Habit #4: Decide with Data with Peter Horsburgh
It’s my pleasure to welcome back Peter Horsburgh. He is the author of the ‘Five Habits of Extraordinary Reliability Engineers.’ He has a long history within reliability engineering. He essentially started in manufacturing, then moved to mining, then to power generation, and to aluminum smelting.
In this episode we covered:
- Now we’re here to discuss habit number four; decide with data. So, what does this mean to you?
- You mentioned data-driven debate. What is it?
- In the book, you said problem solving should follow a framework. You mentioned problem-cause-solution-value framework. Can you elaborate a bit on that?
Role of Data in Reliability
With the advent of Industry 4.0, data and connectivity have enabled equipment to become continuous factories of data. Whether process data, master data or realtime data, it has never been a more challenging time to adjust and harness the power of seemingly huge piles of potential information. Traditional maintenance and reliability are undergoing a tremendous shift as organizations become more data-driven. Sean Rosier and Nathanael Ince of PinnacleART are on the show to help us put in context, the relevance of data in reliability.
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Leveraging Failure Data for Better Decision Making with John Reeve
Failure data is very important for leaders to make effective and efficient decisions. That’s why they are always looking for industry best practices and new implementation techniques to understand failure codes and failure modes in a better way. The organizations always focus on understanding failure codes so that they can design their CMMS. They always configure their CMMS to better analyze the failure data. They have a process in place that allows them to run failure data against different fields based on the failure codes. Failure data must be synchronized to hold RCM analysis results. CMMS can be built in way that it perfectly serves the purpose.
In this episode, we covered:
- What is failure data?
- How can we get failure data out of the CMMS?
- What can we do with failure data?
- and much, much more!
Sensor Data From Manufacturing and Product Sharing with Vendors
Tim and Fred discuss the feedback loop that connects field performance to the production processes at the supplier, and how advanced sensing technology provides more data to all parties, but doesn’t necessarily lead to greater knowledge or improved reliability.
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What is a Monte Carlo Analysis with Fred Schenkelberg
In today’s episode, the guest Fred Schenkelberg explains the Monte Carlo simulation in a fair amount of detail. Before you get in the depth of how the tool works, you need to understand what basically this method is. The Monte Carlo simulation has been around since World War 2 and it is a mathematical technique that works based on probability functions, random variables, and the distribution of statistical data. The main concept of it to give the decision maker the most obvious choices while facing any risks to get the best out of every possible outcome. The tool serves the purpose for getting a better insight of the consequences relative to each choice the person making decisions has to make. When you’re looking for the reliability of your assets and checking the integrity of your different maintenance programs, it is a really powerful tool.
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Failure data and the CMMS with John Reeve
In today’s episode, the guest is John Reeve and the agenda of discussion is failure data and CMMS. As everyone knows that to maintain and solve issues with the equipment you need to get all the basic data or information available. Computerized maintenance management system helps greatly in this task but first of all, you have to know exactly what kind of data are you looking for and what it means. Now failure data can be anything related to your asset that is the foundation data or then transactional data comes which includes the processing data related to the asset such as the actual cost. Now, next thing to understand is the failure mode which consists of the failed component, component problem and cause code. Once you have this failure data, it becomes very easy to run the basic failure analysis which is necessary at certain levels.
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