Analysis of factors influencing smart product development using Interpretive Structural Modelling: a case study

ABSTRACT

Manufacturing in the viewpoint of Industry 4.0 is expected to be highly reactive, innovative, versatile, and agile. In order to facilitate this aspect, product development needs to be smarter to augment manufacturing developments. This article is focused on the analysis of factors of smart product development. 20 factors are analysed using Interpretive Structural Modelling (ISM) method, and key factors are identified. The identified key factors include computation and data storage, facilitating user interaction. MICMAC analysis has been done, and the practical inferences are derived. The study enabled product designers and practitioners to focus on the effective development of smart products.

KEYWORDS:

• Product development
• smart manufacturing
• factors
• Interpretive Structural Modelling
• MICMAC analysis

Introduction

The manufacturing paradigm has been presently oriented towards developing smart factories. In line with these developments, product design and development practices have to be oriented. Smart product development characteristics include Pro-activity (Gutierrez et al. 2013), Growth and Sustainability (Jaiswal, Basu, and Bhola (2019); Majumdar and Sinha (2018); Tomiyama et al. (2019); Manjunatheshwara and Vinodh (2017); Doualle et al. (2019)), Computation and data storage (Dawid et al. (2017); Gogineni, Riedelsheimer, and Stark (2019); Nunes, Pereira, and Alves (2017); Tomiyama et al. (2019)), Supply chain design (Dawid et al. (2017); Majumdar and Sinha (2018); Pan, Zhong, and Qu (2019); Swan, Luchs, and Creusen (2015)), and Degree of autonomy (Dawid et al. (2017); Tomiyama et al. (2019)). To facilitate effective deployment of smart product development practices, its key factors are to be analysed. 20 factors are analysed from literature review. There are many Multi-Criteria Decision-Making (MCDM) methodologies like Weighed sum method (WSM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA), ELimination and Choice Expressing REality (ELECTRE), and Analytic Hierarchy Process (AHP). Along with finding the relevant factors, the present study also focuses on understanding the interactions between various factors. Thus, Interpretive Structural Modelling (ISM) was preferred over other MCDM techniques. ISM is a pairwise comparison technique developed by John N Warfield (Olsen 1982). ISM explores the relationship whether two factors relate to each other or not (YES or NO), there is a grey area between YES and NO (0 and 1), which will tell us how much one factor relates to the other. MICMAC analysis helps in identifying how much each factor is able to contribute to the study by classifying the factors based on dependence and driving power (Saxena, Sushil, and Vrat (1990)).

In the past, there have been many studies in various sectors that used ISM and MICMAC analysis to study the relationships between different factors and rank them according to their dependence/driving power. Shankar, Morghan, and Frank (2011) used ISM and MICMAC to analyse barriers in the development of landfill communities. Stanujkic and Djordjevic (2013) used a similar approach to identify the most influential factors that control solid liquefaction. Mattar (2019) identified 15 critical barriers that obstruct the application of big data analytics–based smart sustainable auditing system and dynamic interrelations among barriers were explored using ISM approach. Janssen et al. (2019) studied the challenges for adoption of IoT in Smart cities using ISM – MICMAC approach. Sonar, Khanzode, and Akarte (2020) analysed factors responsible for the implementation of additive manufacturing in Indian manufacturing sector using ISM and MICMAC approach. Therefore, a structural model is derived based on ISM method. The derived structural model depicts the key factors for smart product development. The structural model would enable the practitioners to focus on key factors for developing smart products.

Research objectives of the present study are:

• To identify factors of smart product development based on Literature Review

• To develop a structural model for analysis of factors of smart product development using ISM approach

The article is organised as follows: Section 2 presented literature review on smart product development; section 3 presents ISM steps; section 4 details factors identified and application steps of ISM for smart product development factors analysis; section 5 presents results and implications; section 6 presents conclusions derived.

Literature review

Review approach and related studies are presented in the following subsections:

Literature review approach

The literature review methodology of various studies was considered (Fargnoli and Lombardi 2020) and literature review approach was followed as described below:

The scientific articles that focused on Smart Product Development, Product development for Industry 4.0, Sustainable product development were considered in the scope of the literature review.

Google Scholar and Scopus databases were used to find relevant research studies. A keyword search was employed, and keywords like ‘Smart Product’, ‘Smart Product development’, ‘Industry 4.0’ AND ‘Product development’, ‘Sustainable Product development’ were used to obtain the relevant articles. To obtain the papers relevant to the ISM approach, keywords like ‘Interpretive Structural Modelling’, ‘ISM’, ‘ISM’ AND ‘Product Development’ were used. Studies in the timeframe 2013 to 2020 were considered in the study, which covers time duration in which Industry 4.0 and Smart Product development were considered buzzwords.

The abstracts of the obtained papers were analysed to classify whether the collected articles can be considered for the study. If the abstract is not compatible with our research problem, then the article is excluded. Further, the content of the selected papers is also studied to ensure that they are compliant with our study. The selected articles are classified into conceptual and empirical studies. A conceptual study focuses on delivering theoretical concepts, literature review on Smart product development and Industry 4.0 (Tomiyama et al. (2019), Pagliosa, Tortorella, and Ferreira (2019), Gogineni, Riedelsheimer, and Stark (2019)). In contrast, an empirical study addresses novel technical solutions, like a practical case study that focuses on Smart product development and Industry 4.0 (Shukla and Mattar (2019), Nunes, Pereira, and Alves (2017)). The classified articles are then thoroughly studied, and research targets from the literature review were defined. In the present study, the goal had been to find important factors that drive Smart Product Development.

Review on related studies of smart product development

Gutierrez et al. (2013) analysed different definitions of Smart Product available in the existing literature. During the systematic review of 26 studies published between 2008 and 2013, the commonalities and differences in the definitions were observed. Therefore, a consensus definition of smart product that can be used by all groups was provided. The authors pointed out the need to standardise definitions of Smart products since it had various characteristics distinguishing themselves from other products, thus require different approach during product development. Dewulf (2013) discussed the importance of Front End Innovation (FEI) and activities involved in FEI during New Product development (NPD). The literature also analysed sustainable design considerations, drivers and barriers of incorporating sustainability in FEI. The study focused on tackling the barriers at early stages, and incorporating sustainable design in both operational and strategic activities. Swan, Luchs, and Creusen (2015) described research findings obtained in the field of product design from an extensive literature review of 252 articles conducted over the 20-year period from 1994 to 2014 and provided directions for future research. Various research opportunities were provided from the observation of current and emerging technological and sociocultural trends. In the field of sustainability, future research was welcomed in the fields like integrating the principles of sustainability into traditional Product development process (PDP), adopting standards to increase the modularity in PDP. In the field of IoT and 3D-Printing, further research was encouraged in analysing how the consumers will respond to the increasing product intelligence and the ways to make an accurate and cost effective prototypes.

Salgado et al. (2014) identified wastes within the product development process using a methodological, quali-quantitative, along with an exploratory outline and a taxonomy development model. The causes of wastes in product development process were identified using value stream mapping approach and a product development process model was constructed that allowed its simulation, mechanisms were obtained which led to the decision-making process to optimise product development process. They encouraged more research to be conducted on the integration of lean tools and lean thinking into new product development process. Dawid et al. (2017) discussed technical capabilities along with various fronts like input, output, resource efficiency, interoperability and scope for improvement of smart products and future trends of smart consumer products. With the introduction of smart products, the economic challenges on an intra-firm level involving supply chain management, innovation management, marketing were considered to be the scope for future research and research perspectives for management science in this field were highlighted. A three-phase planning and decision framework was provided for the successful design and commercialisation of smart products that consists of new product idea generation and screening, product concept development and testing and technical implementation. Filho et al. (2017) stressed that importance should be given to smart product development during the fourth industrial revolution. Modern perspective on smart products was explored through a systematic literature review. From the literature review conducted on 937 sentences consisting ‘smart product’ and ‘intelligent product’, the evolution of the definition of smart product development, its enabling features and current applications were observed. Also, conceptual design of a smart product and the prototype of a small-scale Industry 4.0 scenario for the production of self-aware smart products were generated. Further research in the area of designing smart factories is encouraged in the future. Nunes, Pereira, and Alves (2017) conducted a comprehensive literature review to provide knowledge about smart product development and changes in product development processes, methods, and approaches in developing smart products. Computation, data storage, interaction between product and environment were considered as important factors for smart products. In order to develop effective smart products, they emphasised the focus on Iteration, Integration, and Innovation along with the implementation of lean thinking and utilising the resources optimally. With the help of emerging technologies from Industry 4.0, it is suggested that physical prototyping can be combined with virtual and augmented prototyping to reduce time and cost as well increasing the flexibility of product development process.

