Your Reliability Engineering Professional Development Site
In the intricate landscape of modern manufacturing, the efficiency and quality of the production system are paramount. These two pillars underpin a company’s competitiveness, profitability, and customer satisfaction. A critical factor influencing these metrics is the maintenance strategy employed. This research delves into the profound impact of maintenance strategies on production efficiency and quality, exploring various approaches, their implications, and real-world case studies.
The project hasn’t been going too well; KPI’s indicate problems, staff are demotivated, the customer is complaining, the schedule is in double digit revisions, rework and resubmissions reflect quality, and you may be on your third or even fourth project manager, and cash is all but flowing. Phew…what will happen next?
Despite these symptoms the project is allowed to struggle on until, one day, one brave soul has the courage to admit that enough is enough! The self-help applications of Band Aids and a couple of paracetamols obviously haven’t worked. Making an appointment to see your local GP or even a trip to the emergency room at this juncture may also be a tad late. It’s time for a surgeon or, in project terms, a troubleshooter.
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Many managers think that high equipment reliability needs a reliability mindset. They think you need the right beliefs and values to get reliability. Reliability requires both correct thinking and correct behaviour, but behaviour is by far the most important. Your plant and machinery will deliver outstanding reliability only if their parts are not heavily stressed. Reliability is the result of properly doing the right actions to the parts in your machines—the right belief comes later, once the evidence is in. You make your machines and equipment reliable; you do not think them into being reliable with a good attitude. You do not need to have the right mindset to get highly reliable machines; you only need to deliver to your machinery parts the right environment for high reliability. If reliability is mostly the result of the behaviours that you do, it means that great reliability can be created everywhere.
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People claim poor correlation of predicted and observed MTBFs. That is understandable because handbook failure rates and fudge factors for quality and environment were derived from unknown populations or samples. People also claim there is no basis for applying statistics or probability to MTBF predictions. MTBF predictions use failure rate averages that lack statistical causation. Why not incorporate Paretos in MTBF predictions?
Paretos are fractions of equipment failures caused by each type of part or subsystem. They represent what really happens. Incorporating Paretos requires statistics to adjust MTBF predictions. That causes Paretos in MTBF predictions to match field Paretos. A 1992 ASQ Reliability Review article “MIL-HDBK-217G” proposed using observed Paretos to adjust handbook MTBF predictions with a “Reality” factor.
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Dear friends, Institute of Quality and Reliability is happy to release this video on Reliability Sampling Plans. In this is Part-1 of the video, Hemant Urdhwareshe has explained the basic concepts in Sampling plans. These include Sampling Risks and Operating Characteristics. We are sure, viewers will find this video useful!
We will release part-2 of the video where Hemant will explain Fixed Length Reliability Test Plans and Sequential Test Plans (PRST).
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In this video, George discusses how clean, dry, well balanced, and consistent air can be a huge opportunity for continuous improvement in a facility. If you’re looking to improve the efficiency and reliability of your facility, you won’t want to miss this!
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Underpinning the coherence of statistical process control, process capability analysis and numerous other statistical applications is a phenomenon found throughout nature, the social sciences, athletics, academics and more. That is, the normal distribution, or less formally, the bell curve. Because of its ubiquity, this normal distribution is arguably the most important data model analysts, engineers, or quality professionals will learn.
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A brief introduction to the statistical hypothesis test called the t-test. Useful when examining if there is a difference between the means of two groups.
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The MTBF calculation is widely used to evaluate the reliability of parts and equipment, in the industry is usually defined as one of the key performance indicators. This short article is intended to demonstrate in practice how we can fool ourselves by evaluating this indicator in isolation. [Read more…]
In a previous CERM Insights I mention ESG (Environmental, Social, Governance) Risk. The U.S. and European governments are starting to stress ESG risk management. This article looks at the proposed ESG requirements of the Security and Exchange Commission (SEC), and the implication for quality management.
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Value Driven Maintenance is a financial modelling method to pick maintenance process improvements. Used alone VDM gives you a “starry-eyed” view of maintenance savings. Once you combine VDM with Plant Wellness Way system-of-reliability analysis you get practical solutions.
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This is my second video on Sample Size in Reliability Testing! In this video, we will explain the Weibayes Approach to estimate sample size and estimating test length when sample size and shape parameter is known.
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Insert these pages into your copy of MIL-HDBK-217. The boldface text is changed to MIL-HDBK-217E [1], section 5.2, on parts count reliability prediction. The changes explain how to use “Paretos,” proportions of parts failing in the field, to compute a reality factor that makes predicted Paretos match field Paretos. You can use field Paretos to calibrate predictions for new equipment. You probably have field Paretos on related parts used in your other equipment, which is now in the field. Remember, the field determines reliability.
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I have a service that regularly monitors my home for termites. Using RCM, let’s determine if this Condition Based Maintenance task is both technically appropriate and worth doing.
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The bottom line is reliability engineers must balance honesty and transparency with the pressure to meet business and customer expectations. The second E in the FINESSE fishbone diagram stands for Ethics. Three types of ethics are virtue, consequential, and duty-based. The most important aspect is understanding your ethical framework as you make decisions and communicate to others as a trusted advisor. These three tips will help you improve.
