Internet of Things and Analytics on a Flexible Manufacturing Cell.

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RELAZIONE PER IL CONSEGUIMENTO DELLA LAUREA MAGISTRALE IN INGEGNERIA GESTIONALE

Internet of Things and Analytics on a Flexible

Manufacturing Cell

RELATORI IL CANDIDATO

Prof. Ing. Gino Dini Filippo Giusti

Dipartimento di Ingegneria Civile e Industriale filippogiusti18@gmail.com

Dr. Christos Emmanouilidis

Through-Life Engineering Services Centre, Cranfield

Dr. Maurizio Bevilacqua

Through-Life Engineering Services Centre, Cranfield

Sessione di Laurea del 04/10/2017 Anno Accademico 2016/2017

SOMMARIO

La presente tesi è stata svolta presso la Cranfield University nell’ambito del Double Degree Agreement tra l’ Università di Pisa e la Cranfield University. L’obiettivo della tesi è dimostrare come le tecnologie dell’ Internet of Things possano essere impiegate per il monitoraggio di una linea di produzione. L’obiettivo è stato raggiunto attraverso lo sviluppo di un modello concettuale di sistema di monitoraggio e la sua concreta implementazione sulla Festo FMS, una linea di produzione didattica. L’integrazione di vari sensori permette di raccogliere dati e inviarli ad un’infrastruttura cloud, dove sono elaborati. L’analisi dei dati permette di mettere in luce vari indicatori di performance, che sono visualizzati in streaming su un pannello (dashboard), semplificando la comprensione dello stato del sistema produttivo. Inoltre, in caso di anomalie nella linea di produzione, il sistema di monitoraggio genera degli allarmi su dispositivi mobili, allertando immediatamente gli operatori della presenza di un evento indesiderato. Il progetto dimostra come semplici ed economici dispositivi IoT rappresentino una soluzione efficace per fornire nuove capacità di monitoraggio per sistemi industriali non dotati dellle suddette capacità.

ABSTRACT

The thesis was performed at Cranfield University, as part of the Double Degree Agreement between Pisa and Cranfield University. The aim of this project is to demonstrate how Internet of Things technologies can bring benefits regarding remote performance monitoring. The intended aim was achieved through the development of a monitoring system concept and its concrete implementation on the Festo FMS, a small-scale didactic production line. The integration of various sensors allows data collection and communication to a cloud infrastructure, where data are processed and analysed. Data analytics can highlight key performance metrics that are visualised in streaming on a dashboard, facilitating the understanding of process conditions. Furthermore, the system generates alarms on mobile devices in case of anomalies in the Festo system, allowing users to immediately realise whether an undesired event is occurring in the system. Moreover, the cloud infrastructure enables remote visualisation and monitoring. The project demonstrates how the implementation of simple and inexpensive IoT devices represents an efficient way to provide new monitoring capabilities for old machines.

Keywords:

Industry 4.0, IoT, production management, visual management, remote monitoring, cloud computing, process management, performance monitoring.

ACKNOWLEDGEMENTS

The researcher wants to express his gratitude to the project supervisors Professor Gino Dini, Dr Christos Emmanouilidis, Dr Maurizio Bevilacqua. Their continuous support throughout the project and their knowledge were fundamental to the achievement of the project aim.

Secondly, the researcher acknowledges the support from Cranfield staff, technicians and research fellows that provided their support in various project activities, including: Prof Rajkumar Roy, Dr Yifan Zhao, Samuel Court, Dr Pavan Addepalli, Luke Oakey, Lawrence Tinsley, Matthew Caffrey, Teresa Bandee and Charmaine Bassindale.

Finally, the researcher thanks his father Fabrizio, his mother Patrizia and his brother Jacopo. Their endless love and help contributed to the researcher’s development in all the aspects of his life and he will be forever grateful to them.

TABLE OF CONTENTS

SOMMARIO ... ii

ABSTRACT ... iii

ACKNOWLEDGEMENTS... iv

LIST OF FIGURES ... vii

LIST OF EQUATIONS ... ix

LIST OF ABBREVIATIONS ... x

1 INTRODUCTION ... 1

2 METHODOLOGY ... 3

2.1 Introduction ... 3

2.2 Definition of project aim ... 4

2.3 Project objectives ... 4

2.4 Experiments ... 5

2.5 Discussion and conclusions ... 6

3 LITERATURE REVIEW ... 7

3.1 Introduction ... 7

3.2 The Fourth Industrial Revolution ... 8

3.3 Industry 4.0, IoT and the Industrial Internet ... 10

3.4 IoT Implementations ... 13

3.5 Performance Data Management ... 15

3.6 Research gaps and identification of the need of the research ... 16

4 RESULTS ... 19

4.1 Introduction ... 19

4.2 Monitoring System Concept ... 19

4.3 Festo Flexible Mechatronic System Functionality ... 20

4.4 Monitoring System Instantiation ... 22

4.4.1 Introduction ... 22

4.4.2 Sensors and monitoring capabilities ... 23

4.4.3 Wi-Fi Connection ... 24

4.4.4 Dashboard ... 24

4.4.5 Alarm on mobile devices ... 26

4.5 Experiments’ Results ... 27 4.5.1 Experiment 1 ... 27 4.5.2 Experiment 2 ... 29 4.5.3 Experiment 3 ... 32 4.5.4 Experiment 4 ... 35 5 DISCUSSION ... 36 5.1 Introduction ... 36

5.2 Benefits enabled by the monitoring system ... 36

5.2.1 Number of parts processed and productivity ... 36

5.2.3 Cost savings through inexpensive IoT... 39

5.2.4 Integration with Virtuino App ... 39

5.3 Experiments’ results ... 40

5.4 Comparison with previous findings of the literature ... 42

6 CONCLUSION ... 46

6.1 Monitoring System ... 46

6.2 Future Work ... 47

REFERENCES ... 49

APPENDICES ... 52

Appendix A Festo System Stations ... 52

LIST OF FIGURES

Figure 1-1 Industry 4.0 & IoT publications over the years ... 1

Figure 2-1 Research Methodology ... 3

Figure 2-2 Project Objectives ... 4

Figure 3-1 Literature Review Methodology ... 8

Figure 3-2 The Four Industrial Revolution - Source: (Kagermann, Wahlster, and Helbig, 2013) ... 9

Figure 3-3 Fourth Industrial Revolution Enablers - Source: (Rüßmann et al., 2015) ... 10