Majumdar and Sinha (2018) identified 12twelve important barriers based on literature review and questionnaire survey and the contextual relationship among barriers were derived using ISM approach. From the study, it was established that ‘complexity of green process and system design’ was the most influencing barrier with maximum driving power. Cooper (2018) identified the success drivers of New Product Development (NPD). 20 success drivers are presented which are majorly classified into three categories, the first category explains the tactical factors incorporated during NPD like market-driven and customer-focused NPD. The second category presents the organisational and strategic factors in the business perspective like top management support, innovation strategies. The third category represents factors relating to the systems and methods that the firm has in place for managing NPD, like quality of execution and adaptive response to changing conditions. The research also highlighted the challenges faced by modern day product developers in a faster, less predictable, and more ambiguous environment. Areas like lean product development, open innovation, design thinking can be explored considering new environment and their constraints. Shukla and Mattar (2019) identified 15 critical barriers that obstruct the application of Big data analytics based smart sustainable auditing system and dynamic interrelations among barriers were explored using ISM approach. It was found that advancing immature technology, resolving the complexity of data management, lack of skilled labour were the most critical barriers that were to be rectified. Further, MICMAC analysis was also conducted to analyse the driving power and dependence among the identified barriers. Tomiyama et al. (2019) described the development capabilities of smart products and reviewed the status and trends of the technologies namely model-based systems engineering and digital twin. Different smart products were reviewed and the common features were identified and analysed. Technologies like AI, big data and data analytics were determined to be most impactful in smart product development. Pagliosa, Tortorella, and Ferreira (2019) provided an overview of the existing literature related to Industry 4.0, Lean Manufacturing, and their relationship. They also pointed out the research gaps to pursue future research. In their literature review on Industry 4.0 and Lean Manufacturing using Systematic Search Flow (SSF) methodology, 93 studies were analysed, and nine Industry 4.0 technologies and 14 Lean Manufacturing practices were identified. The synergy between identified technologies and practices was analysed through an interactive matrix based on citation frequency of I4.0 technologies and LPs, implementation of I4.0 technologies and LPs at various levels of value stream, and relevance of the evidence found in existing literature. From the results obtained based on analysis, future research opportunities on integrating Internet of Things (IoT) and Cyber Physical Systems (CPS) into lean systems and further empirical validation of the relationship between I4.0 technologies and Lean were suggested.

Jaiswal, Basu, and Bhola (2019) identified five critical factors on smart product development with reference to Industry 4.0 in Indian environment and prioritised them by applying TOPSIS methodology. Technology and Innovation emerged as the key factor followed by System Integration. Sustainability was found to be the least important factor. They pointed out the need to find and segregate driving and dependence factors in the upcoming studies. Further work on design management issues pertaining to smart product development is also encouraged. Although this study identifies critical factors, it does not provide information on the relationship between factors as well very few factors were considered in their study. Gogineni, Riedelsheimer, and Stark (2019) provided a methodology for developing smart customisable IoT devices by identifying, merging, and improving existing product development methodologies. Concepts like variant management and smart service management were used to develop an effective customisable smart product development. In the future, they look to focus on the integration of information technology and aiding tools into product development methodology. Pan, Zhong, and Qu (2019) provided a brief literature review on the design of Smart product service systems, incorporation of digitalisation and service orientation in supply chain and interoperable logistics with the help of concepts like digital twin, and Cyber Physical systems. They also pointed out the need for further research in the areas of sustainable supply chain and logistics, as well designing smart product service systems for physical Internet and interoperable logistics. Miranda et al. (2019) proposed S³ (Sensing, Smart, and Sustainable) framework which can be incorporated into NPD in order to incorporate emerging technologies to produce better quality products at a lesser cost that comply with rapidly changing customer demands as well as minimise the environmental impacts. Integrated product, process, and manufacturing system development (IPPMD) reference model was used in building S³ framework. The framework was then applied to the development of a second-generation (2G) Reconfigurable Micro-Machine Tool (RmMT). The results of the study revealed that 2G RmMT showed 96.6% improvement over its 1G counterpart.

Based on the literature review, it has been found that there are studies on smart product development. But it is essential to analyse each factor, as well as how the factors interact with each other on a collective basis, and prioritise them which would be helpful in decision-making and making tradeoffs. Such analysis would help organisations to come up with feasible products, incorporating emerging technologies as well as customer requirements, which could potentially create huge business value.

Interpretive Structural Modelling (ISM)

It is a qualitative research technique that helps to build relationships with elements and establish the flow of causality, with the help of expert opinion (Watson 1978). The unclear mental models are converted to a well-defined, clear, hierarchical models, which shows clear relationships among the elements. The steps in ISM technique are listed below:

Factors affecting the system are considered and listed. This can be done through a literature review. From the factors recognised, a contextual relationship is formed between factors, with regard to the pairs of factors are being studied. This is done by obtaining expert opinion in a particular field. Structured Self-Interaction Matrix (SSIM) is formulated for the identified factors, which indicate the relationship between factors with the help of the rating system. There are four kinds of rating system in ISM, which are mentioned as follows:

V: the parameter i will lead to parameter j;

A: the parameter j will lead to parameter i;

X: it indicates the relationship between factor i to j i.e. both factors will depend on each other;

O: it indicates the relationship between factor i to j i.e. both factors will not depend on each other.

Initial Reachability Matrix (IRM) is developed where the rating given to relate different factors are now converted to binary form following the logic stated below:

For rating V: (i, j) rating will be substituted by 1, and (j, i) will be substituted by 0;

For rating A: (i, j) rating will be substituted by 0, and (j, i) will be substituted by 1;

For rating X: both (i,j) and (j,i) ratings are substituted by 1;

For rating O: both (i,j) and (j,i) ratings are substituted by 0.

Final Reachability Matrix is developed where the matrix formed in step 4 is checked for its transitivity, that is, if the variable A depends on variable B and variable B depends on variable C, then variable A will depend on variable C. If transitivity holds true, 0 is replaced by 1*. The final reachability matrix attained in the previous step is partitioned to various levels and based on the relationship between driving power and dependence of the factors, a directed graph (digraph) is drawn, and the transitive links are removed. ISM framework is developed by following the previous steps and is re-checked for conceptual inconsistencies and essential changes are done to remove the inconsistencies.

Case study

The study has been done in an automotive component manufacturing organisation. The organisation has been in the process of applying smart product development practices. Appropriate factors are identified from literature review and discussed with industry practitioners. Twenty critical factors for smart product development have been identified and are summarised in Table 1. ISM approach has been applied for prioritisation of factors.

