Towards a Data-oriented Optimization of Manufacturing Processes

A Real-Time Architecture for the Order Processing as a Basis for Data Analytics

Methods

Matthias Blum and Guenther Schuh

Institute of Industrial Management(FIR) at RWTH Aachen University, Campus Boulevard 55, 52074 Aachen, Germany

Keywords: Industrie 4.0, Data Analytics, Digital Twin, Digital Shadow, Real-Time Architecture.

Abstract: Real-time data analytics methods are key elements to overcome the currently rigid planning and improve

manufacturing processes by analysing historical data, detecting patterns and deriving measures to counteract

the issues. The key element to improve, assist and optimize the process flow builds a virtual representation of

a product on the shop-floor - called the digital twin or digital shadow. Using the collected data requires a high

data quality, therefore measures to verify the correctness of the data are needed. Based on the described issues

the paper presents a real-time reference architecture for the order processing. This reference architecture

consists of different layers and integrates real-time data from different sources as well as measures to improve

the data quality. Based on this reference architecture, deviations between plan data and feedback data can be

measured in real-time and countermeasures to reschedule operations can be applied.

1 INTRODUCTION

Dynamic environment conditions, shorter product life

cycles and the increasing complexity in the

manufacturing environment are just some of the

problems companies are facing today. Examples are

turbulences in supply chains, shifts in customer

demands’ or quality problems (ElMaraghy et al.

2012). To counteract these issues, companies need to

adapt their processes to these changing conditions

(Christopher 2016). Success factors are a high

process efficiency and real-time information about

processes and objects. Industrie 4.0 is the driving

force to secure the competitiveness of high-wage

countries such as Germany and to expand their

leading position in production technology

(Kagermann et al. 2013; Groten et al. 2015). The

fourth industrial revolution is mainly driven by the

internet of the things and services (Gröger et al.

2016). Industry 4.0 leads to a more resource-efficient

and energy-efficient production through the use of

intelligent production systems (Monostori 2014;

Bauer et al. 2013). Thus, the collection and optimized

usage of data within the manufacturing environment

is essential to develop intelligent production systems

(Gröger et al. 2016; Jeschke et al. 2017). Many

example show that the usage of data analytics

methods has a high potential to increase the efficiency

of value-adding processes (Blue Yonder; IBM;

Terradata; Clear Story Data)

The basis for analytics approaches builds a

virtual representation of a product on the shop-floor -

called the digital twin or digital shadow. The digital

twin or digital shadow illustrates the virtual

representation of the production through the

manufacturing data. Similar to a flight data recorder

the relevant data is stored in a time series format

(Blum and Schuh 2016). Although, existing IT

systems provide feedback data from the shop-floor,

they lack a data structure which provides a virtual

representation of a product in real-time. Furthermore,

the data quality is an important issue that is not

addressed in current publications (Abraham et al.

2016). The current state of planning systems can be

summarized to:

• insufficient real-time image of the current

situation of the production in terms of

feedback data

• unstructured data in the variety of IT-

systems

• rigid structures and a lack of adaptability of

planning systems

• no continuous check of the data and their

quality

Blum, M. and Schuh, G.

Towards a Data-oriented Optimization of Manufacturing Processes - A Real-Time Architecture for the Order Processing as a Basis for Data Analytics Methods.

DOI: 10.5220/0006326002570264

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 1, pages 257-264

ISBN: 978-989-758-247-9

257

• no counter-measures to address the poor

data quality

Considering these problems predictions about the

future state of the production and reliable statements

about the current situation of an order are not

possible. In order to overcome the described issue and

successfully implement methods of data analytics

inside the manufacturing environment this paper

presents a reference architecture that overcomes the

described issue and provides a virtual representation

of a product. Section two addresses basic principles

towards a data-oriented optimization of processes.

