The Maintenance Jack-knife chart

Photo by Shane McLendon on Unsplash
A hospital ward white-board

How can this idea of “grasping situational awareness at a glance” be extended in the reliability domain?

An industry example of Jack-Knife
Basic Jack-knife showing component labels
Jack-knife showing detectability criticality using plotted point size
  • We could use the B-20 Weibull measure using the inverse of B-20 Age for the frequency of failure. In this previous blog, we discussed how B-20 may be a more informative reliability metric than MTBF or an averaged failure rate.
  • We might include attributes such as Safety or operational factors, material, labour and logistics costs, as well as factors for logistic delay or lead time to the impact score.
  • Colour coding based on Weibull Shape. Parts with failure patterns that are premature, random or aged. It is probably that using B-20 as the major frequency dimension score the premature failure pattern will score highest. Premature failures are predominantly caused by low-quality issues in operations and maintenance. These causes should be preventable by the operating company.
  • Part obsolescence risks may be included, where the supply may become restricted as parts may no longer be available.
Jack-knife combining size and colour for criticality and patterns of failure
  • If there is strong market demand from the organisation’s output and / or commodity prices are high, then the emphasis may be put on improving reliability and availability of the assets downwards on the Y axis. This means primarily focusing on decreasing the likelihood or frequency of failure of the most critical parts. Eliminating defects and premature failure will pay the highest beneficial returns.
  • If the market takes a down-turn and profits are reduced then cost cutting may be the primary focus, whilst maintaining quality. This means focusing at reducing the highest scoring components on the impact dimension along the x axis.
Jack-knife showing support for business strategy and component improvement opportunities

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23 years in operating and maintaining complex plant, 17 years in leading the development of predictive maintenance. Involved with international standards

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The Maintenance Guru

The Maintenance Guru

23 years in operating and maintaining complex plant, 17 years in leading the development of predictive maintenance. Involved with international standards

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