The benefits of Model-Based Definition (MBD) are well known. Considering this there is a need to understand the reasons why MBD is utilized rarely in the manufacturing ecosystems of low volume, customized products. For studying MBD, manufacturing ecosystems and their mutual relations a literature study and a case study of manufacturing ecosystem were applied. It was noticed that there are differences between the maturities of digital product processes between the actors of the ecosystem, while MBD itself is a matter affecting the whole ecosystem. Investing in education and renewing, integrating and harmonizing processes, legacy data and software lower barriers towards adopting MBD. The motivation of the actors of an ecosystem has to ensure investments and commitment to change. The investment decisions require an ecosystem wide value definition and business case for each actor. The role of an actor within an ecosystem, how common or niche the utilized product and production technology is and the economic situation of a company appear to impact companies' willingness to change into MBD. Also, the motivation to adopt MBD depends on the amount of legacy data and the complexity of manufactured parts. Adopting PMI-data can increase the efficiency of manufacturing processes, such as quality control.

The paper focuses on comparative experiment on manufacturing and inspection of two different prismatic one-off parts, which have different complexity. Our experiment shows that transforming product definition method from the Drawing Centric Definition (DCD) to the Model Centric Definition (MCD) enables 28%-29% time savings in manufacturing and inspection phases of machined one-off part's life cycle. Furthermore, transition from MCD to Model-Based Definition (MBD) enables 5%-9% time savings, respectively. Applying of MBD enables more time savings in complex part compared to a less complex part.

Model-Based Definition provides several benefits for communicating between engineering and other downstream stakeholders. Particularly, semantic PMI information included in 3D models benefits both CAM programming and inspection phases. However, the efficiency of generating CAM- and CAI codes automatically according to the semantic PMI information varies due to the compatibility of different systems. This paper focuses on the experiment that we made on inspecting three different parts with the Coordinate Measuring Machine (CMM). We compared four different programming methods and found that the efficiency of inspecting depends on the serial size of the parts. The completely automatic CAI-programming method does not necessarily produce the most effective CAI code compared to the competent human programmer. This is notable, especially in large series due to achieved cumulative time savings in the inspection of each part. With small series, fully automatic PMI method and human-assisted automatic PMIfp method provide significant benefits in the inspection process due to time savings in CAI-programming work.

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