{"id":38282,"date":"2026-03-18T00:05:28","date_gmt":"2026-03-18T00:05:28","guid":{"rendered":"https:\/\/www.argonandco.com\/?post_type=article&p=38282"},"modified":"2026-03-18T04:11:21","modified_gmt":"2026-03-18T04:11:21","slug":"asset-and-maintenance-excellence-the-system-that-keeps-the-system-running","status":"publish","type":"article","link":"https:\/\/www.argonandco.com\/en\/news-insights\/articles\/asset-and-maintenance-excellence-the-system-that-keeps-the-system-running\/","title":{"rendered":"Asset and Maintenance Excellence: The system that keeps the system running"},"content":{"rendered":"

Long-haul jet engines used to come off the wing every\u00a05,000 hours. Today they\u00a0run\u00a040,000.\u00a0Haul truck engines went from\u00a010,000 hours\u00a0between rebuilds to 30,000.\u00a0Two decades of disciplined asset management and reliability engineering delivered that.<\/span>\u00a0<\/span><\/p>\n

Those gains need defending. Traditional asset management\u00a0has to\u00a0continue, or the hard-won performance walks backward. But each improvement cycle delivers less than the last. Good operators will tell\u00a0you\u00a0reliability on\u00a0its\u00a0own is not worth much. OEE is what they can plan on. At least in theory. And OEE is the\u00a0number\u00a0everyone games.<\/span>\u00a0<\/span><\/p>\n

The next uplift will come from treating maintenance excellence as an operating system that aligns maintenance, operations, supply, and planning around the same constraint and targets. Simple problems still respond to experienced eyes on the ground\u00a0hunting\u00a0the handoffs between teams. Complex ones need simulation, digital twins, and AI<\/span>\u2011<\/span>supported integrity testing. Both matter. Neither replaces the other.<\/span>\u00a0<\/span><\/p>\n

The pattern we see\u00a0<\/strong><\/h3>\n

Maintenance teams are working hard and hitting plan compliance. WO backlogs are real but\u00a0manageable\u00a0and OEE dashboards are green. Yet production still misses its\u00a0targets,\u00a0shutdowns overrun, and the same unscheduled losses show up quarter after quarter. Both sides point fingers.<\/span>\u00a0<\/span><\/p>\n

The gap is rarely inside any one function. It lives in the handoffs between them.<\/span>\u00a0<\/span><\/p>\n

Parts are not on site when maintenance needs them. Shutdown\u00a0scopes get\u00a0reopened a week before execution. Operations push equipment past service intervals to hit a daily target and then wear the unscheduled failures next month. Each team\u00a0optimises\u00a0for its own goals, and the system pays the price.<\/span>\u00a0<\/span><\/p>\n

I have seen this pattern across asset-intensive industries over 30 years.\u00a0It is not a maintenance problem. It is a system problem.<\/span><\/p>\n

\"\"<\/p>\n

Figure 1<\/span><\/span>\u00a0Container & Roll on Roll off ferry<\/span>, Melbourne<\/span><\/span>\u00a0<\/span><\/cite><\/p>\n

What\u00a0it costs\u00a0<\/strong><\/h3>\n

A gold mine in PNG had been\u00a0acquired\u00a0from a global major, but production was disappointing. The mine produced\u00a0ore on\u00a0only\u00a0289 days\u00a0a year. Benchmarking against over 100 similar assets suggested 330 to 340 was achievable. Unscheduled losses accounted for\u00a058 days. Scheduled maintenance\u00a0at\u00a0just\u00a018 days\u00a0was well below benchmarks. The operation\u00a0was under<\/span>\u2011<\/span>maintaining\u00a0and paying for it in breakdowns.<\/span>\u00a0<\/span><\/p>\n

\u200bMost of the unscheduled loss occurred right before and\u00a0immediately\u00a0after major\u00a0shuts. The team implemented a shuts readiness program tracking over 100 critical data points to hit 100% completion on time. Maintenance crews walked dress rehearsals on site before execution. At the same\u00a0mine, the mill shutdown always ran long and blew the production plan. One\u00a0option\u00a0the team suggested was a simple change to how bolts and liners were installed that took hours out of the task.\u00a0<\/span>\u00a0<\/span><\/p>\n

