Identification and ranking of the risk factors involved in PLM implementation

Journal Pre-proof Identification and Ranking of the Risk Factors Involved in PLM Implementation

Shikha Singh, Subhas Chandra Misra, Sameer Kumar PII:

S0925-5273(19)30316-0

DOI:

https://doi.org/10.1016/j.ijpe.2019.09.017

Reference:

PROECO 7496

To appear in:

International Journal of Production Economics

Received Date:

03 May 2018

Accepted Date:

26 September 2019

Please cite this article as: Shikha Singh, Subhas Chandra Misra, Sameer Kumar, Identification and Ranking of the Risk Factors Involved in PLM Implementation, International Journal of Production Economics (2019), https://doi.org/10.1016/j.ijpe.2019.09.017

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Journal Pre-proof Identification and Ranking of the Risk Factors Involved in PLM Implementation Shikha Singh Graduate Student Department of Industrial & Management Engineering Indian Institute of Technology - Kanpur India Email: [email protected] Subhas Chandra Misra Associate Professor Department of Industrial & Management Engineering Indian Institute of Technology - Kanpur India Email: [email protected]

Sameer Kumar* Professor of Operations and Supply Chain Management and CenturyLink Endowed Chair Opus College of Business University of St. Thomas 1000 LaSalle Avenue Minneapolis, MN 55403-2005 USA Email: [email protected] (*Corresponding author)

Journal Pre-proof Identification and Ranking of the Risk Factors Involved in Implementation of PLM Systems ABSTRACT Risk is a threat to every activity or system due to uncertainty with each component. Uncertainty of a system rises as the number of components increases. While uncertain risk elements cannot be assessed deterministically, a probability measurement helps an organization to combat those threats. Introduction of the Product Lifecycle Management (PLM) concept in an organization has the potential to offer substantial benefits. However, there are multiple risks in implementing PLM systems, and adequate care must be taken during PLM initiatives to get the desired benefits. If there are lacunae in the management of a PLM initiative, the whole purpose of adopting PLM may be lost partially or totally. The risk assessment of PLM systems’ implementation is performed by employing integrated grey DANP (DEMATEL-based ANP) and grey TOPSIS techniques for the first time. By considering the category and attribute of each risk factor, this integrated model reveals the critical risk factors: an inappropriate choice of a PLM system, mismatch in required data formats, and inefficient resource estimation. Keywords: Risk factors, PLM implementation, aircraft manufacturing, grey DEMATEL, ANP, DANP, and grey TOPSIS. 1. INTRODUCTION In recent years, manufacturing industries have witnessed tremendous progress in the adoption of Product Lifecycle Management (PLM). The industries better utilized all the information regarding different products throughout the product’s lifecycle (Stark, 2015). PLM allows the business processes to be managed more efficiently by facilitating the dissemination and sharing of information (Jupp and Nepal, 2014). By adopting this management system, it is possible to have a ‘universal, secure, managed access’ and to better utilize product information (Gecevska

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Journal Pre-proof et al., 2011). Adopting PLM ultimately helps to manage and share the live product data along the value chain (Chiang and Trappey, 2007; Oliveira et al., 2016). The benefits of PLM are experienced not only by designers and manufacturing partners but also by customers and suppliers. Thus, PLM handles the management of the complete lifecycle of different products across their entire supply chain efficiently (Jupp and Nepal, 2014). Rangan et al. (2005) pointed out that the product complexity demands the integration of the electrical/electronic systems, mechanical systems, and software systems with the product data management systems. Hence, the PLM systems are developed to be able to integrate all such components of product design and to manufacture in order to efficiently manage the engineering data (Saaksvuori and Immonen, 2008). However, different risks are involved in PLM systems implementation, which is primarily based on information and communications technology (ICT), similar to other IT initiatives. Considering products as intellectual property of a firm (Stark, 2015) and the PLM systems as a common data repository (Singh and Misra, 2018a), the risks associated with PLM systems entail the intellectual risks to a firm. Risks can be related to different elements of a PLM system. For example, some risk may be related to technology, while other risks may be related to data and business processes. The risks vary at different stages of the product lifecycle. Yet, the proper anticipation and management of risk help to reduce the probability of PLM adoption failure. Risk management occupies the topmost position in the list of actions that must be executed during and after the PLM project implementation. However, little research on PLM implementation has been carried out so far. There exist empirical studies on PLM to establish barriers to PLM institutionalization by Singh and Misra (2018a), and exploration of critical success factors to PLM implementation by Singh et al. (2019). Additional discussions on risk management and its impact were made by various authors in the contexts of implementing a variety of projects (Hung et al., 2014; Liu

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Journal Pre-proof and Wang, 2014), but their assertions have seldom considered the attributes of risk factors. Moreover, none of the projects are concerned with PLM adoption. The available Multiple Criteria Decision Making (MCDM) techniques were developed to deal with various situations, but each technique is appropriate for particular situations only (Kujawski, 2003). In the literature, there exist different integrated approaches to studying the risks involved in the implementation of distinct IT systems. The present study proposes an integrated research approach of DEMATEL, ANP, and TOPSIS combined with grey number theory to get a more in-depth insight into the assessment of risk factors. The DEMATEL (Decision Making Trial and Evaluation) was proposed by Fontela and Gabus, (1974) while ANP (Analytic Network Process) was proposed by Saaty in 1999. This type of hybrid model of DEMATEL and ANP (DEMATEL-based Analytic Network Process) is called DANP, in which the DEMATEL method is used for structuring criteria and weighting the clusters as well as handling the inner and outer dependencies. DANP has been applied by distinct researchers to solve the different complexities of problems, as discussed by Gölcük and Baykasoğlu (2016). Further, the resulting influential weights of the factors are considered for finalizing the risk score through grey TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Grey TOPSIS was proposed by Hwang and Yoon (1981) to select an alternative based on the comparison between the distances and the ideal solution. The present work is proposed to contribute as follows: 1. This paper proposes a hybrid decision-making technique of grey DANP and grey TOPSIS, and this hybrid is the new contribution to the domain of decision-making. The risks associated with the implementation of PLM systems are investigated for the first time using the proposed integrated decision-making technique. 2. This work adds to the literature related to the risk assessment of the implementation of PLM systems.

