Unplanned downtime is a persistent challenge in manufacturing which leads to production delays, financial losses, and disruptions. Unlike scheduled maintenance, unplanned downtime occurs unexpectedly and is often due to equipment failures, process inefficiencies, or unforeseen operational issues. The consequences can be severe and impact productivity, profitability and customer satisfaction.
Traditional maintenance strategies are not well suited to prevent unplanned downtime. Break/fix repairs only address issues after the equipment breaks down and scheduled maintenance – while reducing the risk of unplanned breakdowns – cannot always prevent them. The solution to this conundrum is AI-driven predictive maintenance: it offers a proactive approach by using real-time data to anticipate failures and allows manufacturers to intervene before the breakdown happens. Predictive maintenance is therefore the best approach to minimize disruptions and avoid the cost associated with unplanned downtime.
The Costs and Operational Impact of Unplanned Downtime
Unplanned downtime carries direct and indirect costs that can significantly impact a company’s bottom line. Here is a list of possible expenses associated with sudden failure of equipment:
- Lost Production Time – Every minute of downtime reduces output, impacting order fulfillment and revenue.
- Increased Labor Costs – Emergency repairs often require additional labor hours, overtime pay or expensive specialists.
- Equipment Repair or Replacement – Sudden failures may lead to costly repairs or even the need for new machinery.
- Increased Waste and Scrap – Breakdowns can lead to products on the affected lines spoiling generating more waste and scrap.
- Supply Chain Disruptions – Delays in production can affect supplier schedules and inventory levels leading to further inefficiencies.
- Customer Satisfaction Issues – If delivery commitments are not met, customers may look elsewhere affecting long-term relationships.
- Regulatory and Compliance Risks – Some industries have strict uptime requirements, and non-compliance can result in fines or damage to the company’s reputation.
These costs can be significant and add up very quickly. Here is an example from our own experience working with an automotive supplier: despite scheduled maintenance the manufacturer experienced occasional unplanned breakdowns of their assembly robots. In addition to the lost production, repairing the broken part cost more than twice what replacing the part in the course of routine maintenance costs.
In addition to the financial losses, unplanned downtime often also creates operational inefficiencies that extend beyond the immediate breakdown such as:
- Production Schedule Disruptions – Unexpected failures can cause cascading delays requiring rescheduling and resource reallocation.
- Workforce Productivity Decline – Employees may be left idle or reassigned to non-optimal tasks while equipment is repaired.
- Recurrence of Failures – Without root cause analysis, breakdowns may reoccur leading to repeated downtime and compounding costs.
- Lack of Visibility into Equipment Health – Traditional maintenance methods often rely on guesswork rather than data-driven insights, increasing unpredictability.
The solution to these challenges is AI-based predictive maintenance.
The Role of AI in Preventing Unplanned Downtime
AI can fundamentally change maintenance strategies on the shop floor. The powerful models we now have can crunch through large amounts of sensor readings, detect early warning signs of equipment failures, and generate real-time insights into equipment health that helps manufacturers prevent unplanned downtime. What makes AI such a powerful tool is its ability to analyze performance metrics such as temperature, vibration and pressure as well as historical data such as maintenance logs and records. Based on that data AI identifies patterns that indicate potential breakdowns.
The table shows a high level comparison of break/fix, scheduled and predictive maintenance.
The result of these analyses are optimized maintenance schedules that ensure that servicing occurs only when needed. This reduces unplanned downtime but also makes sure that perfectly good parts are not replaced simply because they are due to be replaced based on a rigid, fixed maintenance schedule.
Cheaper Sensors – Measuring Lots, Let AI Decide What’s Important
There is another important factor that has contributed to making predictive maintenance a reality on the shop floor: affordable sensors. The average cost of sensors has decreased dramatically from about $1.30 in 2004 to less than 40 cents in 2024. Cheap sensors allow manufacturers to collect large amounts of data from different pieces of equipment as well as record ambient factors. e.g., temperature, humidity, etc. Since nobody knows upfront which parameters play an important role in causing unplanned downtime and which don’t, it makes sense to measure comprehensively and then let AI do its job of figuring out which of the parameters actually play a critical role in causing unplanned downtime.
In the use case of the assembly robots mentioned above, we developed and deployed a machine learning model that determines the remaining useful life of the robots. This information is then used to build a maintenance calendar that lists the equipment by estimated days to repair. A list like this clearly indicates what needs top be fixed next and which of the assets still have plenty of life left. (Find the use case here).
The business case for this application was easy to establish and is very convincing: between the cost due to lost production and significantly higher repair cost the investment in AI paid for itself within a few months.
Preparing for AI to Avoid Unplanned Downtime
Preventive maintenance is a key application for AI on the shop floor. Successful implementations, however, take some planning. The number one challenge we have experienced is lack of relevant data to feed into the AI model. AI needs large amounts of data to detect correlations that might indicate trouble down the road. Most companies do not have this data. It has either never been collected – understandably so, because there was no reason for collecting it until AI came along – or it isn’t complete and/or readily available in a format that AI can use.
The first step in every AI implementation for predictive maintenance is therefore deciding which data to collect, establishing what data is available, where it is located, and how it needs to be cleansed. Next, you need to install sensors to collect the missing data so the AI can do its job of detecting patterns. This process needs to start as early as possible because collecting enough data takes time. The rarer the the problem, the longer it will take to collect enough data for patterns to emerge.
During this process experienced personnel on the shop floor are valuable contributors, they generally have a pretty good idea of what might cause problems.
In one of the cases we worked on, experienced personnel could tell from the sound of the equipment, that it needed maintenance soon. This was an important hint that we should track vibration.
For another customer we built 60-parameter model and used SHAP analysis to determine which of these parameters contributed significantly to the maintenance issue. Of the top 10 factors we identified, the line operators found five or six unsurprising, they had known or suspected these parameters all along. The rest came as a surprise and together with quality data from the line they combined to create an accurate predictive tool.
While we generally advise customers to not start their AI journey with predictive maintenance because of the data availability and quality issues as well as long timelines, it is a very important application for AI with a very convincing business case. (Btw, visual inspection is a great place to start).
We are happy to discuss your maintenance challenges and how AI can address them. Just reach out.