Technische Universität Bergakademie Freiberg Institute of Management and Information SystemsSilbermannstraße 2, 09599 Freiberg (Saxony), Germany

Industry 4.0 from the Viewpoint of Business Analytics

Dipl. Wi.-Ing. Tom Hänel

1st PhD Conference "Moments of

Finding", 9th June 2016, Freiberg.

Key Facts on Industry 4.0

Establishing of internet technologies on the shop-floor

Production of individual products in time of mass production

Increase of production efficiency

Increase of automation and IT usage across the whole value chain

Decentral and self-organized production processes

Development of new business models

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The Way to Industry 4.0

Different IT systems in industry

– execute,

– monitor,

– model or

– control

actions of manufacturing and product development.

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Support of technical functionsIntegration of processes and

value chainsDigitalization and globalizationInnerorganizational integration

Industry 4.0

NC, CNC,

DNC

Digital

Factory

CAD, CAE,

CAM, CAP,

CAQ

PPS, CIM Lean Production,

Agile ManufacturingERP, MES,

PDM, PLM

Cyber-physical Systems

The technological basis of Industry 4.0 build up cyber-physical systems as well as the internet of things and services.

The terms characterize an integration and synchronization of information from physical and digital environments.

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Network

Physical world

Sensor

Sensor

Computation Computation

Computation

Actuator

Physical interface

Physical interface

Industry 4.0 requires Concepts of Business Analytics

Characteristics of CPS solutions:

– Horizontal and vertical integration of production systems

– Integrated technology concepts throughout the entire life cycle of resulting products.

In order to plan, simulate, describe and evaluate user-driven CPS solutions, interdisciplinary data models need to be created.

These data models have to represent the as-is and the to-be situation of production environments.

The corresponding data organization encompasses methods to collect and analyze decision-relevant information across different CPS, and to provide these information to decision makers.

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Industry-Driven Concepts with Analytical Functions

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Manufacturing Intelligence

Manufacturing ExecutionSystems

Smart Manufacturing

Statistical Process Control

AdvancedProcess Control

Six Sigma

KaizenTotal Cycle Time

Changing Information and Communication Structures

CPS describe flexible, adaptive, self-organizing and self-configuring production systems.

This requires informal networks and the ubiquitous availability of data and analytical services.

Data and services are retrieved and executed at respectively suitable locations in a Smart Factory.

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Enterprise management

Production management

Production operation

Process control

Process recording

Process executionLevel 0

Level 1

Level 2

Level 3

Level 4

Process execution

Components of a Smart Factory

The Smart Factory aims at flexible handling of individual customer requirements, mastery of complex production tasks, minimizing of vulnerability and increase of efficiencies in production.

The focus is on a communication of people, equipment and resources in an Internet-based network.

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Smart Factory – Building Blocks

Intelligent

Dashboard

Visualization

Decision SupportSocial

Collaboration

Workplace

Learning

Mobile DevicesHead Mounted

Displays

Wearables (e.g.

Smart Watches)Desktop / Machine

Data Mining and

Analytics

Semantic

TechnologiesSocial Software

Visualization

Framework

Worker

Environment

(Sensors)

Manufacturing IS:

ERP, MES,

SCADA, ...

Knowledge

Management

Systems

Big Production

Data

Smart Factory

Data

Smart Factory

Infrastructure

Worker-Centric

Service Building

Blocks

Worker-Centric

HCI/HMI Building

Blocks

Business Analytics in the Upcoming Smart Factory

The data basis consists of sensor data from working environments, task-specific IT systems, knowledge management systems, and a mass of production data generated by CPS.

A human-machine interaction is concerned by the topics of Data Mining, Analytics, Intelligent Visualization and Decision Support.

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Smart Factory – Building Blocks

Intelligent

Dashboard

Visualization

Decision SupportSocial

Collaboration

Workplace

Learning

Mobile DevicesHead Mounted

Displays

Wearables (e.g.

Smart Watches)Desktop / Machine

Data Mining and

Analytics

Semantic

TechnologiesSocial Software

Visualization

Framework

Worker

Environment

(Sensors)

Manufacturing IS:

ERP, MES,

SCADA, ...

Knowledge

Management

Systems

Big Production

Data

Smart Factory

Data

Smart Factory

Infrastructure

Worker-Centric

Service Building

Blocks

Worker-Centric

HCI/HMI Building

Blocks

Concept of an Analytical Platform in Industry 4.0

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Decision support

Data source

Integration

Presentation

Data repositories

Production processes

Meta data RulesService

description

Mobile Devices

Head Mounted

Displays

Wearables (e.g.

Smart Watches)

Desktop / Machine

Worker

Environment

(Sensors)

Manufacturing IS:

ERP, MES,

SCADA, ...

Knowledge

Management

Systems

Big Production

Data

Data mining and analytics

Intelligent dashboard

visualization

Event engineOrchestration

engineRules engine

Current Experiences of Analyzing Production Data

We use production data from a rod and wire rolling process to gain experiences in analyzing production data.

Different IT tools and methods for data acquisition, data provisioning and data analysis have been used in this context.