Figure 3-4 Industrial Internet Elements – Source: (Evans & Annunziata, 2012)11 Figure 3-5 Industrial Internet Data Loop - Source: (Evans & Annunziata, 2012) ... 12

Figure 3-6 Manufacturing companies hierarchy according to the ISA-95/IEC62264 standard - Source: (Vermesan & Friess, 2016) ... 13

Figure 4-1 Monitoring System Concept ... 20

Figure 4-2 Festo MPS System ... 20

Figure 4-3 Festo system process map ... 21

Figure 4-4 Arduino Uno board ... 22

Figure 4-5 TSC230 RGB sensor ... 23

Figure 4-6 DHT11 temperature sensor ... 24

Figure 4-7 esp8266 Wi-Fi shield module ... 24

Figure 4-8 Example of data visualisation on the dashboard ... 25

Figure 4-9 Cycle Time Distribution ... 27

Figure 4-10 Normal Probability Plot ... 28

Figure 4-11 Experiment 2 - Black Parts ... 29

Figure 4-12 Experiment 2 - Red Parts ... 29

Figure 4-13 Experiment 2 - Metallic Parts ... 30

Figure 4-14 Experiment 2 - Average Productivity ... 30

Figure 4-15 Experiment 2 - Temperature ... 31

Figure 4-17 Experiment 3 - Red Parts ... 32

Figure 4-18 Experiment 3 - Metallic Parts ... 33

Figure 4-19 Experiment 3 – Productivity ... 33

Figure 4-20 Experiment 3 – Temperature ... 34

Figure 4-21 Experiment 4 – Temperature ... 35

Figure 5-1 Data visualisation on the dashboard ... 38

LIST OF EQUATIONS

Equation 4-1 Average Productivity ... 23 Equation 5-1 Cycle Time Range ... 41

LIST OF ABBREVIATIONS

Business Process Model and Notation First In First Out

Key Performance Indicators Internet of Things

Information Technology Small Medium Enterprises Short Message Service

1 INTRODUCTION

Industry 4.0 and Internet of Things are gaining importance and interest in the research during the recent years. This is proved by the increase in the number of published papers about the topic, as shown in Figure 1-1. The investigation is based on a research of papers in Scopus database, using the keywords “Industry 4.0”, “Industrie 4.0”, “Fourth Industrial Revolution” and “Internet of Things”.

Figure 1-1 Industry 4.0 & IoT publications over the years

Research and case studies show that Industry 4.0 will enhance companies and supply chains integration, enabling machine-to-machine and machine-to-human interaction. The integration of physical assets with computing and networking technologies leads to the creation of Cyber-Physical Systems. The mutual influence between computers and physical resources provides new capabilities and benefits, supporting the optimisation of a variety of companies’ business processes (Lee, 2008).

Despite the benefits that Industry 4.0 can generate for enterprises, the lack of a track record of successful implementations is still inhibiting its spread (Vermesan & Friess, 2016). Moreover, although research has been already undertaken to enhance companies’ business processes through ICT and

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Industry 4.0 technologies, further investigations regarding how to manage and visualise data are required (El Kadiri et al., 2015).

The project aims to illustrate the advantages that Industry 4.0 and IoT can bring to manufacturing companies through the development of a monitoring system concept and its practical implementation. The installation of IoT devices and the integration with cloud and analytics technologies enables user access to real-time information regarding process performance. The benefits are the improved users’ awareness of process conditions as well as remote monitoring capabilities generated by the monitoring system. This will help companies to improve machines’ availability and the whole production management process. Moreover, the project focuses on automating data capturing and transmission, as a valid method to facilitate information sharing across distinct organisational levels of the companies. Furthermore, information visualisation was investigated, to support a fast and easy understanding of current process conditions for the users.

The report is structured as follows. Section 2 contains a literature review of Industry 4.0 and IoT current practices and challenges. Section 3 describes the methodology developed to achieve the intended aim. An experimental research method was followed, based on the development of a monitoring system concept and its implementation on the Festo system, a didactic mechatronics production line, capable of handling and sorting different work parts. The methodology also includes several experiments to evaluate the monitoring system capabilities, which results are shown in section 4. Section 5 includes a discussion to evaluate the developed monitoring system as well as the research project as a whole. The monitoring system concept has a wider applicability that goes beyond the implementation on the Festo system carried out in the project. Therefore, the advantages that can be originated by its deployment on real production lines are also described in this part of the report. Finally, Section 6 provides a summary of the monitoring system capabilities and limitations as well as recommendations regarding the direction that the future work should take.

2 METHODOLOGY

2.1 Introduction

An experimental research method was followed in this project, as in addition to a literature review, it includes the development of an IoT-based lab prototype monitoring system to show how IoT can bring benefits regarding remote performance monitoring for a production line. After the monitoring system was developed, different experiments were performed on the Festo system to assess the monitoring system. The investigated research question was how to use inexpensive IoT, cloud-based analytics and visualisation technologies to demonstrate their applicability in production performance monitoring in a lab environment. The project also aims to highlight how the benefits introduced on the Festo system can be extended to real production lines and, thus, improve companies’ performance. The whole research methodology consists of the following four steps, illustrated in the picture:

Figure 2-1 Research Methodology

Definition of

project aim

Development of

project objectives

Experiments' results

evaluation

Discussion

and

conclusions

2.2 Definition of project aim

IoT offers various advantages to industrial companies but its implementations are still limited, as identified in section 2.6 “Research gaps and identification of the need of the research”. The aim of the project is to fill the identified gaps in the literature review as well as encourage IoT spread in manufacturing through the development of a lab prototype monitoring system to show the concrete advantages and capabilities enabled by IoT devices. These include the collection of production data and their communication to a cloud-based infrastructure. Furthermore, data are elaborated and visualised on a dashboard to demonstrate how visualisation tools are a valuable solution to provide a clear picture of current process performance. In addition to this, the monitoring system is linked to an app to notify users in case of anomalies, as a valuable mean to alert users of unexpected events occurring in the Festo System.

2.3 Project objectives

To achieve the intended aim, the following project objectives were set:

Figure 2-2 Project Objectives

System Understanding

Literature Review

Monitoring System Design

Devices Selection and Installation

Remote Information Access

System Evaluation

1. System Understanding: Study of Festo training material and documents to understand the functionalities of FESTO Flexible Mechatronics System. Moreover, the system was run to develop a deeper knowledge of its functionalities.

