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What is Machine Downtime: Example And Calculation

The overall cost of unplanned downtime in all industries globally is “certainly around $1 trillion,” according to a post by the International Society of Automation.

Manufacturers must always look for methods to increase production efficiency, equipment efficiency, as well as process efficiency because this figure doesn’t even include money lost during scheduled downtime.

We’ll talk about “what is machine downtime” in this post,  and then give you the calculation and example.

What does machine downtime mean?

Machine downtime refers to any time period (scheduled or unscheduled) where a piece of machinery is inoperable or unable to function.

Unplanned downtime frequently happens in the event of human mistakes or inefficiency, including delayed changeover or a lack of expertise or skill. Unplanned downtime is frequently brought on by poor maintenance and ineffective practices.

However, intentional machine downtime is typically ascribed to equipment upkeep, cleaning, and modernization. We won’t be concentrating on scheduled downtime as it is a recognized and regulated sort of downtime.

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How is unplanned machine downtime calculated?

Although the majority of production managers can count their downtime in hours, not all of them consider how much it really costs. It is frequently necessary to let key stakeholders know how much money will be lost and to not ignore any expenses in order to get their support for the planned adjustments.

You should take into account a variety of factors, including lost income from not manufacturing actual items, loss in worker productivity, expenses associated with rescheduling cycles and fixing equipment, as well as indirect costs like public affairs, when calculating the actual cost of downtime.

Considering these factors, your downtime calculation would be:

Cost Of Downtime = Lost Revenue $ + Lost Productivity $ + Recovery Cost $ + Intangible Cost $

Unplanned-machine-downtime-calculation
Unplanned machine downtime calculation

How to cut down on unplanned machine downtime

Recognize your equipment

When performing routine maintenance, look out for the following:

  • Do you have replacement parts, blueprints, and instructions for resolving frequent malfunctions on hand?
  • Is the technology outdated? Plan to replace that equipment as soon as it is practical if you are unable to swiftly fix it (for example, because replacement parts are no longer being produced or because you must order components and/or tools that will take a large length of time).

Establish a safe workplace

Accidents result in lost production. On your factory floor, look out for these things:

  • Do workers have on working protection gear and appropriate clothing?
  • Is machinery in excellent operating order and do they have protective features? Are they being kept properly?
  • Do employees understand and adhere to best practices and standards for safety?

Lower the cycle time

Reducing cycle time in manufacturing ensures that your customers are satisfied and receive the products they require when they require them, as well as preventing downtime. Here are some strategies for cutting cycle time:

  • Ensure that the workspace is neatly arranged. Workers may cut down on time spent searching for tools by removing unneeded equipment, shortening the distance needed to fetch items, and properly marking all gear. Make sure your workspaces are organized according to the 5S lean manufacturing method to achieve optimum efficiency.
  • Ensure that industry standards and best practices are created, followed, and often evaluated. When a new method is found to produce superior results, it becomes the standard, making ongoing development feasible.
  • Learn more about your factory floor. You may identify trouble areas and make improvements that will lessen and avoid future downtime by using shop floor management software. Seek for a cloud-based system with seamless integration, rules engines, add-on systems, dispatching features, and pacing clocks for production.

Although there are many aspects of machines that are beyond your control, you should regulate the roles that people play in downtime.

Explaining Downtime

Machine downtime, or the period when a machine is not in use, is one of the most frequent problems managers encounter. There are virtually obvious information gaps when assessing the primary factors that contribute to a business’s downtimes.

Explaining-Downtime
Explaining Downtime
  • How much downtime is there on the shop floor?
  • Why are the devices not working?
  • How much does downtime cost?
  • Where are the most important places to improve?

A downtime analysis includes providing answers to these queries. In an effort to decrease downtime, process engineers, shop floor managers, and other stakeholders can pool information from the shop floor to perform analytics to find problems, opportunities, as well as other insights.

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How Can Machine Downtime Be Tracked?

Manufacturing companies require performance data before they can do a downtime assessment, even if this should go without saying.

In the past, companies manually recorded downtimes on a board, an Excel sheet, or using ink and paper.bIt is frequently loaded into an ERP or MES system later.

The Challenge of Manual Tracking

As you can expect, gathering downtime statistics in this manner is rather challenging:

  • Unknown downtime durations.
  • Incomplete entries lacking essential downtime information.
  • The stated justifications for stoppages do not accurately represent the real reason for machine downtime.
ATTACHMENT DETAILS Saved. The-Challenge-of-Manual-Tracking
The Challenge of Manual Tracking

The issue here is that manually obtained data is typically erroneous and delayed, especially when trying to do a downtime assessment. This leads to a distorted perception of the plant’s real production efficiency and stops managers and operators from acting fast on the data.

Operators may choose not to disclose downtimes that are frequent, such as tooling changes. Key information may be overlooked or omitted from a report even though downtime is documented. Overworked operators may just note that the machine was stopped and guess the duration from their gut.

Additionally, manual data collecting takes a lot of time and necessitates unneeded data compilation. Not to mention that individuals doing the analysis are probably going to become lost in the contextless data, which is much harder to grasp and use successfully

Automating the Collection of Machine Data

Many of the issues with human techniques are addressed by automating data collecting through machine monitoring.

