Using the End user s Perspective on Product Lifecycle Management Critical Factors to Enhance the Success Rate of the Implementation

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1 Using the End user s Perspective on Product Lifecycle Management Critical Factors to Enhance the Success Rate of the Implementation J. S. Miller Department of Computer Graphics Technology Purdue University, West Lafayette, IN N. W. Hartman, Ed.D Department of Computer Graphics Technology Purdue University, West Lafayette, IN ABSTRACT - Product Lifecycle Management (PLM) is a new business strategy being implemented by many companies. Currently, there is a need is to understand how critical factors affect the end user in a PLM implementation and influence on the overall timeline of the PLM implementation. This leads to two research questions: 1.) In a PLM implementation what critical factors are perceived to influence the implementation?, 2.) How do these PLM critical factors affect the end user s productivity in the overall PLM implementation timeline? To answer these two research questions, a mixed methodology of a qualitative and quantitative approach is suggested to collect the data through a two-part survey. Participants will be selected from the aerospace, automobile, and IT Service & Business Strategy industries as these industries tend to represent the highest levels of PLM toolset investment and implementation. This paper is meant to suggest a methodology that will enhance the success rate of PLM implementations by aiding in the development of project timelines, project costs, and the necessary number of employees. I. Introduction Product Lifecycle Management (PLM) has evolved to more then just thinking about the product itself. PLM needs to be thought of as strategic plan to manage products across their entire lifecycle (Stark, 2005). Thinking of PLM as a strategic plan encompasses the need for a company to complete an assessment of the product development process, including vision, strategy and roll-out plan (Stark, 2005) which leads to understanding present situations, processes, needs, and identifies what outcomes are desired prior to the implementation (Tomovic, Walton, Grieves, Bednar, & Birtles, 2007). An important area of concern in this company assessment is the people and toolsets that are affected by the implementation. This ensures that all the necessary information is collected and distributed properly to enhance the success of the implementation. Understanding of how individuals are affected by a toolset change, as in changing Computer-Aided Drafting (CAD) systems; Kasik, Buxton, and Ferguson in 2005 stated this quote reflecting CAD geometry: A key component of all CAD applications is the end user. A user s success or failure in interacting with CAD products ultimately governs success of the products themselves (p.81). This research is based on PLM implementation critical factors, but as CAD systems are seen as a system component of PLM (Stark, 2005) the quote provides relevance stating a user s performance reflects the success or failure of the implementation. If the end user affects the success or failure of the final products or projects, then there is a need for identifying the critical factors that affect the end users during implementations. After the identification of the