The state of the art regarding real-time reference-

architectures is presented in section three and

analyzed in section four. Section five introduces the

concept for a real-time architecture for the order

processing and specifies the different layers. For a

prototypical implementation we present and discuss

an application scenario in a real production

environment. Finally, we discuss und propose further

research directions.

2 MOTIVATION

In this paper, the term analytics in association with

business intelligence is defined as follows: It is

understood as a scientific process of mathematical-

logical transformation of data to improve decision

making. The maturity level of analytical capabilities

can be classified in four stages: descriptive,

diagnostic, predictive and prescriptive analytics

(Sherman 2015; FAIR ISAAC Cooperation 2013).

To differentiate the four stages, the level of data

analysis and human input is analyzed. The descriptive

analytics, the first stage, aims at analyzing large

amounts of data to get an insight of what happened in

the past. It answers the question “What happened?”.

By analyzing the interactions within the data with the

purpose of getting a conclusion of why it happened in

the past. Thus, diagnostic analytics answers the

question “Why did it happen?”. The question “What

will happen?” is covered by predictive analytics.

Predictive and prescriptive analytics support

proactive optimization. Future behavior is predicted

by methods of pattern recognition and the use of other

statistical methods. The last stage, called prescriptive

analytics, answers the question “What should be

done?” by using simulation and optimization

algorithms to suggest or directly implement a

concrete measures. (Stich and Hering 2015).

3 STATE OF THE ART

Although, several publications focus on approaches

regarding real-time architectures which assist in

planning and controlling the manufacturing process,

a scientific investigation of a real-time representation

of a product is only performed in very few research

activities and not dealt with in detail. Furthermore,

measures to improve data quality are not mentioned.

In the following chapter these approaches will be

outlined.

HANG ET AL. 2014 develop a framework for a

real-time data acquisition in production and the

integration of the internet of manufacturing things

(IoMT) into business information systems. The

AutoID-system based IoMT provides real-time

information and status for a dynamic decision

making. Supplied by RFID tags and machine data the

sensor network enables real-time tracking of the

resources and forms an interface between the

production and the superior management information

systems. The data structure of the processing layer

was developed according to the international standard

ISA95 and operates with the B2MML for data

exchange. (Zhang et al. 2014)

UO ET AL. 2015 develop and implement a

RFID-based system architecture for the decision-

making process in the production monitoring and

planning of a decentral manufacturing. It is developed

for the application of production planning in the

decentral textile industry, which is characterized by

highly fluctuating order processes, to generate more

transparency about capacities of the locations. It uses

RFID-technologies to collect data, which is analyzed

and reconditioned in a module for a business wide

access. This data is presented in a task-specific way

and every incoming order will be assigned by a

production model to fabrics and production. (Guo et

al. 2015)

UO ET AL. 2015 develop a real-time capable

production planning for a hybrid flow shop using a

RFID technology linked production environment.

Luo et al. aim at optimizing the production planning

considering the current progress of an order. To create

a distributed and linked production within the

meaning of ubiquitous computing active RFID reader

and passive RFID tags are used as parts of smart

objects. The shopfloor gateway connects every

working cell gateway, which combine and represent

all RFID objects, with the equivalent production and

it also connects the production with the superior

information system. In addition, the shopfloor

gateway processes the production data by means of

the workflow management module, a MS-UDDI

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

258

server for data distribution and module for monitoring

the working cells. For the schedule of the hybrid flow

shop a multiple periodical hierarchical production

planning algorithm is used. (Luo et al. 2015)

ZHANG ET AL. 2015 develop a method for

controlling a non-clocked material flow based on

real-time information. Based on the real-time

information from the assembly line, exceptions in

order sequence as well as the reaction to failures

should be better controllable, since information about

disturbances is recorded directly at the place of origin.

To collect and distribute real-time information, Zhang

et al. desig an RFID-based system to support the flow

through the use of recorded data. The recorded data is

processed by the developed method in the three

service processes, who track and support the

workflows on the shop floor. Besides the three service

task a second core task handles the data exchange

with the upstream and downstream workstations. The

information can be used to adjust the order scheduling

process based on real-time information. (Zhang et al.

