Integration of Digital tools in Product Realization Process

Integration of Digital tools in Product

Realization Process

PAPER WITHIN Production Development and Management AUTHOR: Abdul Salaam & Sultan Mehmood

This exam work has been carried out at the School of Engineering in Jönköping in the subject area Production system with a specialization in production development and management. The work is a part of the Master of Science program. The authors take full responsibility for opinions, conclusions and findings presented.

Examiner: Krestin Johansen Supervisor: Gary Linnéusson Scope: 30 credits (second cycle) Date: 2021-07-04

Abstract

The market has been evolving lately, with the introduction of more and more

digital tools that industries are making use to improve their overall operations

within the Production process. The integration of digital tools with in the

Product realization process has major advantages in improving the production

performance. Many large industries make use of digital tools to digitize their

products making them smart products. Implementing these digital tools can

be beneficial for reshaping the organisation which can lead to better customer

satisfaction and improve business strategy. This project explores different

digital tools that can be integrated with the product realization process and

how these tools contribute to the different production development phases.

This thesis presents a detailed study of the digital tools Simulation,

Visualization, Emulation and Digital twins which can be integrated with the

production development process. A pre-study is conducted to gather

knowledge regarding the application of these tools and further discover how

these tools can support the Product realization process and is used to describe

which tool works best at which stage of product realization process, which

can be used to improve the efficiency and accuracy of the production process.

Implementing these digital tools within the production facility can be

associated to smart factory paradigm of the Fourth industrial revolution

Industry4.0.

This research aims to contribute to the use of digital tools in the production

processes and aids in bridging the gap between traditional and modern

manufacturing

methods.

The outcome of this study is to clarify how the above mentioned digital tools

are linked to the product realization process to support an efficient and

digitalized production development, also mentioning the strengths and

weaknesses of these tools. The resulting analysis has provided with a

framework developed to support an efficient digitalized production

development and preparation process for assembly task utilizing human and

robot collaboration. This research paper can be used as a guide for companies

that want to explore how implementing digital tools in their product

realization process and how it may improve their productivity.

KEYWORDS: Product realization process, Production development process, Digital Tools, Simulation, Emulation, Visualization, Virtual manufacturing, Digital Twins, Human Robot Collaboration.

Contents

I.

List of Figures ... 6

1

Introduction ... 7

1.1 BACKGROUND ... 7

1.2 PROBLEM DESCRIPTION ... 8

1.3 PURPOSE AND RESEARCH QUESTIONS ... 9

1.4 DELIMITATIONS ... 9

1.5 OUTLINE ... 9

2

Theoretical background ... 11

2.1 PRODUCT REALIZATION ... 11

2.1.1 Product life cycle management: ... 12

2.1.2 Product Life cycle and Product realization: ... 12

2.2 DIGITAL TOOLS ... 13

2.2.1 Simulation: ... 13

2.2.2 Overview of Discreate event simulation: ... 14

2.2.3 Virtual Manufacturing... 14

2.2.4 Emulation ... 15

2.2.5 Visualization ... 16

2.2.6 Virtual Reality (VR)... 16

2.2.7 Augmented Reality (AR) ... 17

2.2.8 Human Robot Collaboration ... 17

2.2.9 Digital Twins... 18

2.2.10 Application of Digital Twins: ... 18

3

Method and implementation ... 20

3.1 RESEARCH DESIGN ... 20

3.2 BRAINSTORMING KEYWORDS ... 20

3.4 DATA ANALYSIS ... 23

3.5 VALIDITY AND RELIABILITY ... 24

4

Findings... 25

4.1 SIMULATION IN PRP ... 25

4.2 VISUALIZATION IN PRP... 26

4.2.1 Visualization and Simulation ... 28

4.3 EMULATION IN PRP ... 28

4.4 DESIGN OF ROBOT WORK CELLS... 29

4.4.1 Digital Twins in Human robot work cell. ... 30

4.4.2 Using Digital Twin as a tool for HRC production process ... 30

5

Analysis ... 33

5.1 ANSWERING RESEARCH QUESTION 1: WHAT ARE THE DIFFERENT DIGITAL TOOLS THAT CAN SUPPORT THE PRP? ... 33

5.2 ANSWERING RESEARCH QUESTION 2 ... 35

5.3 ANSWERING RESEARCH QUESTION 3: ... 37

6

Discussion ... 39

6.1 DISCUSSION OF METHODS ... 39

6.2 DISCUSSION OF FINDINGS AND ANALYSIS ... 39

7

Conclusion ... 40

7.1 FUTURE SCOPE ... 41

I.

List of Figures

Figure 1- Product Realization Process, Modified From (Säfsten, 2005) ... 11 Figure 2 Product Life Cycle Modified From (Stark, 2020) ... 12 Figure 3: Product Realization A Stage In A Product Life Cycle Modified From

(Säfsten, 2005) ... 13 Figure 4 Example Of Industrial Robot Collaboration (Riccardo Gervasi, 2020) ... 18 Figure 5: Schematic Representation Of The Literature Review ... 22 Figure 6: Framework For Collaborative Assembly Work Cell For Human And Robot

In A Digital Twin Workspace ... 37

List of Tables

Table 1: Documents Found In Databases. ... 23 Table 2: Selection Of Digital Tools Supporting Prp ... 33 Table 3: Digital Tools Contributing Production Development Phase ... 35

Abbreviations

PRP - Product Realization Process CAD - Computer-aided Design CPS - Cyber Physical Systems PLM- Product Lifecyle Management VM- Virtual Manufacturing

AR- Augmented Reality VR- Virtual Reality BS- Brainstorming

DES- Discreet Event Simulation 2D- 2-Dimensional

3D- 3-Dimensional

HRI- Human Robot Interaction HRC- Human Robot Collaboration DTW/DT- Digital Twins

1

Introduction

This chapter describes the background of industrialization and its importance in product realization process and an introduction to digital tools that are available in today's market is provided. Further is the research gap described together with the research questions to bridge them and the delimitations to narrow down the research scope.

