Key takeaways:
Food and beverage is moving the wrong way. While nearly every manufacturing sector cut its unplanned downtime hours over five years, fast-moving consumer goods is the one exception where hours rose, and per-plant downtime costs roughly doubled to over $10 million a year.
The aging-equipment bill is coming due. U.S. fixed assets now average 24 years old, the highest since 1947, and recovery from each stoppage takes longer than it used to as experienced maintenance staff retire.
The fix is a strategy shift, not just a capital line. Most plants call preventive maintenance their core approach, yet spend most of their time reacting to breakdowns.
The world’s 500 largest companies lose about $1.4 trillion a year to unplanned downtime, equal to 11% of their revenue, and the average large plant bleeds $253 million a year when lines stop unexpectedly. Across manufacturing, those losses have held roughly steady only because companies cut the number of hours their lines sit idle. Food and beverage is the conspicuous exception.
Almost every sector has reduced its unplanned downtime hours since 2019, according to five years’ worth of survey data from industrial firms. Automotive and heavy industry cut theirs roughly in half. Fast-moving consumer goods, the category that covers food and beverage, was the only one where downtime hours went up. Per-plant downtime costs in the category doubled to just over $10 million a year. That number adds significant pressure to an industry that already runs on thin margins and high volume.
Why food plants are sliding while others recover
Food equipment doesn’t fail more often than equipment in other sectors. Siemens’s downtime report attributes the FMCG lag to slower adoption of the predictive maintenance and Industry 4.0 technology that let other sectors get ahead of failures. Because an hour of food plant downtime costs less than an hour in automotive or heavy industry, around $36,000 at the low end versus $2.3 million in automotive, predictive maintenance looked like a weaker investment case. So the sector waited.
A cost that’s lower per hour is easy to treat as tolerable, right up until the hours pile up and the annual total doubles. The sectors that invested early now lose fewer hours at a higher cost per hour. Food and beverage loses more hours at a lower cost per hour, and those hours add up.
The equipment is old, and it’s getting older
Underneath the downtime numbers is a physical problem. U.S. fixed assets now average 24 years old, the oldest since 1947. Capital investment that got deferred through the pandemic and the supply-chain disruptions that followed left a backlog of aging machines now reaching the point where they demand attention.
While older equipment fails in less predictable ways and needs more frequent service, the people who knew those machines best are leaving.
Siemens found that the time to recover from a stoppage has climbed from 49 minutes in 2019 to 81 minutes, and points to the loss of skilled maintenance labor and the resulting knowledge gap as a leading cause. And in MaintainX’s 2026 survey of more than 2,200 maintenance leaders, 36% named labor shortages as a reason downtime increased, and 28% pointed to a lack of necessary skills on the team.
Plants with both aging assets and a smaller, underskilled maintenance team have a recipe for disaster.
Most plants plan to prevent failures, then react instead
Though 64% of leaders said they run a preventive maintenance program, half of all teams spend less than 40% of their time on planned work. The rest goes to reacting to equipment failures.
A reactive approach is far more expensive. Emergency repairs run on premium-priced parts, overtime labor, and expedited shipping, and a breakdown that takes a critical line down ripples into scrapped product, missed shipments, and contractual penalties. Escaping the downtime trap relies on shifting hours from firefighting to scheduled and predictive work.
The momentum is finally moving that direction. More than half of maintenance teams (58%) are already using AI in some form, and 75% of leaders who adopted it reported measurable returns within six months. For a sector that under-invested for years, the cost of the enabling technology is proving its worth, and it’s getting increasingly expensive to operate with aging equipment.
What to do before the next line goes down
Find your reactive ratio. Measure how much of your maintenance time goes to planned work versus unplanned breakdowns. If most of it is reactive, that number is your single best predictor of avoidable cost.
Rank your oldest and most critical assets. Aging equipment on a critical line is where a failure does the most damage. Target condition monitoring there first, where the payback is strongest.
Treat the knowledge drain as a maintenance risk. Capture what your senior maintenance staff know before they retire, because longer recovery times track directly to that lost expertise.
Reprice the predictive-maintenance case. The per-hour cost that once made the investment look marginal has been overtaken by rising annual losses and falling technology costs. Run the numbers again on current figures, not the ones from five years ago.
Food and beverage didn’t fall behind on downtime because the work is harder, but because the rest of manufacturing acted on a problem the sector treated as tolerable. But the annual cost has doubled, the equipment is the oldest it has been in three generations, and the people who kept the old machines running are walking out the door.
The tools to reverse it are proven and finally affordable. What’s left is deciding to use them before the next unplanned stop makes the decision for you.
