PLM today is Internet of Things (IOT). It both affects and is affected by every single thread in the organization. One of the largest area where clients spend money is – How to design a robust PLM process in the organization? This cost is recurring as it varies based on the opinion and expertise of the various consulting firms. It is also highly dependent on the PLM tool that the client selects. Traditionally, a PLM programs starts with a client’s wish-list. This wish-list is transformed into requirements, which in turn are translated into the customizations and configurations in the PLM tool that the client has chosen. The only place where data plays a role is during data migration. PLM Data science is no where in the picture.
There are vast data resources available with the client. This data is in both connected and disconnected form in the PLM and connected system. What if we are able to connect the dots in the PLM process using this data? There are endless possibilities one can think off – predictive analytics, market analysis, R&D analysis, supplier and sourcing prediction, Inventory Prediction etc., to name a few possibilities.
This data science based PLM transformation approach will have many advantages. One can foresee the future and make the current process flexible enough to accommodate that change. Risks, security, compliance and regulatory requirements can be easily connected using data science. Historical data can help us in avoiding mistakes that we have done in the past.