizational and Human Challenges"

prostep IVIP Symposium – 02.09.2020

Dr.-Ing. Julian SchallowIndustry Coordinator at IPS TU Dortmund,

Founder & CEO of IPS Engineers GmbH

Dipl.-Ing. Andreas EidenInstitute of Virtual Product Engineering (VPE)

TU Kaiserslautern

What is Industrial Data Science (IDS) all about?

201.09.2020Dipl.-Ing. Andreas Eiden, Dr.-Ing. Julian Schallow, Joint Project AKKORD

Data Science is the extraction

of informationen and knowledge from data.

The application of Data Science in an industrial context

is also called Industrial Data Science.

01.09.2020 3

Are you already on your way to apply Data Science?

Dipl.-Ing. Andreas Eiden, Dr.-Ing. Julian Schallow, Joint Project AKKORD

“How present is the Topic of (I)DS in your Company?” ??

01.09.2020 4

There are two common Ways to approach IDS.

“We want to explore thepotential of Data Science!”

“We use Data Science to solve an existing problem!”

Use-Case-specific

IDS-Project

[Gavalas 2015]


?

?

?

01.09.2020 5

Which are the Key Requirements for successful IDS-Projects?


?

?

01.09.2020 6

A well-defined Procedure is a Key Requirement.


01.09.2020 7

CRISP-DM provides a well-defined Project Structure.

Business

Understanding

Data

Understanding

Data

Preparation

Modeling

Evaluation

Deployment

Data

Selection and configuration of suitable prediction models

Measuring and processing of test and process data

Implementation in an IoT-platform Data aggregation and cleansing

Minimizing the slip rate

SMD-Value Stream: Short quality control loops

reduce the scope of the radiographic examination


?

01.09.2020 8

The second Key Requirement is the Maturity of Data.


01.09.2020 9

Data Maturity can be assessed based on defined Criteria.


CriteriaMaturity level

1 2 3 4

Data collection manual entryelectronical, must be triggered

manually

data acquisition is carried out

automatically in most cases

fully automated

data collection

Completeness of

data collection

unilateral and incomplete recording of

relevant characteristics

recording of the essential

characteristics

recording of a large part of the

relevant characteristics

recording of all relevant,

(un)influenceable characteristics

Sample size no historic data small sample per object grouplarge sample per object group, but

unbalanced data

large sample with large number per

object group and class

Data sources paperbased recordsdecentralised data storage with

simple software (e.g. Excel)

different data management systems

with central data storagecomprehensive Data Warehouse

Data formatformats that are difficult to process

(e.g. scans, photos)

formats with limited processability

(e.g. PDF)

different, directly processable

formats (e.g. CSV, XML)comprehensive standard format

Data structureunstructured text

or images

semi-structured data

(e.g. XML, JSON)

structured,

mixed-scaled data

structured, metrically scaled data

and standardized codes

Feature type only set points highly aggregated actual valuesaggregated actual values or raw

data with low sampling rateraw data in real time

Reference levelvalue characteristics at the highest

reference level

value characteristics at the upper

reference level

value characteristics at the next

higher level

value characteristics at individual

element level

Consistency of data no consistency/integritymassive amount of logical

differencesfew logical differences full integrity/consistency

Traceability no ID/ time stamp different ID/ timestamp comprehensive ID/ time stampcomprehensive ID/ timestamp on

same reference level

Reference: Eickelmann et al. (2019):

Bewertungsmodell zur Analyse der Datenreife.

In: ZWF Jg. 114, 1-2, S. 29-33

01.09.2020 10

Working in interdisciplinary Teams is urgently required.

Industrial

Data

Science


01.09.2020 11

Interdisciplinary Teams enable Leaps in Knowledge.

Industrial Production

Industrial Production

Machine

Learning

Digital

Manufacturing

Industrial

Data

Science

Production

Engineering

StatisticsComputer

Science

Prof. Jens Teubner:

▪ Data Management Basics

▪ Data Warehouses

Prof. Claus Weihs:

▪ Association Analyzes

▪ Data Transformations

▪ Concepts for Model Selection

Prof. Jochen Deuse:

▪ Introduction to Industrial Data Science

▪ Data in the industrial environment

▪ Data analysis in the industrial environment

Prof. Kristian Kersting:

▪ Deep Learning

▪ Tree-based procedures

▪ Ensembles


Six

Sigma

Prof. Jens C. Göbel:

▪ Engineering Data Management across the Product Lifecycle

▪ Data Analytics for Smart Product Development

▪ Data Modelling for Digital Twins

01.09.2020 12

The three Key Requirements built the Basis for advanced Research.

Organisation

Industrial

Data

Science


Technology

HumanOrganisation

?

AKKORD - Overall Objective - Concept Graphics

1301.09.2020

Competencies and

recommendations for action

Analysis modules and configuration

Integrated, data-driven reference model kit & online service platform

Consulting Services

Recommendation Assistants

Digital Knowledge Service

Use Case Scenarios

Collaboration and

business modelsManagement

IntegratorDepartment

Data

Analytics

Information

Technology

Data backend system

Industrial data analysis &

linking of data

Product DesignProduct Development ProductionProduct Planning Use and Service

Supplier Customer

Recycling


Competencies and




Consulting Services



Use Case Scenarios

Collaboration and



Data

Analytics

Information

Technology

Data backend system


linking of data


Supplier Customer

Recycling

Goals - New Collaboration Opportunities and Business Models

1401.09.2020

• Development of a data analysis-based recommendation assistant for innovative business models

• Learning models provide an assessment of the situation and recommendations for action based on

real-time and historical data

• Business models are linked to their associated key resources (people, machines) and key activities

(production, work, product development and product use processes)

