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How to Use SOPs to Achieve Zero Failure Operations

How to Use SOPs to Achieve Zero Failure Operations

To get world class operation and profits your Asset Lifecycle Management System must prevent every opportunity for defect-creation in all life cycle phases, for all your operating equipment

Every physical asset has a lifecycle starting at conception through to its eventual disposal and site remediation. In each phase of the lifecycle errors can occur that cause defects within the asset. In operations with hundreds of assets there easily can be tens-of-thousands of defects. Each defect is a risk that eventually contributes to breakdowns, losses, wastes, safety incidents during operation, and shortens the asset’s service lifetime. Eliminating thousands of defects and risks forever is what Plant Wellness Way EAM does for its users.

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Defining Your RCM Analysis Scope to Achieve the Results You Need

Defining Your RCM Analysis Scope to Achieve the Results You Need

Join me, Nancy Regan, as I guide you through the process of effectively defining the scope of your Reliability Centered Maintenance (RCM) analysis to ensure you achieve your desired analysis goals. In this video, I’ll illustrate the importance of tailoring the RCM analysis scope using a practical example of a Plant Air system. Key Takeaways: • Understanding Scope: Learn how to avoid common pitfalls by appropriately narrowing down your analysis to a manageable size. • Adapting to Environments: Gain insights into how identical equipment can behave differently in varied operational environments and why specifying your analysis scope is crucial for clarity and effectiveness. • Goal-Oriented Analysis: Understand how the goals of your RCM analysis, such as developing a proactive maintenance plan, dictate the scope and focus of your analysis. What’s Next?: Stay tuned for tomorrow’s video where I delve deeper into why accurately identifying the scope of analysis is foundational to successful RCM analysis planning. Subscribe and hit the bell icon to follow along with the “100 Days of Reliability” series and enhance your Maintenance and Reliability knowledge with expert insights. Thank you for watching! Remember, the success of your RCM efforts starts with the right scope!

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Hypothesis Testing Part-2: One Sample t-test, t-distribution, Degrees of Freedom and P-Value

Hypothesis Testing Part-2: One Sample t-test, t-distribution, Degrees of Freedom and P-Value

Dear friends, we are happy to release this second video on Hypothesis Testing! In this video, Hemant Urdhwareshe explains One Sample t-test along with illustrations of Student’s t-distribution. Hemant has also explained the concept of degrees of freedom and p-value in this video.

We recommend viewers to watch Hypothesis Testing, Part-1 video before this video.

We are sure, you will find this useful!

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Sample Sizes for Hypothesis Testing

Sample Sizes for Hypothesis Testing

As an industrial statistics consultant for the past 25 years, I have frequently fielded questions related to sample size determination.  Unfortunately, I have encountered many instances where simple rules of thumb were used for any purpose (like always use 30).  Sample size guidance really depends on what the goal of the study is, the type of data we are dealing with, what statistical method we are using and some other factors as well.  Common activities which typically require sample size determination include:

  • Hypothesis Testing (including Equivalence Testing)
  • Estimation of statistics like means, standard deviations, proportions
  • Calculation of Tolerance Intervals (range of data a process uses)
  • Designed Experiments (number of replicates)
  • Statistical Process Control Charts (e.g. X-bar charts)
  • Acceptance Sampling (to disposition lots or batches of raw materials or finished products)
  • Reliability Testing to estimate Reliability performance
  • Reliability Testing to demonstrate Reliability performance

All these applications require different assumptions and calculations to determine an appropriate sample size.  In this article, we focus on Sample Size determination for Hypothesis Testing.  It is assumed that the reader is already familiar with Hypothesis Testing.

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Why Facilitators Should Ask Questions When They Think They Know the Answer

Why Facilitators Should Ask Questions When They Think They Know the Answer

The urge to ask questions can sometimes feel redundant, especially when you already have the answers. When you’re on the verge of holding back a question you already know the answer to, pause and consider your motives. As a facilitator, what impact are you aiming for? Are you looking to guide the group toward understanding, or are you merely asserting dominance with your knowledge? Assessing the significance of arriving at a predetermined answer versus fostering an environment of collaboration and openness is essential.