2015)

HONG ET AL 2015 design a model for an

advanced production planning and control based on

RFID technology. In addition to improve production

planning and control via a second-level hierarchical

approach, the goal is to develop and disseminate

guidelines for the implementation and use of a linked

production based on a RFID system. Therefore, the

production is equipped with smart manufacturing

objects and the production planning and control is

dimensioned for a multi layered hybrid flow shop.

The first level of PPS sets the sequence of orders

considering its priorities. The second level determines

the production plan, where orders are divided into

small tasks and added to a job pool. The RFID

network provides real-time data for the planning and

the evaluation of the planning and control regarding

the usability and benefit. (Zhong et al. 2015)

ASSNER ET AL. 2015 present a platform and

reference architecture for the integration and analysis

of structured and unstructured data. The presented

platform ApPLAUDING consists of three layers to

integrate, analyze and present the structured and

unstructured data form different sources. To integrate

the data, a mechanism similar to the ETL-Process

extracts and provides it to the second layer. The

second layer is divided into core analytics and value-

added analytics. The last one provides fully-fledged

analytics. The presentation layer provides a user

interface where the analyzed data is presented.

(Kassner et al. 2015)

ANG ET AL. 2016 provide an RFID-enabled

indoor positioning method for a real-time

manufacturing execution system using an extreme

learning machine (ELM). For the localization the

signal levels of RFID tags are analyzed by an adaptive

regression algorithm. The ELM is an algorithm with

just one layer of hidden neurons, which is

characterized by a high adaptability and ability to

generalize. The algorithm for localization is

imbedded into a RT-MES layer and transfers position

data to superior layers. The ELM needs the data and

various activation functions for training and

validation. (Yang et al. 2016)

RÖGER ET AL. 2016 provide with Stuttgart IT

Architecture for Manufacturing (SITAM) conceptual

IT architecture for a data driven factory. The

architecture consists of three layers that process the

data of the digital factory and provide it to the user.

In the first layer the data will be collected and a

flexible integration of heterogeneous IT systems is

guaranteed. The second layer of the SITAM analyzes

the data and provides it to the third layer, where the

data is send to manufacturing-specific mobile apps.

Gröger et al. give an overview of possible

implementation scenarios and the benefits of the

SITAM. (Gröger et al. 2016)

CHUH AND BLUM design a data structure for the

order processing which aims at providing a virtual

representation of a product during manufacturing,

called the digital twin or digital shadow. This data

structure provides a real-time feedback data in a time

series format and overcomes the lack of current IT-

systems. Therewith, conclusions about past incidents

and a real-time status of an order contribute to

improve manufacturing processes. (Blum and Schuh

2016).

4 REFERENCE ARCHITECTURE

AS A BASIS FOR DATA

ANALYTICS

In this paper we present a real-time reference

architecture for the order processing as a basis for

data analytics. The reference architecture consists of

three layers. A data layer, an integration layer and a

presentation layer (see Figure 1). Based on the

derived requirements for a real-time architecture we

will detail the different layers in the section.

Towards a Data-oriented Optimization of Manufacturing Processes - A Real-Time Architecture for the Order Processing as a Basis for Data

Analytics Methods

259

Figure 1: Reference Architecture as a basis for data analytics.

4.1 Data Layer

The data level integrates data from different sources

into a database. For a holistic image of a product on

the shop floor tangible aspects (e.g. the product, the

workstation, etc.) as well as intangible aspects (e.g.

process plans, the geolocation, the status, etc.) are

required. A detailed description of the data structure

can be found in (Blum and Schuh 2016). The

identification of the required data can be determined

by with regard to the following aspects:

 Relevance of the data to the production

control

 Automatic data acquisition based on sensors

without manual data input

 Real-time acquisition of the data

The primary application for the developed data

structure will be single or small batch production.

