An Open Platform for Smart Production: IT/OT Integration

in a Smart Factory

Dan Palade

, Charles Moller

, Chen Li

and Soujanya Mantravadi

Department of Materials and Production, Aalborg University, Denmark

Keywords: Industry 4.0, Smart Production, Smart Factories, Enterprise Information Systems, ERP, MES, IoT, IT/OT

Integration, Vertical Integration.

Abstract: As industries are becoming increasingly digitalized, new manufacturing concepts require redesigning the

information systems architecture. The Smart Production Laboratory is used as a learning factory aimed at

exploring new industry 4.0 technologies and for demonstrating Smart Production solutions. The initial Smart

Production Laboratory was built on a proprietary software stack. Experimenting with the information systems

architecture using proprietary systems has shown to be difficult, which is why we built a complete modular

open-source software stack for the Smart Production Laboratory intended to enable high-speed and low-cost

development of demonstrators for research, teaching, and innovation. Therefore, the purpose of this research

is to capture the development of the software stack and identify the required target architecture for the

platform. This is further used for discussing potential future challenges in demonstrating new and innovative

Smart Production concepts.

1 INTRODUCTION

The industry 4.0 movement has seen a vast interest

from the manufacturing world as well as academia

[Xu, Xu & Li]. It entails a complex transformation of

the industrial environment focused on digitalization

and connectedness that leads to enterprise

transparency and holism. As production is being

increasingly digitalized, everything inside and

outside a factory is becoming instrumented,

interconnected and intelligent.(Martin et al., 2010)

The new technologies driven forward by the

Industry 4.0 movement have challenged the

manufacturing industry to transform and to develop

new and innovative solutions (Kagermann, 2015).

Those new solutions often require substantial changes

in the information systems and the architecture of the

enterprises (De Jong, Lalla-Sewgoolam and

Vainberg, 2019). Planning and managing changes in

the enterprise architecture is a complex and long-term

endeavor, and consequently, the architecture is not

https://orcid.org/0000-0001-6595-7215

https://orcid.org/0000-0003-0251-3419

https://orcid.org/0000-0001-6249-8957

https://orcid.org/0000-0001-9382-8314

designed but emerges as a consequence of legacy

systems and past IT-projects (Ross and Weill, 2006).

Smart Production is a research capturing the

industry 4.0 challenges from an industrial point of

view, and one of the elements, smart factories, have

been in the center of a number of research projects

over the last few years at Aalborg University (Madsen

and Møller, 2017). We have established and built a

Smart Production Laboratory environment, further

called Smart Lab, where we can emulate end-to-end

industrial manufacturing in a scaled-down version,

but with real industrial-grade technologies and

systems (Nardello, Madsen and Møller, 2017).

During the research and innovation projects, a

larger number of industrial demonstrators have been

developed. A demonstrator uses Smart Lab as a

platform to demonstrate a particular solution in a

scaled-down but realistic context. A central aspect of

these demonstrators is the underlying information

architecture and the platform they are built on.

The objective of this paper is to build and capture

the development of a modular open platform for

Palade, D., Moller, C., Li, C. and Mantravadi, S.

An Open Platform for Smart Production: IT/OT Integration in a Smart Factory.

DOI: 10.5220/0010436807070714

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 707-714

ISBN: 978-989-758-509-8

707

smart production (OP4SP). The OP4SP is a

simplified enterprise software stack built on open-

source components and designed to be easily

integrated with any production system. The

foundation of the paper is a state-of-the-practice

analysis of the enterprise systems architecture with

the outset in (Li, Mantravadi and Møller, 2020),

leading to formulating the high-level platform

requirements. The next chanpter presents and

discusses the background of the research and the

concrete challenge leading to developing OP4SP,

while chapter 3 present the methodology for this

research. In chapter 4 we scrutinize the need for such

experimental platform through the analysis of four

research engagements where part of the solution was

applied. This led to formulating the target architecture

for the platform, where the selected components are

outlined. In the last chapter, we identify the gaps and

recommend further use and development of the

platform.

