10 Continuous Improvement

We are in unprecedented times. Covid-19 wreaked havoc on supply chains; decreased production during times of increased demand. Labor shortages, chip shortages, long lead items turning into “maybe next year, if you’re lucky” items. The Russia Ukraine war added further stress to supply chains through sanctions, port closures, fuel shortages and much more. What once was reliable is now unreliable. So how can companies overcome an unreliable supply chain to maintain their reliability? There are several ways to mitigate unreliability; scenario planning, supplier management, and technology. There is no one size fits all and what may work for one company will not necessarily work for another. Scenario planning involves reviewing every potential situation that could occur, then working through to see how the company would be impacted. Ultimately this results in mitigation plans for each scenario. These can then be reviewed and implemented. Proper Supplier Management includes ensuring all suppliers have their scorecards reviewed on a regular basis. Their information updated and kept current. It can also include reviewing which suppliers can become substitutes for others in the event one is not able to provide the required product in time. Technology is important as it links all the information together. Algorithms can be created to let management know that certain parts are low, equipment is wearing out sooner, it also collects information on suppliers for the scorecards. Overall technology is the glue that binds and provides real time information updates. This presentation will review how to best use technology to help mitigate reliability and supply chain issues.

Recently there was a lot of discussion on various forums around the term ‘Human Asset’. It was prompted by a paper/presentation made by Dr. Monique Beedles in Australia where she questioned the term and the appropriateness of using it based on the definition of an asset in ISO 55000 – ‘an item, thing, or entity that has value or potential value to the organisation (ISO, 2014)’.There were questions posed on the various forums as to the use of ‘thing’ to describe humans – it’s legal meaning, it’s cultural implications and though most posting felt the term was appropriate, there was definitely a difference of opinion. What wasn’t brought out in most of the discussions was the intent or suitability of the decision to word the definition that way. No-one disagreed with the idea that a human ‘has value or potential value to the organization’ and so rather than argue about the term, I’d like to explore how we could apply the tools and lifecycle approach we use for physical assets, to humans. Let’s walk through the lifecycles and see if we can find how it might apply to humans.Concept Design Acquire Operations and Maintenance Decommission So as I said I won’t address the term ‘human asset’ but hopefully you can see that taking the same approach to ensure that the ‘value or potential value to the organization’ is certainly a good alternative.

Within the mining industry, the library of KPIs has not kept up with data that has become increasingly available through digitization, therefore leading to an overuse of lagging performance indicators. Additionally, due to the fact that strategic corporate goals are oftentimes built on the basis of soft or perceptual measures from stakeholders, which are subjective in nature, organizations struggle with the process of linking strategic level goals to KPIs on the shop floor. Therefore, creating a communication gap between technical teams and senior management. As a consequence, maintenance leaders have a difficult time demonstrating the added value that maintenance activities create with an organization, leading to challenges in securing the resources required for things such as continuous improvement projects. Considering that the implementation of a robust KPI framework is an alignment of three key areas: people, process, and technology, the first portion of this presentation will be investigating a holistic approach to developing maintenance KPIs that are integrated into corporate strategic goals. Therefore, outlining the steps required for organizational leaders to begin standardizing data gathering procedures and creating a trustworthy system that can be leveraged for decision making. Aside from the previously discussed administrative challenges associated with data gathering within organizations, it must also be highlighted that KPIs are collected on a monthly or quarterly basis, meaning that training sets for forecasting models are extremely limited. Therefore, in order to address the reliance on lagging performance indicators within maintenance departments, a variety of time series models capable of achieving high accuracy on small datasets will be discussed along with direct business applications. Through achieving these goals, the intended outcome is to create a more contemporary data driven methodology for selecting organizational KPIs, as well as directly demonstrating the added value that the various business units create within the organization.

Recent developments in AI, ML and related techniques see wide adoption in many industries. However, in the asset management area, such technical advances are still in their infancy, especially in the maintenance, repair, and operations (MRO) area. Part of the reason is that, contrary to production, MRO data has its unique characteristics (e.g. incompleteness, inconsistency and heterogeneous), and most organizations are still planning to introduce diagnostic sensors dedicated to maintenance and equipment reliability. We face both challenges and opportunities in advancing data-driven continuous improvements within the asset management world. This presentation shares the findings of our current research and development focus. Titled “How to use historical data to find opportunities to improve the effectiveness of equipment reliability programs, optimize MRO inventory operations, and enhance MRO workflow management processes”, we will first examine the characteristics of MRO data as their uniqueness to a specific company, plant or equipment and their commonality across all sectors. Then we evaluate the feasibility of applying AI/ML techniques with MRO history for better operational efficiencies. We need to understand what data is related to human knowledge, human interaction and process, and what data is associated with the actual condition of the asset, and if there are patterns and models that can be learned. Last, we will demonstrate that AI/ML can find equipment agnostic models and patterns which help continuously improve MRO operations across different industries. Based on the findings, we will also show how AI/ML models learned from historical MRO data can be translated into prescribed actions for improvements in equipment reliability, MRO inventory and workflow operations for individual organizations.

