A Smart Product Co-design and Monitoring Framework Via

Gamification and Complex Event Processing

Spyros Loizou

, Amal Elgammal

, Indika Kumara

, Panayiotis Christodoulou

Mike P. Papazoglou

and Andreas S. Andreou

Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus

Department of Computers & Information, Cairo University, Egypt

Department of Economics and Management, Tilburg University, The Netherlands

panayiotis.christodoulou@edu.cut.ac.cy, mikep@uvt.nl

Keywords: Product-service Systems, Smart Product, Customization, Product-oriented Configuration Language, PoCL,

Gamification, PSS Monitoring, Complex Event Processing.

Abstract: In the traditional software development cycle, requirements gathering is considered the most critical phase.

Getting the requirements right early has become a dogma in software engineering because the correction of

erroneous or incomplete requirements in later software development phases becomes overly expensive. For

product-service systems (PSS), this dogma and standard requirements engineering (RE) approaches are not

appropriate because classical RE is considered concluded once a product service is delivered. This paper

proposes a novel framework that enables the customer and the product engineer to co-design smart products

by integrating three novel and advanced technologies to support: view-based modelling, visualization and

monitoring, i.e., Product-Oriented Configuration Language (PoCL), gamification and Complex Event

Processing (CEP), respectively. These create a “digital-twin” model of the connected ‘smart’ factory of the

future. The framework is formally founded on the novel concept of manufacturing blueprints, which are

formalized knowledge-intensive structures that provide the basis for actionable PSS and production

“intelligence” and a move toward more fact-based manufacturing decisions. Implementation and validation

of the proposed framework through real-life case studies are ongoing to validate the applicability, utility and

efficacy of the proposed solutions.

1 INTRODUCTION

Industry 4.0 is progressively transitioning

conventional factories to smart components and smart

machines to enable an ecosystem of connected digital

factories. A key enabler of Industry 4.0 is the “digital-

twin” model of the connected ‘smart’ factory of the

future, where computer-driven systems create a

virtual copy of the physical world and help make

decentralized decisions with much higher degree of

accuracy (Grieves, 2014).

The digital-twin approach enables manufacturers

to overlay the virtual, digital product on top of any

physical product at any stage of production on the

factory floor, and analyze its behavior so that product

designers and engineers can make informed choices

about materials and processes using visualization

tools, e.g., 3D CAD/CAM tools, during the design

stages of a digital product and immediately see the

impact on a physical version of the product. The

ability to combine the digital-twin approach with

support for smart products, improved processes and

empowerment of human operators is the key to

unlocking the real underlying value of Industry 4.0.

A few recent studies (Sierla et al., 2018; Schluse

et al., 2018; Lu and Xu, 2018; Ameri and Sabbagh,

2016; Nee et al., 2012; Berg and Vance, 2017) have

applied the digital-twin approach and visualization

tools to support product design, production process

monitoring and control, and product services, such as

maintenance. However, these studies still suffer from

severe drawbacks. First, they do not provide an

integrated and comprehensive digital-twin approach

to support the complete smart product lifecycle from

the stages of requirements elicitation, product design,

customization, and production monitoring. Second,

they lack the integration of product, service and

production-related knowledge with advanced

visualization support. Finally, these approaches lack

intuitive user-friendly interfaces that expedite a

Loizou, S., Elgammal, A., Kumara, I., Christodoulou, P., Papazoglou, M. and Andreou, A.

A Smart Product Co-design and Monitoring Framework Via Gamiﬁcation and Complex Event Processing.

DOI: 10.5220/0007720902370244

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 237-244

ISBN: 978-989-758-372-8

237

particular activity (e.g., product customization), and

do not use emergent advanced techniques such as

gamification for improving the user engagement in

different activity.

To address the aforementioned limitations in the

existing works, the research presented in this paper

realizes the digital-twin approach to support the key

phases in the lifecycle of a smart PSS (product-

service system). In our earlier work (Papazoglou and

Elgammal, 2018), we have introduced the PSS

lifecycle. It provides a closed monitoring feedback

loop that enables continuous product and service

improvements based on the novel concept of

manufacturing blueprints, which formally captures

product-service and production-related knowledge

(Papazoglou and Elgammal, 2017; Papazoglou and

Elgammal, 2015). Blueprints integrate dispersed

manufacturing data from diverse sources and

locations, which includes and combines business

transactional data and manufacturing operational data

to gain full visibility and control, and provides the

basis for production actionable “intelligence”.