1.1 Background

The increase in demand for better product design and product development process requires continuous technological advancements. Organisations have to produce new products with better quality and affordable prices in order to stay competitive in the market. Determining the customer’s needs, developing parameters, formulate conceptual design and design the final product along with its support processes are a part of the Product realization process (PRP). PRP can help maximize the potential for a successful product development (Dwivedi & Thota, 2006). Industrialization works as a driver for both growth and development in larger industries (Kruse et al., 2021). Due to globalization the situation in the market is evolving rapidly; companies need to adapt new technologies and many new concerns need to be addressed, such as the sustainability and environmental impacts of their activities and goods. The Fourth Revolution in Industrialization means Decentralized' smart' factories, with a high degree of networking, automation, and information/data sharing, as well as operation tracking (Person, 2016). According to Thomas Ditlev Brunoe (2019), integration of digital tools in the product development process has a positive impact on a variety of performance metrics, including manufacturing unit costs, standardization, efficiency, and volume flexibility, and can even help incorporate creative strategies.

From the current study, it is known that the way the organisation has been working is completely changed by digitalization. The term "digitalization" refers to the representation of a product or service in digital representation. The use of digital representation allows for more efficient distribution and manipulation. Digital tools enable businesses to deliver new digital solutions for manufacturing and customers. Processes can also be digitalized and distributed over long distances (Balakrishnan et al.,1999).

With the rising regularity in new digital tools like simulation and visualization have the potential and significant effect in the production development process. Simulation is a method of examining models that is primarily experiential or experimental in nature. The primary goal of simulations is to aid decision-making, prediction of system output, gaining knowledge of the system at various life-cycle stages, problem identification, and evaluation of expected results. During the development of complex products, tools like simulation allows for reduction in development times and optimizes resource consumption (Nunes et al., 2017). These digital tools enable business process automation to become more effective, agile, improve customer service and improve the overall quality of deliverables (Shishkov, 2019).

Digital experimenting allows for new design solutions to be explored based on a continuous innovation loop, using a combination of advanced CAD software and experimental statistical methods. And virtual reality tools like Simulation and Visualization, have significantly improved the quality of concept PRP. Researchers discovered that virtual simulation can dramatically contribute to design in terms of time, expense, and quality assurance by simulating assembly operations using a computer-aided system, highlighting the benefits of virtual reality technology (Ghosh & Pal, 2017).

As a new phase of the industrial revolution, Industry 4.0 has progressed from embedded systems to cyber-physical systems (CPS). CPS is an embedded system that communicates data in an intelligent network to promote smart output, and it is one of the core components of Industry 4.0. CPS bridges the gap between the physical and virtual worlds, allowing users to access real-time information and services from anywhere (Nunes et al., 2017).

Digital tools allow new forms of interactions between humans and robots, that will change the industrial work force and have huge importance for the nature of work and safety of the operators. Industrial robots have been able to assist human workers with complex and high-precision, repetitive, and hazardous activities in the last few decades. Paint and sealant applications, welding, assembly, material handling, testing, and other routine activities are examples. However, it was not safe for humans to operate alongside robots. As a result, the robots are normally held in a cage or in an environment where humans must keep their distance (Kolbeinsson et al., 2019).

1.2 Problem description

Technological advancement has driven industrial efficiency and thus its dynamics where manufacturing firms must be able to deliver consistent flexibility while maintaining a high reliability of products more than ever before. Adoption and efficient use of a wide variety of emerging manufacturing technology is one of the most important requirements for achieving and sustaining success in turbulent markets (Kroll et al., 2018). In modern industrial practice, innovative methods for the development of operations or whole manufacturing processes, are constantly being proposed. There are several options available, but the challenge is determining the accuracy of the decisions and their effect on the operation of the entire production system after the changes are implemented. Digital representations of production systems using conventional virtual tools often struggle from shortcomings such as a lack of detail, unrealistic depiction, and inaccuracies (Gregor et al., 2018). Despite the potential for increased operational productivity, many businesses are either hesitant or unable to fully adopt new technology. Many studies have shown that this is due to obstacles and shortcomings within the institution and organization, as well as a lack of understanding of how to restructure market and management structures (Kroll et al., 2018).

There are many factors that create a complicated picture for many industries for using these different digital tools within the manufacturing process. The main challenge is to

figure out which of these tools can be helpful and understand the individual contribution of these tools within the PRP. Moreover, describing which of the digital tools can help in preparing a production layout for human and robot collaboration in assembly process.

1.3 Purpose and research questions

The purpose of this study is to explore the state of art of digital tools for an effective PRP. A pre-study is carried out to investigate how these tools can be utilized for an effective digitalization process in production development and discover new opportunities for human machine interaction within the assembly tasks. The aim of this paper is to illustrate the numerous strengths and shortfalls, as well as the challenges and opportunities discovered in the research literature, including the research gaps that needed to be further explored in the production development stage.

Research question

The following research questions are formulated to address the main purpose of this study.

RQ1: What are the different digital tools that can support the PRP?

RQ2: How can these tools be related and to what degree do they contributed to the PRP especially focussing on the Production development phase?

RQ3: How does these digital tools contribute to the engineering work for preparing a production layout for human and robot collaborative assembly?

1.4 Delimitations

This study is limited to how different digital tools can contribute to PRP. This study does not include all the tools that support PRP available in the market. The authors exclude implementation of these digital tools in other phases of industry like R&D, supply chain and management. This study only focuses on the tools which can contribute to product development phase, not all the tools are a part of this study. This research does not address the design of these digital tools.

1.5 Outline

This section briefly describes how the thesis is organised into chapters. Chapter 2: Theoretical background

This chapter presents the different theories derived from various studies and create the theoretical framework for the thesis.

This chapter includes a comprehensive overview of the applied research methodology and implementation, including the research design, research procedure, and literature review methodology for this thesis work.

Chapter 4: Findings and Analysis

This chapter contains the theoretical and empirical findings that support the research objectives as well as answers to the research questions.

Chapter 5: Discussion and Conclusion

This chapter contains the study's discussion. The research results for methods are discussed first, then the three research questions are discussed.

2 Theoretical background

This chapter comprises relevant theories about Digital tools related to Industry 4.0, which is subjected to address its uses and importance in the PRP.

2.1 Product realization

Product realization is the process of defining new product designs, as well as the necessary manufacturing and field support processes, by combining consumer requirements, technical capacities, and resources. Product realization refers to the design and manufacture of goods that are appealing to consumers. All activities needed to develop solutions that meet a defined customer need, as well as all activities required to realize these solutions in terms of physical products with associated services, are included in product realization. Product development and product realization are also used synonymously (Säfsten & Bellgran, 2005).

Product realization is a wider term in which product creation and production development are interconnected processes that depend on one another for efficient development and implementation (Xiao et al., 2000). Product realization enhances design methodology, which is widely acknowledged as the single most important step in achieving industrial excellence and competitive advantage. The concurrent and collaborative process of evaluating the best solutions at each point in the design and manufacturing process, as well as ensuring that the entire process is configured with the best information and resources, is known as Integrated Product Realization (IPR) (Dwivedi & Thota, 2006). PRP are shown in Figure 1.