2 BACKGROUND

In this section, we provide the background for the

research and conceptualize the enterprise software

stack in regard to smart production. An analysis of

state-of-the-practice is presented, which drives the

research objective.

2.1 The Smart Production Laboratory

In order to make sense of this complex

transformation, a smart production concept covering

horizontal and vertical integration, end-to-end

engineering integrations, as well as customer and user

interactions was created that provides a lens of clarity

towards the impact of Industry 4.0 on a

manufacturing company.

The Smart Factory is the concepts within the that

framework. A common understanding of the term

refers to a highly digitized shop floor with connected

machines and devices that collect and share data

through information systems. It shares features and

goals with related areas such as smart home and smart

city.

At our institution Smart Lab was developed in

order to drive the research related to smart factories.

The Smart Lab (see overview in figure 1) acts as a

learning factory to enable collaborative projects

between researchers, students and enterprises. It is a

fully automated small production line integrating and

demonstrating various Industry 4.0 concepts and

technologies.

The Smart Lab is composed of several standard

FESTO-CP factory transportation modules (conveyor

belts), process modules like parts dispenser, drilling

module, and assembly module, as well as dedicated

integrated robots and mobile robot platforms. From a

data perspective, everything is IP enabled. All

modules have at least one PC controlling the sensors

and actuators. It is integrated using an MES system,

which acts as a unified system for controlling the

production line. It is a proprietary solution that came

from FESTO (MES4) with the procurement of the

line, and incorporates a couple of ERP specific

jurisdictions, like order creation and bill of materials.

In the last couple of years, several projects with the

scope of bringing transparency and connectedness to

the Smart Lab took place, which culminated with

demonstrators being presented to the industry.

Through these projects, we observed that a common

obstacle was to integrate the information systems and

allow for shared data and interoperability within the

system. To bypass this issue, we strive to create a

platform that encompasses minimal required software

applications to ensure streamlined order management

and monitoring. For this, we will use open-source

tools paired with standard protocols. The rest of the

chapter will present the hierarchical model of relevant

information systems and the argument for using open-

source software.

Figure 1: The representation of AAU Smart Production

Laboratory (Nardello, Madsen and Møller, 2017).

2.2 Enterprise Systems

Enterprise systems are information systems directed

at helping enterprises to collect, store, and distribute

relevant information with the aim to support

operations, decision-making, and general

management. There are multiple tools characterized

as enterprise systems: ERP (Enterprise Resource

Planning), MES (Manufacturing Execution System),

CRM (Customer Relations Management), PLM

(Product Lifecycle Management), CPM (Corporate

Performance Management), BA (Business

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Analytics), and other (Møller, 2005). However, in a

manufacturing context, the information systems do

not have the same weight (in terms of importance),

and there is a form of hierarchy backed by the

standard protocol and architecture (ISA-95) in order

to map them according to their scope.

ISA-95 firstly distinguishes between two

domains: Enterprise Domain and Control Domain.

Enterprise Domain refers to the strategic management

of the enterprise and correlates with the ERP system.

It encompasses Level 4 Business Planning &

Logistics from PERA model. The Control Domain

refers to actions within a factory wall, and

encompasses Level 3 Manufacturing Operations and

Control, which is popularly called the MES

(Manufacturing Execution System) or MOM

(Manufacturing Operations Management when

reffering to extended functionalities) layer, and Level

2,1,0, which are also known as OT (Operations

Technology).