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Journal Pre-proof 3. The proposed research technique can be used to identify and analyze the interrelationships and ranks of risk factors involved in the implementation of any other enterprise system. The identification of risk is also an integral part of the proposed research process. Implementation of any enterprise system such as ERP, CRM, and SCM entails different risk factors that need an altogether different literature review than this work. Hence, the proposed research process (in Fig. 1) may be helpful for any enterprise system. The application of the proposed research process for risk analysis of PLM systems implementation in an aircraft manufacturing firm may be used as an example to help the researchers/industry practitioners in applying the procedure. 4. The proposed integrated approach helps to study the interrelationships of factors and their roles. Further consideration of attributes of each risk factor helps to find their ranking by covering the uncertainty and ambiguity of experts’ assessment. Hence, it has been proposed to integrate MCDM techniques further to investigate and analyze the different risk factors associated with the implementation of PLM systems. Organization of the present work is as follows: Section 2 explains the proposed integrated decision-making technique along with its justifications and need. The literature review for risks involved in the implementation of PLM systems is given in Section 3, which includes an account of articles relevant to the current research. It also provides a table with the possible risk factors that we have identified based on the literature review. Section 4 presents the details of an illustrative example that we carried out for a large aircraft manufacturing company to identify the risks involved in PLM implementation. The data and observations presented in Section 4 are discussed in Section 5, while Section 6 provides the conclusions based on the findings of the present study. 2. PROPOSED INTEGRATED RESEARCH TECHNIQUE

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Journal Pre-proof MCDM techniques are used in situations where the decisions are made based on several criteria. The list of criteria may vary depending on the problem’s context and the requirement of the decision-maker. In many cases, only one technique cannot be sufficient, so various researchers combine the techniques in order to solve and effectively analyze complex problems. Such combinations of techniques help the researchers or decision-maker to either overcome the difficulty with a technique or analyze the problem in depth. Several MCDM techniques (AHP, ANP, DEMATEL, VIKOR, TOPSIS, etc.) were introduced in the last few decades to handle different issues and make decisions among the available choices. In this present study, authors utilize a hybrid MCDM technique to find the interdependencies, interrelationships, and ranking of the risk factors. DEMATEL helps to find the cause-effect interdependencies. In the case of DEMATEL, the interdependencies are denoted through the relation values, while the relative importance can be judged using the prominence values of factors (Büyüközkan and Güleryüz, 2016; Singh and Misra, 2018b). The influential weights of the factors cannot be calculated using the DEMATEL technique; however, ANP helps to identify the influential weight of each factor (Büyüközkan and Çifçi, 2012; Tavana et al., 2013; Büyüközkan and Güleryüz, 2016). Similarly, ANP can solve the structural dependency of the problems, while DEMATEL helps to explain the causal dependency of the criteria with visual interrelationship maps (Gölcük and Baykasoğlu, 2016). ANP records the one-sided preference of factors only and assumes the reciprocal values for the preference of reverse relationships of the factors, which may not necessarily be correct (Büyüközkan and Güleryüz, 2016; Govindan et al., 2016) (for example, ANP is not suitable for some problems where factor A is preferred to factor B x times but B may not be preferred to A 1/x times). So some researchers (Gölcük and Baykasoğlu (2016), Deng et al. (2018), etc.) prefer combining ANP with DEMATEL. Unlike ANP, DEMATEL does not consider the reciprocal values (Büyüközkan and Güleryüz, 2016) and records the absolute input for each

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Journal Pre-proof relationship. Hence, DEMATEL is preferred to reveal the interrelationships among the factors and their strengths (Govindan et al., 2016). In the case of ANP, the range of inputs is from 1/9 to 9 (an approximation to ratio scale) (Saaty, 1999), while the scale in DEMATEL generally considers the absolute values from 0 to 4, such as in the studies by Lu et al. (2013) and Deng et al. (2018). The absolute scale is preferred over the ratios scale to collect the experts’ input (Hung et al., 2012) and simplify the mathematical computations. Hence, according to the previously discussed points, DEMATEL and ANP concepts are combined in such a way that problems that ANP can be applied are enhanced. In this combination of DEMATEL based ANP, the DEMATEL technique is used to collect the data and analyze the interrelationships and interdependencies, while the ANP technique is utilized to derive the unweighted supermatrix, weighted matrix, and the limit matrix to finalize the influential weights of the factors under study. Further considering the risk factors, it has been observed that after finding out the index of each risk factor using the DANP technique, it is essential to consider different attributes of risk factors, such as the type of risk, its probability, its impact of occurrence, and its mitigation to finalize their overall rank. TOPSIS helps to decide the overall rank of the factors based on the values of attributes. This overall rank includes not only its prominence and relation with the other factors but also its individual attributes. By considering different attributes and their weights of importance, the TOPSIS technique helps to calculate the distances from ideal solutions to set the rank of factors (Tavana et al., 2013). TOPSIS needs both the influential weights and the attributes’ levels of each factor to decide its rank. Tavana et al. (2013) utilized ANP and TOPSIS together to assess e-government readiness. Although, as it has been done by Chemweno et al. (2015), the selection of risk assessment methodology is prioritized using ANP only. The integrated technique of ANP and TOPSIS has also been utilized by many other researchers, such as Wu et al. (2010), to select the best marketing strategy. Some authors

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Journal Pre-proof (Rajesh and Ravi, 2017; Bai and Sarkis, 2013; Xia et al., 2015; Özdemır and Tüysüz, 2015) used grey DEMATEL to analyze the various management problems for different organizations, while others (Samvedi et al., 2013; Oztaysi, 2014) used TOPSIS in their analyses of risk management and information technology. As discussed earlier, the adoption of product lifecycle management has been found useful, especially for manufacturing industries. Knowing this, the present study has been devoted to the assessment and ranking of risk factors involved in PLM systems’ implementation. The research technique of hybrid grey DANP and grey TOPSIS model is shown in Figure 1.