Table 1. Critical factors for smart product development

S.No	Critical Factor	Description	Reference
1.	Innovation Management (F1)	Innovation is an important component in every development process. It is enhanced by the incorporation of strong leadership, collaboration and technology. Innovation managers have the task of looking up for new markets that the smart products may create and are responsible for its successful market introduction.	Dawid et al. (2017)
2.	Networking & Co – operation (F2)	Intra organisational cooperation and networking is necessary to create and adapt dynamic strategic plans. Business networks can also be formed by more than two firms to generate mutual benefits.	Dawid et al. (2017); Gutierrez et al. (2013)
3.	Adaptability (F3)	It refers to the ability of a firm to adapt and modify smart product according to the situations, needs, customers, etc.	Gutierrez et al. 2013
4.	Growth and Sustainability (F4)	Refers to the ability of the product to sustain in the market for a long period of time amid rapid changes in the market requirements and global economy.	Jaiswal, Basu, and Bhola (2019); Majumdar and Sinha (2018); Tomiyama et al. (2019); Manjunatheshwara and Vinodh (2017); Doualle et al. (2019)
5.	Pro-activity (F5)	Refers to the anticipatory behaviour which includes the anticipation of user’s plans and intentions and acting in advance rather than reacting to a situation.	Gutierrez et al. (2013)
6.	Facilitate user interaction and enhanced user preferences (F6)	Proper and attractive user interface is a critical element of smart products, as they perform numerous functions which are both visible and invisible to the user. Big data collected by the smart devices has helped the manufacturers analyse and understand the user preferences and that helps them to develop future products.	Cooper (2018); Dawid et al. (2017); Tomiyama et al. (2019)
7.	Computation and data storage (F7)	The ability of smart products to record and store information, so that it will be able to record the user trends and preferences which can be used for product development and maintenance.	Dawid et al. (2017); Gogineni, Riedelsheimer, and Stark (2019); Nunes, Pereira, and Alves (2017); Tomiyama et al. (2019)
8.	Business awareness (F8)	It involves understanding the skills, attributes and infrastructure required to develop a smart product. It also requires understanding of the business environment, constraints and the competitors.	Dawid et al. (2017); Gutierrez et al. (2013)
9.	Data & Information Management (F9)	Data & Information management generally refers to the processes and technologies that help in collection, storage, analysis and disposal of information and other sources of data assets. Need for qualitative and reliable data is one of the important and challenging aspects of the smart product development.	Filho et al. (2017); Shukla and Mattar (2019)
10.	Product idea generation and screening (F10)	It refers to the level of integration between external factors like user requirements and internal factors like organisational capabilities. Generally, the concept of product idea generation is used more for newer products, while idea screening is used more for product updates and improvements.	Cooper (2018); Dawid et al. (2017); Swan, Luchs, and Creusen (2015)
11.	Smart Product Lifecycle (F11)	It refers to the timeline of activities from its conceptualisation, through its design, prototyping, manufacturing and usage, to service and disposal. It also includes management of the information that is being generated during the usage phase to create higher business value.	Filho et al. (2017); Gutierrez et al. (2013); Nunes, Pereira, and Alves (2017)
12.	Clear understanding of user and task requirements (F12)	User-centred smart product development focuses on assessing consumer response from the data generated from smart products with the help of big data and data analytics.	Cooper (2018); Dawid et al. (2017); Swan, Luchs, and Creusen (2015)
13.	Supply chain design (F13)	It refers to the efficiency of the supply chain. The supply chain design depends on various factors like suppliers, infrastructure, inventory, logistics, material and cash flows and it is important to properly manage these factors to achieve enhanced performance.	Dawid et al. (2017); Majumdar and Sinha (2018); Pan, Zhong, and Qu (2019); Swan, Luchs, and Creusen (2015)
14.	Interoperability (F14)	In the context of smart product development, it is the ability of the systems, applications, devices and services to share information and work together in a reliable way.	Dawid et al. (2017); Gogineni, Riedelsheimer, and Stark (2019); Gutierrez et al. (2013); Pan, Zhong, and Qu (2019); Filho et al. (2017)
15.	Degree of autonomy (F15)	It refers to the level of autonomous operation. Smart products are required to have high degree of autonomy where it will be able to anticipate and adapt to the unforeseen circumstances, eliminating the need of manpower.	Dawid et al. (2017); Tomiyama et al. (2019)
16.	Technical and Organizational Integration (F16)	Any organisation that develop smart product has certain internal characteristics like organisational structure, culture, management style as well certain technical characteristics like business model, incorporation and utilisation of technology. It is essential for an organisation’s internal and technical factors to successfully connect together.	Swan, Luchs, and Creusen (2015)
17.	System Integration (F17)	It is the process of integrating software along with physical systems. This act as an important tool for the exchange of information.	Gogineni, Riedelsheimer, and Stark (2019); Tomiyama et al. (2019)
18.	Resource optimisation and waste elimination (F18)	Resource optimisation consist of a set of methods that helps in achieving desired results with minimal usage of resources. Wastes generated due to Waiting, Transport, Unnecessary movement, Inventory, Overproduction, resource limitations must be eliminated in order to optimise product development process.	Nunes, Pereira, and Alves (2017)
19.	Collaborative Engineering (F19)	Generally, it refers to the process of various departments of engineering working together towards a certain objective. Collaborative engineering for smart product development can be looked in as integration and collaboration between systems to foster productivity and effectiveness.	Jaiswal, Basu, and Bhola (2019)
20.	Lean Operating Systems (F20)	Lean Operating systems focuses on creating more value to the consumers, to make product development and production processes flexible by utilising less resources and eliminating wastes to enhance productivity and sustain benefits.	Cooper (2018); Jaiswal, Basu, and Bhola (2019); Pagliosa, Tortorella, and Ferreira (2019); Salgado et al. (2014)

Application steps of ISM methodology

To develop an interactive structural model among the critical factors responsible for Smart Product development, ISM is used. Figure 1 presents steps in ISM method.

Figure 1. Steps involved in ISM method

Structured self-interaction matrix (SSIM)

Table 2 presents Structural Self Interaction Matrix. For developing contextual relationships among variables, expert opinion is considered. For stating the relationship between different factors for smart product development, four symbols (V, A, X, O) are being used to denote the direction of relationship between the elements i and j (here i < j):

• V: The element i will lead to element j. (i is linked to j);

• A: The element j will lead to element i (j is linked to i);

• X: The elements i and j lead to one another (i and j are linked to each other);

• O: The elements i and j are not linked.

Following statements explain the use of symbols V, A, X and O in SSIM:

• Pro- Activity (i) leads to Business Awareness (j) but Business Awareness (j) does not lead to Pro-Activity (i) (V);

• Interoperability(j) leads to Innovation Management (i) but Innovation Management (i) does not lead to Interoperability (j) (A);

• Adaptability and Growth & Sustainability leads to each other (X);

• Pro-Activity and Computation & Data Storage are unrelated (O).

Initial Reachability matrix (IRM)

SSIM has now been transformed into a binary matrix where the symbols (V, A, X, O) are substituted by 1s and 0s according to the case. The resultant matrix is termed as Initial Reachability matrix. The exchange of 1s and 0s are governed by certain rules as indicated in research studies (Watson (1978); Ruben, Vinodh, and Asokan (2018)). The rules could be referred from those research studies.

In the Initial Reachability matrix shown in Table 3, the entry ‘1ʹ infers that the barrier in the row impacts barrier in the column. In contrast, Entry ‘0ʹ infers that the barrier in the row does not impact the barrier in the column.

Table 2. Structured self-interaction matrix (SSIM)

CRITICAL FACTORS	Innov. Mgmt.	Co-op. & Netwr	Adaptability	Growth & Sust.	Pro-activity	User Interaction	Comput. & data Stor.	Busin. Aware.	Data & Info. Mgmt.	Prod. Idea gen. & Scr.	Smt. Pdt. Lifecyle	Unders. User Req.	Sup. Chain Desgn.	Interoperability	Deg. Of autonomy	Tech. & Org. Inetg.	System Integ.	Resource Optm.	Collab. Engg.	Lean Oper. Syst.
Innovation Management	X
Cooperation & Network	V	X
Adaptability	V	V	X
Growth and Sustainability	A	A	X	X
Pro-activity	V	V	X	V	X
Facilitate user interaction and enhanced user preferences	A	A	A	V	A	X
Computation and data storage	V	V	V	V	O	V	X
Business awareness	V	A	V	V	V	A	A	X
Data & Information Management	V	V	V	V	V	A	X	A	X
Product idea generation and screening	A	A	A	V	A	A	A	A	A	X
Smart Product Lifecycle	A	A	A	V	A	A	A	A	A	A	X
Clear understanding of user and task requirements	O	V	V	V	A	X	A	A	A	V	V	X
Supply chain design	A	X	A	V	A	X	A	A	A	A	X	X	X
Interoperability	A	V	X	V	A	A	A	A	X	V	V	A	V	X
Degree of autonomy	V	V	V	V	X	V	V	O	V	V	V	V	V	X	X
Technical and Organizational Integration	A	X	V	V	A	A	V	A	X	V	V	V	X	V	A	X
System Integration	A	V	V	V	A	A	X	A	X	A	V	A	X	V	A	X	X
Resource optimisation and waste elimination	A	X	A	V	A	A	A	A	A	V	V	V	V	A	A	X	V	X
Collaborative Engineering	A	X	A	V	A	V	X	A	A	V	X	V	X	A	A	X	X	V	X
Lean Operating Systems	A	V	A	V	A	A	A	A	X	V	V	A	V	A	A	X	X	X	V	X