2 necessary critical factors, the end users need to be surveyed on how these critical factors personally affect them. The end users responses need to be organized in a manner that allows for the information to be easily accessible and understandable by others. This will allow information to be used and incorporated into the implementation project timeline, which increases the chances for success of the implementation can occur. II. Review of Literature The topical areas that inform this subject include brief overview of Computer-Aided Design (CAD) systems as a component of PLM and characteristics of PLM implementation critical factors. These two areas provided the necessary framework for this research so that the results would be meaningful to those implementing a PLM strategy. Through the last 30 years, CAD systems have evolved to more than creating production drawings or diagrams (McMahon & Browne, 1993) to creating solid models. Solid modeling stores volumetric information (Bertoline & Wiebe, 2007). CAD systems continue to add new features in an effort to provide useful functionality for their users to capture design intent, a concept of PLM, and the geometric definition of their products (Stark, 2005). These 3D CAD packages contain unique features or toolsets for specific operations which can enhance a company s performance. However, the most substantial impact of the use of CAD technology in PLM is not the creation of the geometry itself, but the ability to leverage the 3D database throughout the enterprise to communicate requisite design information to internal consumers throughout its lifecycle. In investigating PLM implementations, The Boeing Company has invested in such a project with the 787 Dreamliner. In this project, The Boeing Company is the first with the notion to mandate that the entire airplane is to be designed in a single CAD system; the selected choice was Dassault Systemes CATIA V5 (Stackpole, 2007). In this implementation, Stackpole (2007) suggests that one challenge was making CATIA V5 the standard CAD package for suppliers, because of the implementation costs to install CATIA V5 and the learning curve of CATIA V5. This research is intended to provide general end user response on how users are affected by a PLM implementation, but using this example as a case study demonstrate a CAD implementation as a component of a PLM implementation. As CAD is a system component of PLM, it allows a company to create, edit, and view the 3D model which reduces development time and costs during its lifecycle (Stark, 2005). Stark defines PLM as a business strategy from the cradle to grave to explain how PLM manages the product from the conception, and until it becomes obsolete and discarded (p15). As PLM is thought of as a business strategy, it is needed to understand what a obstacles may occur. These obstacles can be classified as critical factors. So an understanding is necessary for what a critical factor is and which critical factors affect an implementation. This will potentially increase the likelihood of a implementation. Critical factors have been topic of discussion for many years, Rockhart (1979) defines them as factors that affect the company performance and need attention in order for the company to remain competitive. In the initial investigation of critical factors on PLM implementations some information was found on PLM implementations. The investigation was expanded to other implementation type including that of Informational Technology (IT) and Enterprise Resource Planning (ERP), suggesting implementation have similar critical factors. The found critical factors were compiled by using triangulation of sources and when the information became saturated it was determined to

3 be a critical factor of an implementation. The identified critical factors were complied and placed into categorical groups for the future use in the surveys which included: Business Plan and Vision, Education and Training, Communication, Management Support, Organizational Culture, Project Management, and Team Commitment. Business Plan and Vision For a new PLM endeavor, companies must have a clear understanding of their present processes though a needs assessment (Imerman, 2004). Within this needs assessment, the areas of concern include current company needs, reason for implementation, scope of implementation, and the necessary resources for the implementation (Stark & SofTech Inc., 2006). This all forms the implementation plan, as it provides the path for the implementation and encompasses the project mission or scope (Nah & Lau, 2001) which may include the possibility of a new strategy or process (Zhang, L., Lee, Zhang, Z., & Banerjee, 2002). Education and Training Education and training is a necessary tool at all levels in the implementation, including end users, middle management, and the top level executives (Stark & SofTech Inc., 2006). This educational training is needed to increase the knowledge of those affected (Zhang et al., 2002) and by re-skilling those affected by the implementation tools so that their job can be completed correctly(nah & Lau, 2001). It also serves to educate other workers in the organizations. Communication One aspect of the implementation of any new design system is communication. Communication is the element that allows for the business strategy and implementation scope to be expressed (Nah & Lau, 2001). Communication not only acts as a critical factor, but is needed to communicate other factors to all levels of the company (Stark & SofTech Inc., 2006). An effective method for overcoming poor communication is to have regularly scheduled meetings or briefings that give the current status of events (Zhang et al., 2002), thereby allowing everyone to be informed of the new processes or tools that are presently available (Imerman, 2004). Management Support Management provides the necessary support for the organizational culture so that each level of the company can view what the higher (and lower) levels are doing (Zhang et al., 2002). Management support is needed during the entire implementation process (Stark & SofTech Inc., 2006). Management support through the entire implementation allows the company to create policy for future uses (Imerman, 2004) or give the mandate regarding project priorities and how the implementation fits with larger organizational goals (Nah & Lau, 2001). Organizational Culture Organizational culture is used and needs to be incorporated into each of the other critical factors. Organizational culture plays the role of allowing values to be shared among others to overcome obstacles for success (Nah & Lau, 2001). Within this organization culture, one area to include is the idea of sharing success stories relative to the implementation (Stark & SofTech Inc., 2006). It is important for people to know when there are successes and challenges. Project Management Project Management touches the entire toolset implementation, and is a place where CAD implementations frequently go wrong. While a single group should be responsible for it (Nah & Lau, 2001), their work impacts many other groups within the organization, especially given the significance of leveraging the CAD database by entities such as analysis, procurement, customer service, and marketing. Within the implementation group, any

