Why Experienced Maintenance Engineers Are Critical for Factory Operations

In almost any manufacturing facility in Sri Lanka, you will find at least one person who seems to hear things other people do not. They can recognize even the slightest change in a machine whether it's an anomalous vibration, tiniest change in pitch of a motor or smallest change in sound of a moving part. They can without a doubt narrow it down and say what the issue is, not by guessing but purely by years and years of experience and accumulated knowledge over time. They have seen this before, many times, and their diagnostic instinct has been built by years of accumulated pattern recognition. This type of experienced maintenance engineer is one of the most valuable people in the factory, but also one of the most significant operational risks.

What experience actually gives you

There are two types of industrial knowledge, explicit knowledge and tacit knowledge and they are quite distinct from each other. Explicit knowledge is what you can write down like maintenance procedures, machine specifications, SOP documents, component tolerances etc. Tacit knowledge is what you cannot easily document like the intuitive judgment developed through years of handling the same equipment, the sense of which symptoms actually matter and which can wait, the understanding of how a particular machine behaves differently in humid conditions or at the end of a long production run.

What sets apart experienced maintenance engineers is that they carry an enormous amount of tacit knowledge. When a breakdown occurs, they do not follow a checklist from the beginning. They recognise patterns, form hypotheses quickly, and narrow the diagnostic space from a decade of similar situations. This is not something that develops over years, and it is what separates a troubleshooting process that takes twenty minutes from one that takes three hours.

In complex technical roles, the productivity difference between an experienced specialist and an average performer can reach several hundred percent (TSET, via McKinsey, 2025). In maintenance, that gap shows up directly in downtime duration. The experienced engineer's judgment reduces the time that a production line is stopped.

The knowledge crisis building in manufacturing

The challenge in manufacturing industries globally including Sri Lanka is that this expertise is concentrated in a generation that is approaching the end of their careers.

Deloitte and The Manufacturing Institute's 2024 study projects that global manufacturing will need to fill 3.8 million jobs by 2033, with 2.8 million of those vacancies created directly by retirements (Deloitte / Manufacturing Institute, 2024). In the United States, the share of manufacturing workers aged 55 and older has grown from around 10% in 1995 to approximately 25% by 2025 (McKinsey, 2025). The pattern is visible across manufacturing economies in Asia, Europe, and beyond (Automate.org, 2026).

The significant impact of this in maintenance roles is that the knowledge leaving is not the kind that can be replaced by recruiting someone with the right qualification or explicit knowledge. A new engineer can be trained on the procedures, but they cannot be trained on twenty years of watching how specific machines behave on a specific production floor. When the person who holds it retires, it leaves a large void in the factory maintenance that simply cannot be replaced immediately.

This is known as the "silver tsunami," or the "industrial brain drain" in the industry. (Assembly Magazine, 2026; FP360, 2026) and it needs to be addressed consciously.

Why documentation alone does not solve it

The standard solution to knowledge loss risk is documentation. It’s generally to write down the procedures, create SOPs and record the troubleshooting steps. These are all useful, but they capture explicit knowledge which is the what and the how but not the why and the when.

For example, an experienced engineer knowing that a specific machine’s overheating usually traces to the cooling system when it follows a long run, but to an electrical fault when it occurs within the first hour of a shift, has a contextual understanding that a standard procedure document might not be able to capture. They have not gained that knowledge through a manual but have built it through dozens of instances of observing, diagnosing, and resolving the same issue and then remembering it.

In most factories, the documentation that does exist is also scattered. Machine manuals are in one location, SOPs in another, maintenance records in a different place. Historical repair logs exist in SAP or on paper, but are rarely structured in a way that makes patterns visible. A new engineer looking for guidance during a breakdown has to find and cross-reference these sources under time pressure. Even then the documentation would still lack the experiential context to interpret what they find the way a senior engineer would.

Making knowledge visible and usable

The goal of digital knowledge management in maintenance is not to replace experienced engineers but to extend what they know and to make their accumulated understanding accessible to the rest of the team. It is to provide the knowledge in a format that is searchable when it is needed, and preserved when they eventually move on.

To achieve this, first operational history must be captured in a structured, connected form. It means documenting not just that a repair was completed, but documenting what was found, what was replaced, what was ruled out, and what was suspected for the future. Secondly, that history must be accessible in context so that when an engineer is facing a problem at 2am on a night shift, they can query what happened the last three times this machine showed this behaviour, rather than starting from nothing.

Protonest Connect is one such platform built around this operational challenge. The platform connects the sources that experienced engineers currently consult through memory, informal networks, and manual search along with maintenance records, machine manuals, SOPs, sensor data, KPI information, and inventory history into a single environment that is useful during a real breakdown.

The intelligent analysis layer of Protonest Connect enables an engineer to answer the questions that an experienced engineer would naturally figure out through memory and pattern recognition every day. A connected platform makes them answerable from data so that the institutional knowledge built over years is not trapped in one person's head, and does not retire when they do.

The engineer is still the decision-maker

Engineering judgement will always be one of the most critical aspects in any running factory. A system can surface patterns, compile repair history, and flag recurring failure modes. However, it cannot replace an experienced engineer. A platform cannot feel the machine, apply context that was never recorded, or make the call on an unusual situation that does not match any prior pattern. That will always remain the engineer's role.

What the system can do is make every engineer, including less experienced ones, better equipped at the moment a decision is needed by providing the collected data and analyzing them intelligently. It reduces the gap between the engineer who has seen everything and the one who is still learning. It means the factory does not have to rely on one person being in the right place at the right time, and that the knowledge built by decades of operational experience does not disappear quietly when that person leaves.

For Sri Lankan manufacturers investing in operational capability for the long term, it is important to not replace the experienced people, but to make sure what they know survives even after them.

References

• Deloitte and The Manufacturing Institute, 2024 Digital Skills Report — 3.8 million manufacturing jobs needed by 2033, 2.8 million from retirements. Via FP360: https://fp360group.com/industrial-brain-drain-knowledge-gaps-manufacturing/
• McKinsey, manufacturing workers aged 55+ grew from ~10% (1995) to ~25% (2025): https://www.mckinsey.com/industries/aerospace-and-defense/our-insights/investing-in-the-manufacturing-workforce-to-accelerate-productivity
• TSET (citing McKinsey), productivity differences up to 800% in high-complexity roles: https://tset.com/blog/manufacturing-productivity-at-risk-from-retirements
• Automate.org, tacit knowledge at risk as manufacturing veterans retire (Boston Dynamics VP quote): https://www.automate.org/robotics/industry-insights/the-great-knowledge-transfer-safeguarding-manufacturing-expertise-as-veteran-workers-retire
• Assembly Magazine, "silver tsunami" and maintenance knowledge gaps: https://www.assemblymag.com/articles/100027-manufacturers-risk-losing-critical-knowledge-as-workforce-retires
• FP360, industrial brain drain in manufacturing: https://fp360group.com/industrial-brain-drain-knowledge-gaps-manufacturing/
• Protonest Connect: https://www.linkedin.com/company/protonest-connect/