Figure 1- Product realization process, modified from (Säfsten & Bellgran, 2005)

The PRP is a set of product development activities that take place from design to manufacturing. The creation of a product has developed into a multi-step, collaborative process. A product development process can be broken down into stages, activities, and tasks. The distinct stages of a product's development are referred to as phases. An event

is a set of product development events that may take place in order to solve a problem or sub-problem. A task is an operation that involves solving a particular problem or making a decision ( Xiao et al., 2000).

2.1.1 Product life cycle management:

Product Lifecycle Management (PLM) is the corporate practice of effectively handling a company's goods during their lifecycles, from the very first proposal for a product all the way before it is withdrawn and disposed of. It regulates all of its parts and components, as well as the product portfolio, in an organized manner. PLM oversees the entire process, from individual parts to individual products to the entire product portfolio (Stark, 2020). The product is imperative. The business makes money by releasing new and improved goods on a regular basis. It is the market leader in its business field because of its excellent products. Successful products would result in a significant profit (Kerin & Karakaya, 2007).

Figure 2 Product life cycle modified from (Stark, 2020)

The product lifecycle is divided into five stages as shown in the Figure 2. The substance is in a different state in each of these five steps. The product is just an idea in people's heads during the ideation process. The concepts are transformed into a detailed description during the definition process. The commodity remains in its final form, ready for use by a consumer, by the end of the realization process. The product is with the customer who is using it during the service process. The commodity eventually reaches a point where it is no longer useful. The corporation retires it, and the consumer disposes of it. The consumer, the business, or a third party can recycle it. Product management entails tasks like organizing and coordinating product-related resources, making decisions, setting goals, and monitoring outcomes (Stark, 2020). A product must be handled over its entire lifecycle to ensure that it functions properly, and that the product generates a profit for the business (Wiktorsson et al., 2010).

2.1.2 Product Life cycle and Product realization:

Product realization is a stage in the invention process, which is itself a stage in the product life cycle. From applied research and development to product design and planning, process production planning, distribution, and sales, and use and operation, the innovation process encompasses all activities required to bring a new product to market. Product realization a stage in a product life cycle shown in figure 3 (Säfsten & Bellgran, 2005).

Figure 3: Product realization a stage in a product life cycle modified from (Säfsten & Bellgran, 2005)

Figure 3 depicts the operations in a sequential order. However, it is critical to emphasize the necessity of product and manufacturing process integration in order to achieve productive product realization process (Säfsten & Bellgran, 2005).

2.2 Digital tools

Digital tools are software and programs that can be integrated with product realization process for enhancing and visually analyzing the production process.

2.2.1 Simulation:

Simulation is the process of creating a model of a real system and conducting experiments with it for the purpose of either analyzing the system's actions or testing different strategies for its operation (Ingalls, 2008). Simulation is a method of examining models that is primarily experiential or experimental in nature. A model is a software program that is used to represent another object for a specific purpose. Models, in general, are simplistic abstractions that only provide the scope and level of detail required to meet particular study objectives. When investigating the actual device is impractical or prohibitive, models are used. Simulation is similar to field testing in theory, except that the device of interest is replaced by a physical or computational model. Simulation entails constructing a model that mimics the desired behaviors, playing with the model to produce insights, and attempting to comprehend, summarize, and/or oversimplify these behaviors (White & Ingalls, 2009). Every simulation is designed to help you make better decisions in some way. Good decisions lead to greater productivity and lower costs, which are typically two of a company's primary objectives (Pfeiffer et al., 2003). According to Sharma (2015) simulation model is created to investigate how a system works as it progresses over time. A fully developed and validated model can address a wide range of real-world questions. Discrete-Event Simulation is a simulation technique that models the function of a system as a discrete series of events in time.

2.2.2 Overview of Discreate event simulation:

Discrete event simulation represents the real world quantitatively, simulates the mechanisms on an event-by-event basis, and produces a comprehensive performance report (Babulak & Wang, 2008). Discrete-event modeling is appropriate for systems that may be characterized as a collection of interconnected entities that only change their state (and hence the state of the system) at discrete moments in time as a result of their own or other entities' actions (Lückerath & Ullrich, 2017). Since the 1960s, discrete event simulation software has advanced steadily, and several systems have been developed by industry and academia to address a variety of industrial problems. Thanks to the widespread availability of powerful computers, it has long been one of the most widely used computer-aided decision-making techniques. In summary, there have been four generations of simulation software products (Babulak & Wang, 2008): 1st Generation (late 1960s): In a high-level language (H.L.L) programming can be done. FORTRAN is an example of (H.L.L). The modeler was required to develop both the model functionality as well as the code that controlled the activities and events in the model.

2nd Generation (late 1970s): Simulation languages with commands like "engine" for event management, statistical distribution generation, reporting and so on. To create an executable model, a simulation language model was compiled and then connected with the supplied subroutines. GPSS (IBM), See Why (AT&T), and AutoMod (ASI) are some examples.

3rd Generation (early 1980s): Front-end packages that produce code in a simulation language are known as simulation language generators. To create an executable model, the created code is compiled and connected. It cut the time it took to create a model in half, but it still allowed the modeler to understand every aspect of the simulation mechanism. SIMAN (Systems Modeling) and EXPRESS(AT&I) are two examples.

4th Generation (late 1980s): Interactive simulation packages that allow "what you see is what you get" and allow models to be changed at any time and speed up "what-if" analysis. Industrial managers and engineers may easily build simulation models, allowing those with practical knowledge and experience with the problem to do so. ARENA (System Modeling) and WITNESS (AT&T) is an example.

2.2.3 Virtual Manufacturing

Virtual engineering is a new technology that combines geometric models and related engineering tools like design, analysis, simulation, optimization, and decision-making tools in a computer-generated environment to aid multidisciplinary product development. Virtual Manufacturing is a key component of virtual engineering in the manufacturing industry (VM). VM use computer aided design models and simulations of manufacturing for production of manufactured products. VM provides the capability to manufacture in the computer and has the ability to interchange models between their use in simulation and control environments. (Patil & V. G, 2015). At all process stages, VM is the integrated application of simulation, modelling, and analysis technologies

and tools to improve manufacturing design and production decisions and control. The term "virtual manufacturing" was first used in the early 1990s by the US Department of Defense's Virtual Manufacturing Initiative. Virtual reality has been the foundation of virtual manufacturing in product manufacturing strategies and organisation, aimed at meeting the needs of users/buyers of goods, including low cost and lead time. A computer-generated interactive and immersive 3D environment simulating reality is known as virtual reality (Chablat et al., 2007).