The real breakthrough sat underneath. The liners were designed for nine months. The risers had an MTBF of six months. The risers were driving shutdown\u00a0frequency, not the liners. Once the team reset shutdown frequency off actual failure data, scheduled maintenance days increased from 18 to 24 and production days rose by\u00a0roughly 12%\u00a0to 325 over two years.<\/span><\/p>\n

\"\"<\/p>\n

Figure 2<\/span><\/span>\u00a0Crew discussing through the wall bolting<\/span>,\u00a0<\/span>PnG<\/span><\/span>\u00a0<\/span><\/cite><\/p>\n

A large iron ore mine had missed production targets for over a decade by about 4%. The third leading cause was \u201cwaiting for parts,\u201d\u00a0roughly 25%\u00a0of the shortfall. We started at the warehouse. The first KPI was to achieve same<\/span>\u2011<\/span>day receipt, improving from an average of three days. Parts with an identified source of supply went from around 50% to over 95%, reducing ordering time delays and wrong parts being delivered. Criticality<\/span>\u2011<\/span>based stocking was overhauled for production<\/span>\u2011<\/span>critical and long<\/span>\u2011<\/span>lead items. Within six months the warehouse was hitting targets, and that 25% production delay \u201cwaiting for\u00a0parts\u201d dropped out of the top ten\u00a0losses.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u200bSometimes the answer is simpler than anyone expects. A Canadian\u00a0aluminium\u00a0smelter was asked to take on 20% more mobile equipment maintenance from sister operations without adding\u00a0labour\u00a0or expanding the facility. The new GM and I\u00a0walked\u00a0the site and\u00a0agreed that by doing 5S\u00a0really well\u00a0they had a chance. Throughput increased by 25%,\u00a0rework\u00a0dropped by about 5%. No capital. No\u00a0additional\u00a0headcount. Just discipline.<\/span>\u00a0<\/span><\/p>\n

\u200bThese are not unusual stories. In my experience, every heavy industrial operation has\u00a0upside\u00a0in the interactions between maintenance, shutdowns, inventory, and operations. The solutions are often straightforward. The hard part is seeing them, and that requires time on the ground.<\/span>\u00a0<\/span><\/p>\n

\"\"<\/p>\n

Figure 3<\/span><\/span>\u00a0<\/span>Tool crib after 5S, Canada<\/span><\/span>\u00a0<\/span><\/cite><\/p>\n

Why “better maintenance” is not enough<\/span><\/span><\/h3>\n

\"\"<\/p>\n

Figure 4<\/span><\/span>\u00a0Crew solving operational constraints with data<\/span>, Pilbara<\/span><\/span><\/cite><\/p>\n

At a major LNG facility running five trains plus wells and shipping, a single train produces around 3.5 million dollars of gas a day. On one routine inspection, an operator\u00a0identified\u00a0a flange bolt that needed replacing. The maintenance team found a box of the right size on site. Good news. The\u00a0bad news\u00a0was\u00a0the material data sheet was missing, and the plant manager rightly decided not to risk using unverified bolts on an LNG train. New bolts had to be ordered and hot<\/span>\u2011<\/span>shotted to site. Three days. Over 10 million dollars in lost revenue from one missing piece of paperwork on a bolt.<\/span>\u00a0<\/span><\/p>\n

That is what asset management looks like when you trace the full cost. Not the price of the part. The price of the production you lose while waiting for it.<\/span>\u00a0<\/span><\/p>\n

Maintenance excellence is not a maintenance department initiative. It is an operating system. Asset management, work management, parts, people, and information all need to work together. When they work in isolation, you get local wins and system losses. The best shutdown plan fails when work orders are added after the cutoff. A missing data sheet on a box of bolts can cost more than the maintenance budget for the month.<\/span><\/p>\n