4 possible delays should be identified and have an alternative plan to counter their activity (Stark & SofTech Inc., 2006). One important aspect of project management is communication, as it is needed not only for information to develop the project plan but also to explain it to others (Zhang et al., 2002). Team Commitment Just as education and training is important, team commitment is needed at all levels in the company (Zhang et al., 2002). This involves the superiors trusting their employees and coworkers (Imerman, 2004), and it directly affects the trust placed on shared, common information went it is received (Stark & SofTech Inc., 2006). In review, CAD is a system component of PLM that allows user to view the product in 3D through its lifecycle. PLM needs to be thought as a business strategy along with an understanding of what critical factors are and how they affect the implementation. III. Methodology of Collecting Data Survey-based research will be used while trying to answer the two following research questions: 1.) In a PLM implementation, what critical factors are perceived to influence a CAD implementation? 2.) How do these critical factors affect the end user s productivity in the overall PLM implementation timeline? To collect this information, a web-based, two-stage survey will be used. Computer-based surveys can be considered to increase the accuracy of the data collected because software can be used to identify any foreseen errors (Sekaran, 2003). In eliminating possible errors to increase accuracy, computer-based surveys also have the advantage of allowing the researcher to start viewing and analyzing the retrieved data as data collection period is still in progress (Sekaran, 2003). The selected participants for this research are from the aerospace, automobile, and IT service& business strategy industries. These industries were selected were selected for two reasons: 1.) these industries tend to be industry leaders in CAD packages and the needed toolsets 2.) through the researchers past experiences, managerial contacts have been made at leading companies in these industries. The managerial contacts have been contacted and have agreed to allow the use of their employees to serve as participants in this research. In return for their cooperation, a detailed summery will be provided to them upon the conclusion of this research. Measureable variables are involved in both the qualitative and quantitative surveys. The measureable variables in the first survey are defined as the responses provided from the participants. The second survey measurable variable is a numerical response. This numerical response indicates the number of days for the end user to adapt for to the PLM implementation. This numerical response can be viewed as measuring the perceived time needed against the baseline. The baseline is defined as the normal productivity of the user prior to the PLM implementation. For best results, the first survey will be given in two steps. The initial step will be asking each participant to list their personal top five critical factors that affect them during a PLM implementation. The second step uses a forced choice response by asking the participants to rank the critical factors identified in the review of literature. At the completion if the first survey, a correlational analysis will be completed with the data from both the first step and the second step. The anticipated results are that perceived critical factors by the participants will be very similar to those identified through the review of literature. If correlated, the results will provide two outcomes: 1.) it validates the identified

