DRIVING VALUE FROM PLM INFORMATION

Are organisations able to extract value from PLM information through insight and analytics?

Summary of Findings

Driving value from PLM Information - Summary of Findings
Driving value from PLM Information – Summary of Findings

Can you access your PLM data to analyse it?

Being able to access the information in PLM through trusted and reliable mechanisms will help organisations realise its value.

Figure 17: Can you access your PLM data to analyse it?
Figure 17: Can you access your PLM data to analyse it?

Key Findings

  • 47% of respondents export to Excel to analyze their PLM data.
  • 19% use data analytic tools provided by PLM vendors
  • 11% of respondents do not use PLM data for analysis due to no access.
  • 15% use 3rd Party analytics tools.

Conclusions

  • Although PLM vendors provide data analytics tools, only 19% are able to use it. A vast majority still use Excel defeating the idea of a single source of truth, increasing operational complexity and developing silos.
  • Data is useless when it cannot be processed. PLM tools must have inbuilt reporting capabilities or integrate with third party analytics tools to utilize data contained in PLM system to bring value to its users.

Does your PLM vendor provide the access and tools to analyse PLM information?

To get to the valuable information inside of PLM, the vendors need to supply simple and intuitive ways both to access and visualise this data in a trusted and secure way.

Figure 18: Does your PLM vendor provide the access and tools to analyse PLM information?
Figure 18: Does your PLM vendor provide the access and tools to analyse PLM information?

Key Findings

  • 53% of respondents state that, while tools are available, access is still complex.
  • 13% of respondents lack tools to extract data from their PLM system.
  • 13% of respondents can easily access the data even without vendor-provided tools
  • 10% of companies cannot access the PLM data

Conclusions

  • Since a majority of companies have difficulty accessing their PLM data with vendor-provided tools, it is interesting to explore the evolution of PLM interfaces.
  • We should try to understand how PLM data is used in the organisations where it cannot be accessed.

What are the main measures and KPIs you take from PLM?

The maturity in the way businesses use information from PLM is directly related to the value that they can return from the system

Figure 19: What are the main measures and KPIs you take from PLM?
Figure 19: What are the main measures and KPIs you take from PLM?

Key Findings

  • 29% of respondents report basic product information.
  • 25% of companies extract information about engineering changes.
  • 14% report component characteristics
  • 13% of respondents do not extract any KPIs from the PLM system

Conclusions

  • Reporting basic product information under-utilises the capabilities of PLM. Organisations must explore key Product Information like quality, services, cost analysis to benefit their customers.
  • It is interesting to understand whether this low utilisation of the available data in PLM is due to difficulty of access, or to the company not understanding what possibilities PLM offers.
  • Since only 14% of respondents extract part characteristics from PLM, we can conclude that for 86% of respondents there is additional value that can be extracted from their existing PLM systems, with relatively low investment.

Does your company’s Big Data strategy have connections to PLM?

If PLM should be the source of a companies product information and knowledge, it is logical that PLM should be connected to a companies ”Big Data” strategy.

Figure 20: Does your company’s Big Data strategy have connections to PLM?
Figure 20: Does your company’s Big Data strategy have connections to PLM?

Key Findings

  • In 24% of the answers, the company’s Big Data strategy disregards PLM.
  • 36% of companies do not have formal Big Data strategies.
  • 25% of companies plan to include PLM in their strategy.
  • Only 15% of companies already consider PLM when planning their Big Data approach.

Conclusions

  • Since only 24% of respondents disregard PLM in their Big Data approach, we can assume that PLM is slowly being seen as more than an Engineering-only tool.
  • For the companies that include PLM in their strategy, it is interesting to further explore what is its place – what data is captured and how is it being used.