Thus, a special focus will be on linear and divergent

production structures, the use of alternative resources

and different operations and a semi-automated

production with a high degree of manual process

steps. Based on the production structure transport and

temporary inventories will be considered. The

derived data structure is presented in Figure 2.

To meet the different needs for a real time image

of an order in the form of a digital shadow time

related data is required to specify the time and date

when the data is recorded. Herewith, an entire data

record from the release until the completion of an

order on the shop-floor is made possible. This concept

is comparable to an airplane’s flight data recorder

where data is collected in a time series format and

Figure 2: Data structure for a real-time image of an order. (Blum and Schuh 2016).

Date Time Order Product Geolocation Workstation Process Status

10.01.2016 10:00:01 4711 A1234546788 90, 40 140 -- waiting

10.01.2016 10:00:02 4711 A1234546788 90, 40 140 -- waiting

10.01.2016 10:00:03 4711 A1234546788 90, 40 140 10 set-up

10.01.2016 10:00:04 4711 A1234546788 90, 40 140 10 set-up

10.01.2016 10:00:04 4711 A1234546788 90, 40 140 10 failure

………… … … ……

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

260

stored. The database keeps time-related features by

storing sequences of each value that change with

time. In contrast, a relational database usually stores

just the most recent value. The time-series format

allows to find unique patterns in the data, which are

usually related to trends of changes. In the case of an

airplane trends related to velocity or oil pressure can

be revealed. Transferred to the order processing

deviations from the schedule (e.g. geolocation,

workstation and set-up time) may occur and can be

detected in real-time. Time data represents the

leading characteristic of the data structure. Therefore,

other items of the data structure must refer to it.

Overall aim of a production system is the

manufacturing of the right products in the right way

and quantity, in the correct quality to a specified date

and acceptable costs (Westkämper and Decker 2006).

With the use of operational resources, a

transformation process of raw materials or semi-

finished products into finished parts or products takes

place (Westkämper and Decker 2006). Initial object

of each product is an order from a customer. In a

manufacturing environment, we assume there exist

different orders and products which belong to these

orders, thus each order is a unique identification

number assigned. Furthermore, order data

incorporates different product identification numbers

to determine between different products of an order.

Based on the integration of new sensor

technologies (e.g. real-time location system (RTLS)

and radio frequency identification (RFID)) a live

tracking of an order is possible. RTLS tags are

applied to the product or the container and transmit

the geolocation. Tracking the geolocation is

necessary in order to ensure the routing of the order

between two points. This enables to determine the

current location of the order. Featured by the use of

sensor technologies and a real-time routing, the status

of an order between different steps in the working

plan can be obtained.

A production process consists of different process

steps which are needed to produce a product. Items

are tracked in relation to the working plan, e.g., the

workstation and the process. These processes can be

distinguished according to N

YHUIS A. WIENDAHL into

the following process steps:

 laytime before and after processing,

 transport time

 set-up time and

 processing time.

Furthermore, the resources on which the

operations are performed need to be specified. VDI-

Norm 2815 specifies the different resources in a

manufacturing environment. These include machines

as well as means of transport (VDI 2815).

Based on the current process step carried out the

status can be derived and logged. The attributes are

defined by the different timestamps in the database.

To calculate time related data (e.g. absolute

production time, set-up time, transition time) the

status is needed. With these information conclusions

about the current state of an order as well as ex post

analysis are conducted. Based on the data record

orders with the same production processes can be

compared and reasons for deviations can be revealed.

This enables the user to determine on which

workstations operations have been performed to

complete the order as well as the current operation

status.