Virtual Manufacturing can provide a new approach to execute the product development process by proposing a virtual interactive design-build-test cycle in which design, validation, and manufacturing processes can be completed digitally before being carried out physically in the real world (Porto, 2006).

2.2.4 Emulation

Emulation is the method of duplicating or mimicking all of the hardware and software features of a particular system in real life, with the primary goal of evaluating equipment or processes (Pfeiffer et al., 2003). Emulation can be carried out on three levels: application software, system software (operating system), and hardware. Emulating both application and system software necessitates a thorough understanding of their architecture and implementation. These items are sophisticated and, in many cases, unique, making them difficult to duplicate. Another challenge with application-level emulation is that each program requires its own emulator. Emulation of a hardware platform necessitates the replication of the original platform's functional behavior in such a way that the original program is unable to discern between emulation and reality. Certain RWS hardware platform specification requirements must be completed using emulators available on the Internet. In short, it must function with an x86 platform with sufficient internal memory and hard drive space, as well as a compatible display adapter, sound card, and networking capabilities (Wijingaarden & Hoeven, 2005). The function of an emulation in manufacturing is to emulate the behaviour inside the manufacturing cell behaviours utilizing data from the real world, including actual equipment, genuine controllers, and realistic management software. An animation function that synchronizes the results of processes occurring in manufacturing cell to visualize 3D manufacturing cell emulation models. A connection function that uses the soft-wiring mechanism to connect the emulator world data to the actual world data. A Production system emulation is a device or piece of software that allows a program or piece of equipment designed for one type of computer or equipment to be used with the same exact results on a different type of computer or equipment ( Hibino & Fukuda, 2008). Production system emulation refers to the mixing and synchronization of manufacturing system emulators, real equipment, real controllers, and management applications in the situation that elements of the manufacturing system, control programs, and manufacturing management applications are not accessible ( Hibino & Fukuda, 2008).

Manufacturing System Emulation:

The use of manufacturing system emulator and emulation are described by ( Hibino & Fukuda, 2008);

• A manufacturing system emulator is a tool or set of application that enables a program or set of tools designed for one kind of computer or equipment to be utilized with the same exact results on a different type of computer or equipment.

• Manufacturing system emulation refers to the integration and synchronization of manufacturing system emulators, real equipment, real controllers, and management applications in a manufacturing system.

2.2.5 Visualization

Most of the researchers have stressed the role of visual representation in decision-making. Raw data is transformed into pictures that people can understand easily through visualization. It is a user-friendly method of information representation. When managers adapt their problem-solving processes to the problem representation, it can perform better (Chen, 2004). Visualization is a "visual" vehicle of thinking that helps managers make decisions (Platts & Tan, 2004). People's perception has been shown to improve when they visualize pictures and models, which can help with training and learning. Using virtual software, large volumes of data can be visualized in a single virtual representation. Manufacturing firms have shown how to use virtual tools, as well as a few issues and inconsistencies with the approaches used to visualize manufacturing processes. There are a variety of resources available for visualizing industrial applications. The production structure and its facilities are often designed and recorded using CAD-based applications. It's also used to create and envision manufacturing, transportation, and material handling equipment, as well as tooling and other hardware installations (Lindskog et al., 2012). There are a variety of conceptual framework for presenting visual representations for single user or multiple users. Virtual Reality (VR) and Augmented Reality (AR) are the two main concepts identified for visual representation (Saavedra et al., 2020).

2.2.6 Virtual Reality (VR)

A virtual reality (VR) environment is a three-dimensional virtual environment that is created in real time and managed by the users. The aim is to give users the sensation of being inside the virtual world by displaying the 3D virtual environment on some form of display. The lack of knowledge provided by conventional 2D models prompted the use of virtual reality (Smith & Heim, 1999).

VR has a wide range of applications. The workshop can be run in parallel with the real-world development process, with modifications made virtually and then implemented in the real-world system. However, even though VR technology has great potential for visualizing virtual representations of production processes, the issue of gathering data for the virtual representations remains (Filho et al., 2010).

2.2.7 Augmented Reality (AR)

AR is a key technology for making Industry 4.0 ideas into reality. Employees will use augmented reality to bridge the gap between the physical world and the critical digital world. AR is one approach to augmenting the worker, as such approaches foster a human-centric industrial climate. Humans can access the digital world via a layer of knowledge built on top of the physical world using this technology (Masood & Egger, 2019). AR is a set of technologies that use an electronic system to display a real-world physical environment that is coupled with virtual elements, either directly or indirectly (Furht & Carmigniani, 2011).

The global augmented reality market is rapidly expanding, and the widespread acceptance of AR technology means an undeniable influence on society. AR's implementation in the industry domain is significant because it significantly enhances coordination in product design and development, it aids in the early detection and avoidance of design defects, decreases the number of physical prototypes, and saves time and money for businesses (Manuri et al., 2018). According to Masood (2019), AR can be used to view the real time information on-site in an intuitive way, immersing the operator in the digital environment of Industry 4.0 solutions.

2.2.8 Human Robot Collaboration

Manufacturing companies must adapt their processes by enhancing production quality, flexibility, and sustainability as modern production structures increasingly move from mass production to mass customization. The definition of the fourth industrial revolution is used to explain the real industrial transition that is affecting global businesses. One of the main cyber-physical enabling technologies of Industry 4.0 is industrial collaborative robotics. Human-robot interaction (HRI) aims to merge automation capabilities with special human skills by allowing for secure and profitable task sharing in a shared workspace. A good illustration of “human-centred design” would be the correct use of collaborative robots to facilitate operators during assembly tasks (Lucca gualtieri, 2020; Bzhwen A Kadir, 2018).

Human-Robot Collaboration (HRC) is a method of primary interaction between humans and robots that is primarily used to achieve a common goal. HRC's main concept is to combine human and robot abilities. Humans have inherent flexibility, intelligence, and problem-solving abilities; robots, on the other hand, offer precision, power, and repeatability. Collaborative robots are robots that are designed to work safely alongside humans or to assist them with specific tasks. These robots can be used in a variety of settings, including factories, homes, and hospitals (Shah & A. Lasota, 2015). According to Riccardo Gervasi (2020) HRC is one of the cornerstones of Industry 4.0, which is defined by intelligent and autonomous systems that are powered by data and machine learning.