\"\"<\/p>\n

Figure 5<\/span><\/span>\u00a0<\/span>Rodding furnace, Australia<\/span><\/span>\u00a0<\/span><\/cite><\/p>\n

At a joint Suzuki and GM fabrication plant in Ontario, two identical production lines sat side by side. One built the Suzuki Vitara, the other the GM Tracker. Same vehicle. Same plant. Same workforce.<\/span>\u00a0<\/span><\/p>\n

In practice,\u00a0vastly\u00a0different. One production engineer told me she would flat out refuse to drive the GM version on the test track, while she happily would own a Vitara. The difference was the engineering change order system. Suzuki\u00a0processed\u00a0changes\u00a0instantly and electronically. GM used batch processing on a quarterly cycle. That gap flowed through to inventory\u00a0and to\u00a0maintenance of the production machines. The Suzuki side used Teian, operator<\/span>\u2011<\/span>driven continuous improvement, feeding operator knowledge directly into how equipment was\u00a0maintained. The GM side relied on OEM\u00a0install\u00a0standards.<\/span>\u00a0<\/span><\/p>\n

Same plant. Same people. Dramatically different outcomes. How information flows, how changes are processed, and how operator knowledge is captured drives maintenance quality as much as any maintenance strategy.<\/span>\u00a0<\/span><\/p>\n

At a submarine operation, the refit team was consistently outperforming the original build. The refit crews had years of hands<\/span>\u2011<\/span>on experience with the equipment and were\u00a0applying\u00a0it every day.\u00a0But that experience was not\u00a0being shared\u00a0back to the new build team.\u00a0Two teams, same platform, different results. When knowledge stays in one part of the system, the\u00a0whole system\u00a0underperforms.<\/span>\u00a0<\/span><\/p>\n

Solve for\u00a0the system outcome. Align maintenance, operations, supply, and planning around the same targets. Make sure information flows to where it is needed. The prize is not a better maintenance department. It is a more reliable, safer, higher<\/span>\u2011<\/span>performing operation.\u00a0<\/span>\u00a0<\/span><\/p>\n

Matching the strategy to the asset\u00a0<\/strong><\/h3>\n

The maintenance strategy itself matters. We all use run<\/span>\u2011<\/span>to<\/span>\u2011<\/span>failure, scheduled preventive, condition<\/span>\u2011<\/span>based, predictive, and prescriptive maintenance. The winning condition is to match the strategy to the criticality of the asset. Run<\/span>\u2011<\/span>to<\/span>\u2011<\/span>failure is right for some items. Predictive is essential for the assets that drive your production constraint.<\/span>\u00a0<\/span><\/p>\n

Practitioners\u00a0leverage\u00a0valuable methods such as RAM, FMECA, RCM, and FRACAS. Our article, Constraint to Control, showed that performance models like Lean, Theory of Constraints, and flow analysis cannot solve complex system problems on their own. The skill is the same here. Bring asset management and maintenance methods together into one integrated system\u00a0view\u00a0so decisions improve the operation not just help a function hit its KPIs.<\/span>\u00a0<\/span><\/p>\n

For straightforward operations, walking the floor with experienced hands and hunting handoffs for unscheduled loss gets you there. For complex operations where multiple assets interact and the consequences of failure are severe, you need these frameworks\u00a0feeding\u00a0into coordinated simulation with clean failure data.\u00a0<\/span>\u00a0<\/span><\/p>\n

\"\"<\/p>\n

Figure 6<\/span><\/span>\u00a0Iron ore conveyors, Pilbara<\/span><\/span>\u00a0<\/span><\/cite><\/p>\n

On conveyor inspections, experienced operators walking the line could spot faults, but they were exposed to moving machinery, noise, and weather, and false positives were common. Introducing drones, with experienced operators reviewing footage and training the analysis, cut false positives by more than half, lifted actual fault detection by\u00a0<\/span>roughly 5%<\/span>, and dropped\u00a0<\/span>the all<\/span><\/span>\u2011<\/span><\/span>injury frequency rate from 2.0 to 1.85 in a year, with crews off the line of fire.\u200b<\/span><\/span>\u00a0<\/span><\/p>\n