Figure 1. Proposed Integrated Research Technique

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Journal Pre-proof The MCDM techniques are utilized in most of the practical, complex, and uncertain conditions, where such techniques can only be understood through the experiences of the involved persons. In many places, these persons are called experts because they are the individuals who know the most about that domain. Experts of a firm are considered the decision-makers, who have a common goal to solve the problem with the best alternative (Büyüközkan et al., 2017). A group of decision-makers is preferred over a single expert, as it reduces the bias and uncertainty in evaluations. Such uncertainties are taken care of in the standard ANP technique by the eigenvalue method (Satty, 2004; Chemweno et al., 2015), but the eigenvalue approach is not the part of the mathematical computation of hybrid DANP. Hence, such uncertainties and subjective errors are possible to treat with different methods such as grey, fuzzy, and rough sets theories (Liu et al., 2012). The uncertainty in the responses may arise due to the differences in the level of perception, experience, and knowledge of each expert (Büyüközkan et al., 2017). Hence, it is imperative to reduce these errors. For instance, DEMATEL, ANP, and Intuitionistic Fuzzy Sets (IFS) are used by Büyüközkan et al. (2017) to find suitable partners for customer relationship management. Similarly, Büyüközkan and Çifçi, (2012) integrated DEMATEL, ANP, and TOPSIS in a Fuzzy Environment, while Tavana et al. (2013) utilized only ANP and TOPSIS in a Fuzzy Environment. Samvedi et al. (2013) have consolidated the fuzzy TOPSIS scores with the weights obtained from fuzzy AHP in order to get the final indices for supply chain risks. Bai and Sarkis (2013) utilized grey DEMATEL to analyze the implementation factors of business process management, while Hashemi et al. (2015) employed grey relational analysis with ANP to choose the appropriate green supplier. Fuzzy numbers deal with the cognitive uncertainty by defining the membership function, while grey numbers deal with the uncertainty of a small sample and less information (Liu et al., 2012). The grey number can be expressed as

 J  [J , J ] ,

where J and J are the lower

and upper limits of the grey number, respectively. Basic mathematical expressions of grey 8

Journal Pre-proof theory and grey numbers are discussed and proven by Li et al. (2007) as shown in Eq. (1) to Eq. (4).  J1   J 2  [J1  J 2 , J1  J 2 ]

(1)

 J1   J 2  [J1  J 2 , J1  J 2 ]

(2)

 J1 *  J 2  [min( J1 J 2 , J1 J 2 , J1 J 2 , J1 J 2 ), max( J1 J 2 , J1 J 2 , J1 J 2 , J1 J 2 )]

(3)

 1 1   J1   J 2   J1 *  ,    J2 J2  

2.1.

(4)

Mathematical Steps of Grey DANP-TOPSIS Technique The grey DEMATEL method is the combination of the basic DEMATEL

technique with grey numbers which has been extensively utilized in various studies during the last decade. Grey DEMATEL considers the uncertainty of the assessments made by the experts. The basic DEMATEL technique is an MCDM technique used in structuring complex problems to identify the cause and effect relationship among multiple criteria. In the case of very small datasets, many researchers find grey numbers, defined in grey theory (Julong, 1989), to be more convenient than the crisp numbers to treat the biases and subjectivity of the experts in decision making. Based on the description of the grey DEMATEL method adopted by Bai and Sarkis (2013), Singh and Misra (2018b), and Singh et al. (2019), steps to the grey DEMATEL method are briefly described below. Step 1: Obtaining the aggregate grey direct relation matrix The direct response matrix from each respondent has been presented in the grey matrix format, as shown in Eq. (5). Thus, a total number of ‘m’ individual grey direct- relation matrices are obtained as the input for further computation.

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Journal Pre-proof [0, 0] b12m L  m b [0, 0] L m B   21  M M L  m m  bk1 bk 2 L

b1mk   b2mk  M  [0, 0]

(5)

In Eq. (5), ‘m’ represents the number of respondents, ‘k’ the number of factors, and

 bijm the grey pairwise impact of the factors. The aggregate grey direct-relation matrix is calculated by taking the simple average of all the corresponding inputs. The aggregate grey direct-relation matrix is calculated in Eq. (6), given below. 𝐻 = () 𝑚

(6)

Step 2: Finding the normalized grey direct-relation matrix The grey normalization factor (  v ) is calculated by considering the maximum among all row sums and column sums. Each element in the normalized grey direct-relation matrix ‘N’ are calculated by using the Eq. (7), given below.

 bij bij   𝑛𝑖𝑗=  ,   v v 

(7)

Step 3: Establishing the grey total relation matrix (T) The grey total relation matrix is established by considering the impact of direct and indirect relations expressed through a normalized grey direct-relation matrix. Hence, the grey total relation matrix is calculated by a combination of Eq. (8) to Eq. (11), given below. T = D  D 2  D 3  D 4  .......  D m = D(I - D) -1

T  [t ij ] kXk ;

 tij  [tij , tij ]

(8) (9)

Matrix [  tij ]  D( I  D) 1

(10)

Matrix [tij ]  D( I  D) 1

(11)

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Journal Pre-proof Step 3(a): Conversion of the grey total relation matrix into a crisp total relation matrix Total relation matrices of factors and dimensions are converted into crisp matrices by using the CFCS (Converting Fuzzy data into Crisp Scores) method, as suggested by Opricovic and Tzeng (2003). This method is utilized by Özdemır and Tüysüz (2015) and Xia at al. (2015). More detailed steps of the CFCS method have been discussed by Opricovic and Tzeng (2003). The brief steps are shown from Eq. (12) to Eq. (14) to calculate the final crisp total relation matrix, as illustrated in Eq. (15).  f ij 

 t

ij

 min  t ij

 max min

 f ij  (t ij  min  t ij ) max min

(12) (13)

where max min = max  t ij  min  t ij

Gijk   f ij (1   f ij )  ( f ij )X ( f ij ) (1   f ij   f ij ) C ijk  min  t ij  Gijk max min

(14) (15)

Step 3 (b): Plotting the cause-effect diagram Each row sum is denoted by ‘R’, and each column sum of the grey total relation matrix is denoted by ‘D’. Net prominence for each factor is calculated as P  R  D , while net relation for each factor is calculated as E = R  D . The positive values of E show the ‘causal’ character of the factor, while negative values of E show the ‘affected’ character of the factor. After obtaining the total relation matrix of dimensions and related factors, its further combination with ANP helps to calculate the unweighted supermatrix, weighted matrix, and limit matrix to finalize the influential weights. Detailed mathematical steps for DANP are explained by Hung et al. (2012), Hsu et al. (2013), Gölcük and Baykasoğlu (2016), and Deng et al. (2018).