Human Robot Applications

• Collaboration between humans and robots will aid in the construction of future factories, a place where humans and robots can work together and complete tasks. It relieves human operators from repetitive or potentially dangerous activities, allowing them to concentrate on operations of high value added and involving significant levels of proficiency (Murtua, 2017).

• Human–robot cooperation is a new frontier for robotics, and human–robot teamwork will be a key factor in improving production lines' efficiency and versatility in the future (Matheson et al., 2019).

• Economic motivations, workplace health (ergonomics and human factors), and effective usage of factory space all play a role in the decision to use human– robot collaboration systems (Murtua, 2017).

2.2.9 Digital Twins

With companies progressing into the industry 4.0 era, companies are moving towards a smart manufacturing paradigm to improve production capabilities. DT is an approach to continuous improvement in human well-being and quality of life by offering a tool to monitor, optimize, and forecast processes. By illustrating the physical status of systems in a virtual environment, DT technology is a useful tool to meet the requirements of smart manufacturing (Hong Lim et al., 2020). DTW is generally referred to as data linkage between a physical object and its virtual representation, generated using computational simulations and techniques to enhance the performance of the physical component (Pang et al., 2021). DT is a digital replication of organizations with real-time two-way contact between cyberspace and physical space, designed to increase output performance (Hong Lim et al., 2020).

2.2.10 Application of Digital Twins:

1. DTWs draw interest from the fields of activities of various industries, such as product design, logistics, development, and maintenance. DTWs can also be used to improve the productivity and automation of production, maintenance, and after-sales service levels (Pang et al., 2021).

2. In the stages of production planning and execution, a digital twin is a useful tool for drafting plans and optimizing processes. The digital twin shopfloor can replicate the blueprints in virtual space and identify possible problems before the production process even begins (Qi et al., 2018).

3. DTW can be a very useful tool in the manufacturing system in the product definition, design and development stages. It is also used effectively to understand the efficiency and behaviour of individual devices, making it easier to integrate the production line (Pang, et al., 2021).

4. Digital twins are used to model complex assets or processes that communicate with their environments in a variety of ways, making it difficult to predict results over the course of a product's life cycle (Warshaw & Parrott, 2017).

5. According to Pang et al., (2021), DTW can be used as an intelligence tool in the field of services, such as after-sales services, to provide additional value to consumers by being able to make better forecasts of potential activity and the remaining lifespan of an asset and its components. lifespan of an asset and its components. DTWs may also be used to gather valuable information to drive modification of design, enhance product efficiency and improve the overall production planning period.

3 Method and implementation

3.1 Research design

Research design can be thought of as the framework of research. It is a set of criteria for data collecting and analysis that tries to balance relevance to the study goal with efficiency and procedure (Akhtar, 2016). The procedures for collecting, analysing, interpreting, and reporting data in research studies are known as research designs. It is the overarching strategy for connecting conceptual research problems with relevant empirical research. In other words, the research design establishes the procedure for collecting and analysing the required data, as well as how all of this will be used to answer the research question (Boru, 2018).

Phases in Research Designing:

The Research process proceeds in five phases by (Akhtar, 2016): 1. Defining the problem or topic to be researched

2. Framing research questions 3. Collecting the data

4. Analysing the data 5. Preparing the report

These are the key points to remember before formulating any research. Research design is the preparation of a strategy for conducting research. To prevent uncertainty later, both measures should be clearly defined. Therefore, study design work begins after the problem is selected and ends before data is obtained (Akhtar, 2016).

3.2 Brainstorming Keywords

Brainstorming (BS) is a technique for encouraging group creativity in which members freely share ideas and thoughts in order to find answers to practical problems. Many prior studies have suggested that the process involved in the idea generation job has the potential to play a significant role in enhancing people's ability to generate creative solutions that can be further examined and, eventually, implemented in practice. The amount or novelty of the generated ideas is frequently used to assess an individual's competence during a BS session (Samarraie & Hurmuzan, 2017).

According to Isaksen (1998), there are certain guidelines to carry out the brainstorming process.

• Refrain from criticizing: While sharing their thoughts, none of the members should criticize another’s ideas, even if it is completely absurd.

• Focus on Quantity: The greater the number of thoughts the easier it is to classify them.

• Freewheeling is welcomed: The wilder the idea, the better; it is easier to tame down than it is to come up with. Since criticism is temporarily prohibited, it is okay and desirable to communicate truly odd and unusual thoughts.

• Combine and enhance ideas: Ideas should be finalized before they are evaluated. The idea that was offered impulsively are now revised and finalized as a result of this.

For gathering the relevant data from the literature review, the important task is to find the relevant articles. The authors have brainstormed various keywords related to this study which can be used in combination with other keywords to look for the relevant data from the databases. Brainstorming is best suited method for the group. It could also help you convince team members to buy into the solution you have selected- after all they helped build it, they are more inclined to stick with it. Furthermore, because brainstorming is engaging, it enhances team bonding by allowing members to tackle problems in a positive, productive environment (Mutairi, 2015). Therefore, brainstorming the keywords related to this study helped the authors in collecting the right data in short amount of time. Different keywords and combination of keywords used for this study is shown in the Table 1.

3.3 Literature review

In order to address our research question, A literature review have been conducted. According to (Bolderston, 2008) a literature review will include a comprehensive summary of evidence in a specific field. The analysis of literature is used to set the stage for a subject of primary research and may therefore be reasonably precise. This preliminary assessment can also be used to reassure the reader that the researcher has considered the previous published work on the subject and that the new research they have carried out is relevant and adds to this domain of research.

The researcher may use a variety of methods, principles, and guidelines developed specifically for performing a literature review, depending on the intent of the review. A literature review can be the most effective analytical tool for answering a variety of research questions. When a researcher needs to evaluate theory or evidence in a particular field or investigate the validity or accuracy of a particular theory these reviews are very valuable (Snyder, 2019). Below figure 4 shows the basic schematic representation of how literature review has been conducted.

Figure 4: Schematic Representation of the literature review

The literature search has been conducted on databases like Scopus, Primo, Google scholar. On database Scopus the research was carried out by combining different keywords and Boolean operators. The main reason for using Scopus was because it is one of the few databases in which it provides possibility to read articles abstract easily. Since this is a wide area of research a single database was insufficient to gather enough data for this study. Therefore, additional databases like Primo and Google scholar were used.