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Data Modelling

Data Acquisition

Data Provisioning Data Analysis

iba Process

Data Acquisition

Pentaho

SQL Server

MicroStrategy

Rolling ProcessADAPT

Schema of the Rod and Wire Rolling Process

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Inductive Heating

Driver 42

Temperature 10

Revs per Minute – Actual

Voltage – Actual

Armature Current - Actual

Two-high Reversing Mill

Mill Force Front Left

Mill Force Front Right

Mill Force Back Left

Mill Force Back Right

Momentum

Temperature 2 (Inflow)

Temperature 3 (Outflow)

Revs per Minute - Actual

Voltage - Actual

Finishing Mills

Mill Force (F1, F2, F3, F4)

Momenta (F1, F2, F3, F4)

Temperature 4 Before F1

Temperature 6 Before F3

Temperature 7 Before F4

Temperature 8 After F4

Revs per Minute - Actual (F1, F2, F3, F4)

Voltage - Actual (F1, F2, F3, F4)

Armature Current - Actual (F1, F2, F3, F4)

Engine Speed - Actual (F1, F2, F3, F4)Armature Current - Actual

Trigger Pyrometer

Temperature

Cooling Line

Temperature 9

Looper

Revs per Minute - Actual

Voltage - Actual

Armature Current - Actual

Roughing Mill Final Rolling Pass

Data Modelling

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Dimension Date

Dimension Time

Dimension Transaction

Dimension Experiment

Date Hierarchy

Month{ }

Day{ }

Time Hierarchy

{ }

{ }

{ }

{ }

{ } Experiment

Transaction Hierarchy

Subprocess{ }

Rolling Phase{ }

{ } Monitoring Point

{ } Determinant { } Temperature

{ } Force

Feature

Experiment Indicators

Rolling Phase Indicators

{ } Duration

{ } Temperature

{ } Force

{ } Momenta

{ } Revs per Minute

{ } Duration of Phase

{ } Voltage

{ } Armature Current

{ } Engine Speed

Dimension Experiment

Dimension Date

Dimension Time

Dimension Transaction

Experiment Indicators

Rolling Phase Indicators

Rod and wire rolling { } Material

Year{ }

Multidimensional datamodel represented in

ADAPT

4 dimensionsrepresenting process

design, 2 dimensions forprocess operation

Data Provisioning

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ETL-Processimplementedwith Pentaho.

Galaxy Schema implemented with

SQL Server

Data Analysis

The data analysis was performed on the analytical platform of MicroStrategy.

We created several reports that are presented via a web interface for example in report documents or analysis dashboards in context of basic reporting.

In addition, the development environment provides extensive possibilities to investigate the data according to various aspects.

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Business Analytics compared to the Traditional Approach of Analyzing Production Data

Traditional Approach Business Analytics

ConciseRepresentation

&Consistent

Representation

Two-dimensional presentation of measurement parameters and values

Experiment-related presentation Manual and unstandardized reports Presentation by simple graphs or

spreadsheet programs

Multidimensional presentation of descriptive information, measurement parameters and values

Use of hierarchies Process-related presentation Automated and standardized reports Various presentation options

(reports, documents, dashboards)

Interpretability&

Understandability

Difficulties to consider external or additional parameters

Static analysis perspectives

Different levels of detail Opportunity of add data

perspectives or parameters Flexible analysis perspectives

Ease of Operation

Limited data manipulation options Time-consuming aggregations and

calculations

Flexible options for data manipulation (drilling, pivoting, filtering, sorting)

Simple aggregations and calculations

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Industry 4.0 and Resource Management

General aspects

The VISION: 50 % more productivity and 50 % less use of resources

IT systems model, monitor and optimize energy efficiency or the use of materials

Protection of resources as energy, water, air, or rare materials is realized by a technical communication of IT systems

Simulation and virtual models for resource management

Generation and analysis of data to provide balances of material and energy consumption

Planning and implementation of resource efficiency on product level

Operation of energy-intensive processes in times of low energy prices

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Industry 4.0 and Resource Management

Application scenarios

Recycling of complex goods– E.g. automotive products or electronic devices

– Refurbishment or recycling of products using a product`s knowledge about its components and production process

– Communication of products and recycling machines

– A product is able to find its way in a recycling process automatically.

Measurement of factory energy consumption– Determination of local consumers

– Dimensioning of machines according their task specifics

– Optimization of product`s movement profiles in factories

– Identification of hardly moved masses

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Last, but not least…

Industry 4.0 focusses on improvements and technological advances of the actual way of production and related services.

Industry 4.0 is an international hot topic, which is subject of several governmental funding initiatives.

This opens up a broad area for research projects and new developments.

However, Industry 4.0 is a vision about future manufacturing in ten to 15 years from a today`s point of view.

It is part of an evolutionary process of IT usage in industry and certainly not the final stage.

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Questions?

http://tu-freiberg.de/fakult6/wirtschaftsinformatik

Tom.Haenel@bwl.tu-freiberg.de

Dipl. Wi.-Ing. Tom Hänel

Thank you for your attention!

TU Bergakademie FreibergInstitut für Wirtschaftsinformatik