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Journal Pre-proof Step 4: Forming the unweighted supermatrix It can be formed by separating the factors into clusters of dimensions in the total relation matrix. Each cluster of factors is normalized by dividing each row element by its respective row sum. Finally, position the transpose of normalized clusters of factors in their respective positions. The unweighted supermatrix is derived from the total relationship matrix in compliance with ANP (Lu et al., 2013; Deng et al., 2018). Step 5: Calculating the weighted supermatrix It is calculated by weighting the factors in the unweighted supermatrix with the weights of their corresponding cluster dimension. Step 6: Obtaining the limit supermatrix It is obtained by increasing the power of the weighted supermatrix for very high values. The limit supermatrix shows stable values, which are considered the influential weights of the respective factor. The TOPSIS method evaluates both the positive and negative ideal solutions. The alternative is selected based on its proximity to the positive ideal solution. As the distance from the positive ideal solution increases, the alternative becomes important. Zhang (2014) used the grey TOPSIS approach to rank project risks, while Mavi et al. (2016) and Oztaysi (2014) used grey TOPSIS to rank the alternatives for different management issues. The inputs on each risk attribute are received from the experts of the case company. After calculating the influential weight of each risk factor, further steps have been adopted to finalize the ranks of risk factors.

Step 7: Normalizing each attribute

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Journal Pre-proof In a decision matrix, X  [ xij ] pXq ; i = 1 to p; j = 1 to q, p represents the number of the alternative and q stands for the number of the attribute. The normalized value of each attribute will be calculated by dividing by the column sum, as presented in Eq. (16).

 xij xij   rij   ,   u j u j 

(16)

where  u j  [u j , u j ] represents the sum of each column corresponding to each attribute (j). Step 8: Defining positive and negative ideal solutions The positive ideal attribute ( V  ) is assumed to be the best of the attributes, and the negative ideal solution ( V  ) is assumed to be the worst of the attributes. V  = { max ( rij )}= { r1 , r2 , r3 ,......., rp } i

V  = { min (rij ) }={ r1 , r2 , r3 ,......,.rp } i

Step 9: Defining the differences from the positive and negative ideal values The measure of separation between the positive ideal and negative ideal attribute values,

d i and d i , is calculated using the Euclidean distance and expressed in Eq. (17) and Eq. (18).

d i 

1 m  wi {[r j  rij ] 2  [r j  rij ] 2 } 2 j 1

d i 

1 m  wi {[r j  rij ]2  [r j  rij ]2 } 2 j 1

(17)

(18)

Step 10: Calculating the relative closeness to the positive ideal solution Relative closeness to the positive ideal attribute decides the performance index and rank of the risk factors based on the attributes to choose the best one. Relative closeness is the

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Journal Pre-proof relative index of the attribute and is represented in Eq. (19). This relative index decides the rank of the factor.

Ci 

d i , where 0  Ci  1 .   di  di

(19)

3. LITERATURE REVIEW FOR RISKS INVOLVED IN IMPLEMENTATION OF PLM SYSTEMS Implementation of PLM systems is the act of developing a PLM solution and deploying it appropriately. The PLM solution should be regarded as tentative before the deployment, and the solution comes to reality only after it is implemented. According to the Project Management Institute (PMI, 2013), a project’s risk may be looked upon as “an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives such as scope, schedule, cost, or quality.” Probable uncertainties that are related to the technology, process, and human factors must be well considered for each component of the entire implementation process. The implementation of PLM systems is a significant step towards PLM institutionalization (Singh and Misra, 2018a). Hence, the risk related to PLM systems implementation may threaten PLM institutionalization. The cost of software systems, the estimated schedule of implementation, and the resource requirements often exceed the anticipated figures. These factors are vital to system development, so the mistakes in estimating these factors can cause PLM implementation to fail. Some managers of organizations who adopt PLM have experienced its values and benefits, while others have found that PLM adoption has failed to derive benefits to the level they expect (Graham, 2010). This observation emphasizes that the risks associated with PLM adoption and the root causes of failures should be investigated appropriately. In order to counter the risks, it is advisable to involve the users in the organization to get better insights and solutions.

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Journal Pre-proof Other types of risk can arise if the roadmap for institutionalizing PLM is not adequately planned and the software procurement budget is not correctly estimated (Graham, 2010). Machac and Steiner (2014) suggest that the tasks of identifying the risks and assessing their impact on implementation and maintenance should be performed simultaneously as the adoption of PLM progresses in the organization. According to Stoneburner et al. (2002), risk assessment refers to assessing the related threat components along with their vulnerability and control. Various researchers studied risk factors in the research area of enterprise systems. Ziemba and Kolasa (2015) examined the risk factors to information systems by considering case studies for two companies in Poland. Schmidt et al. (2001) did surveys in three different countries (USA, Hong Kong, and Finland) on the identification of risks involved in adopting software projects. While Sumner (2000) attempted to identify the risk factors in adopting MIS projects and ERP projects, Tesch et al. (2007) discussed risk factors associated with implementing systems development in IT industries and suggested some specific strategies for compiling risk data. Based on an architectural model of a PLM solution, Bokinge et al. (2012) proposed a method to identify the risks involved in the implementation of PLM systems. Based on their observations from the electronic industry, Carbone and Tippett (2004) discussed the risk management of a project to improve risk controls. Barki et al. (2001) proposed an integrative model for managing software project risks. They made the important observation that if levels of internal integration and formal planning are both high, even the success of high-risk projects can be expected. The relationship between the success in implementing a project and the management of risks was discussed by Carvalho and Junior (2015). Cagliano et al. (2015) proposed a theoretical framework in which a particular model should be used to explore the risk management process for a given project implementation. They claimed that the

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Journal Pre-proof work bears the potential to integrate the two processes of knowledge management and risk management. Castro et al. (2008) observed the inadequacy of available risk management information systems and proposed a software system that, according to them, can manage the complexities of risks. The basic functions of PLM systems are to collaborate and integrate all the productrelated data, people, processes, and systems in the organization to manage the complete lifecycle of the product (Stark, 2015). Hence, the PLM systems’ implementation involves the proper knowledge and the proper information exchanges among all the stakeholders of the organization. The implementation of PLM systems requires a change in communication to generate coherent and consolidated product information, and the culture of the organization also needs to be re-aligned. Organizational culture is considered a common way to perceive the problems and manage them to solve for survival; with the long experience of dealing in the external environment, these perceptions get establish as patterns of collective behavior in an organization (Schein, 1990; Watkins, 2013). The founders or the senior managers are majorly responsible for developing and improving the organizational culture (Schein, 1995). Hence, management support is considered as motivational guidance to the employees at the time of technology change management in an organization. A lack of support from the senior managers and leaders may turn into a risk for the implementation of such IT-based systems (Gibson, 2003). Hence, the lack of communication and lack of management support are considered risks generated by ignorance of cultural changes required for the implementation of PLM systems. The identified risks have been categorized into three groups: technology-related risks, process-related risks, and human-related risks. It is known that PLM implementation requires the integration of different systems to compile the data into one