Although ISA-95 clearly dictates boundaries

between ERP and MES functionality, this view is

being challenged by technology providers that extend

the functionalities of their tools to incorporate the

other ones. For example, the ERP is extended to

include order tracking, and MES includes resource

management and planning/scheduling (Franzosa,

2019). Moreover, the shift to the Cloud has the

potential to disrupt the whole hierarchy. De Jong,

Lalla-Sewgoolam and Vainberg (2019), outline the

challenges of the transformation from presented

decoupled information systems architecture towards

the future of the integrated modular components. This

transformation is central to most of the demonstrators

in the Smart Lab, which is the reason why we didn’t

develop the enterprise software stack as one entity,

but rather as a collection of modular systems with

clear integration protocols in place which are easy to

replace, extend, and deploy according to our needs.

The integration of the components found in the

PERA hierarchy model is called Vertical Integration

in Industry 4.0 ontology (on account of them being

presented in a vertical hierarchy) and is also

commonly known as IT/OT integration. In a nutshell,

IT/OT integration deals with connecting the data and

the processes from the manufacturing floor to the

strategic level. Internet of Things (IoT) is one of the

technologies enabling this integration.

2.3 Open Source

At the time of this writing, there are no less than 50

proprietary IIoT platforms; however, the proprietary

solutions often rely on an ecosystem of partnership,

thus making system integration difficult. However,

given the heterogeneous nature of data within IoT, it

is no surprise that open-source technologies,

standards, and protocols may be chosen as an

alternative and even preferred to proprietary IoT

solutions. The drivers for choosing open-source

technologies for IoT over proprietary solution are

summarized here from (Kim, Lee and Jeong,

2019): (1) The cost of implementing open source IoT

frameworks is low; (2) open-source code leads to

open innovation, thus making operability across

operating systems more probable; (3) the use of Open

APIs (Application Programming Interface) is

preferred, thus leading to the common gateway

(backed by standard protocols) for connecting

software and hardware; (4) the open-source

framework usually offers a wide range of working

libraries; (5) solves the problem of interoperability;

(6) open-source software is more secure than

proprietary software.

2.4 Research Objective

We strive to use the gained experience from building

Smart Lab demonstrators to construct a generalized

enterprise software stack incorporating minimal ERP,

MES, and IoT functionalities, for vertically

connecting information sources, to be able to explore

new concepts and easily make new demonstrators for

research and innovation purposes.

3 METHOD

This research is structured as a DSR (Design Science

Research) approach, with the creation and evaluation

of an artifact (OP4SP) at the center. The OP4SP

represents a concrete technological instantiation of a

full factory software stack that includes an ERP, an

MES, and an IoT solution. It is created and refined in

4 design cycles, which are presented in figure 2.

The knowledge base is provided by standards,

specifically ISA-95, and guides the architecture and

functionalities of each level of the artifact, which

ensures the rigor of the research. A study (Mantravadi

et al., 2020) deduced that ISA 95 structure is indeed

helpful in securing IIoT interconnections in a factory.

Furthermore, an open-source enterprise solution was

beneficial in the implementation of virtual intelligent-

assistant systems on the shop floor for operator

assistance (Mantravadi, Jansson and Møller, 2020).

Moreover, the identified principles related to

Industry 4.0 technology creation, also called Industry

4.0

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Figure 2: DSR Research Overview presenting the three

domains and relevance/rigour cycles.

Application is underlined by providing a means to

have industrial demonstrators built on top of the

physical instantiation presented in this research. The

demonstrators take place because there is interest

from industrial partners. It is important to note that

the artifact (enterprise software stack) and its

abstraction (the platform) should be used as a means

to an end, which is a concrete solution to a

demonstrator project, and not as an end of itself, thus

the measurement by which the artifact is assessed is

by how easy it is to build industrial demonstrators

around it.

There are a set of high-level requirements that

drive the design and development of the

platform. They are: (1) the use of open source is

mandatory; (2) no hard coding necessary; (3)

inclusion of ERP, MES, and IoT; (4) the architecture

must be modular; (5) real-time capability is required

(6) allow virtualization and information

transparency.