The proposed framework considers smart product

ideation and customization, as well as monitoring of its

actual production. The framework consists of an

integrated product designer component and a

monitoring Dashboard, which enables the customer, in

collaboration with the product designer/engineer, to

co-design customized PSS via a unique gamification

experience. The user-friendly 3D product designer

component offers a fancy gaming experience during

the product design and customization process. To

enable on demand PSS customization and a customer-

centric approach, the PSS lifecycle supports

complementary stakeholders’ perspectives by making

use of a novel Product-oriented Configuration

Language (PoCL) (Elgammal et al., 2017). Utilizing

PoCL in conjunction with gamification, customers, in

collaboration with product designers, can specify the

desired product and service characteristics. The

monitoring Dashboard displays the products,

machines, sensors and other artefacts in a dedicated

interactive interface, and is able to provide a 3D

representation of the graphical objects. The Dashboard

also serves as a mediator between the shift in-charge or

control room manager / operator, who supervises and

monitors the manufacturing process, and the factory-

floor environment. The monitoring framework utilizes

CEP technology (Etzion and Niblett, 2010), which is

event-based processing that combines data from

multiple sources, to infer events or patterns that suggest

more complicated circumstances. Implementation and

validation of the proposed framework through a real-

world case study (taken from the H2020 ICP4Life EU

Project) is performed to validate the applicability,

usability and efficacy of the proposed solutions.

The remainder of the paper is structured as

follows: Section 2 discusses related efforts in the

areas of digital-twin and visualization approaches for

production co-design and production process

monitoring. Section 3 presents the proposed PSS co-

design framework via PoCL, gamification and CEP.

This is followed by presenting the current implement-

tation efforts in Section 4 Finally, Section 5 concludes

the paper and highlights future work directions.

2 RELATED WORK

We consider related studies from the two key research

issues in this paper, that is, digital-twin, and

visualization platforms for product co-design and

shop floor monitoring.

2.1 Digital-Twin

Tao et al. (2018) proposed a framework that utilizes

the raw data from the physical product and its digital-

twin to support product design, production, and

services, such as maintenance. Sierla et al. presented

the concept of digital-twin centric control, where the

digital twin derived from a product model creates

assembly plans, and orchestrates the resources in a

production cell. Schluse et al. (2018) introduced the

concept of experimentable digital-twins, which are

model-based simulations of digital-twins. The studies

in (Lu and Xu, 2018; Ameri and Sabbagh, 2016) use

ontologies to represent the resources in a factory-floor

to create their digital-twins.

As opposed to these works that mostly consider

only limited aspects of the Product Lifecycle Manage-

ment (PLM), the approach presented in this paper is

built on top of the manufacturing blueprints approach

(Papazoglou and Elgammal, 2017; Papazoglou and

Elgammal, 2015) that enables an integrated and

comprehensive digital-twin approach to support the

complete smart product lifecycle from the stages of

requirements elicitation by means of the Product-

oriented Configuration Language (PoCL), to product

design, customization, and production planning.

2.2 Visualization Platforms for Product

Co-Design

According to the recent reviews of the related

literature (Nee et al., 2012; Berg and Vance, 2017),

the advanced visualization technologies, such as

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virtual/augmented reality and 3D CAD/CAM, have

been used in collaborative manufacturing

environments to support such activities as product

inception, co-design, production planning, and

maintenance. However, overall, they have several key

limitations. First, they lack the integration of

product-service and production-related knowledge

with their visual supports. Such knowledge can

enable consistency checking of visual models, and

support informed decision making during a particular

user activity (e.g., product design and customization)

(Rocca, 2012; Chandrasegaran et al., 2013). Second,

these approaches lack intuitive user-friendly

interfaces that expedite a particular activity (e.g., a

user interface tailored to product customization).

They also do not apply the techniques for improving

the user engagement in the activity, such as

gamification. Several recent works have concluded

that gamification in manufacturing environments can

improve the quality of the work and the performance

of the users/workers (Korn and Schmidt, 2015).

