In this article, we reference Doug Plucknette's top 5 signs of a reliable plant, hypothesise that top performing sites are more likely to use a predictive maintenance software.
Executive Summary / TLDR to help decide if reading this article will be valuable to you:
Doug Plucknette's 5 Signs of a Reliable Plant: In this article, we make reference to Doug Plucknette's top 5 signs of a reliable plant, based on his extensive experience in manufacturing. These signs include a clean environment, effective Management of Change (MOC), measurement of reliability, adherence to Standard Operating Procedures (SOP), and planned and scheduled maintenance.
Reliability Measurement (Sign 3): The article highlights the importance of measuring reliability in top-performing plants, as indicated by the ability to answer questions about asset reliability metrics. It acknowledges the challenges in obtaining reliable measurements and suggests that using predictive maintenance software can help address these challenges. We hypothesise that top performing sites are more likely to use a predictive maintenance software like Factory AI’s platform given it will offer them these key reliability measurement points.
Maintenance Planning and Scheduling (Sign 5): We emphasise the significance of using data in maintenance planning, scheduling, and execution in achieving high reliability. We note that trusted leaders in the field agree that Predictive Maintenance (PdM) should represent a substantial portion of the maintenance workload and discusses the challenges faced by some teams in reaching this level.
Role of Predictive Maintenance (PdM): We suggest that starting a Predictive Maintenance proof of value can be a low-risk way to enhance reliability and reduce maintenance costs. We mention that initial maintenance costs may increase but should eventually decrease as asset reliability improves.
In a timeless article from 2015, Doug Plucknette, formerly associated with Allied Reliability Group, shares his top 5 indicators of a reliable plant. Drawing on over 15 years of experience touring manufacturing plants, Doug's insights serve as a beacon of guidance. Despite his retirement a year ago, his enduring passion for reliability shines through his ongoing LinkedIn posts.
Explore Doug's original article [Link here] to glean wisdom from observations like the intriguing "dumpster mention," showcasing the seasoned professionalism inherent in his perspectives.
Doug's insights set the stage for our exploration into the realm of reliability, emphasising that in top-performing manufacturing sites, reliability is a measured. In this context, we assert that tools such as continuous condition monitoring and predictive maintenance software are integral components of effective measurements.
Our goal in this article is to help shed more light into how exactly continuous condition monitoring and predictive maintenance software help you measure reliability.
Let’s dive in.
Author's Note: It's important to note that I, the author, am not an experienced reliability engineer. I’ve spent the past year immersing myself in the subject through extensive reading, including recommendations from our readers [A RE I spoke with even shipped me his copy of Ramesh Gulati's Maintenance and Reliability Best Practices!], and remain a perpetual student of reliability engineering through our engagements with our customers. We, at Factory AI, collaborate with diverse reliability teams across the greater Sydney area and Australia, spanning industries such as food and beverages, building materials, and packaging. Our goal is to elevate uptime and reduce maintenance expenses through the implementation of our predictive maintenance software.
The 5 signs of a reliable plant
Going back to the initial article references, here’s a brief overview of Doug Plucknette's five principles, they are as follows:
Sign 1: A clean plant environment.
Sign 2: Effective Management of Change (MOC) processes.
Sign 3: Implementation of reliability metrics.
Sign 4: Establishment and adherence to Standard Operating Procedures (SOP).
Sign 5: Planned, scheduled, and well-executed maintenance activities.
As the complete article is already concise, there is no need for further summarisation.
What we will do instead is focus on the two points within the article particularly pique our interest.
#1 - Sign 3 - In Top Performing Sites, Reliability is measured.
A reliable plant places a strong emphasis on measuring reliability. Doug Plucknette points out that when he begins an RCM analysis, and the plant's team can readily provide the reliability data of the asset in question, it's a promising sign.
High-performing plants are proactive in measuring OEE/TEEP (Overall Equipment Effectiveness/Total Effective Equipment Performance) on critical assets. They actively employ proven tools and methodologies to enhance reliability. In contrast, plants facing reliability challenges often claim they don't have the time to measure reliability.
In my early interactions, I noted that many reliability engineers didn't readily provide answers to questions about asset metrics, such as MTTR and MTTB for critical assets, the proportion of planned maintenance work, or the ratio of time-based preventive maintenance (PMs). Initially, I speculated that they might be uncomfortable sharing such metrics. However, as I delved deeper by asking more questions, including those related to sensitive areas like finances, which they were willing to address, I realised that they simply lacked the data to answer our inquiries.
It's essential to acknowledge and sympathise here; reliability leaders often face daunting challenges. Many are relatively new in their roles and inherit complex situations and deeply ingrained cultural norms. We're not here to pass judgment.
Instead, we propose that while understanding the difficulties associated with establishing reliable measurements, using predictive maintenance software can be a valuable asset in this endeavour.
At its core, initiating monitoring for your most crucial assets provides seamless access to vital operating data. By simply logging in, you can not only assess the performance of individual assets but also gain insights into critical segments of your production line. Additionally, proactive alerts keep you informed of any changes demanding attention.
This approach not only gives you real-time performance metrics but also accumulates a comprehensive dataset over time. With Factory AI, you can effortlessly visualise and analyse these metrics through our main dashboard, empowering you with a good foundation for informed decision-making. This can also tell you whether or not you are working in the direction of your goals, and if reliability is improving overtime, and the cost of your maintenance decreasing also.
#2 - Sign 5 – Maintenance is planned/scheduled and performed.
“Condition Maintenance (aka Predictive Maintenance (PdM) or Condition Based Maintenance (CBM)) represents 40% or more of their maintenance workload, and PM (Preventive Maintenance) is in the range of 7% to 10% of their work. Companies that struggle with planning, scheduling and performing routine maintenance will never achieve the level of reliability they need to accomplish Lean Manufacturing or work in a Just-In-Time environment. “
Perhaps this is another area where we are sympathetic; some teams are starting from so far behind that these figures may seem like an unachievable goal.
Whilst we agree with Erik Hupjé that you certainly don't need every new piece of shiny new software (Referenced from here), we've also seen reliability and maintenance teams benefit immensely from starting to capture key data on important assets that can cause a lot of pain when they fail. This pragmatic approach not only delivers measurable return on investment (ROI) but also allows for straightforward comparisons with other ongoing initiatives aimed at enhancing site reliability.
How we help with this
Firstly, Doug's interpretation of predictive maintenance in the quote above might seem a bit direct. You can read more about how we perceive it here.
In simple terms, to achieve a maintenance strategy where Predictive Maintenance (PdM) accounts for 40% of your planned workload, you must start somewhere.
Initiating predictive maintenance (we wrote a detailed 10-step roadmap here) can be a low-risk method to enhance reliability and reduce maintenance costs. Even considering Ramesh's insight that "initially, when beginning an M&R improvement plan, maintenance costs may rise, but they should eventually decrease as asset reliability improves," (Referenced from Maintenance and Reliability Best Practices, 2020) the value of predictive maintenance software offers a substantial return on investment to reliability teams through reductions in unplanned downtime costs and diminished maintenance labor.
In conclusion, we've discussed key points about reliability-centered maintenance (RCM) and how predictive maintenance can enhance RCM by offering key data points in your journey.
We want to emphasise that we don't claim to have all the answers in this field. If you believe we've missed something crucial or would like to engage in a constructive discussion about our perspectives, we welcome your input and feedback.
JP is the Co-Founder and CEO of Factory AI. Previously, he held senior sales leadership roles at Salesforce and Zipline, supporting executive teams in their digital transformation journeys. His passion for reliability and maintenance grows as Factory AI partners with clients to tackle unique challenges