Moreover, the design and development of the

artifact is constrained by external factors: (1) the

artifact should be tested at our Smart Lab, because it

will be used there; (2) be easily customizable or

programmable; (3) allow Plug and Play feature.

The artifact evolved because of specific needs

from four previous demonstrator projects, each of

them guiding the development from specific

perspectives having different scopes and

requirements, but still acting as a stage of iterative

development. The contextualization of each

demonstrator is presented in figure 3, and in the

further section.

3.1 Demonstrators Scenario

The platform evolved through as a result of four

demonstrators, which are presented further.

Figure 3: The Design Domain from DSR, presenting the

design cycle.

1) Collaborative Part Assembling: In this scenario,

the MES provides the instruction to the mobile robots

to deliver the materials to different robot cells to assist

product assembling. The production line consists of

two robot cells, one mobile robot and materials (i.e.,

PCB). In this scenario, the MES receives the

production order from the Odoo Sales (part of Odoo

ERP) module and checks the mobile robot’s state to

see if it is available or not. After receiving the

command from the MES, the mobile robot will pick

up the specific PCB and deliver it to the robot cell.

This task requires the MES to be able to communicate

with the mobile robots through OPC UA protocol.

The order information and mobile operations are set

as parameters transferring between MES and mobile

robots.

2) Assisted Production Control: The second scenario

we tested relates to the task of production control.

There are four pieces of equipment involved in this

scenario, counting machine one, counting machine

two, robot station, and manual station. This task

simulates the LEGO bricks assembling process. The

counting machine one and two counts two types of

bricks separately and put them to the pallet according

to the work order. The robot station can select the

other three types of bricks for assembling. All the

bricks in the pallet will be assembled at the manual

station. In this task, MES helps to schedule the

production, balance the workload of the equipment,

and control the production process. To synchronize

the order states and avoid the same order is produced

at the same time, MES also introduces a synchronize

mechanism to create a production sequence of the

work order.

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3) Hybrid Architecture Designing: The third scenario

is focused on a hybrid architecture that provides an

integration solution that introduces the IoT platform

(Thingworx is chosen as the IoT platform in this case)

to the traditional hierarchical model. The proposed

architecture follows the Industrial standards ISA-95

and includes the key activities of different functional

hierarchy layers. It defines the several interfaces to

support data collection and transformation between

the MES and IoT platform. The integration between

MES and IoT platform is through the ISA-95 middle

layer which helps to generate an industrial standard

followed, standardized, and formalized data

structure.

4) Data Driven Production: The fourth scenario is

focused on creating a simple monitoring system for

production lines with outdated machinery without

compromising or infiltrating the legacy systems

already in place. A Node-Red (simple programming

tool) instantiation was installed on a Raspberry Pi,

and was receiving vibration sensory data from

ESP8266 microcontrollers, that were mounted on a

conveyor, that would declare if a machine is

functioning or idle. Moreover, a fully customizable

dashboard accessible from PC would present the

necessary data, which was also collected in a database

for statistical purposes. The setup is modular and

easily customizable with minimum programming

knowledge.

Figure 4: Levels of the hierarchical model, detailing where

the demonstrators’ scope correlated with the enterprise

stack.

3.2 Development

Based on the knowledge acquired from these

demonstrators, an architecture was proposed, and an

instantiation of a platform was developed that would

facilitate further demonstrators. Figure 4 presents the

levels of the platform's architecture, and specifically

where previous demonstrators augmented the design

of it. The next chapter presents in detail the road to

designing and developing the platform.

4 STAGES IN THE EVOLUTION

OF THE PLATFORM

The Smart Lab is the learning factory at our research

institution. It is the foundation on which new industry

4.0 concepts are tested, and artifacts designed and

deployed as part of industrial demonstrators. It has a

proprietary MES solution, and at procurement it had

an ERP system from SAP, which was discontinued

due to the inability to customize it. This is the outset

from which we start our journey. The direction of our

journey is top to bottom from the vertical integration

perspective, and the demonstrators mirror this

approach.