2. Literature Review: Investigation of current practices and implementations of IoT devices to improve process monitoring and control.

3. Monitoring System Design: Definition of which system capabilities the lab prototype monitoring system will track to enable performance monitoring. 4. Devices Selection and Installation: Selection of the required devices to be

installed and connection to the server to communicate data and consequently allow information generation for performance monitoring. 5. Remote Information Access: Development of a dashboard to display

production data in real time, supporting user access to shop floor performance data. Moreover, the connection to the app was accomplished to enable users alerting in case of undesired events.

6. System Evaluation: Assessment of monitoring system capabilities through different experiments.

2.4 Experiments

To assess the lab prototype monitoring systems robustness, different experiments in the Festo system were performed to check whether the lab prototype monitoring system evaluated them as it was supposed to. Four different experiments were carried out:

1. First of all, Festo system cycle time distribution was obtained through the sampling of sixty parts. This made possible to calculate the mean and the standard deviation of the cycle time and then set suitable thresholds for the interval between two parts are processed by the system.

2. Four parts of each type were processed by the machine. After that, a shortage of parts in the inventory was simulated, leaving the system running for additional two minutes.

3. The experiment represented a higher production of red parts (ten) while five black and metallic parts were processed. After this, the Festo system was blocked and the monitoring system ran for other two minutes.

4. The conveyor belt was run continuously for nine minutes to see if there was a significant increase in the temperature of the motor that is present in the Festo system.

2.5 Discussion and conclusions

The last step of the methodology comprises the discussion of the experiments’ results and to which extent the project has filled the literature review gaps. Furthermore, limitations of the system are discussed as well as future work to further improve the lab prototype monitoring system, including additional devices that could be installed to obtain an improved comprehensive understanding of Festo system’s conditions.

3 LITERATURE REVIEW

3.1 Introduction

A literature review was performed to develop an understanding of how Industry 4.0 and specifically IoT devices can be implemented on a production line to improve process monitoring. The technologies contributing to the fourth Industrial Revolution create new opportunities as well as challenges for the companies; four big challenges have been identified (El Kadiri et al., 2015):

1. Data value chain management: it refers to understanding the value of data generated during the execution of business processes and how they can be used to improve companies’ performance. Moreover, the challenge includes how to process, analyse and manage data to make information available to users and develop a deeper knowledge of companies’ business processes and products.

2. Context awareness: advanced information systems should provide to users only the information they need instead of generic, huge amount of data. This will support and simplify users understanding and consequently streamline the decision-making process.

3. Usability, interaction and visualisation: acquiring and analysing data is just one of the steps for developing knowledge from such data. An additional challenge is to find the best method to visualise data and improve users’ interaction and data usability.

4. Human learning and continuous education: as new technologies are becoming protagonists of the manufacturing landscape, there is the need for sharing the expertise among industrial workers and engineers. As a result, it is fundamental to find the best way to develop the required professional competencies for taking advantages of the new discoveries in the manufacturing industry.

The project, applying IoT, data management and visualisation on an educational production mechatronics system, is targeting the challenges 1, 3 and 4 of the above ones. To achieve so, different research areas were covered in the literature review.

Figure 3-1 Literature Review Methodology

The research includes a review of new capabilities enabled by Industry 4.0 technologies to understand how data value chain management can provide business competitiveness advantages for manufacturing companies. Secondly, a more focused analysis of IoT, including specific implementation case studies, was carried out. This area was investigated to facilitate the development of the lab prototype monitoring system concept. Finally, a research about how to process and visualise performance data was performed. The purpose was to study how to obtain valuable information from data and present it in an effective way to the users. A critical analysis of the most relevant papers was carried out, assessing the extent to which past and current work sufficiently address the problem and identify areas where further research is needed.

3.2 The Fourth Industrial Revolution

The history of the manufacturing sector was marked by three Industrial Revolutions. The first one began in the eighteenth century with water and steam powered machines (Kagermann, Wahlster, and Helbig, 2013). The second one was characterised by the employment of electric power and the Ford Production

System that led to an increase in production facilities’ capacity and in a reduction of manufacturing costs. Afterwards, the third Industrial Revolution was marked by the automation of repetitive processes, obtained by the application of IT technologies, enabling a higher level of efficiency. Nowadays, a new shift is happening in the manufacturing environment, described as the fourth Industrial Revolution. The creation of Cyber-Physical Systems is generating smart and digitalised manufacturing facilities, enhancing their capabilities as well as creating new opportunities.

Figure 3-2 The Four Industrial Revolution - Source: (Kagermann, Wahlster, and Helbig, 2013)

Industry 4.0 will make all the elements of manufacturing plants, and even supply chains, to be connected into Cyber-Physical Systems. Machines, robots, transportation systems, work parts and control systems will communicate with each other, reaching a higher level of integration. Machines will control themselves and change their tasks according to the different situations (Kagermann, Wahlster, and Helbig, 2013). The huge amount of data produced

by the different elements of smart factories will be used to optimise manufacturing processes, making them more efficient and flexible whilst reducing production costs and enhancing product quality (Rüßmann et al., 2015).

3.3 Industry 4.0, IoT and the Industrial Internet

Various benefits are generated by the improved communication and integration inside manufacturing plants. This can be achieved through the application of different technologies. The Boston Consulting Group identified nine key technology enablers that will support the achievement of the Fourth Industrial Revolution, as shown in Figure 3-3.

Figure 3-3 Fourth Industrial Revolution Enablers - Source: (Rüßmann et al., 2015)

Among other technologies, IoT will play a fundamental role in the Industry 4.0 Revolution. IoT is defined as the network of physical objects that have the technology to communicate and sense with themselves or the external environment (Vermesan & Friess, 2016). IoT devices enable the gathering of a huge amount of real-time data. In the manufacturing context, this includes information about process performance and machines’ health status. Furthermore, the implementation of IoT allows the integration between industrial

systems, analytics and Internet connectivity, creating what is called Industrial Internet (Evans & Annunziata, 2012). Industrial Internet is characterised by the integration and collaboration of three key elements: machines, analytics and people. This can be reached through the installation of sensors on industrial machines to collect data, together with data analysis through advanced algorithms and providing information accessibility to users.