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3 Ways to Expose MTBF Problems

3 Ways to Expose MTBF Problems

MTBF use and thinking is still rampant. It affects how our peers and colleagues approach solving problems.

There is a full range of problems that come from using MTBF, yet how do you spot the signs of MTBF thinking even when MTBF is not mentioned? Let’s explore the approaches that you can use to ferret out MTBF thinking and move your organization toward making informed decisions concerning reliability. [Read more…]

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Discoveries on the Path to World-Class Maintenance and Reliability Excellence

Discoveries on the Path to World-Class Maintenance and Reliability Excellence

Learn what you need to know and do to quickly have world class maintenance and reliability excellence in your operation

In 2002 I wrote a small book after visiting Sumitomo Chemicals in Japan called the Japanese Path to Maintenance and Reliability Excellence. It explained the maintenance and reliability philosophy and attitudes that got Sumitomo Chemicals amazingly high chemical process plant uptime.

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Save 20 % of your Maintenance Costs

Save 20 % of your Maintenance Costs

Saving 20% of your maintenance costs is achievable in many operations. Are you running your production equipment to failure? Is your maintenance spending consistently higher than you budget allows? Are you frustrated that breakdowns cause delays in delivery schedules? If you answer “yes” to any of those questions, then savings and production gains are possible. The 20% figure is arbitrary but truly indicative of what is possible. Reliable production equipment is far cheaper to maintain simply because it is reliable, but it must be maintained. It requires the right maintenance being done the right way, and at the right times. Get that right and you save operating costs, AND you gain productive uptime with its attendant revenue.

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Targeted RCM Analysis: How to Identify Equipment That Needs It

Targeted RCM Analysis: How to Identify Equipment That Needs It

Join Nancy Regan in the serene setting of Ave Maria Grotto in Cullman, Alabama, as she demystifies one of the biggest Reliability Centered Maintenance (RCM) analysis misconceptions – the necessity of applying RCM to all assets. In this enlightening video, Nancy draws parallels between the selective craftsmanship of Brother Joseph Zoettl — who created 125 meticulous replicas of the world’s most significant structures — and the strategic approach required for effective RCM. What You’ll Learn:

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Hypothesis Testing Part-1: Introduction and One-Sample Z-Test

Hypothesis Testing Part-1: Introduction and One-Sample Z-Test

Dear friends, Our best wishes to all for a great Quality Month and Year Ahead for your Quality Initiatives! In this Quality Month, we are starting a new series of videos on Hypothesis Testing in our Channel! we are happy to release our first video on Hypothesis Testing! We will be releasing a complete series of videos on Hypothesis Tests! In this first video on the subject, Hemant Urdhwareshe explains the basic concepts and discusses an illustration of One-Sample Z-Test!

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Understanding the Difference Between Statistical and Practical Significance

Understanding the Difference Between Statistical and Practical Significance

Data-driven decision-making is central to designing and improving products and processes. Professionals are often presented with statistical analyses, with key outputs such as p-values or confidence intervals that indicate whether results are “statistically significant.” However, statistical significance doesn’t always translate into meaningful changes on the shop floor or within a product’s design. Understanding the difference between statistical significance and practical significance is crucial to making well-informed decisions that genuinely impact the business.

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Using Contour Plots in RCAs

Root Cause Analysis Overview

A Root Cause Analysis (RCA) is a structured approach to identifying the underlying factors that result in the unwanted/unexpected outcomes of chronic or sporadic events. It is a highly methodic and rigorous process. And highlights what assets, systems or behaviors need to be modified in order to to limit or eliminate the recurrence of similar outcomes. The fundamental driver is to address, correct or mitigate the root causes that lead to the unwanted event rather than addressing the symptoms. Bob Latino, a renowned RCA expert, summarizes this concept as “the establishing of logically complete, evidence based, tightly coupled chains of factors from the least acceptable consequences to the deepest significant underlying causes.”

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