The Value of Robust Materials Information Management

Centrally managing materials information in a holistic manner across the enterprise is critical for manufacturing organizations ultimately seeking to enhance products, processes and profitability. Selecting and applying the right materials data is not just for R&D or materials engineers – it concerns every design and support activities and is especially relevant to describe all aspects of the materials life-cycle, mechanical, thermal properties, processing and other contextual properties, standards and codes, purchasing information, security, source and export traceability, certification, product management and other sub-functions.

Materials Life-cycle Management is integral to effective end-to-end Product Life-cycle Management (PLM).

Effective materials information management can contribute to avoidance of problems and risks:

  • Valuable, consolidated, rationalised (concise but exhaustive), centrally managed and up-to-date materials, including properties which are fit-for-purpose to enable Digital Engineering and Simulation (kinematics, stress, thermal, etc.).
  • Increased product innovation, quality, integrity, safety, accuracy and full traceability of materials data – from test data to Finite Element Analysis (FEA), from data capture, analysis, deployment and maintenance.
  • Increased efficiency of data management and analysis, saving time and money, and increasing competitiveness.
  • Better accessibility of material data, ‘what you need, when you need it, in the format that you require’.
  • Better management and introduction of new materials.
  • Better knowledge capture for future re-use.

Simulations and materials data represent a huge amount of data to manage, which can be easily lost, often available in pockets, which means that it is difficult and time-consuming to find and understand (it sometimes referred as “engineering archaeology” – this is perhaps why most materials management gurus are PhDs). Challenges related to materials information management typically relate to productivity and data integrity due to:

  • Data stored in different sources.
  • Data losses.
  • Data misuses or lack of use.
  • Data inconsistencies.
  • Data source (un)traceability.

These issues need to be addressed through a combination of good practice, robust processes, and appropriate PLM information systems.

What are your thoughts?

This post was originally published on LinkedIn on 14 February 2015.

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