Figure 3-4 Industrial Internet Elements – Source: (Evans & Annunziata, 2012)

An example of the interaction between the three elements of the Industrial Internet is shown in Figure 3-5. IoT devices collect and send data from machines and the whole manufacturing shop floor to a cloud-based infrastructure to be analysed. Afterwards, Big Data analytics tools and algorithms process the data to facilitate fast decision-making. The analysis of the data produced by the various devices will provide more accurate and real-time information about the resources composing manufacturing systems. Based on the understanding provided by data analysis, a feedback control is provided to the machines to optimise their utilisation and business processes. In addition to the data generated by an individual machine, multiple sources can be utilised for developing a deeper knowledge of machines' status. For example, historical

data or data from different plants using the same machines can be jointly analysed to determine a more precise estimation of time to failure for an asset.

Figure 3-5 Industrial Internet Data Loop - Source: (Evans & Annunziata, 2012)

Recent studies show that Industrial Internet might have a remarkable impact on different manufacturing sectors such as aviation, rail transportation, power generation, oil and gas development and health care (Evans & Annunziata, 2012). Industrial Internet has the capability to bring different benefits. First of all, the optimisation of each individual machine. The knowledge of historical data as well as real-time machines’ health status enables improved maintenance process and consequently lower operating costs. Furthermore, Industrial Internet can generate positive effects on the whole production and distributing systems. It supports supply chain tracking and coordination, quality compliance and distribution network optimisation.

Moreover, IoT and Industrial Internet can support the integration of the different hierarchical organisational levels of a production system. According to the ISA-95/IEC62264 standard, manufacturing companies’ production systems are characterised by five layers, as shown in Figure 3-6 (Vermesan & Friess, 2016).

Figure 3-6 Manufacturing companies hierarchy according to the ISA-95/IEC62264 standard - Source: (Vermesan & Friess, 2016)

The standard provides guidance and suggestions regarding operations management of manufacturing companies. Nonetheless, the authors have identified some limitations regarding the integration and communication across different levels, including information flow from the lower levels to the higher ones (company visibility) and from sensing devices to all over the company. Consequently, different ways of facilitating communication and integration between different organisational levels and devices were investigated in this project.

3.4 IoT Implementations

IoT supports the enhancement of different business processes, such as production, logistics, purchasing and aftersales. It can be beneficial also for the maintenance of industrial machines and their components. IoT devices can be used to track various parameters of an asset and identify its health status.

Monitoring of a machine components’ health is essential to support a condition-based maintenance strategy, which constitutes an optimised maintenance approach compared to predetermined preventive or corrective maintenance. In predetermined preventive maintenance, components are maintained trying to avoid failures, based on mean time between failures whilst corrective maintenance implies reparations after failures (Vermesan & Friess, 2016). Both predetermined preventive and corrective maintenance strategies are characterised by shortcomings. Predetermined preventive maintenance’s drawbacks are the possibility to change parts too early, before the end of their lifetime, and a more frequent access to equipment, causing downtime. On the other hand, corrective maintenance’s main disadvantages are the shortened equipment lifetime and the alteration of production schedules due to unexpected failures. Condition-based maintenance’s aim is to maintain parts only when it is needed. A continuous monitoring of parts parameters allows predicting when they will fail and, thus, identify the optimum moment to change or maintain the components. As a result, it increases equipment utilisation and uptime, reducing maintenance costs (Xu, Chen and Minami, 2012). Maintenance optimisation is gaining importance as manufacturing industry is moving towards servitisation (Roy, Shehab, and Tiwari, 2009). Servitisation is a business model in which companies, instead of selling a product, ensure the availability of it at an agreed level of readiness and for a specified period (Erkoyuncu et al., 2014). This kind of contracts implies that the responsibility for the maintenance of the products is shifted from customers to manufacturers. The change originates further challenges for manufacturing companies that have to manage and maintain huge fleets of products. As maintenance becomes a key process for the companies offering this service, gathering and analysing data of the products through IoT represents a valuable support in the management and maintenance of their fleets, in order to increase products availability and performance (Grubic & Peppard, 2016).

3.5 Performance Data Management

One possible purpose of IoT implementation is to enable remote monitoring. This has been seen in the case of SmartFarmNet, a platform for the collection of data regarding farming (Jayaraman et al., 2016). The platform consists of a collection of sensors to gather information regarding important farming parameters, such as field moisture, pH level of soil and Nitrogen depletion. The system automates the collection of this data without the need of human operators. In addition to this, it allows remote visualisation of the farming condition. Farmers can access the cloud database through a User Interface and keep in control the farming environment even when they are not in the farm. The need for manufacturing processes optimisation is stressed by the evidence that manufacturing industry’s complexity is growing (Hwang et al., 2017). Recent studies affirm that manufacturing sector will require a more rapid product development and a higher flexibility (Brettel et al., 2014). To maintain or increase their competitiveness, companies will have to reduce inventory level and standardise their processes to achieve a greater level of efficiency whilst increasing product customisation (Hwang et al., 2017). In addition to this, consumers are nowadays more concerned about sustainability and authenticity of products, increasing the importance of traceability and visibility of supply chains (El Kadiri et al., 2015). As manufacturing industry is becoming more complex and IoT can support processes optimisation, different studies about how to implement IoT in manufacturing have been already undertaken. An IoT-based model to enhance performance measurement systems was recently developed (Hwang et al., 2017). After selecting appropriate KPIs, the researchers developed a virtual model of a job shop composed of two production lines with twelve machines each. Afterwards, an experiment consisting of a purchase order was simulated and the KPIs were calculated. This study demonstrates that the implementation of IoT devices supports companies to assess defined process KPIs. On the other hand, the limitation of this work¸ as acknowledged by the authors, is that production lines are virtual and it assumes that IoT implementation is carried out without any issue. In particular, it is assumed that data sent from the devices are always correct and

no anomalies are associated with the devices. All the challenges related to the installation of the devices as well as their programming and connection with a server are not covered in this work.