4.1 Design Cycles

The first step was to replace the SAP system with

something that is customizable, modular, and easy to

deploy. The Odoo ERP system was considered

(formerly Open ERP). The community edition of

Odoo ERP is open source and can be deployed both

onsite and in the cloud. Odoo is structured in

modules, each of which has a specific authority.

In the (1) Collaborative part assembling

demonstrator the Odoo Sales and Odoo

manufacturing modules were tested. Odoo

manufacturing was customized specifically to fit the

Smart Lab, and specifically this demonstrator.

Through OPC-UA (OPC-Unified Architecture) the

manufacturing module would check the availability

of resources (in this case, mobile robots) and assign a

task to them. On completion, it would receive a signal

and take the next task. Through this demonstrator, we

observed that Odoo fits the need of the Smart Lab and

thus it was decided to use it further.

In the (2) Assisted production control

demonstrator, we further tested the customizability of

the Odoo manufacturing module by designing a

production planning operation that would

synchronize the order states and balance the workload

of the resources used. Through this, we also tested the

real-time capabilities of the system, deemed it

acceptable for our purposes.

Further, in (3) Hybrid architecture designing

demonstrator we complete the vertical integration of

information systems by introducing an IIoT platform

(Thingworx). The integration follows the industrial

standard ISA-95 through a middle layer that supports

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standardized and structured data. The solution was

deployed on the Cloud and represented a modular

software with a digital backbone that was

customizable, and quickly deployable. However,

customizing the IIoT platform required a lot of hard

coding and with each new use case the legacy would

grow, thus bringing complexity into maintaining the

platform. Thus, it was decided that Thingworx is not

fully suited for our purposes, and that testing another

solution is required. Although there are other open

source IIoT platforms (FIWARE, Mobius,

SiteWhere, Kaa, DeviceHive), we decided to test an

IoT platform designed for automating your house,

Home Assistant, because it has a much larger

audience and is designed specifically for ease of use.

In the last couple of years, Home Assistant was

appreciated with grants and awards from open-source

societies for its flexibility and ease of integration with

smart resources and third-party software. Although it

was criticized in the beginning for its file-specific

integration (the use of YAML setup files), presently,

it shifted to a more web-based GUI. Home Assistant

was tested on the premises of the Smart Production

Lab by creating a local version on a raspberry pi and

connecting it to the Festo CP factory, through OPC-

UA.

In (4) Data driven production demonstrator, we

tested the possibility of using a cheap setup of

vibrational sensor and ESP32 (cheap microcontroller

architecture) to bring smartness to old machinery. An

often-cited motive for the difficulty to integrate

machinery into a central system (like an IoT platform)

was the lack of functionality and connectivity

embedded in its system. We bypassed this problem by

adding inexpensive smart sensors instead that are

easy to install and connect, thus completing the

skeleton of vertical integration.

Finally, the platform was tested and evaluated at

our facility. Our artifact, which constitutes a skeleton

of a full enterprise automation stack, is able to fully

control and monitor the state of the order and

machinery.

4.2 Target Architecture and Workflow

The artifact is designed to correspond to the levels of

functionality dictated by the ISA-95 standard. At each

layer there are containers with relevant applications.

Figure 5 presents an overview of the architecture.

It was decided to have a clear hierarchical model,

but also to divide the functionalities in separate

containers in order to be able to migrate them either

to the Cloud or to the edge according to our needs and

keep the distinct integration protocols in place.

The ERP layer includes Odoo Community

edition, deployed locally as the MVP (minimum

viable product) with sales, employment, inventory,

purchase, and accounting modules. It is considered to

deploy the enterprise edition on the Cloud in the

future release. The MES layer includes another

module from Odoo called manufacturing, which was

customized according to our needs and maybe further

changed to fit specific demonstrator scopes. There is

also the Festo Agent, which is a python based

program that acts as a control layer for the machines

through OPC-UA. The IIoT layer includes an

instance of Home Assistant core running add-ons like

partainer, ESPHome, and Node-RED in separate

containers, and a Home Assistant OS.