Many keywords used with a combination of Boolean operators to conduct this study were Simulation, Emulation, Digital Twins, Product assembly, Product realization, Visualization, Production development, Virtual Manufacturing. These keywords were used to cover all the aspects needed to address the research question for this study. After initial search, the abstract of these papers was read, and relevant articles were shortlisted for detailed study. The article in most cases were “Peer-reviewed”. When an article is peer reviewed it has gone through an audit by experts in the field before it is published (Moberg, 2020)

To limit the number of hits, filters were used in the databases example: all peer reviewed articles were used; the articles were filtered to focusing only on engineering; final publication stage; article from journals and articles only in English language and selecting familiar keywords in the filtered list. These filters reveal articles that were related to the study.

Table 1: Documents found in Databases.

Databases Keywords No of hits No of

hits after filtering

No of relevant papers

Scopus Simulation AND Product realization

1173 12 4

Scopus Visualization AND

Production Development

2721 12 4

Scopus Virtual Manufacturing AND Product Realization

1022 27 12

Primo Digital Twins AND human machine collaboration.

1533 92 6

Primo Emulation AND Product realization

4329 221 10

The following procedure that was presented in the table are used as an example on how literature review was carried out. For the article search made for Simulation AND product realization there are 1173 no of hits, after applying appropriate filters 12 article abstract are reviewed in order to keep track on the relevant article. After going thoroughly from the following relevant article, 4 articles were selected that are applicable for the current situation. In the similar pattern other keywords are search and similar procedure were followed.

However, in addition to the Scopus database, a more search was conducted using the Google Scholar database. This systematic search review makes it possible to have a direct access to reports, journal article and conference paper that are available on another database.

3.4 Data Analysis

The data for this research study is mostly gathered through a literature review. By doing literature review qualitative data was collected. According to Gibson (2003), the most critical step in data analysis is to properly organize and structure the data. Data should be structured in a way that makes it simple to read and helps the researcher to pick up concepts and themes as they go through each topic. One method to accomplish this is to construct a table with all of the information from the text. The data analysis was carried out by the authors of this study is through arranging and compiling the data in the form of tables in order to analyze it further for the findings.

3.5 Validity and reliability

There are some good suggestions for including a compelling context in research. The correctness of findings is referred to as validity, whereas the accuracy of findings is referred to as reliability (Mohajan, 2017). Data gathered in the findings to analyse the research questions was extracted from secondary data available on the databases which were peer-reviewed and using literature review as a method chosen for data gathering can prove the validity and reliability of this study. Validity and reliability increase transparency while reducing the chances of researcher bias in qualitative research. An assessment of the methods used to acquire data is required for a full evaluation of reliability and validity for all secondary data (Kimberlin & Winterstein, 2008).

4 Findings

In this chapter the authors describe the detailed information of Digital tools linked with PRP. This information is utilized to relate with respective theories in the theoretical background chapter to carry out a detailed analysis in the next section.

4.1 Simulation in PRP

A paradigm shift in product development processes and the role of both physical and virtual simulations is needed to realize the full potential of virtual simulation in reducing product development time and costs (El-Sayed, 2011). Simulation is a key technology within the digital factory concept, which provides an integrated approach to improving the product and production engineering processes. Different forms of simulation, such as discrete event or 3D motion simulation, may be used in virtual models to enhance product and process planning at all levels (Kuhn, 2006).

Allocating orders to the factory floor on single, parallel, and multiple lines, as well as separating and joining lines, generally requires precise details and, in some cases, complex rules and strategies. Software support is essential, particularly when a large number of different products and variants must be created, and order sequencing is constrained by a large number of rules. A simulation-based sequencer tool can help reduce the amount of manual work required to create feasible schedules while also improving schedule quality (Kuhn, 2006). Discrete event modelling software may be used to simulate plants, lines, and processes. For all levels of production planning, these methods allow for the analysis of structures and processes in order to maximize material movement, resource use, and logistics (Kuhn, 2006). During the product development process, physics-based models are very useful. Simulation reduces the need for costly manual prototyping, resulting in a faster prototype production time and lower product development costs. Assembly planning, ergonomics research, and testing implementations all use physics-based simulation. 3D CAD data were used to drive physics-based simulations. The number and complexity of geometric features present in the CAD model determine the simulation's computational efficiency. Features that are an essential component of today's CAD models, and they're used in about every aspect of the product life cycle, including architecture, production, research, and maintenance (Thakur et al., 2009).

The use of a DES tool is popular for assisting engineers in designing production lines and planning production processes, as well as providing valuable assistance on what-if analysis and solution calculation and validation for the process. In contrast to standard practice, a DES model has been developed whose purpose is to produce a solution that is close to the optimum, rather than to calculate and/or validate a problem solution (Gironimo et al., 2013). Today, DES methods are used in a variety of fields, ranging from health care to shipbuilding and, more recently, heavy construction, with new applications emerging from research. Nonetheless, manufacturing-related research is scarce, and research activities in such fields, especially in production and process planning, appear to be on hold (Gironimo et al., 2013).

Frequent schedule adjustments, or even the development of new production flow routes, are becoming more common, and the time needed to test the new introduced solutions is short. Using the capabilities of computer simulation and visualization of production flow is one way to deal with such challenges (Olender & Krenczyk, 2015). On the market are a variety of simulation packages, ranging from basic numerical simulations to sophisticated modeling, visualization, and discrete-event computer simulation systems. Manufacturers can check if planned production orders can be completed on time by using computer simulation systems (such as Arena, Taylor II, Enterprise Dynamics, FlexSim, and others). It is primarily discrete simulation systems that are currently used in the automotive industry to assist the decision-making phase in production planning. The use of simulation tools to aid decision-making at the organizational and tactical planning levels allows for easy verification of the viability of developed production plans (Olender & Krenczyk, 2015). The Enterprise Dynamics simulation system was used to prepare simulation experiments as a simple modeling method. The model's implementation, as well as the use of the automated module for experiment execution, has been demonstrated. The experimental module enables the automated execution of several simulations and changes to production flow parameters (e.g., capacities, setup and cycle times, etc.) without the need to run each one individually. Simulation has made it possible to examine various production flow paths for different sets of available resources, which is especially useful when planning production in virtual enterprises - virtual/dynamic manufacturing networks (VMN/DMN) (Olender & Krenczyk, 2015).