Collaboration and



Data

Analytics

Information

Technology


Competencies and




Consulting Services



Use Case Scenarios

Collaboration and



Data

Analytics

Information

Technology

Data backend system


linking of data


Supplier Customer

Recycling


Goals - Integrated and linked Analysis of Industrial Data

1501.09.2020

• Development of a user-oriented configuration assistant for application-specific data analysis

• Development of analysis modules and dashboards that can be pre-configured and assembled

• Development of interfaces, rules and application-oriented tools for semi-automated configuration


Competencies and




Consulting Services



Use Case Scenarios

Collaboration and



Data

Analytics

Information

Technology

Data backend system


linking of data


Supplier Customer

Recycling

Goals - Competence Development in Value Creation Networks

1601.09.2020

• Development of structures to record, link and secure competencies

• Development of situated learning modules for application-oriented competence development

• Development of a digital knowledge service for SME-oriented competence development and networking

in the thematic area "Industrial Data Analysis"

Competencies and



Competencies and




Consulting Services



Use Case Scenarios

Collaboration and



Data

Analytics

Information

Technology

Data backend system


linking of data


Supplier Customer

Recycling

Goals - Creation of a comprehensive and networked Database

1701.09.2020

• Development of data acquisition solutions, data formats, interfaces and communication protocols as

well as application-specific integration and linking modules

• Automated semantic networking of heterogeneous (e.g. unstructured, human-related) data

• Development of modules for the evaluation of data quality

Data backend system


Competencies and




Consulting Services



Use Case Scenarios

Collaboration and



Data

Analytics

Information

Technology

Data backend system


linking of data


Supplier Customer

Recycling

Goals - Integrated, data-driven Reference Model Kit

1801.09.2020

• Implementation of the reference model kit as a collaborative service platform to promote the broad-

based use and expansion of the kit

• Modular provision of application-oriented modules, concepts, guidelines, recommendations for action,

services and exemplary implemented application scenarios

• Realization of the service platform in the sense of an Internet-based marketplace, so that future

developments can also be incorporated after the project has been completed


Consulting Services



Use Case Scenarios


Use-Case 1: overarching, predictive Industrial EngineeringVolkswagen AG

1901.09.2020

Use-Case Goal

Reduction of manual comparison and search work by automated analysis of process data to avoid multiple solutions for the

same work.

Work packages

▪ Development of a method for the Identification of

similar labor systems

▪ Development of a benchmark for the automated

comparison of labor systems with IT-tools for the

design of labor processes

▪ Development of data mining processes for

context-aware suggestion of product and process

planning


Use-Case 2: data driven Quality ManagementMiele

2001.09.2020

Use-Case Goal

Development of a holistic and linked tool for reporting and analysis.

Demonstration of the quality situation with feedback and learning loops.

Enabling of user-specific data analysis and development of

competencies for the data usage inside the company

Work packages

▪ Development of a user-specific tool for visualizing KPI and

provisioning of analysis results from linked data


Data

Analysis

Modules

Data Backend

2301.09.2020

Backend

Fro

nt

-End

Busin

ess U

nders

tandin

g

Data

Unders

tandin

g

Data

Pre

para

tion

Modelli

ng a

nd A

naly

sis

Evalu

ation

Rollo

ut

Building a datamodel

Understanding data sources

Linking Data Enriching Data


Data Analysis Modules

Data Preparation & Analysis Modules

2401.09.2020

Ba

ck-

En

d

Fro

nt

-End

Busin

ess U

nders

tandin

g

Data

Unders

tandin

g

Data

Pre

para

tion

Modelli

ng a

nd A

naly

sis

Evalu

ation

Rollo

ut

Purchasing

Production Preparation and Planning

Production Surveilance and Control

Sales and Distribution Logistics

Services/After Sales

Sales/Marketing

Ord

er

Pro

ce

ssin

g P

roce

ss

Normalization

Binning

Missing

Outliers

Dimensionality


What about the Return on Investment (ROI) in IDS-Projects?


“How economical are your current IDS-activities?” ??

Data Preparation for Data Analytics and Artificial Intelligence (DPDA)

2601.09.2020

Data ScientistData Engineer

Management

Domain experts(Process-, Data- und Systems owner)

APP Developer Data Analyst

Orchestrator(Project owner)

Result example: “Role Model for DPDA projects“

Goal: “Establishing methods and standards to ease DPDA in industrial applications“

• founded in 2019 –> ongoing work in 2021

• user driven project group within PSI association

• use case and application centric collaboration

• integrating all PSI member groups

Future topics:

• Standards and Tools to Extract-Load-Transform

• Room for your input and requirements

• …

Join the DPDA Group in 2021!contact: [email protected]


supported by:

Digitalization enables a Dynamic Value Stream Analysis (DVSA)

Dynamic Bottleneck

27

▪ seamless data acquisition

▪ automated calculation

▪ dynamic visualization

in cooperation with:


Summary

2801.09.2020

▪ Industrial Data Analytics is a complex, interdisciplinary topic.

▪ SME need support to find suitable use-cases, technical support and ways to train employees.

▪ The AKKORD integrated data-driven reference model kit & online service platform provides necessary tools, learning courses and best practices for SME.

▪ The data acquisition & preparation need standards to enable easier analysis and collaboration.

▪ The prostep Data Preparation for Data Analytics and Artificial Intelligence (DPDA) group works on standardization of these topics

▪ Join the DPDA-group if you are interested!


The Factory of the Future will remain a socio-technical System.

2901.09.2020

Organisation

Human Technology


www.akkord-projekt.de

Contact

Thank you for your kind Attention!

Andreas Eiden

+49 (0)631 205 3990

Julian Schallow

+49 (0)231 9700 711

[email protected] [email protected]

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