4.2 Integration Layer

The integration layer includes functionalities for

securing the data quality. From a product point of

view, quality is defined as the processing of a set of

characteristics (9000:2015, 2015-11-00). In statistical

process control quality has a long history where it is

used to ensure product conformity as well as for the

optimization of processes. When it comes to data,

quality is more difficult to define. Compared to

products, data do not have physical properties which

allow to assess the quality. According to Wang and

Strong the dimensions of data quality can be

differentiated in four dimensions and 15

characteristics. For the derived data structure only

characteristics are considered which contribute to an

improvement of the identified influencing factors and

can be measured quantitatively. Therefore, only

characteristics like completeness and accuracy are

considered in detail. Deviations are defined as the

difference between an acquired parameter and its true

value. For the derived data structure deviations can

occur due to signal losses of the sensors or magnetic

interferences. The data is extracted from the different

sources using ETL-functions from the integration

layer. The ETL-process consists of three different

steps. Extraction includes extraction of the data out of

the core system and external sources. The

transformation step applies a set of rules to clean and

transform the data. The last step, loading, ensures that

the data is loaded to a target database.

Within Figure 3 the feedback data of a product in

a time series format is shown. Although, data was

recorded for the date, time, order, product and status

without any errors or wrong data, the geolocation

contains wrong and missing data. As already

explained, these wrong or missing data can occur due

Towards a Data-oriented Optimization of Manufacturing Processes - A Real-Time Architecture for the Order Processing as a Basis for Data

Analytics Methods

261

Figure 3: Changing the data by the application of integrity rules.

to magnetic interferences or signal losses. To detect

wrong or missing data and to be able to improve data

quality a set of rules is applied to the data structure.

These rules are known as integrity rules. For example,

a given value is only allowed in a specified range or

must contain defined symbols. In the following, we

explain how integrity rules are applied to the derived

data structure (see Figure 3).

Missing data can be restored by the application of

mathematical models. In the area of statistics

imputation processes can substitute missing data by

estimating the missing values based on other

available data in the data structure. Based on hot deck

imputation, each of the time stamps is examined and

the most similar value is substituted for the missing

data value. In the example above, the timestamps

before and after the missing data remain at the same

values. Based on the most similar timestamps, hot

deck imputation can be used to substitute the missing

data. In the case of varying geolocation values,

mathematical models can be used to estimate and

substitute missing values (e.g. linear and non-linear

models).

The identification of wrong data inside the data

structure takes place by the application of integrity

rules. For that, the following rules can be applied. To

detect wrong values inside the Geolocation data sets

the Pythagorean theorem is used. The Pythagorean

theorem is suitable for the distance computation in the

two-dimensional space. In this case the theorem is

used to calculate the deviation l of Geolocations

between two time steps (1).

)()(

−−

−+−=

iiii

yyxxl

(1)

For the example shown in Figure 3, the deviation

between the first and second time step is to 41.23

units. If the deviation between two time steps exceeds

a predefined range, e.g. more than 10 units, integrity

checks will mark the data as wrong. The predefined

value could be defined by the distance a production

object could be transported by a pallet transporter or

automated guided vehicle.

In the data structure, the error may occur that a

value of the Geolocation with a deviation occurs

between two equal values. This can result in a

deviation of status, process or machine.

To check for wrong data in the process order, a

state transition model could be used. This model

describes the states (e.g. process steps) and the

actions that lead to state changes. With such a model

a production process order could be described. The

integration rules can use the state transitions model to

check the process step sequence and, if necessary,

enter the correct process step.

