When Every Hour Costs Millions: Strategies to Combat Operational Downtime in Manufacturing

Every unplanned machinery stoppage translates into lost production, wasted labor hours, delayed shipments, and strained customer relationships. A Siemens report (PDF) estimates that the world’s five largest companies lose $1.4 trillion annually to unplanned downtime, representing around 11% of total revenue.

Although the costs are variable across industries, they are significant to each: from $2.3 million per hour of downtime in the automotive industry, to $1 million per hour of downtime in the oil and gas industry, and $100,000–$500,000 in pharmaceuticals. Even at the lower end of the spectrum, manufacturing downtime in the food and beverage industry, or paper manufacturing can mean tens of thousands of dollars lost per hour. Beyond direct losses, downtime introduces ripple effects that compound the cost: emergency spare parts orders, higher labor costs for specialized and/or third-party repair workers, contractual penalties, and long-term damage to customer trust.

These realities are forcing manufacturers to rethink maintenance as more than a back-office function. It has become a strategic imperative, directly tied to profitability, supply chain resilience, and competitive advantage.

The Shifting Maintenance Landscape

There is no question that artificial intelligence (AI) and machine learning (ML) are rapidly changing the shape and scope of manufacturing and industrial automation across all sectors. In particular, the maintenance and operations sub-sections of manufacturing and industrial automation are currently undergoing a rapid shift away from reactionary troubleshooting, towards predictive maintenance.

To illustrate this shift, a 2018 A Maintenance Report from Plant Engineering found that roughly 57% of manufacturing business employed a run-to-failure (RTF) maintenance method. Essentially, an RTF method consists of letting a machine run until it breaks down. Running machines to failure accelerates their deterioration, leading to more frequent and costlier repairs, which often require emergency spare parts with expedited shipping, further adding to the cost of downtime. Further, machinery that is allowed to run to failure often results in more severe breakdowns, and a shorter lifespan.

As a result of the issues arising from RTF strategies, there has been a significant shift toward AI/ML driven Predictive Maintenance (PdM). A recent study by MaintainX – The State of Industrial Maintenance: 2025 – found that the percentage of manufacturers that include RTF in their maintenance methods has shrunk to 38%. An examination of the costs and consequences of unplanned downtime across all manufacturing sectors sheds more light on the need for this strategic shift.

Consequences of Unplanned Downtime

Monetary Consequences

Unplanned downtime is, broadly, any unexpected interruption to the production process, such as equipment breakdown or power outages, that halts operations. Unplanned downtime is one of the most costly and disruptive problems that manufacturers face. In terms of monetary losses, the numbers are staggering. The Siemens Report, noted above, found that in 2022-2023 every unproductive hour costs vehicle manufacturers around $2.3 million, or roughly $600 per second. While this number represents the high-end of the spectrum, other industry verticals also face significant costs for each hour of downtime:

• Oil & Gas: ~$1M/hour of downtime
• Chemical and Plastics: ~$150,000/hour of downtime
• Pharmaceuticals: Between $100,00 and $500,000/hour of downtime
• Electronics Manufacturing: $100,000 or more/hour of downtime
• IT: more than $300,000/hour of downtime
• Energy & Utilities: ~$125,000/hour of downtime
• Overall cross-industry average: $260,000/hour of downtime

In short, manufacturers across all industry verticals are losing anywhere from tens of thousands of dollars to millions of dollars for every hour that production/manufacturing facilities are idle. Beyond the direct impact of the loss of production, there are hidden costs associated with unplanned downtime. Wages and salaries of the production workers are still paid during downtime; emergency replacement parts often come with a high price; contractual shortfalls can result in significant penalties.

Supply Chain & Customer Impact

Unplanned downtime can delay shipments, which disrupts supply chains and can affect vendor relationships. This has the added consequence of introducing more risk into manufacturing contracts and can negatively impact company reputation. Competitors with higher uptime and efficiency can outpace in cost, delivery, and quality of manufactured goods. Without effective uptime/downtime management, leadership may overlook maintenance as a strategic function, and preventative or predictive maintenance can be viewed as sunk cost.

Predictive Maintenance

As has been shown, RTF methodologies lead to many different problems, including accelerated machine deterioration and expensive emergency repairs. As a result, many manufacturers are adopting a PdM methodology. PdM is an AI/ML powered maintenance strategy that uses real-time sensor data coupled with historical analytics to monitor equipment health and predict failures before they occur, enabling proactive maintenance versus reactive fixes or rigid schedules.

PdM models utilize a combination of the following technologies:

• Sensors & IoT devices continuously monitor machine conditions and health
• Machine learning algorithms detect patterns and monitor machine behavior that precede failure
• Predictive models estimate remaining useful life of machine components
• AI software to schedule and recommend interventions at the optimal time

PdM replaces the RTF models and has a significant positive impact on costs, downtime, and the longevity of manufacturing machinery. While it has not reached anything close to full market saturation – the MaintainX study notes around 27% of manufacturing facilities utilized PdM in 2024 – the manufacturers that are employing some form of PdM are seeing positive returns. The Siemens report notes that the companies using PdM have experienced:

• An 85% improvement in downtime forecasting accuracy
• A 50% reduction in unplanned machine downtime
• A 40% reduction in maintenance costs
• Approximately 10-20% lower replacement part costs due to smarter inventory planning, powered by the PdM software

There are several barriers to implementing PdM. The first among these is the implementation and the associated costs. Many systems, specifically legacy systems, do not have the necessary sensors or digital interfaces, so must be retrofitted – according to the MaintainX study, the average age of manufacturing equipment pieces is 24 years old. Further, most of the PdM platforms carry licensing and implementation costs, along with ongoing training and education costs. Finally, there is currently a maintenance workforce shortage, maintenance staffs are busy putting out fires and do not have capacity for new initiatives like PdM.

Conclusion

Unplanned downtime is more than a maintenance problem. It is a multi-dimensional risk that affects costs, safety, supply chain reliability, and the long-term competitiveness of manufacturers. While industry indicators make it clear that downtime is declining for many facilities, the financial impact of each incident is growing, making proactive and predictive maintenance strategies essential.