In addition to performing data gathering and analysis, a well-designed performance measurement system communicates process performance in a simple and clear way, supporting stakeholders’ understanding of current performance and whether the situation is in control. For this reason, many companies integrate performance measurement systems with visualisation tools in their manufacturing plants. Visualisation tools are designed to facilitate process status evaluation at a glance. They improve perceived information quality, simplify the understanding of the information and avoid information deficits (Bititci, Cocca and Ates, 2016). Furthermore, they support comprehension and decision-making (Heer, Bostock and Ogievetsky, 2010). Among various tools, dashboards are employed in many manufacturing plants to display process KPIs and, thus, summarising process performance. Dashboards also support employees’ engagement. They allow all the people on the shop floor to see the same data, encouraging discussion and shared knowledge (Bateman, Philp and Warrender, 2016). Nevertheless, the use of visualisation methods is still limited in management (Lengler & Eppler, 2007). Therefore, an implementation demonstrating the potential of those tools was achieved, encouraging future adoptions in the manufacturing industry.

3.6 Research gaps and identification of the need of the

research

The literature review reveals how Industry 4.0 and IoT will be protagonists of the future manufacturing environment. The reasons for that are the benefits enabled in almost all companies’ business processes, from raw material acquisition to the final customer. Nonetheless, IoT adoptions in manufacturing companies are still limited (Vermesan, & Friess, 2016). This is caused by the presence of barriers to IoT deployment, such as the manufacturers’ culture of reluctance regarding IT technology and scepticism about advantages created by IoT. The previously mentioned attitude originates from the limited number of

recorded improvements generated by IoT (Vermesan, & Friess, 2016). The research undertaken by Hwang et al. (2017) provides useful insights regarding how to develop an improved performance monitoring system through IoT. However, all the work is accomplished on a virtual production line, excluding various issues that may arise when applying the system on a real production line. For this reason, this project will be performed on a real system, though a small-case didactic one. The literature review also reveals that additional research is needed to better manage data value chain in order to develop useful information from process data and make it accessible to users (El Kadiri et al., 2015). A recent investigation shows that one challenge faced by manufacturing companies is the shortage of real-time information of their resources. The accessibility to this information can improve process optimisation and decision-making (Zhang et al., 2014). Therefore, the project targets the automation of the collection and visualisation of the data generated during the execution of the Festo system’s process.

Industrial Internet provides several advantages for the companies, through enhanced monitoring and communication capabilities (Evans & Annunziata, 2012). However, manufacturing companies can be discouraged by the implementation costs of new machines and devices. A cheaper alternative solution is represented by the retrofit of old machines with IoT devices, providing sensing capability and Internet connection. The solution was investigated in this project through the implementation of the lab prototype monitoring system. It aims to show how even simple and inexpensive IoT devices can provide additional capabilities to old machines and realise the benefits of Industry 4.0 and Industrial Internet.

Information flow across different organisational levels of manufacturing companies still needs further improvements (Vermesan & Friess, 2016). For this reason, the project is directly targeting how to support operations and integration in the last three layers identified by the ISA-95/IEC62264 standard, which can then have an impact on the top layer and decision-making process. Appropriate ways of automating information flow from the shop floor to the

management level were studied. Moreover, as the SmartFarmNet case study showed the advantages created by remote monitoring in farming (Jayaraman et al., 2016), the project investigates how to transfer the benefits to the manufacturing sector. Furthermore, as the use of visualisation methods is still limited (Lengler & Eppler, 2007), the project aims to encourage the adoption of these tools showing the advantages through a concrete implementation on the Festo system.

IoT has already started to be employed for supporting the maintenance process, in particular for condition-based maintenance. Although the project is not directly targeting the developing of a maintenance strategy for the Festo system, it includes the monitoring of one of the system’s components as an example of machines’ health assessment through IoT devices.

Finally, as shown by El Kadiri et al. (2015), industrial workers need to develop knowledge and expertise about ICT research outcomes, in order to exploit the possibilities offered by these technologies in the manufacturing industry. The implementation of a monitoring system on a didactic small-case production line represents one way of supporting learning and understanding of IoT technologies. Advantages and drawbacks of this solution are discussed.

4 RESULTS

4.1 Introduction

The first part of the Results section consists of a description of the monitoring system concept developed in the project. In addition to the instantiation on the Festo system, the proposed concept has wider applicability and can be adopted in numerous manufacturing and industrial scenarios, enabling various advantages described throughout the report. Secondly, Festo Flexible Mechatronic System’s components and functionalities are illustrated. The description is meant to facilitate the understanding of the Festo system’s key features and its parameters that are tracked by the monitoring system. Thirdly, the monitoring system instantiation, i.e. the practical implementation of the concept on the Festo System, is described. This part includes a detailed explanation of the system’s architecture and the performance metrics that it can measure. In addition to this, it explains how the information is visualised to enable user understanding of current process performance and how the system can highlight anomalies. Finally, experiments’ results are presented, showing how the various performance metrics were identified and displayed.

4.2 Monitoring System Concept

The monitoring system concept developed in the project can be described as an integrated system of hardware and software, designed to support performance measurement and management. The concept is based on equipping industrial machines with sensors and communication devices, enabling data capture and transmission. Data are sent by the devices themselves to a cloud-based infrastructure where they can be processed by analytics algorithms. The algorithms can transform the data into relevant information for the users, enhancing their awareness of process performance. Furthermore, information can be visualised on mobile devices, allowing real-time remote monitoring and control. Moreover, the system alerts users in case of undesired events, such as production stoppages or hazards. The information provided by the system allows the users to understand process conditions and, therefore, they can

provide a feedback to optimise process parameters. The concept is summarised in Figure 4-1.

Figure 4-1 Monitoring System Concept

4.3 Festo Flexible Mechatronic System Functionality

The Festo system is a small-scale production line, capable of performing handling and sorting operations. It is made up by two stations in series: Distributing and Sorting. The automated system is controlled by a collection of sensors, pneumatic actuators and PLC controllers. It processes three different kinds of parts:

• Red part • Metal part • Black part.

Each part of a different type is transported by the system to a different slide through a conveyor belt, branching gates and grippers. Part type recognition is performed through different sensors. To facilitate the understanding of the tasks accomplished by the two stations, the whole process carried out by the system was mapped through the BPMN technique. Among other process mapping techniques, swim-lane diagrams such as BPMN, are recommended when it is important to show process flow as well as highlight which resource is responsible for each task.

Figure 4-3 Festo system process map

A more detailed description of the two stations and their functionality is provided in Appendix A.

4.4 Monitoring System Instantiation

After the initial study of the Festo system and its training material, functional set up and testing were performed so the researcher could develop a deep understanding of the system. As a result, it was possible to realise which system parameters will be tracked and then to design the monitoring system, that represents a practical instantiation of the concept described before.

