The Cost Of Unexpected Industrial Downtime in the manufacturing industry, the actual cost of dealing with unexpected and unplanned downtime can be staggering. Many manufacturers say it’s the single largest cause of lost production time. The period of time in which a company’s machines are not in production gets lots of attention because of the high visibility of equipment failures and breakdowns. Plus, it results in loss of productivity and erodes customer trust. While nothing of value is being produced, the company’s overhead and operating costs continues to grow. This directly impacts the company’s bottom line.
According to recently released research results, in the past few years more than 80% of manufacturers have had their machinery go down and their production come to a screeching halt. The hourly cost to companies that have their means of production go down unexpectedly can be as high as $260,000 an hour. Whether companies have to let their equipment sit idle because of a hardware failure, unplanned machine maintenance, excessive tool changeover, waiting for the materials necessary to complete a task or the lack of an operator, manufacturers nationwide must deal with often massive losses.
The True Cost of Industrial Downtime Is Grossly Underestimated. Calculating downtime’s true cost to manufacturers is difficult. About 80% of companies significantly underestimate what it really costs them to have their machinery
down for hours at a time. Correctly calculating the true impact of having valuable, expensive, equipment sitting idle and workers unable to be productive requires the companies forced to endure it to take a broader view. Having equipment down means workers are being paid, but not generating income, lucrative contracts are not being fulfilled, the company’s reputation takes a hit and they could lose future contracts.
Productivity And Customer Service Hit Hardest. Studies show the areas that suffer the hardest hit when machines and equipment go down are productivity and customer service. However, the business as a whole feels the reverberations. The work stoppages last 4 hours on average and cost about $2 million.
This caused 46% of companies to fail to provide the services their customers needed. Plus, 37% of critical asset production time was lost and 29% of manufacturers couldn’t provide the service or support specific assets or equipment needed. To control unplanned downtime’s impact, many companies are investing in digital transformation.
Tracking Unplanned Industrial Downtime
For companies to accurately estimate the cost of having their equipment go down several times a year, they must have a record of how many times the equipment went down and an understanding why it happens. Traditionally, it’s has been possible to gather information on the number, frequency and length of times the equipment has gone down from the manual log sheets of the operator. However, many operators don’t log it when the equipment only goes down for a few minutes. To get accurate data on the equipment downtime’s overall duration and cost requires a platform that can track the equipment’s cycle status in real-time.
Neglecting Equipment Maintenance Hurts. Neglecting to do timely, systematic, regular, maintenance, upgrades and replacement of key pieces of equipment is a major reason the machines go down. More than 70% of equipment operators said they don’t know when maintenance services on their equipment are due. The maintenance approach most frequently used by manufacturers doesn’t rely on predictive data and analytics about 76% of the time. As a result, they aren’t very effective at reducing unplanned downtime. It’s surprising that with downtime’s high cost, many manufacturers don’t take a more proactive approach to equipment maintenance.
Typically, the manufacturing or automation equipment at many companies goes down periodically for a few minutes and is quickly brought back on line. Operators tend to log the actual data well after the incident has occurred and been addressed. This throws the accuracy
of the data logged in into the sheet on how long the equipment was down into question.
However, even if the equipment is down frequently for several minutes each time, that adds up and can be a significant total amount of time. If the operator quickly logs and categorizes the data each time there’s an equipment break down, a more accurate accounting the total time the equipment is down will emerge.
Using Data Effectively
Gathering accurate data on when, why and how long a manufacturer’s equipment is down
is key to understanding and solving maintenance related problems. There are excellent platform that can gather data on which machine went down, doing what job, on which shift and who the operator was. This data can be used for comparative analysis. However, 60% of operators in a recent study said it was a major challenge to deal with the outcomes of the data collected. So collecting accurate data is enough. Knowing why it was collected and what to do with the data is essential to use it effectively.
The data on how often, how long and why the equipment is down can be used to improve predictive maintenance and condition-based monitoring of the machines. It can:
A. Help Identify Data-Based Patterns
B. Enhance Cognitive Learning Capabilities
C. Create Opportunities To Do Cross-Organization And Cross-Industry Comparisons
D. Get Additional Analysis And Insights From Trusted Service Providers
With proper guidance, the data can yield a treasure-trove of actionable insights. It can help to get the equipment functioning better and longer and increase the bottom line.
Using Digitization To Reduce Equipment Breakdowns
Using digitization can help manufacturers significantly reduce the number of unplanned hours their machines are down. However, the organization will not become digital simply by doing digital things. It must go beyond just making technological changes in order to fully embrace the many powerful benefits digitization has to offer. With digitization it’s easier for manufacturers to define the problem with equipment breakdowns and devise ways to prevent it. Careful and accurate tracking of where and when the breakdowns occur is an important step towards reducing or eliminating equipment breakdowns.
Reducing Unexpected Production Backups
When a manufacturer begins using digitized platforms to monitor, collect and analyze data related to unplanned instances when machinery and equipment are down, reducing unexpected production backups becomes easier. The platforms can help companies develop the solutions needed to prevent equipment breakdowns and production backups. Knowing how, when and where equipment breakdowns happen, how long they last and what repairs are necessary to get them working properly is data companies can use to find and implement a permanent solution. Investing in digital tools and technology can transform manufacturing companies and prepare them for the next level through continuous improvement.
The Power Of Predictive Maintenance
Ineffective maintenance strategies are a major contributor to poor industrial asset management. Reactively dealing with equipment breakdowns, waiting for replacement parts and spending hours fixing serious issues, is costly. Predictive maintenance strategies employing smart sensors on equipment that constantly monitor and report on vital parameters related to the health and functioning status of industrial assets prevent breakdowns. They analyze sensor data and breakdown patterns and anticipate future failures. Using these valuable insights, manufacturers can prevent untimely equipment breakdowns.
Preventative Maintenance. With timely, accurate, data about their industrial assets companies glean from using predictive technologies, they can replenish spare parts in advance and strategically schedule maintenance services. This helps minimize the time and money lost due to equipment and machinery being down because of the need for unscheduled repairs. Preventative maintenance can play a key role in optimizing asset availability and
extending the lifetime of the assets manufacturers use in their production processes.