Table 3. Initial Reachability Matrix

CRITICAL FACTORS	Innov. Mgmt.	Co-op. & Netwr	Adaptability	Growth & Sust.	Pro-activity	User Interaction	Comput. & data Stor.	Busin. Aware.	Data & Info. Mgmt.	Prod. Idea gen. & Scr.	Smt. Pdt. Lifecyle	Unders. User Req.	Sup. Chain Desgn.	Interoperability	Deg. Of autonomy	Tech. & Org. Inetg.	System Integ.	Resource Optm.	Collab. Engg.	Lean Oper. Syst.
Innovation Management	1	0	0	1	0	1	0	0	0	1	1	0	1	1	0	1	1	1	1	1
Cooperation & Network	1	1	0	1	0	1	0	1	0	1	1	0	1	0	0	1	0	1	1	0
Adaptability	1	1	1	1	1	1	0	0	0	1	1	0	1	1	0	0	0	1	1	1
Growth and Sustainability	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Pro-activity	1	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1
Facilitate user interaction and user preferences	0	0	0	1	0	1	0	1	1	1	1	1	1	1	0	1	1	1	0	1
Computation and data storage	1	1	1	1	0	1	1	1	1	1	1	1	1	1	0	0	1	1	1	1
Business awareness	1	0	1	1	1	0	0	1	1	1	1	1	1	1	0	1	1	1	1	1
Data & Information Management	1	1	1	1	1	0	1	0	1	1	1	1	1	1	0	1	1	1	1	1
Product idea generation and screening	0	0	0	1	0	0	0	0	0	1	1	0	1	0	0	0	1	0	0	0
Smart Product Lifecycle	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0
Clear understanding of user requirements	0	1	1	1	0	1	0	0	0	1	1	1	1	1	0	0	1	0	0	1
Supply chain design	0	1	0	1	0	1	0	0	0	0	1	1	1	0	0	1	1	0	1	0
Interoperability	0	1	1	1	0	0	0	0	1	1	1	0	1	1	1	0	0	1	1	1
Degree of autonomy	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
Technical and Organizational Integration	0	1	1	1	0	0	1	0	1	1	1	1	1	1	0	1	1	1	1	1
System Integration	0	1	1	1	0	0	1	0	1	0	1	0	1	1	0	1	1	0	1	1
Resource optimisation and waste elimination	0	1	0	1	0	0	0	0	0	1	1	1	1	0	0	1	1	1	0	1
Collaborative Engineering	0	1	0	1	0	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0
Lean Operating Systems	0	1	0	1	0	0	0	0	1	1	1	0	1	0	0	1	1	1	1	1

Final Reachability matrix

Final Reachability Matrix (FRM) is shown in Table 4. The entries 1* indicates transitivity links. For example, Adaptability is unrelated to Cooperation & Networking as designated by ‘O’ in SSIM Matrix. However, Adaptability influences Growth and Sustainability as designated by ‘X’ in Table. On the other hand, Co-operation and Networking is influenced by Growth and Sustainability as designated by ‘A’. Thus, by the principle of transitivity, Adaptability will influence Cooperation and Networking.

Table 4. Final Reachability Matrix

CRITICAL FACTORS	Innov. Mgmt.	Co-op. & Netwr	Adaptability	Growth & Sust.	Pro-activity	User Interaction	Comput. & data Stor.	Busin. Aware.	Data & Info. Mgmt.	Prod. Idea gen. & Scr.	Smt. Pdt. Lifecyle	Unders. User Req.	Sup. Chain Desgn.	Interoperability	Deg. Of autonomy	Tech. & Org. Inetg.	System Integ.	Resource Optm.	Collab. Engg.	Lean Oper. Syst.	Driving Power
Innovation Management	1	0	1*	1	0	1	0	0	1*	1	1	0	1	1	0	1	1	1	1	1	14
Cooperation & Network	1	1	1*	1	1*	1	0	1	0	1	1	0	1	1*	0	1	0	1	1	1*	15
Adaptability	1	1*	1	1	1	1	0	1*	0	1	1	1*	1	1	1*	0	0	1	1	1	16
Growth and Sustainability	1*	0	1	1	0	0	0	0	0	0	0	0	1*	0	0	0	0	1*	0	1*	6
Pro-activity	1	1	1	1	1	1	0	1*	0	1	1	1	1	1	1	1	1	1	1	1	19
Facilitate user interaction and enhanced user preferences	1*	1*	1*	1	1*	1	0	1	1	1	1	1	1	1	1*	1	1	1	1*	1	19
Computation and data storage	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1*	1*	1	1	1	1	19
Business awareness	1	1*	1	1	1	0	0	1	1	1	1	1	1	1	0	1	1	1	1	1	17
Data & Information Management	1	1	1	1	1*	1*	1	1*	1	1	1	1	1	1	1*	1	1	1	1	1	20
Product idea generation and screening	0	0	0	1	0	1*	0	0	0	1	1	1*	1	1*	0	0	1	0	1*	0	9
Smart Product Lifecycle	0	0	0	1	0	0	0	0	0	1*	1	0	1	0	0	0	1*	0	1	0	6
Clear understanding of user and task requirements	1*	1	1	1	1*	1	0	0	0	1	1	1	1	1	0	0	1	1*	1*	1	15
Supply chain design	1*	1	0	1	0	1	0	0	1*	0	1	1	1	1*	0	1	1	1*	1	1*	14
Interoperability	0	1	1	1	1*	0	0	1*	1	1	1	1*	1	1	1	1*	0	1	1	1	16
Degree of autonomy	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	19
Technical and Organizational Integration	1*	1	1	1	1*	0	1	1*	1	1	1	1	1	1	0	1	1	1	1	1	18
System Integration	1*	1	1	1	1*	0	1	1*	1	1*	1	0	1	1	0	1	1	1*	1	1	17
Resource optimisation and waste elimination	1*	1	0	1	0	0	0	0	1*	1	1	1	1	0	0	1	1	1	0	1	12
Collaborative Engineering	1*	1	1*	1	0	1	1	0	1*	1	1	1	1	1*	0	1	1	1	1	1*	17
Lean Operating Systems	0	1	0	1	0	0	0	0	1	1	1	0	1	1*	0	1	1	1	1	1	12
Dependence	16	16	15	20	11	12	6	10	13	18	19	14	20	17	7	15	16	18	18	18	299

Level partitioning

The Reachability and the Antecedent sets for each factor are derived from Final Reachability Matrix. The Reachability set consists of the factor itself and all the other factors, which leads to it. The Antecedent set consists of the factor itself as well as all the other factors that it leads to (Singh (2015)). The intersection set consists of the elements which are common in both Reachability and the Antecedent sets. All the factors would lead to a top level factor and a top level factor does not lead to any other factor apart from the factors present in the intersection set. Through this method, a top level factor is identified and separated. Multiple iterations are performed using the above-mentioned logic to partition the levels. Table 5 depicts the level partitioning of the factors.

