**6. Discussion and further research**

PLM systems assemble and manipulate product descriptions, maintaining a product model. These descriptions come from many sources in the product development process including design, simulation, test and field data. To some extent the timely availability of descriptions is dependent on the process model used to organise and manage tasks. This chapter has addressed this issue through examining how a change to process models through integrating activities has an impact on PLM descriptions.

**References**

2011, Jul 28

Springer; 2016:1-35

org/10.1007/s00163-018-0295-6

Aided Design. 2011;**43**(5):464-478

Springer; 2017:267-278

2017;**28**(3):171-204

2010;**73**

041002

Engineering Design. 2018;**29**(2):161-202

Manufacturing Technology. 2004;**53**(2):643-655

Berlin, Heidelberg: Springer; 2008:375-389

[1] Karniel A, Reich Y. Managing the Dynamics of New Product Development Processes: A New Product Lifecycle Management Paradigm. Springer Science & Business Media;

Testing and PLM: Connecting Process and Product Models in Product Development

http://dx.doi.org/10.5772/intechopen.80364

91

[2] Stark J. Product lifecycle management. In: Product Lifecycle Management. Vol. 2. Cham:

[3] Tahera K, Earl C, Eckert C. A method for improving overlapping of testing and design.

[4] Tahera K. The role of testing in engineering product development processes [PhD Engi-

[5] Tahera K, Wynn DC, Earl C, et al. Research in Engineering Design. 2018. https://doi.

[6] Wynn DC, Clarkson PJ. Process models in design and development. Research in

[7] Srinivasan V. An integration framework for product lifecycle management. Computer-

[8] Takata S et al. Maintenance: Changing role in life cycle management. CIRP Annals-

[9] Daily J, Peterson J. Predictive maintenance: How big data analysis can improve maintenance. In: Supply Chain Integration Challenges in Commercial Aerospace. Cham:

[10] Shabi J, Reich Y, Diamant R. Planning the verification, validation, and testing process: A case study demonstrating a decision support model. Journal of Engineering Design.

[11] Engel A. Verification, Validation, and Testing of Engineered Systems. John Wiley & Sons;

[12] Krishnan V, Eppinger SD, Whitney DE. A model-based framework to overlap product

[13] Moullec M-L et al. Toward system architecture generation and performances assessment under uncertainty using Bayesian networks. Journal of Mechanical Design. 2013;**135**(4):

[14] Lévárdy V, Browning TR. An adaptive process model to support product development project management. IEEE Transactions on Engineering Management. 2009;**56**(4):600-620

[15] Bufardi A, Kiritsis D, Xirouchakis P. Generation of design knowledge from product life cycle data. In: Methods and Tools for Effective Knowledge Life-Cycle-Management.

development activities. Management Science. 1997;**43**(4):437-451

IEEE Transactions on Engineering Management. 2017;**64**(2):179-192

neering and Innovation]. Milton Keynes: The Open University; 2014

The main argument of this chapter is delineating further the relationship between the product models of PLM and the process models for planning product development. Karniel and Reich [1] make the case that product models of PLM, updated throughout product development, have the potential to drive the planning of adaptable and dynamic processes for product development. Along with other research (e.g. [14]), they develop methods and algorithms to derive dynamic process models from the updating product models of PLM. This view gives, in a sense, a priority to the product models of PLM. The 'new paradigm' of Karniel and Reich [1] provides a critical role for PLM in planning dynamic processes. Updated product models in PLM are used to update process models. Although, in many industry contexts, available information in PLM and other information systems is necessary for the management and organisation of the dynamic processes of product development, which are by nature contingent and dependent, it is not sufficient. There are imperatives and opportunities in managing processes can drive the modes and forms of information available to PLM.

This chapter has examined one aspect of this mutual dependency between product and process models. Making changes to process models through increasing the integration of test, simulation and acquiring field data, changes the requirements for product models and associated PLM systems. This research adds to the understanding of ways that process models drive the types of PLM systems necessary to support them. It complements the extensive body of research on the how PLM systems can drive dynamic and adaptable process models for product development. Considerable further research is required both in theoretical methods and in industry cases to optimise the costly and time consuming processes of testing, simulation and field data collection as well as integrating them with PLM systems.
