Benjamin Agbesi — New Contributor

Nobody wants to answer that question honestly.

Ask the maintenance manager and he will tell you procurement moves too slowly, does not understand urgency, and treats a critical spares request like a routine stationery order. Ask the procurement manager and he will tell you maintenance submits requests too late, provides incomplete specifications, and expects three-week lead times to disappear overnight. Both of them are right. And that is exactly the problem.

While the two departments argue about whose fault it is, the plant is down. Production is bleeding. And somewhere in a storeroom that nobody properly manages, the part that could have prevented the whole crisis was either never ordered, already expired on the shelf, or listed under four different names in the CMMS so nobody knew it existed.

This is not a story unique to one plant or one country. It is playing out across manufacturing facilities in Ghana, across West Africa, across the continent, every single week. And until we are honest about where the real failure sits, we will keep having the same conversation every time a critical machine goes down.

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“Just Be Good at Your Job”

Nobody tells you this when you graduate.

You spend four, five years studying thermodynamics, machine design, strength of materials. You stay late in the lab. You pass your exams. You step into your first role and you work hard, really hard, because that is what you were trained to do. Be competent. Master the technical side. Deliver.

And for a while, that feels like enough.

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The Question Is Whether You Are Ready for It.

There is a version of the AI conversation happening in industrial circles that I find deeply unhelpful. It goes like this: AI will either save everything or destroy everything. Plants will either become fully autonomous or engineers will become redundant. The future is either utopian or catastrophic.

Neither framing serves the professionals who actually have to make decisions about AI adoption in real operations, under real constraints, with real consequences for getting it wrong.

The more useful conversation is a quieter one. It asks: what specific problems does AI solve well in industrial settings, what does it not solve, and what does an organization need to have in place before AI can actually deliver value? That is the conversation this post is about.

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What It Taught Me About Reliability Engineering

It was a Tuesday morning, the kind that starts quietly and then doesn’t. I was on shift at the cement plant when the alarm hit the control room. One moment everything was running within parameters. The next, the Ball Mill had tripped, and the entire production line was holding its breath.

No dramatic explosion. No single obvious failure. Just silence. The kind of silence in a plant that costs real money by the minute.

I’ve been in reliability engineering long enough to know that the first question is never “what broke?” The right question is always “why did it break here, why now, and why didn’t we see it coming?”

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Every unplanned shutdown has a price tag. Most organizations only count the obvious one: the cost of the repair itself. They rarely account for the lost production, the emergency procurement premiums, the overtime hours, the downstream schedule disruptions, or the quiet erosion of team morale that follows when breakdowns become routine.

Reactive maintenance: fixing things after they fail: is not simply an operational choice. It is a compounding liability. And in industrial operations across Africa, it remains one of the most significant and least-discussed drains on productivity, asset life, and organizational performance.

The engineering profession has the tools, the frameworks, and the data to do better. The question is whether organizations are willing to make the shift: in thinking, in culture, and in investment: before the next failure forces the conversation.

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