Every year, the use of discrete simulation tools for production simulation grows, and this practice will continue in the future. The use of simulation to help production planning has a lot of potential because it allows you to spot problems early on, giving you a production summary and enough time to fix them. It also allows you to test production capacity, experiment with a simulation model, and construct various scenarios that can be checked before they are implemented in real life ( Václav et al, 2018). Simulation is a term that defines an interpretation of reality that is hard to understand on its own for many users (Han et al, 2012).

4.2 Visualization in PRP

The exploration of critical elements used by practicing engineers to visualize the construction of product model during the product development process is the main focus of this Product visualization context. The tasks of sorting, filtering, or highlighting specific aspects of a model to obtain a clearer understanding of the nature of the product are among the techniques used in CAD model visualization ( Adnan & Daud, 2019). Visualization is a powerful medium for facilitating perception and collaboration through disciplines. It is a useful instrument for illustrating and portraying appearances and relationships, as well as for creating a mutual understanding of possible products and processes. When project teams use visualization during the creation of new manufacturing structures, procedures, or objects, it is easier to create a

shared conceptual vision of a potential product or operation. Digital Storage is freed up as a result of visualization, allowing more time for problem solving. Successful work is enabled over a long period of time when a work process is continually visualized in the form of drawings and graphs (Sivarda et al., 2014). Non-contextual content, such as product designs and engineering drawings, requires visualization software to be displayed, shared, and communicated. The most widely used methods are three-dimensional CAD structures (such as CATIA, ProEngineer, and IDEAS). ‘Digital mock-ups' are now eliminating the need more and more for physical prototypes thanks to developments in 3D-CAD technologies (or at least allow designers to inexpensively build many generations of digital mock-ups before building the final physical one). Because of this, designers can test for conflict between products and materials early on and fix them for a low cost (Yassine et al., 2004).

Using data visualization will be extremely beneficial, many scholars have emphasized the role of visual representation in decision-making (Sackett et al., 2006). The product design information, manufacturing resource information, and process design information are transferred to the workshop site in the form of digital quantity, and the system is presented using assembly process visualization technology. Traditional two-dimensional engineering drawings, process design, and two-two-dimensional assembly orders have been largely replaced by digital products, digital process models, and their three-dimensional AO (Assembly Order, that is pre - assembled guidance). This has increased the impact of technical training for employees. It enables workers to be instructed in how to carry out field production in a vibrant and intuitive manner, as well as set up the assembly for field production. An application structure facing field output must be visualized (Song et al., 2018).

Modern manufacturing lines, which are highly digitalized and integrated, conveniently have an abundance of process data. Visualization and visual analysis can give manufacturing supervisors intuitive displays of real-time process data for on-site troubleshooting and production monitoring; it can also give production managers deep insights into non-real-time historical process data for process innovation. Real-time production line monitoring is the most important requirement in the manufacturing industry, and it is also one of the most common real applications for visualization and visual analysis technology in smart factories. The visualization-assisted approach has two distinct benefits over conventional production monitoring. By one side, visual data presentation makes unified management of spatially distributed output equipment possible. Interactive monitoring, on the other hand, may incorporate the professional expertise of operators to determine the operating state of production lines in order to provide prompt and relevant troubleshooting. Visualizations can be used to analyze the process data produced by a complex production system, allowing for user-guided discovery of potential bottleneck process phases and excess devices for improvement of the production system (Lin et al., 2018).

4.2.1 Visualization and Simulation

The use of visualization in conjunction with simulation can provide project participants with a detailed-level model that can help them avoid data misinterpretation and better understand the manufacturing process. Visualization of simulated development process can aid in the analysis and communication of simulation outcomes, which can aid in decision-making. Visualization using realistic graphical depictions will represent virtual operations as they would be in practice (Han et al., 2012). In a virtual environment, the sequence of assembly stages can be viewed. Viewing each procedure step, jumping backward and forth, inspecting and querying information about assembly pieces, and selecting viewpoints are the only ways for the user to interact. The user has the ability to explore the entire virtual scene in order to look into functional dependencies (Schenk et al., 2005). The state of the Virtual environment can reflect the state of the real factory thanks to online simulation concepts and a connection to shop-floor systems. In the event of an emergency (such as a machine failure), the factory operator can plan and test various action plans (fast-forward simulation) before deciding on the best option. The logical step in the development of visual 3D simulations is to combine simulation and virtual reality into truly interactive and immersive environments, which is a logical consequence of the digital factory's requirements (Schenk et al., 2005). The internal world of certain facilities and devices during the production and manufacturing phases involves complicated natural processes. The modeling and analysis of these complicated natural systems is a difficult task. To make the validation of simulation models and concept prototypes easier, scientific visualization will partially or completely substitute physical instruments for simulating and evaluating the internal environment (Lin et al., 2018).

4.3 Emulation in PRP

Emulation is a testing procedure in which the controllers or control code are in their final state but the elements they control are still virtual. It serves as a link between design and execution, making the manufacturing process installation much more effective. Controller validation will be done much earlier in the process, allowing users more flexibility to make modifications to your software and troubleshoot problems. It is used to test the control system under practical conditions (Proctor & Harrison, 2015). An emulation model is one in which a real-world component performs part of the functions of the model. Despite the fact that all models are approximations of real systems, there will be significant different in the performance of the real system and the model. These contradictions frequently result in a "credibility gap," which an emulation model attempts to close by substituting "reality," or a component of the real system, for a component of the model. Emulation models are utilized in a much more realistic way to test the performance of a control system under various system loading situations. The emulation model more accurately reflects the system that will be implemented, and as

a result, it may be used to carry out a specific number of verification processes to confirm the control system's performance or responsiveness (McGregor, 2002). Emulation models are not utilized for testing in the same way as simulation models, they're inadequate to perform the task because they are typically run-in real time. Engineers in charge of commissioning, or the installation and start-up of automated systems of all kinds, are finding that using emulation models to test control systems under realistic conditions, by replacing the production system with a model, is proving to be of great interest. An emulation model is a model that takes the place in real-world feature. They are used in a much more specific sense, such as monitoring the operation of the control system under different system loading conditions and risk-free training of system operators and maintenance staff. As a result, the emulation model more perfectly describes the configuration that will be used, and it can be used to perform a limited range of verification procedures to ensure the consistency or reaction of the control system. There are programs that will make these tests easier to execute and run in parallel, allowing for further tests to be run on a real computer (McGregor, 2002).