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Journal Pre-proof common repository. Ignorance of such integrations may lead to high risks, as pointed out in a PLM report by a consulting firm, CIM data (2017). Moreover, alignment of PLM technology with the organization is not given adequate attention during the decision of PLM adoption. This is also a high risk in implementing PLM. The present study has been carried out by incorporating these factors along with other considerations. An extensive survey of the literature and discussions with executives of the case company brought out the possible risk factors for PLM systems implementation. They have been included in Table 1. Table 1. Risk Factors Identified for Implementation of PLM Systems Dimension

Factors

1

2

Human-related risks

Ignored cultural changes

Lack of technological skills

3

Lack of technologyprocess alignment Process-related risks

4

5

Lack of user acceptance

Description of Risk Factors for PLM systems implementation  Underestimating the need of cultural change  Lack of collaborative environment  Lack of communication  Lack of management support Lack of user participation Change-resistant user Improper user understanding of technology and its use

Hung et al. (2014)

 

User learning and technical skills User may need additional hands-on training to proceed their working

Graham (2010); Hung et al. (2014); Liu and Wang (2014); Lóopez and Salmeron (2012)



Mismatch of process requirements of PLM Lack of PLM technology alignment with an organization’s process

Graham (2010)

Underestimating the data consolidation challenge from various other information systems such as from CAD, CAM, etc. to PLM

Graham (2010)

Poor anticipation of budget needed to deliver the full PLM implementation Compelled executives from the software suppliers/internal management team

Graham (2010); Hung et al. (2014); Liu and Wang (2014); Sundararajan et al. (2014); López and Salmeron (2012)

Mismatch between the systems’ requirements & the organization’s business strategy

Sumner (2010)





 

6

7

Lack of technologybusiness alignment

Graham (2010); Hung et al. (2014); Liu and Wang (2014); López and Salmeron (2012)

  

Improper data migration

Inadequate resources estimation

References



17

Journal Pre-proof PLM systems evaluation and selection for implementation

Industry expert opinion

Lack of infrastructure



9

Lack of adequate technology infrastructure

CIM data (2017)



Lack of integration among other enterprise systems such as ERP, CAD, CAM, etc.

Hung et al. (2014); López and Salmeron (2012)



Alterations in data format during migration

Industry expert opinion

 

Misuse of product-related data Unauthorized changes in the product designs/processes

Industry expert opinion

10

11

12

Technology-related risks



8

Inappropriate choice of PLM system

Lack of systems integration Mismatch in required data formats Threat to data security

4. ILLUSTRATIVE STUDY: Data Collection and Analysis The study has been performed in a large aircraft manufacturing company located in India. The purpose is to identify the risks involved in the implementation of PLM systems. This company manufactures various kinds of aircraft and their variants. The firm has implemented PLM, but it could not completely institutionalize PLM throughout the entire organization. This aircraft manufacturing firm was in a transformation phase of PLM adoption during this study. The transformation was taking place from various isolated systems to one integrated and collaborative PLM system. Five experts were chosen from different working areas, such as design, IT, and management. All the experts had spent a similar amount of work time in the PLM area. Because of this, the weights of all the experts were considered equal. The data collection and analysis was performed by applying the proposed research technique to finalize the ranks of risk factors of PLM systems’ implementation. Similar to Bai and Sarkis (2013), while collecting the data, this study also used the linguistic scale and terms, as mentioned in Table 2. The respondents gave their inputs by using these linguistic terms, which correspond to grey numbers. The grey numbers

18

Journal Pre-proof have been used while determining the grey direct-relation matrix, grey normalized matrix, and grey total relation matrix. Table 2. Grey Linguistic Scale for Impact Assessment Grey numbers [0,0]

Linguistic terms No influence (N) Very low influence (VL) Low influence (L) High influence (H)

[0,1] [1,2] [2,3]

Very high influence (VH)

[3,4]

By using Step 1 to Step 3 in Section 2.1, the grey total relation matrix has been calculated and presented in Table 3. Table 3. Grey Total Relation Matrix of Factors Factors

H1

H2

H3

P1

P2

P3

P4

P5

T1

T2

T3

T4

H1

[0.124, 0.192]

[0.220, 0.334]

[0.210, 0.301]

[0.226, 0.339]

[0.186, 0.279]

[0.202, 0.275]

[0.266, 0.363]

[0.123, 0.22]

[0.249, 0.333]

[0.241, 0.338]

[0.170, 0.267]

[0.213, 0.312]

H2

[0.211, 0.266] [0.211, 0.272] [0.19,0. 245] [0.183, 0.241] [0.195, 0.279] [0.276, 0.324] [0.269, 0.329] [0.223, 0.285] [0.215, 0.266] [0.233, 0.295] [0.171, 0.225]

[0.259, 0.338] [0.362, 0.443] [0.314, 0.392] [0.257, 0.341] [0.278, 0.389] [0.343, 0.42] [0.345, 0.434] [0.314, 0.395] [0.377, 0.449] [0.337, 0.426] [0.258, 0.335]

[0.306, 0.377] [0.231, 0.303] [0.282, 0.35] [0.247, 0.311] [0.249, 0.347] [0.29, 0.363] [0.261, 0.338] [0.270, 0.34] [0.315, 0.385] [0.293, 0.378] [0.19, 0.255]

[0.377, 0.444] [0.395, 0.464] [0.249, 0.319] [0.286, 0.353] [0.281, 0.392] [0.358, 0.428] [0.417, 0.481] [0.387, 0.441] [0.394, 0.458] [0.41, 0.478] [0.226, 0.297]

[0.287, 0.363] [0.319, 0.392] [0.293, 0.357] [0.184, 0.243] [0.243, 0.343] [0.26, 0.326] [0.288, 0.372] [0.278, 0.348] [0.333, 0.397] [0.355, 0.419] [0.171, 0.237]

[0.188, 0.261] [0.242, 0.314] [0.178, 0.246] [0.171, 0.232] [0.131, 0.211] [0.182, 0.251] [0.227, 0.299] [0.216, 0.279] [0.205, 0.269] [0.21, 0.29] [0.157, 0.229]

[0.374, 0.44] [0.399, 0.469] [0.323, 0.4] [0.295, 0.362] [0.294, 0.403] [0.288, 0.355] [0.391, 0.466] [0.382, 0.441] [0.425, 0.482] [0.367, 0.453] [0.288, 0.357]

[0.24, 0.305] [0.245, 0.319] [0.205, 0.278] [0.149, 0.209] [0.176, 0.272] [0.197, 0.264] [0.165, 0.238] [0.184, 0.244] [0.2, 0.28] [0.227, 0.31] [0.173, 0.241]

