Unplanned downtime is all unexpected stops that occur during production. Such a stay occurs without notice and can last for a long time, thus leading to a loss of income. These unplanned interruptions in the production process eat up hours on the workday of the team and inevitably lead to losses and more costs.
Accidents occur for a variety of reasons, and even in well-organized production, they do exist.
The time required to rectify the fault is directly related to the repair materials' availability and the presence of qualified personnel for immediate response. These unplanned shutdowns turn directly into a loss of revenue. Any moment when a machine does not work is a loss of revenue.
Planned and unplanned downtime are crucially important metrics to track.
Planned downtime is a scheduled temporary production stoppage for routine machine maintenance. It may involve replacing equipment parts, software upgrades, or other necessary maintenance work. This scheduled downtime prevents equipment malfunctions and ensures operations run smoothly. To streamline this process, many businesses rely on CMMS software for effective preventive maintenance service.
On the other hand, unplanned downtime is an unforeseen interruption to the production process. It can be caused by factors such as machine breakdowns, power outages, or other operational issues. Accidental equipment downtime events are costly. They also negatively impact productivity and deadlines.
Unplanned downtime can lead to tremendous losses for businesses that rely heavily on technology or machinery to operate. Here are the most common machine downtime consequences:
The intangible costs of downtime are less obvious as they are less specific. Much of this cost is focused on the relationships of the workforce in a given environment and how people and machines interact. Here are some examples:
The real costs of downtime are determined by the impact of the outage on employees and productivity. By identifying the costs of employee stays as well as the costs of losing orders, manufacturers can calculate the specific cost of unplanned operating time.
There are many reasons why understanding the cost of a stay is important for optimizing day-to-day operations throughout production. Understanding the importance of living costs, manufacturers can make data-based decisions with confidence. Operational teams can avoid unnecessary costs and preventive action can be taken to avoid significant amounts of unplanned operational time.
One of the popular ways to calculate the price of a 1-day stay is according to the following formula:
Total losses for only 1-day stay = Lost profit per day (with 8-hour shift) + Costs for employees (their hourly rate and percentage of non-working employees due to an accident)
A GE survey of the oil and gas industry found that only 24% of the global operators involved described their maintenance strategy as "predictive" or an approach based on efficient and effective data collection, management, and analysis. The most common strategies used by the participants in the study are the reactive approach and the planned approach.
The main characteristics of each of these approaches in terms of unplanned operating time can be summarized as follows:
Combined with the additional costs of repairs, labour, transportation, and equipment, reactive and planned approaches result in losses of $60 million per year. A predictive approach to data monitoring reduces these losses by nearly 40%. The strength of the forecast data and analyzes lead to a significantly reduced amount of non-operational time, which can be seen in the production plant.
Applying the latest technology, including CMMS implementation and devices like PLCs in manufacturing, can significantly enhance preventive maintenance strategies.
The perfect solution for reducing unplanned operational time must combine both real-time monitoring and forecast analysis.
Real-time monitoring allows manufacturing companies to access real-time production data. It allows you to see where refraction can occur in the production line, why and when it happened, and much more.
Machine learning and predictive analytics allow for predicting and preventing problems by alerting operators or engineers in a timely manner so that they can take immediate corrective action, which will ultimately cost a 15-minute downtime against 5 hours of unplanned operating time in the future.
Farico is a stay calculation and machine monitoring solution applicable in many manufacturing industries. It can be useful in numerous ways:
Contact us for more information or a demo. Today is the day to start predicting and preventing equipment failure!
Preventing unplanned downtime must be a top priority for any manufacturer. Not doing so, can lead to significant financial repercussions for the business.
In a factory, production processes have to go smoothly. This can happen by incorporating appropriate maintenance strategies along with the right tools. If manufacturing companies take a proactive approach and apply the latest technology in maintenance management, they can expect positive results.
If you’re one of those businesses and are still unsure about applying preventive maintenance software, give us a call. We are here to help!
Unplanned downtime is measured by monitoring the time a machine or production line is not operating due to unexpected disruptions or breakdowns.
Minimizing unplanned downtime is crucial for manufacturers as it can increase productivity, lower maintenance expenses, improve customer satisfaction, and maintain competitiveness.
In manufacturing, downtime is classified into two main types:
Unplanned manufacturing downtime can be caused by various factors, including machine malfunctions, human errors, poor maintenance, and supply chain delays.
Businesses can address the unplanned non-operational time causes by:
How does preventive maintenance help in reducing unplanned downtime?
Preventive maintenance involves regular inspections and equipment servicing. This contributes to identifying potential issues and reducing unexpected disruptions in operations.
Predictive maintenance utilizes real-time data and condition-based monitoring to foresee potential failures and address them. Thus, it helps prevent unplanned downtime and optimize production efficiency.
The unplanned downtime costs in industrial manufacturing include:
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