The monitoring system collects data through different sensors connected to an Arduino Uno board. The board is a microcontroller that can receive input signals from the sensors, processes them through a programme uploaded in the memory and send output signals.

Figure 4-4 Arduino Uno board

The board is equipped with a Wi-Fi shield module to connect the board to the Internet. Internet connection enables data to be sent to a cloud service, where they can be processed and displayed in real-time on a dashboard, allowing remote monitoring. Finally, the cloud service is linked to a mobile app that can send an alarm to managers or operators if the system is not working as it is supposed to.

4.4.2 Sensors and monitoring capabilities

Among different possible functions and parameters that can be monitored, the project focuses on specified features, as an example of monitoring enhancement that can be achieved through IoT implementation. The monitoring system keeps track of the number of parts of each type (i.e. black, red and metallic) the Festo system processes. Although the Festo system is already capable of sorting parts according to the colour and material, it is not tracking how many parts are processed. This is achieved through a TSC230 RGB sensor. The sensor returns different levels of red, green and blue colour when parts of different colours pass in front of it. Therefore, through an Arduino programme developed in the project, it can recognise the different parts processed by the Festo system and count them.

Figure 4-5 TSC230 RGB sensor

Furthermore, as a basic performance metric, the system calculates average productivity level of the Festo system, according to the following formula:

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 [𝑛𝑜.𝑜𝑓 𝑝𝑎𝑟𝑡𝑠

𝑚𝑖𝑛𝑢𝑡𝑒 ] =

𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑑 𝑝𝑎𝑟𝑡𝑠 [𝑛𝑜. 𝑜𝑓 𝑝𝑎𝑟𝑡𝑠]

𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 [𝑚𝑖𝑛𝑢𝑡𝑒𝑠] .

Equation 4-1 Average Productivity

Moreover, the system tracks the temperature of the motor that drives the conveyor belt, which heats up during operation. In addition to this, some of its components may fail, causing an increase in its temperature. As an example of prevention mechanism in case of fires or other possible dangerous situations, a temperature sensor is installed on the motor. Temperature is detected through a

3 pin DHT11 sensor. The system also calculates the time interval between the processing of two parts. If the interval is greater than the process cycle time, it implies that there are problems in the system, such as a shortage of parts in the inventory or a stoppage of one component of the system. Therefore, a signal is sent to alert the users that an undesired event occurred.

Figure 4-6 DHT11 temperature sensor

4.4.3 Wi-Fi Connection

The basic Arduino Uno board is not integrated with a Wi-Fi shield, but it can be equipped with an external Wi-Fi shield and then connect to the Internet. This was achieved through an esp8266 Wi-Fi module.

Figure 4-7 esp8266 Wi-Fi shield module

The Arduino code of the programme is included in Appendix B. The programme enables the previously described monitoring capabilities as well the Wi-Fi connection.

4.4.4 Dashboard

To enable remote monitoring, all the data are sent to a cloud-based infrastructure in real-time. This way, different people involved in the process and

decision-making, can easily access to production data even if they are located away from the Festo system. Among various available solutions, a choice was made to send to the 'Thingspeak' cloud-service, provided by Mathworks1_{. The}

website offers a free data analysis service for a limited number of data captures. It allows users to update real-time data that we will be visualised in a dashboard that users can customise according to their need. This choice enabled: (a) integration with the Arduino board (b) connectivity to a mobile app called Virtuino. The advantage of the connection with the app is explained in the next paragraph.

Figure 4-8 Example of data visualisation on the dashboard

4.4.5 Alarm on mobile devices

Thingspeak dashboards can be linked to a mobile app called Virtuino. The app allows remote visualisation of the information available on the Thingspeak cloud service. What’s more interesting is that it can be set up to send alarms and SMS notifications. The app can read data from the cloud service and if the values are below or above a defined threshold, it generates an alarm on mobile devices or sends an SMS. In the Festo system case, the app is set up to alert users if the motor’s temperature is over a threshold. As a general recommendation, DC motor’s temperature should not exceed 40 °C (Polka, 2003). Therefore, the threshold was set to this value.

Moreover, two thresholds for the interval between two parts reading were set. Details about the thresholds and their meaning are provided in section 5.3 “Experiments’ results”.

4.5 Experiments’ Results

Figure 4-9 Cycle Time Distribution

The figure above shows the cycle time distribution, obtained through the sampling of sixty parts, and its density function. The distribution is characterised by an average of 23.81 seconds and a standard deviation of 0.96 seconds. Its shape distribution can be represented by a normal distribution. This fact was checked, comparing the normal probability plot of the sample with the one of a randomly generated normal distribution characterised by the same number of observations, average and standard deviation. The comparison was obtained through the statistical software “R”.

Histogram of Cycle_Time Cycle_Time D e n si ty 21 22 23 24 25 26 0 .0 0 .1 0 .2 0 .3 0 .4

Figure 4-10 Normal Probability Plot

The figure represents the normal probability plot of the cycle time sample (left) and of the random normal distribution (right). If the population is normal, the points in the plot tend to form a straight line. For the nature of the experiment, this is sufficient evidence of a normal distribution.

-2 -1 0 1 2 22 23 24 25 26

Normal Q-Q Plot

Normal Q-Q Plot

4.5.2 Experiment 2

Figure 4-11 Experiment 2 - Black Parts

Figure 4-13 Experiment 2 - Metallic Parts

Figure 4-14 Experiment 2 - Average Productivity

Productivity starts from zero because during the first reading no parts were already processed by the Festo system. Afterwards, it starts to increase, reaching a steady level of 2.48 𝑝𝑎𝑟𝑡𝑠

recognised the four parts of each type that were processed. At 13:10, no more parts were available in the inventory, so in the period from 13:10 to 13:12 no parts were processed and the total number of parts processed is constant for all the three types of parts. As a result, average productivity started to decrease from 13:10.

Figure 4-15 Experiment 2 - Temperature

While the system was running, motor’s temperature increased from 26 °C to 28 °C, due to the accumulated heat generated by the running of the motor.

4.5.3 Experiment 3

Figure 4-16 Experiment 3 - Black Parts

Figure 4-18 Experiment 3 - Metallic Parts

The system correctly recognised the ten red, five black and five metallic parts that were made available in the inventory, as can be seen in the previous graphs.