[0.284, 0.356] [0.298, 0.377] [0.232, 0.301] [0.195, 0.266] [0.242, 0.343] [0.313, 0.379] [0.285, 0.366] [0.22, 0.282] [0.339, 0.401] [0.281, 0.37] [0.245, 0.31]

[0.328, 0.401] [0.313, 0.393] [0.275, 0.344] [0.246, 0.323] [0.242, 0.353] [0.322, 0.388] [0.347, 0.423] [0.335, 0.398] [0.259, 0.329] [0.345, 0.424] [0.209, 0.287]

[0.255, 0.337] [0.249, 0.334] [0.22, 0.305] [0.284, 0.335] [0.21, 0.314] [0.274, 0.346] [0.279 ,0.362] [0.206, 0.276] [0.26, 0.343] [0.206, 0.284] [0.144, 0.219]

[0.299, 0.376] [0.275, 0.361] [0.255, 0.331] [0.21, 0.284] [0.232, 0.334] [0.317, 0.386] [0.276, 0.37] [0.312, 0.372] [0.317, 0.391] [0.281, 0.375] [0.159, 0.225]

H3 P1 P2 P3 P4 P5 T1 T2 T3 T4

The grey total relation matrix was then converted to a crisp total relation matrix using CFCS, as explained earlier in Eq. (12) to Eq. (15) and expressed in Table 4. Table 4. The Crisp Total Relation Matrix of Factors Factors

H1

H2

H3

P1

P2

P3

P4

P5

T1

T2

T3

T4

H1

0.141

0.258

0.245

0.261

0.215

0.243

0.296

0.155

0.289

0.281

0.208

0.259

H2

0.241

0.289

0.356

0.422

0.332

0.225

0.415

0.285

0.326

0.377

0.308

0.353

H3

0.245

0.42

0.261

0.446

0.369

0.294

0.449

0.299

0.348

0.363

0.302

0.33

P1

0.216

0.358

0.323

0.269

0.331

0.209

0.358

0.247

0.259

0.308

0.265

0.297

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Journal Pre-proof P2

0.209

0.289

0.277

0.312

0.198

0.196

0.318

0.169

0.213

0.276

0.32

0.24

P3

0.24

0.333

0.302

0.331

0.292

0.155

0.34

0.225

0.291

0.291

0.265

0.285

P4

0.314

0.393

0.337

0.402

0.293

0.215

0.309

0.233

0.358

0.364

0.324

0.369

P5

0.315

0.405

0.303

0.468

0.339

0.274

0.443

0.196

0.333

0.403

0.339

0.337

T1

0.26

0.36

0.31

0.425

0.317

0.254

0.42

0.212

0.24

0.378

0.238

0.356

T2

0.243

0.432

0.367

0.441

0.379

0.241

0.47

0.246

0.387

0.289

0.315

0.373

T3

0.273

0.395

0.35

0.463

0.406

0.258

0.42

0.283

0.334

0.403

0.243

0.343

T4

0.192

0.287

0.206

0.241

0.185

0.185

0.31

0.203

0.273

0.23

0.163

0.174

Further, the prominence and relation values were calculated by following Step 3 (b) and are shown in Table 5.

Table 5. Prominence and Relation Values of Risk Factors Factors H1

Cultural changes

H2 H3 P1 P2 P3 P4 P5 T1 T2 T3 T4

User acceptance Technological skills Technology-process alignment Improper data migration Resources estimation Technology-business alignment Inappropriate choice of PLM system Lack of infrastructure Lack of systems integration Mismatch in required data formats Data security

Prominence (R+D) 5.74

Relation (R-D) -0.038

8.148 7.763 7.921 6.673 6.099 8.459 6.908 7.421 8.146 7.461 6.365

-0.29 0.489 -1.041 -0.639 0.601 -0.637 1.402 0.119 0.22 0.881 -1.067

The grey total relation matrix of dimensions is presented in Table 6, and the crisp total relation matrix of dimensions is presented Table 7. Table 6. Grey Total Relation Matrix of Dimensions Dimensions

R1

R2

R3

R1

[0.441,0.63]

[0.9,1.09]

[0.5,0.75]

R2

[0.578,0.784]

[0.516,0.724]

[0.5,0.75]

R3

[0.993,1.196]

[1.219,1.416]

[0.5,0.75]

Table 7. Crisp Total Relation Matrix of Dimensions

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Dimensions

R1

R2

R3

R1 R2 R3

0.479 0.651 1.153

1 0.555 1.381

0.625 0.625 0.625

The prominence and relation values of dimensions are shown in Table 8. Table 8. Prominence and Relation Values of Dimensions R1 R2 R3

Dimensions Human-related risks Process-related risks Technology-related risks

R+D 4.387 4.767

R-D -0.179 -1.105

5.034

1.284

The prominence-relation graph of factors is based on the values in Table 5 and shown in Figure 2. The prominence-relation graph of dimensions is based on the values in Table 8 and shown in Figure 3. 1.5

P5

1

T3 P3

R-D

0.5

0

H3 T2

T1 H1

H2 -0.5 P4

P2 -1

-1.5 5.5

P1

T4

6

6.5

7

7.5

8

8.5

R+D

Figure 2. Overall Prominence-Relation Graph of All Factors

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1.5 R3 1

R-D

0.5

0 R1 -0.5

-1

R2

-1.5 4.3

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

R+D

Figure 3. Overall Prominence-Relation Graph of Dimensions

The prominence-relation graph categorizes the factors above and below the xaxis. The prominence values (expressed on the x-axis) are positive for all the factors. The relation values (expressed on the y-axis) are positive and negative. The factors with positive relation values (i.e., above the x-axis) are found to be the causal factors, which influence the impact of the remaining factors. The factors with negative relation values (i.e., below x-axis) are found to be the affected factors, which are impacted by others. Following Step 4 and Step 5, the unweighted supermatrix and weighted matrix of the factors are calculated. The weighted matrix is expressed in Table 9. Table 9. Weighted Matrix of Risk Factors Factors