To overcome the above key limitations in the

current literature, the visualization platform presented

in this paper utilizes the blueprinting approach that

readily supports production-related activities ranging

from the conception and configuration of a

customized product all the way to planning and

digital production, by gathering, storing and

processing “smart actionable data” from every point

of the product lifecycle. Moreover, the user-friendly

PoCL helps customers to collaboratively and visually

create, validate and optimize manufacturing design

plans with product designers/engineers, augmented

by gamified 3D CAD/CAM interactive capabilities.

2.3 Visualization Platforms for Shop

Floor Monitoring

A few studies have used complex event processing

(CEP) for monitoring and control of production

processes in the factory-floor (Grauer et al., 2011;

Babiceanu and Seker, 2016; Estruch and Heredia,

2012; Izaguirre, Lobov and Lastra, 2011). Grauer et

al. (2011) used CEP to perform real-time monitoring

and control of processes in manufacturing enterprises.

The data is collected from different automation

systems in real-time. The CEP engine detects

complex events (e.g. alarms) from this raw data, and

the dedicated tools visualize the detected complex

events. Estruch and Álvaro proposed a generic

architecture for event-driven manufacturing process

management (EDMPM). It consists of three main

layers: connectivity layer (to enable communication

with existing information systems in the enterprise),

process execution layer (to enact event-driven

manufacturing processes) and a user interface layer

(to support customizable KPIs visualization and

analysis). Izaguirre et al. used CEP to support the

interoperability of the events generated using two

different standards for device communication

protocols by the devices at the factory-floor.

In comparison with the above works, this paper

proposes a platform that applies CEP to derive the

meaningful production process events (e.g., anomaly-

detected event) from the raw sensor data at the

factory-floor, and visualize such events in an

interactive Dashboard. Moreover, we use the

integrated and formalized knowledge (i.e.,

manufacturing blueprints) related to product-service,

and production to drive the production monitoring.

3 SMART PRODUCT CO-DESIGN

AND MONITORING

FRAMEWORK

The proposed framework is shown schematically in

Figure 1. It is firstly concerned with smart product

ideation and customization (upper left hand-side of

figure). This is achieved through the integration of a

set of interplaying advanced technologies including

the novel PoCL that we have previously introduced in

our previous work (reference omitted for blind

review) and gamification. The framework also

supports monitoring of actual requested customized

product production based on CEP and provides an

interactive graphical Dashboard (lower part of Figure

1). More specifically, a user-friendly graphical

gamification tool, which is based on PoCL, is

proposed that allows a user to define in collaboration

with the product designer customized smart product

requirements. In the next sub-sections, the smart

product ideation and customization based on PoCL

and gamification is first discussed, followed by its

integrated monitoring approach.

3.1 Smart Product Ideation and

Customization

PoCL is a model-based user-friendly Domain-

Specific Language (DSL) that helps customers to

collaboratively create, validate and optimize

manufacturing design plans concurrently with

product/service designers during the stages of the

requirement elicitation process. PoCL is a view-based

modelling language that supports different

stakeholders by tailor-made interfaces at varying

A Smart Product Co-design and Monitoring Framework Via Gamiﬁcation and Complex Event Processing

239

Figure 1: Proposed Smart product co-design and monitoring framework.

levels of abstraction that support the associated user

profile. In essence, PoCL allows customers to

imagine and gradually create a virtual product,

amplifying their ideas and clearing the way to better

design and innovation.

PoCL supports the following: (i) Digital product

features framing, which focuses on collation of

product ideas that can inspire new and innovative

products by supporting unique extensions and

adaptations of base products to create customizable

ones; (ii) Progressive product configuration sketching

and framing, which defines the requirements of the

products, produces prototypes by managing product

parameters and evaluates the cost implications of

potential design improvements.

PoCL and its gaming counterparts are formally

founded on the novel concept of the widely tested and

validated manufacturing blueprints (blueprints for

short, which we have previously introduced in

(references omitted for blind review). Manufacturing

blueprints semantically capture product-service and

production-related knowledge. Manufacturing

blueprints rely on model-based design techniques to

manage and inter-link product data and information

(both its content and context), product portfolios and

product families, manufacturing assets (personnel,

plant machinery and facilities, production line

equipment), and, in general, help meet the

requirements (functional, performance, quality, cost,

time, etc.) of an entire manufacturing network. This

information can be collated and put within a broader

operational context, providing the basis for

manufacturing actionable “intelligence” and a move

toward more fact-based decisions.