In this case, average productivity measurement is less accurate compared to the precedent experiment, in particular around 09:02. Apart from this, it correctly starts to decrease at 09:07, when the Festo system was intentionally blocked.

Figure 4-20 Experiment 3 – Temperature

The temperature remained stable at 31°C, apart from two peaks, one at 09:00 and one around 09:04.

4.5.4 Experiment 4

Figure 4-21 Experiment 4 – Temperature

In this experiment, the focus was on the motor's temperature. The experiment started with the motor at the environmental temperature, running for nine minutes. Temperature increase is evident, from 26°C to a maximum of 33°C.

5 DISCUSSION

5.1 Introduction

The implementation of the described monitoring system enables new capabilities for industrial machines that can provide a deeper knowledge of process conditions and useful insights. An extensive description of the advantages that can be obtained through the adoption of the monitoring system are explained in this section. Moreover, experiments’ results are reviewed, evaluating the monitoring system’s robustness. Finally, the research project is compared to the findings of the literature and it is assessed the extent to which the project filled the identified gaps.

5.2 Benefits enabled by the monitoring system

The data regarding the number of parts produced by a manufacturing system is a valuable information for production management. First of all, it allows managers to know whether the production is running according to the schedule. This is very helpful as realising that not all the expected parts can be produced in a period can trigger different solutions. These may be either scheduling the production of some parts in a different line or facility, or outsource the production. The type of the solution relies on the structure of the companies in terms of additional available lines or facilities and their make-or-buy strategy. In fact, it is not always convenient or appropriate to outsource the production (Krajweski, Ritzman and Malhotra, 2012). In the case of infeasibility of producing all the scheduled parts, at least companies can notify the customers of possible delays. Secondly, the monitoring system could be integrated with a system tracking when the parts are shipped from the inventory and consequently know the on-hand inventory level. This is a useful information for production managers, as it supports production scheduling process. The knowledge of the on-hand inventory level allows managers to take decisions about the quantity of each product that should be produced in a period, accepting or refusing orders from customers or outsource the production of

certain parts if the production capacity level is already too high to meet the products demand.

The monitoring system automated the calculation of the system productivity. This represents an easier and more precise solution compared to the data gathering and analysis process based on stopwatch studies. The information about productivity can be used to know the maximum number of parts per period the system can produce and thus support production scheduling. Moreover, the monitoring system can highlight a possible decrease in productivity that can be caused by different reasons, such as a reduction of the performance of the robotic arm or the conveyor belt. After some time, the components may start to reduce their performance and require more time to accomplish their tasks. Therefore, the efficiency of the whole system would reduce, causing higher costs and a reduction in the number of parts that the system can produce in a period. As data are displayed live and remotely, production managers can timely take corrective actions and avoid that the system will work below its ideal performance level for long periods. For example, a reduction in the productivity can trigger a maintenance action or the substitution of one or more system’s components in order to restore the optimal performance level.

5.2.2 Cloud infrastructure

The cloud infrastructure enables remote monitoring and control. It means that even people away from the production system can access to relevant information. This would be beneficial in the case of different production lines working at the same time. The solution allows operators and managers to monitor the production lines from a central area and access to the lines only if needed. Therefore, operators would spend less time checking that the stations are working as expected and, instead, they can focus on more value-adding activities. In addition to this, managers that are away from the plant for different reasons, such as work travels or visits to other plants, can still know the current situation in the plant and take decisions remotely. All the decisions explained in the previous paragraph can be agreed without the need to be in the plant, with

just the requirement of a mobile device (i.e. laptop, smartphone or tablet) and Internet connection.

The cloud infrastructure is a flexible solution. The cost for the service is variable and based on the number of data captures per year the companies require. Therefore, the companies pay only for what they need. Moreover, the service is scalable. It means that if the companies require more data captures, they can purchase an additional amount. This is useful if the companies decide to increase the number of production lines or the number of devices to monitor additional parameters and therefore require further data captures.

The use of a dashboard as visualisation tool represents an effective a fast way to communicate useful information to users.

As can be seen in Figure 5-1, the dashboard allows users to easily know the current production level and whether productivity and motor’s temperature level are in control, at a glance. In this example, users can easily realise a productivity decrease. Although in this case only few basic KPIs are displayed, it represents an example of how production lines performance can be summarised using key performance metrics and then visualised with appropriate tools. This will enable a quick and easy understanding of process conditions for users. This is fundamental, especially in complex manufacturing environments, where the control and optimisation of various parameters of different production lines are required to sustain companies’ competitiveness in terms of quality, lead time, cost, product customisation and thus customer satisfaction.

5.2.3 Cost savings through inexpensive IoT

The integration of the basic Arduino Uno board and the esp8266 Wi-Fi shield represents a cost saving solution compared to the purchase of an Arduino Uno board with an integrated Wi-Fi shield. In fact, the Arduino Uno board with integrated Wi-Fi costs around 30£, while the combination of the basic Arduino Uno board and the esp8266 Wi-Fi module costs around 20£. Although the integration requires some effort for coding and enabling Internet connectivity, it represents a cheaper solution and it can be particularly suitable for projects with a limited budget, for example academic projects or pilot test implementations. Although this is a trivial example, it serves as an example of costs savings through inexpensive IoT choices.

5.2.4 Integration with Virtuino App

The integration of sensors, Arduino board, Thingspeak website and Virtuino App represents a good way to keep under control critical parameters of the system. In the specific case of the Festo system, managers and operators can be notified on mobile devices, such as mobile phones or smartwatches, if the temperature of the motor is above the safe working range. This can trigger a prompt intervention to avoid hazards that can affect system safety. Furthermore, the monitoring of the interval between two consecutive parts are

processed by the system can reveal possible issues in the production line. As the alarm would be sent few seconds after a problem appearance, production managers can timely take corrective actions and avoid long production stoppages and consequent higher production costs. In addition to higher costs, a stoppage can lead to missing the scheduled production and consequently incomplete deliveries to the customers. This would affect companies’ reliability reputation, customer’s satisfaction and therefore companies’ competitiveness. Such cloud-enabled integration with mobile apps is just representative of reduced development time for customised needs, thus benefitting rapid user-oriented solution development.