4.4 Design of Robot work cells

The design, simulation, optimization, analysis, and offline programming of robotic work cells and automated manufacturing processes in the sense of product and production resource data is the objective of Digital Manufacturing and simulation of robotic work cells. To perform reachability tests, collision detection, and cycle time optimization, motion simulation and synchronization of several robots and mechanisms, including 3D path definition, is necessary (Kuhn, 2006; Qibing, 2021). There is a variety of commercial software that can be used to design the layout of a facility with robotic work cells. Additionally, commercial software for industrial robot simulation and visualization has been developed. Prior to purchasing any hardware, simulation and visualization of robotic work cells are needed. Users may use the simulation tools to test reachability, workspace, safety problems, and other aspects of industrial robots (Jen et al., 2008). Facility layout planning software is generally based on the discrete-event simulation concept, in which the system's function corresponds to the sequential order of events. The industrial robot is considered an event inside the simulated robotic work cell by discrete-event simulation software, which gives an overall image of the simulated robotic work cell. There are some drawbacks in using DES, the simulation environment does not focus on the special constraints and ergonomics in the operation. A geometric graphical representation with a constant time interval is provided by geometric or continuous simulation. Geometric simulation, as compared to discrete-event simulation with irregular time intervals, is better suited for 3D visualization, offline robot programming, and collision detection during manufacturing processes. Virtual manufacturing is another name for geometric simulation (Yap et al., 2014).

New generations of social robots have opened new possibilities for automation (cobots). Cobots are outfitted with high-performance sensors and are operated by smart systems that incorporate these sensors with the help of cutting-edge software. Cobots do not need safety fencing and are capable of interacting with the environment well enough to assist humans in work cells (Yap at al., 2014). There is also a potential value in the field of cobots using Digital twins (Brem & Malik, 2021).

4.4.1 Digital Twins in Human robot work cell.

The physical and digital spaces are intertwined in the digital twin structure. The digital space is a three-dimensional virtual image of humans and robots coexisting or interacting, while the physical space is a real-world production environment made up of humans, robots, and other hardware (Kousi et al., 2021). Many researchers have found several contributions with regard to digital twins in production process like Digital twins in product design and service, Digital twins in shop floor, Digital twins in Factory design, production line efficiency, and many more. Despite this, the majority of research is available in the form of conceptual models and simulations, with minimal application to real-world scenarios. (Brem & Malik, 2021). The CIRP (Society of Production Engineering) keynote paper emphasized the role of DTs in HRC production systems. In virtual models, it was shown that a DT can make combining and aligning the role, structure, and actions of an HRC cell with the symbiotic interplay of humans easier (Brem & Malik, 2021). Ali Ahmad malik, (2018) addressed the importance of digital twins for HRC assembly process, as well as the relevance of Digital twin in Human robot Collaboration. It is also concluded that Digital twins can support HRC systems, but the analysis was limited to the HRC in operational process.

4.4.2 Using Digital Twin as a tool for HRC production process

Digital twins can be useful for HRC in three phases: Design, Integration, and operational phase (Brem & Malik, 2020).

The design phase.

To prevent wastes, it is critical to select and position production resources optimally in a production setup. When designing the layout, the coexistence of humans and robots has additional safety consequences. Based on this the following experiment and tests for safe and waste free layout for HRC (Brem & Malik, 2020)

a) Reach and placement evaluation

These experiments can be carried out in a virtual environment. Similarly, the reachability of human arms (depending on body measurements) can be investigated without bending of the body. The target is to have the shortest cycle time, the minimum accidents, and the safest working conditions possible (Brem & Malik, 2020).

Adjusting the human-robot trajectories to travel from one point to another in an HRC where humans communicate with cobots, and regular output changes can be desired. Collisions are bound to happen in such a fast-paced environment. While cobots are built for environments where human-robot collisions are likely to occur, frequent collisions reduce productivity (Brem & Malik, 2020).

c) Ergonomic analysis

Work postures, load on the human body during weight lifts, and task frequency are all linked to poor human performance when it comes to manual labour. Early in the design process, a wide range of these ergonomic problems in production systems can be simulated and evaluated (Brem & Malik, 2020).

Integration phase

When the virtual and physical robots are linked, the virtual robot will follow the physical robot's path. If the real robot is in an empty room, the virtual model can be filled with resources, equipment, and people to build the physical space's future scene. As a result, when the task is completed by a physical robot which allows for secure and safe operation in the virtual space. (Brem & Malik, 2020).

Operation phase

The DT model will evaluate the cycle time for each task by taking into account the robot's main positions (pick and place) and creating a robot trajectory. Based on task sequence, minimum cycle time, and eliminating any idle time, a final assembly plan can be created (Brem & Malik, 2020).

Robot control program

The robot program is intuitively created from the DT environment when using a DT. The robot software is moved to the linked cobot, which begins functioning as the robot in the digital twin once the desired process has been virtually checked (Brem & Malik, 2020).

Cobots (Collaborative Robots)

Collaborative robots, unlike traditional industrial robots, allow humans to work alongside them, removing factory confinement barriers in the process. As a result, implementing HRC in the manufacturing sector allows for the creation of a dynamic and flexible environment in which production lines can quickly adapt to new products and change (Gervasi et al., 2020). Some of the examples of industrial collaborative robots are shown in the Figure 6.

i Sawyer

iii Yumi iv UR5e

Figure 6: Example of Industrial Robot collaboration (Riccardo Gervasi, 2020). ii LBR iiwa

5 Analysis

In this chapter, the research questions are addressed by linking the findings with various theories that are elaborated in the theoretical background chapter. This chapter discuss about the digital tools, and their functions and contributions in relation to PRP.

5.1 Answering research question 1: What are the different digital tools that can support the PRP?

From this research, the authors have found data regarding digital tools that support PRP that is discussed in the Table 2.

From table below, PRP is divided into two categories: Product Development and Production development. These are further divided into Product planning and product Design, Process planning and Production Assembly. From this table we can interpret which of these digital tools support all the phases of the PRP which will be helpful in selecting the digital tools for each process.

Table 2: Digital tools supporting the PRP

Digital Tools PRP Authors

Product Development Production Development Product Planning Product Design Process planning Production Assembly Simulation DES or 3D motion in Virtual model. Physics based Models using 3D CAD data Discreet Event Modelling for analysis Using Physic based simulation for testing (Kuhn, 2006), (Thakur, Banerjee, & Gupta, 2009) Visualization - 3D CAD structure model (CATIA) - Digital mock-ups (Yassine, Kim, Roemer, & Holweg, 2004), (Sivarda, Shariatzadeh, & Lindberg, 2014)