The information in the Blueprints describes,

through ontological forms, the setting of the

machinery at the factory-floor, machines’

capabilities, their sensors and actuators. Blueprints

also offer a way to define certain properties for a

customized product. The latter is used to query and

match the description of the desired product with

existing Blueprints in the repository so as to retrieve

Blueprint product instances stored that are very close

to, or relevant with the desired customization

(represented by the input arrow named “Retrieve all

knowledge for products” in Figure 1). This could

include, for example, components and their

composition, relationships between components,

materials, services (including sensors and their

relationships), etc.

The outcome of the “Customization via

gamification” component is a new “Customer Smart

Product Request” in the format of the adopted

gamification technology/engine. The smart product

request is then transformed into Blueprints

representation (an OWL representation; details are

found in (references omitted for blind review) using a

“transformation engine”, which is eventually stored

in the Blueprints Repository.

The integration of the Manufacturing Blueprint

Data Model with PoCL embodies production-domain

knowledge along with the rules of what type of

knowledge must be recorded about each

manufacturing element, how these elements can be

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connected and how this knowledge can be

aggregated, conserved and reused.

3.2 Smart Product Monitoring

The proposed Dashboard serves as a mediator between

the shift in-charge or control room manager/operator,

who supervises and monitors the whole manufacturing

process, and the factory-floor environment. In this

context, this person is able to define for each machine,

and each sensor installed in a machine, the type of

values it collects, their timing (frequency) and, most

importantly, some monitoring rules and actions based

on threshold values (minimum, or maximum, or both),

which are set to denote ranges of normal operation. The

sensors continuously gather information (e.g.

temperature, pressure, humidity etc.) which is then

compared in real-time against the normal operation

thresholds. In case a deviation is observed from

‘normality’, an alerting process is initially triggered

which produces certain types of alerts to notify the

person in charge that one or more anomalies are

detected at the factory-floor.

This monitoring process, as shown right bottom

hand-side of Figure 1, is enabled by utilizing and

integrating CEP technology. Thus, this process takes

advantage of CEP’s event processing capabilities to

combine data from multiple sources and infer events

or patterns that suggest more complicated

circumstances. The detection of a violation of any of

the defined monitoring rules (and/or thresholds), that

are stored and maintained in the Blueprints repository

may trigger an alerting process, as well as the (semi)

automatic execution of appropriate response action(s)

defined for the same type of violation.

This could be in the simplest case confined to sending

an alarm signal to the operator (displayed on the

Dashboard), and in a more automated/sophisticated

manner, extended to sending signals/actions/controls

to the factory-floor that drive the actuators on the

machines. For example, a possible action as a response

to the detection of a rise in temperature for a welding

machine (as compared to the defined threshold), is to

send a signal to the actuators at the factory-floor to

turn-on specific ventilation or air-conditioning

machinery to cool the place and lower the temperature.

This work currently implements the definition of

the threshold values and the actions to be taken when

emergency cases arise, and assumes that the

execution of these actions is handled by another

(existing) module, the latter being the subject of

another research work by the authors.

4 IMPLEMENTATION

This section presents a demonstration example where

the framework/approach described in section 3 is

applied on a real-world use case. The customization-

gamification process is developed in the Unreal

Engine environment (https://www.unrealengine.

com) Realizing PoCL with gamification offers a

fancy gaming experience during the product

customization process, which significantly improves

the quality of experience of the involved

stakeholders. The graphical Dashboard (as shown in

Figure 2: Dashboard functionality allowing gamified customization in Unreal Engine.

A Smart Product Co-design and Monitoring Framework Via Gamiﬁcation and Complex Event Processing

241

Figure 3: Customer’s Dashboard for sensor and connection to the machine.

figure 2) displays the products, machines, sensors and

other artefacts in a dedicated interactive interface, and

it is able to provide a 3D representation of the

graphical objects.

The Dashboard offers a set of simple and

ergonomic graphical actions with which a potential

customer finalizes the details of customization when

ordering a new product.