Table 5. Level partitioning

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
1.	1,3,4,6,9,10,11,13,14,16,17, 18,19,20	1,2,3,4,5,6,7,8,9,12,13,15,16,17,18,19	1,3,4,6,9,13,16,17,18,19	VI
2.	1,2,3,4,5,6,8,10,11,13, 14,16,18,19,20	2,3,5,6,7,8,9,12,13,14,15,16,17,18,19,20	2,3,5,6,8,13,14,16,18,19,20	VII
3.	1,2,3,4,5,6,8,10,11,12, 13,14,15,18,19,20	1,2,3,4,5,6,7,8,9,12,13,14,15,16,17,19	1,2,3,4,5,6,8,12,13,14,15,19	VI
4.	1,3,4,13,18,20	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20	1,3,4,13,18,20	I
5.	1,2,3,4,5,6,8,10,11,12 13,14,15,16,17,18,19,20	1,2,3,5,6,8,9,12,14,15,16,17	1,2,3,5,6,8,12,14,15,16,17	VI
6.	1,2,3,4,5,6,8,9,10,11,12,13, 14,15,16,17,18,19,20	1,2,3,5,6,7,9,10,12,13,15,19	1,2,3,5,6,9,10,12,13,15,19	XIII
7.	1,2,3,4,6,7,8,9,10,11, 12,13,14,15,16,17,18,19,20	7,9,15,16,17,19	7,9,15,16,17,19	XIV
8.	1,2,3,4,5,8,9,10,11,12,13,15,16,17,18,19,20	2,3,5,6,7,8,9,13,16,17	2,3,5,8,9,13,16,17	XII
9.	1,2,3,4,5,6,7,8,9,10,11 12,13,14,15,16,17,18,19,20	1,6,7,8,9,13,14,15,16,17,18,19,20	1,6,7,8,9,13,14,15,16,17,18,19,20	X
10.	4,5,10,11,12,13,14,17,19	1,2,3,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20	5,10,11,12,14,17,19	II
11.	4,10,11,13,17,19	1,2,3,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20	10,11,13,17,19	II
12.	1,2,3,4,5,6,10,11,12,13,14,17,18,19,20	3,5,6,7,8,9,10,12,13,14,15,16,18,19	3,5,6,10,12,13,14,18,19	IX
13.	1,2,4,6,9,11,12,13,14,16,17,18,19,20	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20	1,2,4,6,9,11,12,13,14,16,17,18,19,20	I
14.	2,3,4,5,8,9,10,11,12,13,14,15,16,18,19,20	1,2,3,5,6,7,8,9,10,12,13,14,15,16,17,19,20	2,3,5,8,9,10,12,13,14,15,16,19,20	V
15.	1,2,3,4,5,6,7,9,10,11,12,13,14,15,16,17,18,19,20	3,5,6,7,9,14,15	3,5,6,7,9,14,15	XI
16.	1,2,3,4,5,7,8,9,10,11,12,13,14,16,17,18,19,20	1,2,5,6,7,8,9,13,14,15,16,17,18,19,20	1,2,5,7,8,9,13,14,16,17,18,19,20	X
17.	1,2,3,4,5,7,8,9,10,11,13,14,16,17,18,19,20	1,5,6,7,8,9,10,11,12,13,15,16,17,18,19,20	1,5,7,8,9,10,11,13,16,17,18,19,20	VIII
18.	1,2,4,9,10,11,12,13,16,17,18,20	1,2,3,4,5,6,7,8,9,12,13,14,15,16,17,18,19,20	1,2,4,9,10,12,13,16,17,18,20	III
19.	1,2,3,4,6,7,9,10,11,12,13,14,16,17,18,19,20	1,2,3,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20	1,2,3,6,7,9,10,11,12,13,14,16,17,19,20	IV
20.	2,4,9,10,11,13,14,16,17,18,19,20	1,2,3,4,5,6,7,8,9,12,13,14,15,16,17,18,19,20	2,4,9,13,14,16,17,18,19,20	IV

Formation of digraph and ISM

A diagraph is a structural model which is generated with the help of Final Reachability Matrix. If factor i leads to factor j, there is an arrow starting from factor i and leading to factor j. Digraph depicts the relationship between each pair of factors. Figure 2 depicts the relationship among each pair of factors driving Smart Product Development. After including the transitivity factor as described in ISM methodology, an Interpretive Structural Model (ISM) is constructed from the digraph shown in Figure 3.

Figure 2. Digraph of factors for smart product development

Figure 3. ISM-based model for smart product development

MICMAC analysis

From the above results, the critical factors are classified into four different categories (Figure 4). The first category consists of ‘autonomous factors’ which have weak driving power and weak dependence. These factors are relatively disconnected from the system, with few weak links. The second category consists of ‘dependent factors’ which has weak driving power but strong dependence. Third category consists of the ‘linkage factors’ which have strong driving power and strong dependence. Fourth category consists of the ‘independent factors’ having strong driving power but weak dependence. The dependence and driving power of each of the factors are listed in Table 4 .

Figure 4. Driving and dependence power diagram

Autonomous enablers include Computation and data storage (F₇), Business Awareness (F₈), and Degree of autonomy (F₁₅).

In the study, most factors fall under dependent enablers, which include Innovation management (F₁), Cooperation and Network (F₂), Adaptability (F₃), Pro-activity (F₅), Facilitation of user interaction and enhanced user preference (F₆), Data and Information management (F₉), Understanding user tasks and requirements (F₁₂), Supply chain design (F₁₃), Interoperability (F₁₄), Technical and organisational integration (F₁₆), System Integration (F₁₇), Resource optimisation and waste elimination (F₁₈), Collaborative engineering (F₁₉), and Lean operating systems (F₂₀)

The other factors that include Growth and Sustainability (F₄), Smart product lifecycle (F₁₁), and Product idea generation and screening (F₁₀) are classified as Linkage enablers.

Results and discussions

From the final reachability matrix, directed graph (digraph) of the factors driving smart product development is constructed, which represent the relationship between factors as shown in Figure 2. The relationship between the factors j and i is indicated by an arrow pointing from i to j. Removing the transitivity as depicted in ISM methodology, the digraph is transformed into ISM as depicted in Figure 3.

From the model, it is inferred that computation and data storage, which is placed in the lowest level, are the most elementary driver for smart product development. Cloud storage facilities, evolution of big data analytics, help in predictive analysis of data generated by various sensors. Proper evaluation of the data stored help us to understand the trends and user priorities. Such proactive behaviours might possibly open up new business opportunities, as well assists in modifying and updating the existing products. It also aids in creating high degree of autonomy, and will be able to anticipate the unforeseen circumstances and adapt to it automatically without human intervention. It is important for the organisation that merge the technical characteristics like its business model, its use of technology and the internal characteristics of the organisation like management style and work culture together to obtain better information flow at both intra- and inter-organisation levels. This process is important as it is essential for the smart product developers to know about customer preferences which is known to the sales and marketing teams of the same organisation as they meet and interact with the customers on a regular basis. Such a collective approach helps in fostering innovation. Therefore, technical and organisational integration leads to cooperation and networking. By system integration, a collaborative engineering environment can be established. In a collaborative environment, where an integrated system of cloud, raw material acquisition, product development, remanufacturing, recycling, recovery and disposal involving logistics facilities, etc. between all phases are established, leading to waste elimination and optimisation of resources.

Product idea generation and smart product lifecycle, which is placed at the second level, are influenced by user requirements, business awareness, pro-activity, system integration, innovation and storage and management of data. Supply chain design and growth and sustainability of the product are positioned at the topmost level. The supply chain design of a smart product highly depends on collaborative networking between customers, suppliers, manufacturers, product managers, etc. The design of the supply chain also depends on its degree of autonomy. Smart supply chain systems are more flexible, transparent, and self-optimised. The data collected from various segments of the supply chain process are analysed to continuously optimise the process. Therefore, supply chain design relies on data and information management and lean operating systems. Growth and sustainability of the product depends on product idea. It must be ideated such that it satisfies the user requirements. Commercial smart products focus on providing the best possible user interface as it plays a major role in enhancing user interaction with the device and thus, are able to generate more useful data. Growth and sustainability of the product is also greatly influenced by the management of product lifecycle. It is essential to alter the existing products according to the necessities of the user as well make sure that the customer queries are addressed especially in the usage and servicing phase of the smart product.