Automating-the-Collection-of-Machine-Data
Automating the Collection of Machine Data

Correct start and end times for each downtime event are automatically monitored when software connected to a machine’s control system is used. Some systems even forbid resuming unless operators enter the causes of the outage, and operators are only given a set selection of possible causes in order to deliver context-sensitive information.

In addition to being necessary for a good audit, automating the collection of data and guaranteeing its correctness lays the groundwork for sound decision-making that goes well beyond downtime assessment only.

This gives manufacturers information about downtimes that is accurate, right from the machine, as well as information about the “why” behind these downtimes. Operators and managers can quickly and simply use out-of-the-box analytics to understand exactly productivity and quality and take immediate action on data.

The complexity and scale of an organization have a significant impact on the automated data gathering technologies chosen.

Yet, companies that use automatic downtime monitoring are not limited to one data gathering technique or a specific downtime tracking program in order to record downtime.

Numerous of the above alternatives may be connected, offering scalability for small firms while enabling tests for bigger corporations.

Using such a data logger with either a CMMS or MES is one example. Even though the data wouldn’t be accessible in real-time, weekly bulk uploads with entire downtime offer a cheap approach to spot possible productivity problems on crucial computers.

What Information Are Required?

To do a successful downtime analysis, you’ll need to gather a range of data from your equipment and operators, such as:

  • Downtime category
  • If the downtime was planned or not the duration of the downtime
  • The machine where the downtime happened
  • When the downtime happened
  • The operator of the machine

To put the downtime’s causes in context, more details could be added.

How Can a Machine Downtime Analysis Be Performed?

Machine-Downtime-Analysis
Machine Downtime Analysis

Data collection, whether human or automated, is insufficient. To be examined, the data must be put into reports.

An automated machine tracking system will make this much easier and more helpful than just doing manually since it brings in the data automatically to conduct analytics, enters reports, and enables you to create customized dashboards and reports.

To better understand the reason why you are facing downtimes and attempt to prevent them, there is a range of metrics and reports you will need to check.

The main factors for machine downtime

The Downtime Pareto report, which compiles all the documented downtime causes, is among the most helpful for determining downtime causes.

The most extreme downtime causes may be quickly found with the use of this report. Additionally, you may divide the data into various shifts, machine cells, or single machines to get a more granular understanding of potential issues.

Overuse of tools, overuse of jobs, lack of operators, and unexpected machine repair are common types of downtime.

Comparing scheduled and unscheduled downtime

Downtime in manufacturing will never end until they are fully autonomous. Fortunately, this is recognized as “scheduled” downtime in expectations.

By using machine monitoring, you can observe where the data deviates from expectations and better understand what proportion of downtime is unexpected.

The performance levels of various machines, jobs, and shifts

Manufacturers can identify inefficiencies by narrowing their focus to a more particular aspect, including a certain task, shift, or machine.

Is there significantly more downtime for the night shift than for the day shifts?

Is one machine having more outages than the others? Does this result in a snag?

Increasing the depth of your analysis

The following are some more questions you will need to make your analysis more detailed

  • How long does it typically take you to fix machine issues?
  • How frequently do you run downtime analyses? monthly or weekly?
  • Which indicators matter the most to your business, and what role does downtime play?
  • What course of action would your analysis lead to? new technology, altered processes, etc.
  • Are your KPIs changing for the better as you make changes?

Manufacturers may follow continuous improvement programs realizing they have fast access to precise production data, enabling them to take practical actions to decrease downtime, with the help of the proper machine monitoring partner.

Machine downtime examples 

Wiscon manufactures high-quality precision components for a variety of sectors. Finding the leading causes of downtime was one of the company’s key concerns. For employees and managers to communicate more dynamically in real-time, the organization needed reliable data that was presented in the right way.

One of the main advantages of using the technology was being able to monitor when the machinery was operating and determine when the manufacture of a certain item was not going as planned. Engineering, for instance, is aware that the cycle count time is to blame if a part objective isn’t being met despite a 99 percent utilization rate.

Wiscon enhanced operator productivity and the company’s total capacity by 30%.

Another example is Carolina Precision, a supplier that specializes in Swiss-turned components with small bores and tight tolerances.

The business was able to determine what was generating the downtime and used Pareto charts to make a $1.5 million save for the first year.

  • An improvement in shop productivity of 20%.
  • 688 more operational hours per machine on average.
  • An ROI in only 11 days.
  • Reducing daily administrative tasks by two hours.

Conclusion

Applications for downtime calculation are helpful in a lot of manufacturing industries nowadays.

Unplanned downtime is costly across the board. Loss of machine parts or deviations from ideal settings not only involves the production of scrap but can also lead to dangerous working conditions for employees.  It will significantly affect the bottom line if resources are improperly manufactured.

Machine downtime analysis will hasten the adoption of a predictive strategy on your production floor in order to significantly reduce unexpected downtime and avert significant revenue loss. Hope you have a good time with Efex

Ngoc Lee

Writing is an art that speaks from my heart!

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