Firstly, as presented in Figure 2, in order to add a

new machine in the system (the product to be ordered

and developed in this case is a machine) a user

(customer) must first select the “Machines” from the

list of the main categories (left sub-figure) and then

define its type (right sub-figure). For the purposes of

our demonstration we have set-up three types of

machines, a CO2 Laser, a Laser Cutter and a Drilling

Machine. Once a machine is added into the system the

user is able to view it in 3D, set its properties, rotate

it, change its skeleton, change its colour etc.

In the next step shown in figure 3, a user wishes

to add certain sensors on a specific machine. In order

to do so (s)he must first select the operation “Sensors”

from a list of main functional categories (left sub-

figure) and then specify the type of sensor, its

thresholds and the machine the specific sensor

operates on (right sub-figure). In our example four

types of sensors with certain properties were defined:

a “Temperature” sensor, with a minimum value of

30°C and a maximum value of 100°C, a “Humidity”

sensor with minimum value of 40%RH and a

maximum value of 80%RH, a “Motion” sensor with

minimum distance of 1m and maximum distance of

15m (denoting the range of distance covered to detect

motions) and a “Light” sensor with start-time set to

21:00pm and end-time to 07:00am (this is the time

frame for the light sensor to perform its action: when

the Motion sensor detects a movement and the time

recorded is within the range of the Light sensor then

a light goes-on; as long as no movement is detected,

the light sensor remains inactive).

Figure 4: An example in xml of the product request

generated by the Unreal Engine.

Figure 4 outlines the data extracted from the

Unreal Engine according to the demonstration

example. First, the data is translated into an OWL

representation and then using PoCL it is converted

into Blueprints. As mentioned in the Methodology

section, users have the ability to process the sensor

Figure 5: Creating an execution plan by using a query.

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242

fields defined in the previous steps and create

execution plans based either on certain queries, as

presented in figure 5, or by creating their own

scenario without having to write a query, as shown in

figure 6.

Figure 6: Alterative way for creating a scenario.

Figure 6 presents the alterative, easier way for

creating a scenario in which the user defines sensors

and threshold values through dedicated GUI forms.

Once the scenario is created, the associated graph

appears as a result of that scenario, as shown in figure

7; the graph displays temperature sensor values above

100°C.

Figure 7: A scenario which displays all temperature sensor

values that are greater than 100°C.

Statistical information is also displayed on the

Dashboard screen as depicted in figure 8.

Figure 8: Statistics Dashboard about real-time values from

temperature sensor.

Figure 9: Notification alert for temperature value greater

than 200°C and action confirmation.

Finally, the case of a notification alert popping-up

on the Dashboard screen and requiring the shift

manager to confirm the action related to this alert is

presented in figure 9.

5 CONCLUSIONS AND FUTURE

WORK

The present paper introduced a new framework that

facilitates smart product ideation and customization,

and provides the means for monitoring the production

process. The framework offers the ability to a

customer to co-design customized PSS via a unique

gamification experience and also integrates with a

Product-oriented Configuration Language to specify

product and service characteristics. A dedicated

interactive monitoring Dashboard displays products,

machines, sensors and other artefacts using a 3D

graphical representation. The Dashboard essentially

connects the control room manager/operator with the

factory-floor environment and assists in the

supervision and control of the manufacturing process

utilizing CEP technology. The latter supports event-

based processing by combining data from the various

sources at the factory-floor. The proposed framework

is being demonstrated and validated using a real-

world case study in terms of applicability, usability

and efficiency.

Future work will concentrate on: (i) (semi-)

automating recovery actions by seeding self-

adaptiveness and self-healing capabilities into the

monitoring component of the framework, moving

towards the vision of self-autonomous smart factory,

and (iii) augmenting the Dashboards with

sophisticated visualization features by supporting

augmented and virtual reality, and (iii) designing and

developing advanced querying and

recommendations/matching capabilities, which will

assist the re-usability of previous customization

efforts during the smart product ideation phase.

A Smart Product Co-design and Monitoring Framework Via Gamiﬁcation and Complex Event Processing

243

ACKNOWLEDGEMENTS

This paper is part of the outcomes of the Twinning

project Dossier-Cloud. This project has received

funding from the European Union’s Horizon 2020

research and innovation programme under grant

agreement No 692251.

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