Managerial and practical implications

The study facilitated industry practitioners to systematically identify factors influencing smart product development and to develop a structural model for analysis of factors. Based on the analysis, vital factors are analysed from viewpoint of smart product development. MICMAC analysis facilitated the categorisation of factors according to their driving power. Dominant factors are being identified and are focused. Appropriate technologies are focused for enhancing effective deployment of smart product development practices. User preferences and requirements can be identified through cognitive computing, which allows computers to have the capability of knowing, thinking, and feeling (Gutierrez-Garcia and López-Neri (2015)). Internet of things (IoT) enhances system integration, thereby allowing physical systems to interact through Internet. Incorporation of such technologies decreases human intervention and thus enhances Degree of Autonomy (Gogineni, Riedelsheimer, and Stark 2019). Evolution of 3D-Printing technologies facilitates prototyping, and customised manufacturing at optimal cost. They also help in resource optimisation and waste elimination when compared with subtractive manufacturing methods. Since additive manufacturing hardly requires tooling like jigs and fixtures, it encourages decentralisation enabling manufacturing close to the customers, which leads to reduction in logistics, warehousing, and thereby simplifying supply chain and enhancing sustainability (Pour et al. (2016)). Big data analytics (BDA) have played a major role in obtaining value from data generated from products which will be largely useful in making the product better, or may even provide ideas that might lead to the generation of new, meaningful products (Tomiyama et al. (2019)). Cloud storage systems had made data storage and handling easier. The evolution of cybersecurity helps in protecting the whole cyberspace that contains data, and all other related assets (Solms and Niekerk (2013); Pan, Zhong, and Qu (2019)). When these emerging technologies are implemented into the NPD, it must be ensured that the changes made in the products as well product development process consider driving factors like Growth and Sustainability, Supply Chain Design, Collaborative engineering, Smart Product Lifecycle, to name a few to produce better quality products that comply with rapidly changing customer demands as well as minimise the environmental impacts. For instance, if an industry wants to implement BDA to analyse data obtained from its existing product, it has to consider the stakeholders involved and their expected response to the paradigm shift beforehand in order to have a smooth supply chain. The additional resources consumed due to the change are to be recorded and optimised by eliminating non-value adding activities. Collaborations with other departments like data analysts, marketing professionals will have to be done to understand user requirements in a better way and working with multiple teams is essential to deliver a successful Smart product.

Conclusions

Product design and development is subjected to several challenges in the context of advanced manufacturing systems. In line with Industry 4.0 requirements, manufacturing system is expected to be smarter and innovative to respond to dynamic preferences of customers and market changes. The present study aimed at analysis of factors influencing smart product development based on expert opinion. The derived structural model indicated ‘Computation and data storage’, ‘Facilitating user interaction’ and ‘Business awareness’ as top three priority factors. Growth and sustainability, Supply chain design are the final driving factors which are dependent on most of the other factors. MICMAC analysis indicated that 14 dependent, three linkage and three autonomous factors in the dependent, linkage and autonomous categories. The study facilitated the practitioners to focus on key factors to enhance the effectiveness of smart product development.

Limitations and future work

In the present study, 20 factors are being considered. In future, additional factors could be considered in line with technological and managerial advancements of smart manufacturing. The results derived from ISM model could be further validated based on statistical analysis using structural equation modelling. The potential interventions and impacts on the drivers can also be analysed in the future studies.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Praneashram D

Praneashram D is an undergraduate student from the Department of Production Engineering at the National Institute of Technology, Tiruchirappalli. His research interest lies in Product Development, Additive Manufacturing, Industrial engineering and Sustainability.

S Vinodh

S.Vinodh is an Associate Professor in Production Engineering Department of National Institute of Technology, Tiruchirappalli, Tamil Nadu, India. He completed his Ph.D. from PSG College of Technology, Coimbatore, India. He completed his Master’s degree in Production Engineering from PSG College of Technology, Coimbatore, India and Bachelor’s degree in Mechanical Engineering from Government College of Technology, Coimbatore, India. He has been awarded National Doctoral Fellowship for pursuing research by AICTE, New Delhi, India. He is the recipient of Institution of Engineers Young Engineer Award for the year 2013-14. He has published over 150 papers in International Journals. His research interests include Lean Production and Sustainable Manufacturing, Agile Manufacturing, Rapid Manufacturing, Product Development and Industry 4.0.

Rohit Agrawal

Rohit Agrawal is a Research Scholar in the Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India. He did his M.Tech. in Industrial Engineering and Management from the Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu, India. He completed his B.Tech. in Mechanical Engineering from Bhilai Institute of Technology, Durg, India. He has published various articles in reputed journals and international conference proceedings. He has skills and expertise of Multi-Criteria Decision Making, Fuzzy Theory, and Grey Theory. His areas of research are sustainability manufacturing, circular economy, additive manufacturing, Industry 4.0, and sustainable supply chain management.

Appendix.

A1 to A14: Level Partition tables

Table A1. ITERATION 1

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
1.	1,3,4,6,9,10,11,13,14,16,17, 18,19,20	1,2,3,4,5,6,7,8,9,12,13,15,16,17,18,19	1,3,4,6,9,13,16,17,18,19
2.	1,2,3,4,5,6,8,10,11,13, 14,16,18,19,20	2,3,5,6,7,8,9,12,13,14,15,16,17,18,19,20	2,3,5,6,8,13,14,16,18,19,20
3.	1,2,3,4,5,6,8,10,11,12, 13,14,15,18,19,20	1,2,3,4,5,6,7,8,9,12,13,14,15,16,17,19	1,2,3,4,5,6,8,12,13,14,15,19
4.	1,3,4,13,18,20	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20	1,3,4,13,18,20	I
5.	1,2,3,4,5,6,8,10,11,12 13,14,15,16,17,18,19,20	1,2,3,5,6,8,9,12,14,15,16,17	1,2,3,5,6,8,12,14,15,16,17
6.	1,2,3,4,5,6,8,9,10,11,12,13, 14,15,16,17,18,19,20	1,2,3,5,6,7,9,10,12,13,15,19	1,2,3,5,6,9,10,12,13,15,19
7.	1,2,3,4,6,7,8,9,10,11, 12,13,14,15,16,17,18,19,20	7,9,15,16,17,19	7,9,15,16,17,19
8.	1,2,3,4,5,8,9,10,11,12,13,15,16,17,18,19,20	2,3,5,6,7,8,9,13,16,17	2,3,5,8,9,13,16,17
9.	1,2,3,4,5,6,7,8,9,10,11 12,13,14,15,16,17,18,19,20	1,6,7,8,9,13,14,15,16,17,18,19,20	1,6,7,8,9,13,14,15,16,17,18,19,20
10.	4,5,10,11,12,13,14,17,19	1,2,3,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20	5,10,11,12,14,17,19
11.	4,10,11,13,17,19	1,2,3,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20	10,11,13,17,19
12.	1,2,3,4,5,6,10,11,12,13,14,17,18,19,20	3,5,6,7,8,9,10,12,13,14,15,16,18,19	3,5,6,10,12,13,14,18,19
13.	1,2,4,6,9,11,12,13,14,16,17,18,19,20	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20	1,2,4,6,9,11,12,13,14,16,17,18,19,20	I
14.	2,3,4,5,8,9,10,11,12,13,14,15,16,18,19,20	1,2,3,5,6,7,8,9,10,12,13,14,15,16,17,19,20	2,3,5,8,9,10,12,13,14,15,16,19,20
15.	1,2,3,4,5,6,7,9,10,11,12,13,14,15,16,17,18,19,20	3,5,6,7,9,14,15	3,5,6,7,9,14,15
16.	1,2,3,4,5,7,8,9,10,11,12,13,14,16,17,18,19,20	1,2,5,6,7,8,9,13,14,15,16,17,18,19,20	1,2,5,7,8,9,13,14,16,17,18,19,20
17.	1,2,3,4,5,7,8,9,10,11,13,14,16,17,18,19,20	1,5,6,7,8,9,10,11,12,13,15,16,17,18,19,20	1,5,7,8,9,10,11,13,16,17,18,19,20
18.	1,2,4,9,10,11,12,13,16,17,18,20	1,2,3,4,5,6,7,8,9,12,13,14,15,16,17,18,19,20	1,2,4,9,10,12,13,16,17,18,20
19.	1,2,3,4,6,7,9,10,11,12,13,14,16,17,18,19,20	1,2,3,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20	1,2,3,6,7,9,10,11,12,13,14,16,17,19,20
20.	2,4,9,10,11,13,14,16,17,18,19,20	1,2,3,4,5,6,7,8,9,12,13,14,15,16,17,18,19,20	2,4,9,13,14,16,17,18,19,20