After checking the data for incorrect and missing

values, the data are cleansed and transformed in the

corrected target data. There are various methods to

clean up the data. To replace missing or wrong

Geolocation data of a moving production object the

method of linear interpolation could be used. The hot

deck imputation can be used to replace missing or

wrong data in other columns of the data structure. The

hot deck imputation uses data values of the actual data

set to fill the or correct the wrong data. If there is no

Date Time Order Product Geolocation Workstation Process Status

10.01.2016 10:00:01 4711 A1234546788 90, 40 -- -- waiting

10.01.2016 10:00:02 4711 A1234546788 50, 30 -- -- waiting

10.01.2016 10:00:03 4711 A1234546788 90, 40 -- -- waiting

10.01.2016 10:00:04 4711 A1234546788 NULL -- -- waiting

10.01.2016 10:00:05 4711 A1234546788 90, 40 --

waiting

………… … … ……

Date Time Order Product Geolocation Workstation Process Status

10.01.2016 10:00:01 4711 A1234546788 90, 40 -- -- waiting

10.01.2016 10:00:02 4711 A1234546788 90, 40 -- -- waiting

10.01.2016 10:00:03 4711 A1234546788 90, 40 -- -- waiting

10.01.2016 10:00:04 4711 A1234546788 90, 40 -- -- waiting

10.01.2016 10:00:05 4711 A1234546788 90, 40 --

waiting

………… … … ……

Raw DataTarget Data

Integrity

rules

Deviation

Time

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

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data for an imputation or a linear interpolation cannot

be applied the data record of this time step must be

deleted.

4.3 Visualization Layer

In order to make proper decisions, users are usually

required to scan and integrate various data sets. Since

users mostly act as final decision makers, the

complexity of the displayed information can have a

substantial impact on the decision quality. To support

the user in the decision process, a visualization layer

is implemented in the reference model to reduce the

complexity of the compared data. The dashboard

visualizes the data and especially deviations between

the real-time feedback data and the plan data found in

the underlying layer of the model. In addition, the

dashboard notifies the user about deviations of the

order processes on the shop floor. This notifications

and visualizations are combined with the upper and

lower limits of the allowed deviations.

Furthermore, the visualization layer’s dashboard

represents both an aggregated view that summarizes

all orders, as well as each order and its deviations.

5 APPLICATION

A prototypical implementation based on the derived

reference model and data structure to a demonstration

factory is ongoing. The Demonstrationsfabrik

Aachen (DFA) within in the Campus Cluster Smart

Logistic will provide an excellent environment to

validate the results of the research in real production

area of 1600 square metres. In the DFA an electric go-

kart is built. During the production, data is generated

and then compared with the plan data to observes

changes in the order process. Based on the real time

data of an order, deviations in the processing can be

uncovered in the moment when the location or the

process step differ from the planned ones. Therefore,

feedback and plan data are compared with maximum

and minimum limit.

6 CONCLUSION AND FURTHER

RESEARCH

In this research paper a real-time reference

architecture for the order processing as a basis for

data analytics is developed which aims at providing a

design aid towards a data oriented optimization of

manufacturing processes. After introducing the

preconditions of the model the structural framework

of a reference architecture for the order processing

was derived. The reference architecture consists of

different layers: a data layer, an integration layer and

a virtualization layer. The data layer provides a real-

time image in the form of a virtual representation of a

product, called the digital twin or digital shadow.

Therefore, different data sources (e.g., order,

geolocation and status) have to be integrated to derive

a holistic image of the order processing. In order to

determine the quality of the data, relevant dimensions

of data quality for the data structure are derived and

integrity rules are formulated. By doing this, wrong

and missing data can be identified and data can be

restored. The virtualization layer provides the user

with the relevant information. Therewith, users can

assess the current status of a product in real-time or

derive counter measures if deviations between

planned and feedback data occur. For enabling the

implementation of the model, practical implications

have been carried out. The reference architecture

builds a framework towards a data-oriented

optimization and a basis for the use of data analytics

methods. Therewith, conclusions about past incidents

and a real-time status of an order contribute to

improve manufacturing processes. Further research is

needed to substantiate the presented solution

principles. Directions of further work include the use

of redundant information provided by sensors, the

handling of the geolocation and the transfer of the

solution principles to other domains (e.g. supply

chain and service).

ACKNOWLEDGMENT

The presented research is result of the Cluster of

Excellence (CoE) on “Integrative Production

Technology for High-Wage Countries” funded by

Deutsche Forschungsgemeinschaft (DFG). The

authors would like to thank the German Research

Foundation DFG for the kind support within the

Cluster of Excellence „Integrative Production

Technology for High-Wage Countries.

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