Businesses often have difficulties achieving end-to-end transparency and forward and backward traceability along the entire digital product lifecycle. They typically face technical and organizational challenges and must change processes as well. Additionally, they face cost pressures due to increasing product individualization and complexity, a shortened time-to-market and innovation cycle, while they must also meet the different needs and market regulations of their global customers.

With our 360° Product Lifecyle Management (PLM) approach, we leverage digital transformation and process automation along the entire value chain. We cover all relevant PLM methods, processes, and systems solutions and can integrate PLM holistically into upstream and downstream processes and IT systems:

Product lifecycle management (PLM)

Product lifecycle management is more than the simple product creation process (PEP): it also goes beyond traditional boundaries. It supports businesses in the integration of sales and marketing and cross-system master data concepts up to the manufacturing, service, and maintenance of your products in PLM, ERP, MES, and MRO systems. PLM is the catalyst for the Internet of Things, Industry 4.0, and Digital Twins. PLM creates transparency, traceability, and the ability to convert product-describing data and further insights into data-driven actions and decisions.

  • PLM strategy alignment – Derivation and evaluation of processual and technological architecture considering client-specific needs and capabilities of selected solutions and their best practices
  • PLM architecture – Definition of PLM IT landscape
  • PLM roadmap and transition – Create a roadmap to achieve PLM excellence and define a concept for a smooth introduction and rollout

Master data management (MDM)

Robust master data management inputs, validations, and rhythms are generally essential in underpinning accuracy throughout an organization – the BearingPoint approach identifies and quantifies opportunities within:

  • Data governance – Defining and implementing a framework of rules
  • Data lifecycle – Designing and implementing rhythms and processes
  • IT architecture – Identifying and selecting appropriate tools to integrate with existing systems
  • Data quality – Implementing appropriate KPIs to continuously measure the data quality and create insight to initiate any data cleaning initiatives or interventions

Would you like more information?

If you want to get more information about this subject please get in touch with our experts who would be pleased to hear from you.

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