Today, the concept of Product Lifecycle Management (PLM) is widely accepted as strategically important. It is used to manage the increasing complexity of products, processes and organizations. The need to adopt PLM is growing rapidly for Small to Medium-sized Enterprises (SME). PLM implementations are costly and require a lot of effort. The business impact and financial risks are high for SME. Also, SMEs seem to have relatively more difficulties to benefit from PLM. The study at hand addresses the question, based on literature research, why these difficulties exist and how they can be overcome. To answer that question, three sub questions are discussed in this paper. 1) A generic PLM implementation process structure. 2) A list of identified PLM implementation challenges, specific for SME. 3) A classification of PLM research for SME, related to the common PLM implementation process structure. A hypothesis for a PLM implementation failure mechanism in SMEs is formulated, based on the findings. Also, a potential research gap on operational implementation knowledge in SMEs is identified.

In many cases PLM implementations are halted in the first phases of larger projects. On average, implementation projects take longer, cost more than planned and not all goals are achieved despite modern software implementation methods like Agile or Scrum. This paper proposes another approach, in which the implementation method is inspired by product development methods in general and set based concurrent engineering in particular. The method is structured in five major steps alongside a method of knowledge management and reuse to support the implementation method. The five steps deal with scope and maturity level, requirements analysis, process mapping, rationale based solution selection and system consolidation. The element of knowledge reuse makes this method also accessible for small and medium sized companies, generally reluctant to conduct a fundamental process analysis before starting a software implementation. From there this knowledge can evolve towards a product configuration framework for PLM implementation. The paper outlines the method in theory and proposes further steps to investigate each step in more detailed research and case studies.

Research on the implementation of Product Lifecycle Management (PLM) has been published since the beginning of the 21st century. Some researchers claim that the success rate of PLM implementation projects is below 50%, but the authors have found no evidence of that figure. In this paper's research, a number of PLM implementation cases have been analyzed for their project goals, implementation challenges, and project results. The research data are retrieved from project files and interviews with project managers. The investigated implementation cases are in Small to Medium-sized Enterprises (SME). The results have been structured and compared with findings from the authors' earlier literature research on SME specific implementation challenges and recommended implementation methods. From this comparison, a conclusion is drawn regarding the implementation success rate and a hypothesis for causes of observed failure.

Product Lifecycle Management (PLM) has matured over the last two decades. The software and the market have evolved, resulting in a small number of dominant vendors offering an array of functionality in their software platforms. Moreover, PLM software is becoming available in the cloud as a service (Software-as-a-Service, SaaS). These developments impact the Value Added Resellers (VARs) who deliver software and services to the users of the PLM software. The VARs need to focus more on their added value by providing contextual application knowledge for PLM software in specific business environments. The management of this knowledge is a challenge for VARs because this relies strongly on tacit, empirical knowledge that resides in the heads of their staff. This paper describes a methodology that uses Benefit Dependency Networks to capture, manage, and reuse this knowledge. The methodology is based on the literature and then modified with insights from case studies with a PLM VAR. All relevant contextual elements in an extended Benefit Dependency Network of a PLM implementation are registered in a Graph Model, allowing consultants to find answers within a given industry context and configure a customer-specific implementation plan. The paper also describes a software design built on a Graph Database to use this knowledge in an operational VAR environment and build a foundation for future machine learning in implementation knowledge.

Industrial companies face significant challenges when they engage in the implementation of Product Lifecycle Management. Research has shown that organizations have difficulties in defining concrete and measurable goals and relating enabling technology to business benefits. Moreover, implementation service providers rely heavily on tacit knowledge when it comes to operational details. This paper proposes a conceptual framework as a methodology for implementation teams. It allows teams to reuse implementation knowledge on a detailed level, related to contribution to benefits and business goals. The methodology is derived from emerging, set-based product and process development methodologies and also from benefit management strategies for information systems. The goal of this methodology is to increase the probability that Product Lifecycle Management implementation contributes to the business benefits of organizations and therefore lower the economic risks. The paper describes the method and the result of two explorative case studies.