The Irving Pulp & Paper maintenance team was the recipient of the PEMAC Maintenance Team of the Year Award for 2021. The team was honored to be nominated based upon long term sustained performance improvements, professional development, controlling reactive maintenance and continuous improvement.

Maintenance Turnaround events involve major expenditures of time and money for any large continuous process plant. Without careful planning and preparation they can quickly and easily overrun their target cost and schedule. The larger and more complex the scope to be executed in the turnaround, the greater the risk is, of cost and schedule overruns. For this reason, it has, in recent years, become the norm for turnaround teams to use a “scope challenge” process, just before freezing the scope list. The purpose of the challenge being to review every work item and decide whether it should be in the scope of the turnaround event. Some of these “scope challenge” sessions can get quite sophisticated, involving a risk assessment, balancing the cost of doing the maintenance repair in the turnaround against the cost of doing it after a (probability weighted) breakdown during normal running. Many turnaround teams stop there, and assume that if they’ve had a “scope challenge”, then they’ve done what they can to optimize and control the scope. But more and more are beginning to realise that a “Risk Based Work Selection” Scope challenge session, while useful and necessary, is only one element in a process for choosing and controlling the work scope at an optimum level. This paper will describe a successful and more holistic approach, using scope control methods that start with the premise document (reference earlier article), include the scope gathering phase, go through the “Risk Based Work Selection” process and carry on to the “Additional Work Request” process after scope freeze. Along the way it will also discuss how to ensure that site leadership and plant operations staff “buy in” to the need to control the scope.

An Asset Health Index or AHI refers to analysis performed using various asset data to determine the state or condition of the asset. AHI can be used to better assess asset condition, used and useful life, progression toward potential failure, and failure probability. Further, using AHI can also enable the development of optimized maintenance and replacement strategies for assets using a set of objective criteria to assess the true health of the asset. However, entities vary widely in whether they develop Asset Health Indexes (AHIs) for their key assets. For those that do, there are marked differences in the level of rigour and sophistication employed in developing and applying AHIs for effective asset management decision-making. AHI calculations involve identifying and collecting data which may include a review of core asset attributes such as manufacturer, inspection data including field observations, destructive and/or non-destructive test data, maintenance data including historical records, operational records, and asset failure/refurbishment data. In other words, some are core inventory data, some work records, and some inspections or tests. This presentation will go through how to make the best use of asset SMEs and how you can start to develop useful AHIs from what you already know/have. Technically, the process begins with identifying the most critical assets and determining which can best benefit from AHI formulation development. The next steps are used to develop proposed condition factors (CF) and weighting factors (WF) that provide insight into the condition of the assets. Finally, CFs and WFs are used to develop a mathematical algorithm or formulas for the Health Index. We will also discuss how AHI can be used to develop asset management and maintenance strategies – the whole point of the data and analysis in the first place.

Business improvements require changes. In reliability and maintenance, some of the change factors are within our control, but many are not. If we stick with small changes, they can often happen but they often fail to achieve their full potential. Why? The short answer is that there are many factors we can’t control and usually we have limited influence. Some of those are related to people and are dealt with by “change management”, but others are related to how our businesses are structured and organized. If we want to make big changes we need to get past that! This presentation will give you something to think about and share with your senior management. If they want miracles from you, then they will need to make it possible!

We know that reliability has value to your business, but many of us with technical backgrounds struggle to present a good business case to decision-makers. We are very often held back by budget constraints and we are not in a position to make decisions involving financial risk-taking. Most of us don't have a business background, nor do we speak "finance". It is a whole different language than maintenance and reliability, yet we all want the same things for our business. This presentation will give you some ideas on what you will need to determine in order to show what reliability is worth, and how to present that to decision-makers.

The ultimate maintenance KPI must show if we are keeping assets from breaking and not just fixing them after they break. While our maintenance forces are responsible for fixing things that break, we have really failed in our primary mission to keep them working in the first place! In Six Sigma terminology, the ultimate maintenance KPI should be about eliminating defects. Emergency and urgent work requests are “defects.” How many do we have? Are we getting better at eliminating them? Simply put, that’s what it’s all about. Modern maintenance knows that the key to reducing emergency and urgent work orders is doing more proactive work such as PM, PdM, and project work to head off reactive work. However, because many plants are overwhelmed with reactive work, planning and scheduling are critical to help us complete the extra proactive work. We track and manage simple supporting planning and scheduling KPIs to achieve super-high productivity and quality of work. These easy-to-measure KPIs are schedule compliance, schedule loading, work order completion rate, planned coverage, creation of reusable job plans, keeping a minimal unplanned backlog, and amount of helpful feedbacks received.