Table A2. ITERATION 2

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
1.	1,3,6,9,10,11,14,16,17, 18,19,20	1,2,3,5,6,7,8,9,12,15,16,17,18,19	1,3,6,9,16,17,18,19
2.	1,2,3,5,6,8,10,11,14,16,18,19,20	2,3,5,6,7,8,9,12,14,15,16,17,18,19,20	2,3,5,6,8,14,16,18,19,20
3.	1,2,3,5,6,8,10,11,12,14,15,18,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,19	1,2,3,5,6,8,12,14,15,19
5.	1,2,3,5,6,8,10,11,12,14,15,16,17,18,19,20	1,2,3,5,6,8,9,12,14,15,16,17	1,2,3,5,6,8,12,14,15,16,17
6.	1,2,3,5,6,8,9,10,11,12,14,15,16,17,18,19,20	1,2,3,5,6,7,9,10,12,15,19	1,2,3,5,6,9,10,12,15,19
7.	1,2,3,6,7,8,9,10,11,12,14,15,16,17,18,19,20	7,9,15,16,17,19	7,9,15,16,17,19
8.	1,2,3,5,8,9,10,11,12,15,16,17,18,19,20	2,3,5,6,7,8,9,16,17	2,3,5,8,9,16,17
9.	1,2,3,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20	1,6,7,8,9,14,15,16,17,18,19,20	1,6,7,8,9,14,15,16,17,18,19,20
10.	5,10,11,12,14,17,19	1,2,3,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20	5,10,11,12,14,17,19	II
11.	10,11,17,19	1,2,3,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20	10,11,17,19	II
12.	1,2,3,5,6,10,11,12,14,17,18,19,20	3,5,6,7,8,9,10,12,14,15,16,18,19	3,5,6,10,12,14,18,19
14.	2,3,5,8,9,10,11,12,14,15,16,18,19,20	1,2,3,5,6,7,8,9,10,12,14,15,16,17,19,20	2,3,5,8,9,10,12,14,15,16,19,20
15.	1,2,3,5,6,7,9,10,11,12,14,15,16,17,18,19,20	3,5,6,7,9,14,15	3,5,6,7,9,14,15
16.	1,2,3,5,7,8,9,10,11,12,14,16,17,18,19,20	1,2,5,6,7,8,9,14,15,16,17,18,19,20	1,2,5,7,8,9,14,16,17,18,19,20
17.	1,2,3,5,7,8,9,10,11,14,16,17,18,19,20	1,5,6,7,8,9,10,11,12,15,16,17,18,19,20	1,5,7,8,9,10,11,16,17,18,19,20
18.	1,2,9,10,11,12,16,17,18,20	1,2,3,5,6,7,8,9,12,14,15,16,17,18,19,20	1,2,9,10,12,16,17,18,20
19.	1,2,3,6,7,9,10,11,12,14,16,17,18,19,20	1,2,3,5,6,7,8,9,10,11,12,14,15,16,17,19,20	1,2,3,6,7,9,10,11,12,14,16,17,19,20
20.	2,9,10,11,14,16,17,18,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,18,19,20	2,9,14,16,17,18,19,20

Table A3. ITERATION 3

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
1.	1,3,6,9,14,16,17,18,19,20	1,2,3,5,6,7,8,9,12,15,16,17,18,19	1,3,6,9,16,17,18,19
2.	1,2,3,5,6,8,14,16,18,19,20	2,3,5,6,7,8,9,12,14,15,16,17,18,19,20	2,3,5,6,8,14,16,18,19,20
3.	1,2,3,5,6,8,12,14,15,18,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,19	1,2,3,5,6,8,12,14,15,19
5.	1,2,3,5,6,8,12,14,15,16,17,18,19,20	1,2,3,5,6,8,9,12,14,15,16,17	1,2,3,5,6,8,12,14,15,16,17
6.	1,2,3,5,6,8,9,12,14,15,16,17,18,19,20	1,2,3,5,6,7,9,12,15,19	1,2,3,5,6,9,12,15,19
7.	1,2,3,6,7,8,9,12,14,15,16,17,18,19,20	7,9,15,16,17,19	7,9,15,16,17,19
8.	1,2,3,5,8,9,12,15,16,17,18,19,20	2,3,5,6,7,8,9,16,17	2,3,5,8,9,16,17
9.	1,2,3,5,6,7,8,9,12,14,15,16,17,18,19,20	1,6,7,8,9,14,15,16,17,18,19,20	1,6,7,8,9,14,15,16,17,18,19,20
12.	1,2,3,5,6,12,14,17,18,19,20	3,5,6,7,8,9,12,14,15,16,18,19	3,5,6,12,14,18,19
14.	2,3,5,8,9,12,14,15,16,18,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,19,20	2,3,5,8,9,12,14,15,16,19,20
15.	1,2,3,5,6,7,9,12,14,15,16,17,18,19,20	3,5,6,7,9,14,15	3,5,6,7,9,14,15
16.	1,2,3,5,7,8,9,12,14,16,17,18,19,20	1,2,5,6,7,8,9,14,15,16,17,18,19,20	1,2,5,7,8,9,14,16,17,18,19,20
17.	1,2,3,5,7,8,9,14,16,17,18,19,20	1,5,6,7,8,9,12,15,16,17,18,19,20	1,5,7,8,9,16,17,18,19,20
18.	1,2,9,12,16,17,18,20	1,2,3,5,6,7,8,9,12,14,15,16,17,18,19,20	1,2,9,12,16,17,18,20	III
19.	1,2,3,6,7,9,12,14,16,17,18,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,19,20	1,2,3,6,7,9,12,14,16,17,19,20
20.	2,9,14,16,17,18,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,18,19,20	2,9,14,16,17,18,19,20

Table A4. ITERATION 4

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
1.	1,3,6,9,14,16,17,19,20	1,2,3,5,6,7,8,9,12,15,16,17,19	1,3,6,9,16,17,19
2.	1,2,3,5,6,8,14,16,19,20	2,3,5,6,7,8,9,12,14,15,16,17,19,20	2,3,5,6,8,14,16,19,20
3.	1,2,3,5,6,8,12,14,15,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,19	1,2,3,5,6,8,12,14,15,19
5.	1,2,3,5,6,8,12,14,15,16,17,19,20	1,2,3,5,6,8,9,12,14,15,16,17	1,2,3,5,6,8,12,14,15,16,17
6.	1,2,3,5,6,8,9,12,14,15,16,17,19,20	1,2,3,5,6,7,9,12,15,19	1,2,3,5,6,9,12,15,19
7.	1,2,3,6,7,8,9,12,14,15,16,17,19,20	7,9,15,16,17,19	7,9,15,16,17,19
8.	1,2,3,5,8,9,12,15,16,17,19,20	2,3,5,6,7,8,9,16,17	2,3,5,8,9,16,17
9.	1,2,3,5,6,7,8,9,12,14,15,16,17,19,20	1,6,7,8,9,14,15,16,17,19,20	1,6,7,8,9,14,15,16,17,19,20
12.	1,2,3,5,6,12,14,17,19,20	3,5,6,7,8,9,12,14,15,16,19	3,5,6,12,14,19
14.	2,3,5,8,9,12,14,15,16,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,19,20	2,3,5,8,9,12,14,15,16,19,20
15.	1,2,3,5,6,7,9,12,14,15,16,17,19,20	3,5,6,7,9,14,15	3,5,6,7,9,14,15
16.	1,2,3,5,7,8,9,12,14,16,17,19,20	1,2,5,6,7,8,9,14,15,16,17,19,20	1,2,5,7,8,9,14,16,17,19,20
17.	1,2,3,5,7,8,9,14,16,17,19,20	1,5,6,7,8,9,12,15,16,17,19,20	1,5,7,8,9,16,17,19,20
19.	1,2,3,6,7,9,12,14,16,17,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,19,20	1,2,3,6,7,9,12,14,16,17,19,20	IV
20.	2,9,14,16,17,19,20	1,2,3,5,6,7,8,9,12,14,15,16,17,19,20	2,9,14,16,17,19,20	IV