\"\"<\/p>\n

Figure 7<\/span><\/span>\u00a0CF-18 Hornets on the flight line, Canada<\/span><\/span>\u00a0<\/span><\/cite><\/p>\n

At the other end of complexity, while performing QA on CF<\/span>\u2011<\/span>18 off<\/span>\u2011<\/span>wing engine remanufacturing for the Canadian Air Force, we could not work out why MTBF was not hitting standards. The answer was a complex interaction between three things. The wind tunnel was out of compliance. Power test sensors had too much variance from\u00a0centre\u00a0line, so the expected power curve was not actually being tested. Some turbine blade machining tools were using an older specification. It took systematic investigation across the full rebuild and test process to find it.<\/span>\u00a0<\/span><\/p>\n

\u200bGet the manual process\u00a0right first. Lock in good logic.<\/span>\u00a0<\/span><\/p>\n

Where to start\u00a0<\/strong><\/h3>\n

Three entry points, depending on what the operation needs.<\/span>\u00a0<\/span><\/p>\n

\u200bIf unscheduled loss is the dominant problem, start with root cause analysis and\u00a0a review\u00a0of your maintenance strategies. Match the strategy to the asset. Challenge whether you are over<\/span>\u2011<\/span>servicing low<\/span>\u2011<\/span>risk equipment while under<\/span>\u2011<\/span>investing in the assets that drive your constraint. Start on the ground and let the evidence lead.<\/span>\u00a0<\/span><\/p>\n

\u200bIf shutdowns are bleeding time and cost, start with shuts readiness. Track critical data points, run dress rehearsals, enforce\u00a0scope cutoffs. Walk the shutdown tasks before you plan them. Check your component<\/span>\u2011<\/span>level MTBF data. The PNG risers were driving shutdown frequency, not the\u00a0liners\u00a0the plan was built around.<\/span>\u00a0<\/span><\/p>\n

If \u201cwaiting for parts\u201d shows up in your loss data, start with how parts get to the people who need them. Trace the path from request to receipt. The gaps in cataloguing, sourcing, stocking, and data quality are where the improvements sit.<\/span>\u00a0<\/span><\/p>\n

These three threads connect. Fixing one often reveals the next. The entry point matters less than starting.<\/span><\/p>\n

\"\"<\/p>\n

Figure 8<\/span><\/span>\u00a0Iron ore ship loading<\/span>, Pilbara<\/span><\/span>\u00a0<\/span><\/cite><\/p>\n

The system that keeps the system running\u00a0<\/strong><\/h3>\n

Sometimes the answer is 5S done properly. Sometimes it is a complex multi<\/span>\u2011<\/span>factor investigation like the CF<\/span>\u2011<\/span>18 engine rebuild. The common thread is getting on the ground and seeing the system for what it is.<\/span>\u00a0<\/span><\/p>\n

In my experience,\u00a0organisations\u00a0that get this right treat maintenance excellence as an operating system, not a department initiative. They align maintenance, operations, supply, and\u00a0planning around\u00a0system outcomes. Information flows between teams, not just within them. The fundamentals come before\u00a0the technology. And the floor gets walked. The hard<\/span>\u2011<\/span>won reliability gains are behind us.<\/span>\u00a0<\/span><\/p>\n

The prize is significant. Maintenance costs\u00a0down\u00a010 to 20%. Production uplift of 2 to 5%. Fewer safety incidents. A team that runs on rhythm instead of reacting to the last crisis.<\/span><\/p>\n

\"\"<\/p>\n

Figure 9<\/span><\/span>\u00a0Celebrating a promotion (second from right)<\/span>,\u00a0<\/span>PnG<\/span><\/span>\u00a0<\/span><\/cite><\/p>\n

If your operation is facing persistent reliability losses, shutdown overruns, or production delays, it may be time to look beyond maintenance alone and examine the system around it. If you would like to discuss how to unlock performance across maintenance, operations, and supply, feel free to get in touch.<\/a><\/p>\n

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