5 critical factors from the review of literature and 2.) will provide the framework for the second survey. If the participants results and the identified critical factors are uncorrelated, the information will not be deemed a failure. This will just demonstrate that the participants perceive a difference in critical factors affecting a PLM implementation. This provides two outcomes as well: 1.) the survey needs to be expanded to many more user to identify if in deed there are undocumented critical factors affecting a PLM implementation and 2.) for in the surveys, the critical factors list needs to be expand to encompasses more than just the seven identified from the review of literature. The quantitative survey will use the correlated critical factors or possibly the new critical factors from survey one and ask for an unstructured numerical response. The numerical response has been defined for the adaptation period for the implementation according to the critical factors. Figure 1 is a graphical representation example to better understand how the numerical response reflects productivity. It is the total number of days that the end user feels that they are below their prior implementation productivity baseline. The response indicates then their productivity meets or exceeds the prior baseline, thus demonstrating an improvement which could lead to the success of the PLM implementation. Figure 1. Example of what end user s numerical response represents In review of the methodology, using the survey approach exposes the critical factors in a PLM implementation. In the first survey, the correlational analysis will help the company increase the chance of a successful implementation by identifying the critical factors. The use of the unstructured numerical in the second survey allows the users to provide an honest response and one that is more relevant to their own experience set. Using this design of collecting the data and analyzing it has been in an attempt to increase the probability of a successful PLM implementation.. IV. Anticipated Outcomes/ Recommendations The results of the second survey will be compiled among all the user/participants according to industry segment creating an average for each group. This average can then be used as a guide for the necessary timeline for the PLM implementation to accurately estimate the needed time for the implementation. At the completion of each industry analysis, all three industries are to be compiled in attempt to provide a generic guideline. These data may provide the following things: Project costs can be more accurately projected, due to more accurate timelines and the ability to predict factors causing delay. Information on the number of employees needed for an implementation may also arise from this survey. This can be enhanced by using the statistical averages for the timeline and can help determining the total number of employees. Understanding the anticipated results of enhancing the implementation timeline and the other relevant information can be viewed as a triangle, where each side affects the other. Figure 2 demonstrates how the timeline can affect the project costs by determining how aggressive or relax the finish date is. Where the project cost can have an effect on the total number of employees available for use and affect the finish date

them. Rockart, J. F. (1979). Chief Executives Define their own Data Needs. Harvard Business Review, 57(2), 81-93. Sekaran, U. (2003). Research Methods for Business: A Skill Building Approach (p. 464).

6 by stating when the budget should cease. Last, the number of employees affects the implementation timeline by how quickly the employees can adjust and the number of employees involved means the budget should accommodate the pay for all of them. Rockart, J. F. (1979). Chief Executives Define their own Data Needs. Harvard Business Review, 57(2), Sekaran, U. (2003). Research Methods for Business: A Skill Building Approach (p. 464). John Wiley & Sons. Stackpole, B. (2007). Boeing's Brave New World of Product Development. Design News, 62(8), Stark, J. (2005). Product Lifecycle Management: 21st Century Paradigm for Product Realisation. Springer- Verlag London Limited. Stark, J., & SofTech, Inc. (2006). Top 10 PLM Pitfalls to Avoid (pp. 1-13). SofTech, Inc. and John Stark Associates. Figure 2. Anticipated Result Triangle. Using the suggested methodology, the anticipated results will increase the chance of success because the end user is taken into account. Too many times a timeline is proposed and has unrealistic goals for getting the project done. This research is intended to provide a guide or template to help educate those creating such schedules and thus increase the chance of success for the PLM implementation through the involvement of the end users. Tomovic, C., Walton, A., Grieves, M., Bednar, B., & Birtles, B. (2007). Measuring the Impact of Product Lifecycle Management: An Assessment Model and Metrics Framework. (Technical Paper: TP07PUB170), Society of Manufacturing Engineers. Wiebe, E.N. (1999). 3-D constraint-based modeling: Finding common themes. The Engineering Design Graphics Journal, 63(3), Zhang, L., Lee, M. K. O., Zhang, Z., & Banerjee, P. (2002). Critical Success Factors of Enterprise Resource Planning Systems Implementation Success in China. In Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 8 - Volume 8 (p. 236). VII. References Bertoline, G.R., & Wiebe, E.N. (2007). 3-D Solid Modeling. In Fundamentals of Graphics Communication.New York: McGraw-Hill. Imerman, J. (2004). Steps for Success and Reliability in PLM Implementations. Huntington Bearch, California. Kasik, D. J., Buxton, W., & Ferguson, D. R. (2005). Ten CAD Challenges. IEEE Computer Graphics & Applications, 25(2), McMahon, C., & Browne, J.(1993). CAD CAM, from principles to practice. Addison Wesley. Nah, F. F., & Lau, J. L. (2001). Critical factors for successful implementation of enterprise systems. Business Process Management Journal, 7(3),

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