5.3 Experiments’ results

The monitoring system showed to be reliable in the experiments, perfectly recognising the parts processed by the system. Productivity level was correctly calculated except for one reading in the third experiment, caused by a small delay in part type recognition. However, it happened only time and the measured productivity differed from the actual one of 4%, a mistake that can be considered acceptable for a prototype. Nonetheless, few additional experiments should be accomplished before a possible deployment on a real production line. The performance metrics were correctly visualised on the dashboard on Thingspeak cloud service in all the experiments, showing that the connection was stable and the integration with Thingspeak is a solid solution. Furthermore, an increase of the motor’s temperature while the system was working was highlighted. The increase in temperature was not large enough to be considered dangerous. Nevertheless, the system was run for at most fifteen minutes, so in future work should be investigated until which level the temperature will increase and whether the level can be considered dangerous.

As shown in section 4 “Results”, the cycle time follows a normal distribution. It implies that, if the system works in stable conditions, the 99.73% of the data fall within the following range (Ross, 2009):

µ ± 3σ

Equation 5-1 Cycle Time Range

where µ is the mean and σ is the standard deviation of the normal distribution. It implies that there is a small likelihood (0.135%) that if the system is working in stable conditions, a cycle time measurement is over the + 3σ from the mean limit. As a result, a measurement falling over the + 3σ limit might be a signal that a shift occurred in the system (Montgomery & Runger, 2010). The shift may cause the system requiring more time to perform the process and that might be some problems or causes of inefficiency in the system. For this reason, a threshold for the cycle time was set. A measurement over the + 3σ limit is considered a yellow alarm, implying that the situation is not critical but should be investigated. It is important to underline that even if the system is working in stable conditions, some cycle time measurements would be naturally over the + 3σ limit. Therefore, a single yellow alarm should not be interpreted as a signal of a problem in the system. However, several yellow alarms in a limited time period can be an indicator that the working conditions of the system are shifting and it should be investigated. Furthermore, a second threshold corresponding to + 6σ limit was set. The probability that a measurement is over this threshold whilst the system is still working in stable conditions is negligible, so it reasonable to say that if a measurement is over the threshold, the system is experiencing some issues, such as missing parts in the inventory, a blockage of the robotic arm or the conveyor belt. Consequently, a red alarm was set in this case, requiring the maximum attention and immediate intervention from the users. Though such alarm settings are defined for a trivial lab case, they are representative of a broader approach, which can be enabled through IoT-based monitoring on more complex performance monitoring problems.

Figure 5-2 Cycle time distribution and alarm thresholds

In all the cases, either when the system was intentionally stopped or no more parts were available in the inventory, red alarms were sent to the app installed on the smartphone of the researcher, demonstrating that the solution is suitable to achieve the intended result. When the system was working in normal conditions, i.e. parts are available and no stoppages were deliberately caused, the system did not generate any yellow alarms. This implies that, at least for what concerns the cycle time, the system was working in stable conditions. Though trivial, this serves as a pointer to what can be achieved at a larger scale via employing IoT and cloud-based analytics for performance monitoring.

5.4 Comparison with previous findings of the literature

The development of the monitoring system concept and its instantiation on the Festo system addressed some of the research gaps found in the literature review. Even though the system is a small-scale production line, the challenges

were the same as a real implementation in an industrial plant, including the selection and actual installation of appropriate devices on the Festo system, collection and analysis of real production data generated while the system is working and remote visualisation on a dashboard developed for the specific case. Furthermore, the project addressed the issue of guaranteeing that the devices are suitable for the purpose and the data gathered were correct. Therefore, it addressed some unresolved challenges left by the work accomplished on a virtual production line by Hwang et al. (2017). Moreover, as it underlines the benefits achieved through a concrete example, it contributes to show the effectiveness of IoT in the manufacturing sector and it helps to reduce uncertainty regarding IoT value for the companies. In addition to this, the project covers different aspects of data value chain management. It describes the necessary steps to gather data from a manufacturing process, their analysis and visualisation, offering useful insights and process awareness to the users. Furthermore, the extensive description of the benefits that can be enabled for companies underlines the importance of data value chain. Real-time visualisation allows a timely description of process conditions that supports management level decisions that can lead to improvements of the whole shop floor performance.

The project shows how the advantages enabled by the Industry 4.0 technologies can be achieved through the retrofit of old machines. It proves that enhancing monitoring and connectivity capabilities of a system don’t necessarily require the substitution of the already existing machines of the companies. All the previously described capabilities and functionalities of the system require an expenditure of roughly £40 for the devices. As it is a cheap solution, it is also suitable for SMEs with limited availability of capital to invest.

The monitoring system automates data flow from the shop floor to the management level. Data are captured through the various sensors and sent to the cloud infrastructure where managers can access to relevant information about the production line. This represents an extension of the guidance already provided by the ISA-95/IEC62264 standard. As highlighted by Vermesan &

Friess (2016), the standard does not fully cover data management and information sharing across the company. The monitoring system, both as a conceptual model as well as its instantiation on the Festo system, represents an example of how IoT can facilitate and even automate information flow in the companies. This improves company visibility as information can be more easily shared across different organisational levels. Furthermore, the system supports the connection and integration not only of all the elements of a single production plant but even more facilities, facilitating the collaboration between geographically spread sites and the whole manufacturing value chain. The project also encourages the adoption of visual tools in manufacturing and management through the development of a dashboard that visualises streaming data. As all KPIs of the process are together visualised in a single dashboard, users can easily understand if the process is under control at a glance. The use of line charts can reveal possible trends that can provide useful insights about the process.

Tracking the motor’s temperature represents an example of system health monitoring. As shown in various experiments, the motor tends to get hotter as the system is running. Apart from this specific case, the concept of monitoring system’s components is relevant for all manufacturing companies. The continuous tracking of key parameters can immediately reveal a shift in components’ behaviour that can affect process performance or cause hazards. As a result, the awareness of the generation of an issue allows the users to timely take corrective actions and restore the best process conditions and a safe working environment.

Finally, the process itself of developing a monitoring system constitutes a way of teaching and developing IoT technologies knowledge among people. Although theoretical lectures can provide a general idea about IoT capabilities and advantages, a practical implementation offers several benefits. First of all, working on a real system allows the learners to develop the required skills to select appropriate devices and install them on a system. This includes selecting the specific devices and ensure that they are installed and programmed in the

correct way to ensure data gathering and analysis. Moreover, the development of a real working solution enhances learners’ awareness of the advantages that the monitoring system can bring. On the other hand, it is a more expensive solution compared to theoretical lectures, as it requires the purchase of the small-case production line and IoT devices to be installed.