H1

H2

H3

P1

P2

P3

P4

P5

T1

T2

T3

T4

H1

0.05

0.062

0.06

0.0856

0.096

0.0975

0.1069

0.109

0.102

0.085

0.098

0.102

H2

0.091

0.074

0.103

0.1419

0.133

0.1353

0.1338

0.141

0.1413

0.151

0.142

0.153

H3

0.087

0.091

0.064

0.128

0.127

0.1227

0.1148

0.105

0.1217

0.129

0.125

0.11

P1

0.106

0.119

0.114

0.0577

0.079

0.0747

0.0839

0.082

0.1141

0.109

0.111

0.094

P2

0.087

0.094

0.094

0.071

0.05

0.0659

0.0612

0.06

0.0851

0.093

0.097

0.072

P3

0.099

0.064

0.075

0.0448

0.05

0.035

0.0449

0.048

0.0682

0.059

0.062

0.072

P4

0.12

0.117

0.115

0.0767

0.081

0.0767

0.0645

0.078

0.1128

0.116

0.1

0.121

P5

0.063

0.081

0.077

0.0529

0.043

0.0508

0.0486

0.035

0.0569

0.061

0.068

0.079

T1

0.083

0.071

0.077

0.0783

0.069

0.0877

0.0864

0.08

0.0392

0.056

0.05

0.064

T2

0.081

0.082

0.08

0.0931

0.09

0.0877

0.0878

0.097

0.0617

0.042

0.06

0.054

T3

0.06

0.067

0.067

0.0801

0.104

0.0799

0.0781

0.082

0.0388

0.046

0.036

0.038

T4

0.074

0.077

0.073

0.0898

0.078

0.0859

0.089

0.081

0.0581

0.054

0.051

0.041

Based on the limit matrix, the influential weights of the risk factors are shown in Table 10. 22

Journal Pre-proof Table 10. Influential Weights of Risk Factors Influential Weights 0.085 0.124 0.108 0.096 0.079 0.061 0.099 0.061 0.071 0.077 0.066 0.072

Factors H1 H2 H3 P1 P2 P3 P4 P5 T1 T2 T3 T4

Cultural changes User acceptance Technological skills Technology-Process alignment Improper data migration Resources estimation Technology-Business alignment Inappropriate choice of PLM system Lack of infrastructure Lack of systems integration Mismatch in required data formats Data security

These weights of the corresponding factors will be further considered in computing the risk index using TOPSIS. Similar to the linguistic terms used by Samvedi et al. (2013), the linguistic scale used to evaluate the attributes of risk factors are presented in Table 11. Table 11. Linguistic Scale for Attribute Assessments Linguistic terms for mitigation

Linguistic terms for type of risk

Very Easy (VE)

Strategic (S)

Grey numbers [0,1]

Easy (E)

Strategic-Tactical mix (T)

[1, 2]

Medium (M)

Tactical (M)

[2, 3]

Difficult (D)

Tactical-Operational mix (TO)

[3, 4]

Very Difficult (VD)

Operational (O)

[4, 5]

Table 12 represents the normalized inputs for different attributes, corresponding positive and negative ideal solutions, the distance of each attribute from the ideal solutions (Step 8 to Step 10), the performance indices, and ranks of the factors. Table 12. Attribute Values and Ranks of the Risk Factors Type of risk

Probability

Impact of occurrence

Mitigation

A-

A+

d+

d-

Performance index (PI)

Rank

H1

[0.048,0.12]

[0.088,0.207]

[0.047,0.108]

[0.065,0.128]

0.047

0.207

0.068

0.043

0.387

12

H2

[0.018,0.074]

[0.088,0.207]

[0.074,0.146]

[0.065,0.128]

0.018

0.207

0.085

0.069

0.451

8

H3

[0.06,0.139]

[0.027,0.103]

[0.042,0.1]

[0.05,0.106]

0.027

0.139

0.001

0.041

0.977

1

23

Journal Pre-proof P1

[0.03,0.093]

[0.068,0.172]

[0.068,0.138]

[0.065,0.128]

0.03

0.172

0.002

0.049

0.962

3

P2

[0.06,0.139]

[0.007,0.069]

[0.053,0.115]

[0.035,0.085]

0.007

0.139

0.045

0.042

0.485

7

P3

[0.042,0.111]

[0.048,0.138]

[0.042,0.1]

[0.04,0.092]

0.04

0.138

0.001

0.025

0.971

2

P4

[0.024,0.083]

[0.061,0.161]

[0.068,0.138]

[0.085,0.156]

0.024

0.161

0.05

0.054

0.523

4

P5

[0.048,0.12]

[0.048,0.138]

[0.089,0.169]

[0.085,0.156]

0.048

0.169

0.038

0.036

0.49

6

T1

[0.077,0.167]

[0.02,0.092]

[0.032,0.085]

[0.04,0.092]

0.02

0.167

0.054

0.038

0.411

10

T2

[0.089,0.185]

[0.041,0.126]

[0.047,0.108]

[0.06,0.121]

0.041

0.185

0.055

0.04

0.422

9

T3

[0.089,0.185]

[0.048,0.138]

[0.032,0.085]

[0.03,0.078]

0.03

0.185

0.057

0.038

0.402

11

T4

[0.06,0.139]

[0.048,0.138]

[0.089,0.169]

[0.085,0.156]

0.048

0.169

0.039

0.041

0.511

5

5. IMPLICATIONS AND DISCUSSIONS Based on the results presented in the previous section, we could identify the causal group of impactful factors using the grey DEMATEL technique. These are ‘Technological skills’, ‘Resources estimation’, ‘Inappropriate choice of PLM system’, ‘Lack of infrastructure’, ‘Mismatch in required data formats’, and ‘Lack of systems integration’. The study further reveals that the factors ‘Cultural changes’, ‘Improper data migration’, ‘Data security’, ‘User acceptance’, ‘Technology-process alignment’, and ‘Technologybusiness alignment’ are the affected risk factors. They do not have a significant impact on other risk factors. Moreover, among the clustered dimensions, technology-related risks are found to be causally related to the other two dimensions: human-related risks and process-related risks. The impact of each causal factor on the remaining factors is discussed as follows: 

‘Technological skills’ are needed to better utilize PLM systems. In the case of most manufacturing organizations, PLM systems are purchased from an outside vendor. In turn, the organizations remain dependent on the external vendors and don’t develop the technical skills within the organization. This dependency invites a risk that may negatively impact the implementation of PLM systems and increase the customization and implementation time.



Improper ‘Resources estimation’ impacts the technological requirements and affects the alignment of technology with business needs. Moreover, the inadequate 24

Journal Pre-proof estimations impact the required IT infrastructure, workforce, and the number of licenses. This is a fatal risk factor that was faced by the case firm—the number of licenses and the required workforce were underestimated, which hampered the implementation and unnecessarily increased the implementation time and cost. Moreover, the non-availability of proper resources may be an obstacle for the proper planning of the desired integration and alignment of the new technology with processes and business strategies. 