Figure 5: High Level Architecture of the enterprise software

stack.

Figure 6 presents the workflow of the order, detailing

the course from sales to execution. It represents a

generic order flow that can be applied in any

manufacturing context, thus ensuring the relevance of

the research. The logical blocks (different colors)

correspond with specific functionalities from ERP

(Purple and Blue), MES (Yellow), and IIoT (Red)

layers.

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Figure 6: Generic Order Workflow model.

5 DISCUSSION

In the next couple of months, AAU plans to host

several industrial demonstrators with multiple

industrial partners. In this section, we will argue that

the presented artifact will facilitate the creation of the

demonstrators by specifically pointing out the

processes that it will perform for each project. First

demonstrator is titled Mass Customization. The outset

of the project is to demonstrate the capability of a

simple setup, to receive an order for a customized

product (made by the customer through a

configurator), sort the correct components, and

package them. The resources include but are not

limited to feeders with counting devices, grippers,

and robots. Our artifact has a major role, specifically

controlling the order management through the ERP

layer, and production processes through the MES

layer. The IoT will collect machine status and order

data for a statistical reason and strategic decision

making. The secondary scope is to demonstrate

traceability and quality assurance. Second

demonstrator titled Paperless Production is directed

toward companies that are at the beginning of the

digitization journey and have no or minimal gain

from using information systems at their site. The

scope is to demonstrate the capabilities of digital tools

through the whole spectrum of the hierarchical stack;

thus, our artifact will be used extensively in this

demonstrator, especially for collecting data. Some of

the goals are: (a) automatic capturing and storage of

information from machines, devices, and employees;

(b) aggregation and visualization of information for

decision support; (c) automatic model-based

decision; (d) reporting and publishing insights for

actions. Third demonstrator titled Predictive

Maintenance has scope is to create a base for

intelligent maintenance of machines and devices

through diagnostic, predictive, and prescriptive

analytics. For this, a stable data collection and storage

procedure should be in place, which is provided by

our platform.

The OP4SP will be the facilitator for conducting

these planned demonstrators and will allow for quick

setup according to new requirements for each

demonstrator.

6 CONCLUSIONS

In this paper we have stated that our research

institution uses its facility, the Smart Lab, to create

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industrial demonstrators for showcasing Industry 4.0

concepts and technologies. We argue that the Smart

Lab misses a digital backbone that would allow us to

easily collect data and track the production. This

digital backbone would help us as researchers to

explore new concepts and create industrial

demonstrators faster. The digital backbone entails a

full enterprise automation stack (as explained in ISA

– 95 standard) developed from open-source tools. The

architecture includes an ERP system (Odoo ERP), a

MES (MES – based on Odoo Manufacturing), and an

IoT Platform (Home Assistant), which was the result

of a DSR approach with 4 design cycles. Our artifact

is not the final state and will continue to be refined as

it will be used in the upcoming industrial

demonstrators.

The presented platform fulfills almost all the

requirements presented earlier. It is composed of

open-source tools (1), has little hard coding involved,

only in the FESTO Agent (2) , includes ERP, MES,

and IoT (3), has a modular architecture through the

containers that hold the programs (4), the response is

close to real-time (5), the virtualization and

information transparency is in place (6). Further

development also seeks to fully complete the

requirements (2) and (5).

The contribution of this paper includes proposed

architecture of the enterprise software stack, a

concrete instantiation of the platform, and the generic

order workflow model.

It is also planned that the OP4SP will be used

further in the context of helping small and medium

sized enterprises (SMEs) by providing minimum

necessary enterprise functionalities and observe their

digitization journey. Moreover, the stack will

facilitate research into implementation of industry 4.0

technologies, with respect to SMEs as well.

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