Table A5. ITERATION 5

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
1.	1,3,6,9,14,16,17	1,2,3,5,6,7,8,9,12,15,16,17	1,3,6,9,16,17
2.	1,2,3,5,6,8,14,16	2,3,5,6,7,8,9,12,14,15,16,17	2,3,5,6,8,14,16
3.	1,2,3,5,6,8,12,14,15	1,2,3,5,6,7,8,9,12,14,15,16,17	1,2,3,5,6,8,12,14,15
5.	1,2,3,5,6,8,12,14,15,16,17	1,2,3,5,6,8,9,12,14,15,16,17	1,2,3,5,6,8,12,14,15,16,17
6.	1,2,3,5,6,8,9,12,14,15,16,17	1,2,3,5,6,7,9,12,15	1,2,3,5,6,9,12,15
7.	1,2,3,6,7,8,9,12,14,15,16,17	7,9,15,16,17	7,9,15,16,17
8.	1,2,3,5,8,9,12,15,16,17	2,3,5,6,7,8,9,16,17	2,3,5,8,9,16,17
9.	1,2,3,5,6,7,8,9,12,14,15,16,17	1,6,7,8,9,14,15,16,17	1,6,7,8,9,14,15,16,17
12.	1,2,3,5,6,12,14,17	3,5,6,7,8,9,12,14,15,16	3,5,6,12,14
14.	2,3,5,8,9,12,14,15,16	1,2,3,5,6,7,8,9,12,14,15,16,17	2,3,5,8,9,12,14,15,16	V
15.	1,2,3,5,6,7,9,12,14,15,16,17	3,5,6,7,9,14,15	3,5,6,7,9,14,15
16.	1,2,3,5,7,8,9,12,14,16,17	1,2,5,6,7,8,9,14,15,16,17	1,2,5,7,8,9,14,16,17
17.	1,2,3,5,7,8,9,14,16,17	1,5,6,7,8,9,12,15,16,17	1,5,7,8,9,16,17

Table A6. ITERATION 6

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
1.	1,3,6,9,16,17	1,2,3,5,6,7,8,9,12,15,16,17	1,3,6,9,16,17	VI
2.	1,2,3,5,6,8,16	2,3,5,6,7,8,9,12,15,16,17	2,3,5,6,8,16
3.	1,2,3,5,6,8,12,15	1,2,3,5,6,7,8,9,12,15,16,17	1,2,3,5,6,8,12,15	VI
5.	1,2,3,5,6,8,12,15,16,17	1,2,3,5,6,8,9,12,15,16,17	1,2,3,5,6,8,12,15,16,17	VI
6.	1,2,3,5,6,8,9,12,15,16,17	1,2,3,5,6,7,9,12,15	1,2,3,5,6,9,12,15
7.	1,2,3,6,7,8,9,12,15,16,17	7,9,15,16,17	7,9,15,16,17
8.	1,2,3,5,8,9,12,15,16,17	2,3,5,6,7,8,9,16,17	2,3,5,8,9,16,17
9.	1,2,3,5,6,7,8,9,12,15,16,17	1,6,7,8,9,15,16,17	1,6,7,8,9,15,16,17
12.	1,2,3,5,6,12,17	3,5,6,7,8,9,12,15,16	3,5,6,12
15.	1,2,3,5,6,7,9,12,15,16,17	3,5,6,7,9,15	3,5,6,7,9,15
16.	1,2,3,5,7,8,9,12,16,17	1,2,5,6,7,8,9,15,16,17	1,2,5,7,8,9,16,17
17.	1,2,3,5,7,8,9,16,17	1,5,6,7,8,9,12,15,16,17	1,5,7,8,9,16,17

Table A7. ITERATION 7

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
2.	2,6,8,16	2,6,7,8,9,12,15,16,17	2,6,8,16	VII
6.	2,6,8,9,12,15,16,17	2,6,7,9,12,15	2,6,9,12,15
7.	2,6,7,8,9,12,15,16,17	7,9,15,16,17	7,9,15,16,17
8.	2,8,9,12,15,16,17	2,6,7,8,9,16,17	2,8,9,16,17
9.	2,6,7,8,9,12,15,16,17	6,7,8,9,15,16,17	6,7,8,9,15,16,17
12.	2,6,12,17	6,7,8,9,12,15,16	6,12
15.	2,6,7,9,12,15,16,17	6,7,9,15	6,7,9,15
16.	2,7,8,9,12,16,17	2,6,7,8,9,15,16,17	2,7,8,9,16,17
17.	2,7,8,9,16,17	6,7,8,9,12,15,16,17	7,8,9,16,17

Table A8. ITERATION 8

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
6.	6,8,9,12,15,16,17	6,7,9,12,15	6,9,12,15
7.	6,7,8,9,12,15,16,17	7,9,15,16,17	7,9,15,16,17
8.	8,9,12,15,16,17	6,7,8,9,16,17	8,9,16,17
9.	6,7,8,9,12,15,16,17	6,7,8,9,15,16,17	6,7,8,9,15,16,17
12.	6,12,17	6,7,8,9,12,15,16	6,12
15.	6,7,9,12,15,16,17	6,7,9,15	6,7,9,15
16.	7,8,9,12,16,17	6,7,8,9,15,16,17	7,8,9,16,17
17.	7,8,9,16,17	6,7,8,9,12,15,16,17	7,8,9,16,17	VIII

Table A9. ITERATION 9

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
6.	6,8,9,12,15,16	6,7,9,12,15	6,9,12,15
7.	6,7,8,9,12,15,16	7,9,15,16	7,9,15,16
8.	8,9,12,15,16	6,7,8,9,16	8,9,16
9.	6,7,8,9,12,15,16	6,7,8,9,15,16	6,7,8,9,15,16
12.	6,12	6,7,8,9,12,15,16	6,12	IX
15.	6,7,9,12,15,16	6,7,9,15	6,7,9,15
16.	7,8,9,12,16	6,7,8,9,15,16	7,8,9,16

Table A10. ITERATION 10

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
6.	6,8,9,15,16	6,7,9,15	6,9,15
7.	6,7,8,9,15,16	7,9,15,16	7,9,15,16
8.	8,9,15,16	6,7,8,9,16	8,9,16
9.	6,7,8,9,15,16	6,7,8,9,15,16	6,7,8,9,15,16	X
15.	6,7,9,15,16	6,7,9,15	6,7,9,15
16.	7,8,9,16	6,7,8,9,15,16	7,8,9,16	X

Table A11. ITERATION 11

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
6.	6,8,15	6,7,15	6,15
7.	6,7,8,15	7,15	7,15
8.	8,15	6,7,8	8
15.	6,7,15	6,7,15	6,7,15	XI

Table A12. ITERATION 12

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
6.	6,8	6,7	6
7.	6,7,8	7	7
8.	8	6,7,8	8	XII

Table A13. ITERATION 13

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
6.	6	6,7	6	XIII
7.	6,7	7	7

Table A14. ITERATION 14

S.NO	REACHABILITY SET	ANTECEDENT SET	INTERSECTION SET	LEVEL
7.	7	7	7	XIV