‘Improper choice of PLM systems’ has the potential of severely prohibiting effective PLM adoption. The PLM systems must be chosen carefully, as this is a preliminary requirement of PLM adoption and fulfilling the business’s need. If the adopted systems are not aligned with the business, the business will no longer be able to maintain the user’s interest. This hurt user acceptance and the successful utilization of PLM systems in organizations (Singh and Misra, 2018a). This risk may not even be realized in the entire PLM implementation process. Only the previous experiences and expertise can help to take care of this risk. This risk directly hits the whole purpose after the investments on inappropriate PLM systems.



‘Lack of infrastructure’ affects the information flow among all stakeholders by hampering the data migration among the enterprise systems, which impacts the firms’ performance. This risk can be eliminated by upgrading the existing infrastructures at the appropriate time.



‘Mismatch in required data formats’ causes improper data migration, which may require additional efforts to convert the data into a coherent format. Such risk can directly cause user resistance.



‘Lack of systems integration’ calls for working on different enterprise systems separately. This risk has been faced by the case company. A single system is utilized 25

Journal Pre-proof to work on PLM systems. It is not integrated with other systems such as CAD, CAM, CATIA, and ERP. Employees need to use separate CPUs to work on different enterprise systems. Moreover, this calls for manually re-entering the list of parts and other product-related data. The lack of system integration is considered one of the primary reasons for the limited use of PLM systems (Singh and Misra, 2018a). 

‘Cultural change’ is observed as the most affected risk factors. It concerns the work culture of the organization, which is associated with the behavior of employees and senior management towards the adoption and use of the new technology.

All the causal risk factors impact the risk of resistance to accept and change the culture, but to a large extent, user acceptance of the new technology depends on the organizational culture. Hence, all these causal risk factors deserve special care and priority from the inception of PLM adoption in order to get the maximum benefits of PLM. Further, the integration of ANP and grey TOPSIS helped to rank all the risk factors. Table 12 in the preceding section shows the rank of each risk factor based on its performance

index.

‘Technological

skills’,

‘Technology-process

alignment’,

‘Technology-business alignment’, ‘Resources estimation’, and ‘Data security’ are ranked among the top 5 risk factors. The risk indices indicate the serious need for a mitigation plan for action to deal with such risks. Because the process of PLM adoption and the subsequent transformation is very long, it requires sustained efforts. The risk weights computed in the present study serve as clear indicators of the risks’ gravity. They guide the risk management during PLM implementation and the eventual transformation that takes place in organizations. The risk index of a risk factor is likely to vary at different stages of the transformation, so it is necessary to evaluate and examine the risk factors at regular intervals throughout the transformation process. This study suggests that the evaluated risk indices should be 26

Journal Pre-proof circulated among the team members who are associated with the work. This will enable the team to take the necessary care to eliminate risks so that the organization can expect better success in PLM implementation and its institutionalization. 6. CONCLUSION Risk identification is an important component of an organization’s decision-making process. In the present study, an integrated model has been developed for the DEMATEL, ANP, and TOPSIS techniques by using the grey concept. This enables us to consider the subjectivity and uncertainty of inputs when identifying the risk factors in a grey environment. The DANP hybrid technique was found to be useful in determining the weights of different risk factors, while the grey TOPSIS technique is found appropriate to identify the ranks of risk factors by considering their attributes. The risk index of the individual risk factors can be used by industry managers to finalize the mitigation and improvement plans. Although the analysis can be improved by collecting inputs from more experts and by considering more organizations, this study establishes the risk factors associated with the implementation of PLM systems and verifies the success of the proposed integrated decision-making model. Researchers may also find the results of the present study useful to validate results of more complex studies in the future. The present study is an illustration of the proposed research techniques to investigate risk factors associated with the implementation of PLM systems in a single firm. In order to conduct a more general study, this study may be extended to a bigger group of PLM experts from different firms. This work concludes the successful illustration of the integrated research model. This model can be further applied in many other areas to identify and investigate the risk factors and their roles. APPENDIX: Survey Questionnaire for Illustrative Study in an Aircraft Manufacturing Firm

27

Journal Pre-proof Brief of Survey: The survey is to investigate the interrelationships among the identified risk factors and their dimensions. The first row defines the impact level with which a factor in column affects the factor in the rows. A row starting with P1 signifies the impact of P1 factor on the factors listed in each column. Similarly, a column named P1 reflects the impact received by P1 from other factors. Impact Assessment of Risk factors: Factors H1

Cultural changes

H2

User acceptance

H3

Technological skills

P1

Technology-Process alignment

P2

Improper data migration

S1

Resources estimation

S2

Technology-Business alignment

S3

Inappropriate choice of PLM system

T1

Lack of infrastructure

T2

Lack of systems integration

T3

Mismatch in required data formats

T4

Data security

H1

H2

H3

P1

P2

P3

P4

P5

T1

T2

T3

T4

… … … … … … … … … … … …

Impact Assessment of Risk Dimensions: Dimensions

R1

R1

Human-elated risks

….

R2

Process-related risks

R3

Technology-related risks

R2

R3

…. ….

Assessment of Risk Attributes: Factors/Attributes H1

Cultural changes

H2

User acceptance

H3

Technological skills

P1

Technology-process alignment

P2

Improper data migration

P3

Resources estimation

P4

Technology-business alignment

P5

Inappropriate choice of PLM system

T1

Lack of infrastructure

T2

Lack of systems integration

T3

Mismatch in required data formats

T4

Data security

Type of risk

Probability

Impact of occurrence

Mitigation

Assessment Scale:

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No influence=No

Very low influence=VL

Low influence=L

High influence=H

Very high influence=VH

For Type of Risk: For Probability:

Strategic=S

Tactical=T High=H

Mix tactical & operational=TO Very high=VH

Operational=O

Low=L

Mix strategic & tactical=ST Medium=M

For Impact of Occurrence: For Mitigation:

Low=L

Medium=M

High=H

Very high=VH

Extreme=E

Very easy =VE

Easy=E

Medium=M

Difficult=D

Very difficult=VD

Extreme=E

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Research Highlights 

Identify associated risks with implementation of PLM systems.



Ranks risks based on their individual risk indices

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MCDM techniques used include Grey-DEMATEL, DANP and Grey-TOPSIS

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Technological skills, resource estimation & technology process alignment as top risks to PLM systems implementation.