Manufacturers are in a constant state of improvement. Increasing efficiency and reducing lead times have brought much attention to machine downtime. Companies often underestimate the cost and amount of downtime they experience, despite the fact that it greatly affects the capacity of an entire shop.
The following will explore strategies and solutions to track, categorize, expose, and ultimately reduce unplanned downtime to ensure manufacturers are generating as much throughput as possible.
Machine downtime is based on a complex series of events; it isn’t related to a single event or type of event. Downtime can be expected or a surprise, depending on the circumstances.
Unplanned Downtime – Unplanned downtime is any unexpected stoppage due to technical problems, equipment or spindle breaks, tooling mishaps, etc. Unplanned downtime may also be due to operators, lack of cross-training, or lack of a good preventive maintenance plan.
Planned Downtime – This time represents when the machine was scheduled to be down during regular operating hours. It may include training, meetings, material shortages, preventive maintenance, or scheduled maintenance during events like changeovers.
Scheduled Downtime – This time encompasses the hours when the plant isn’t operating, such as weekends or holidays.
Cycle or Performance Downtime – This type of downtime occurs when there is a difference in expected versus planned output. Reasons may include quality issues, scheduled maintenance that was poorly done, or other events.
Just as there are several reasons for downtime, there are several different impacts on the company. Planned downtime that isn’t driven by optimized production processes (such as for changeovers and preventive maintenance) drains resources.
Cost – Downtime can be very expensive. Lower efficiency and throughput, higher cost for part replacement, increased spare parts inventory, and other stoppages impact the bottom line and increase the cost per unit.
Safety – Downtime is a safety concern. An injured employee who cannot work due to an accident can cause lost production time. This is compounded when the downtime event is due to training, broken production processes on the plant floor, and maintenance issues.
Lower Service Levels – Any downtime event in a manufacturing plant can reach a crisis level if it threatens repeat orders, the completion of existing orders, or lengthier lead times. Every time downtime occurs, it accrues cumulatively to create a loss that could impact orders.
Lower Morale - Employees know when downtime is a problem. It may be due to broken processes, a lack of cross-training, or improper attention to detail.
Calculating downtime is straightforward. In its simplest form, downtime is:
Planned Time – Actual Operating Time = Hours of Downtime
When data collection is automated and connected to a cloud-based analytics platform, downtime can be broken down by:
These actionable insights can help drive improvement and reduce downtime. If data collection and tracking are manual, deep dives into other aspects of downtime will be difficult.
Manufacturers often know they have problems and cite that the biggest one is downtime. However, that might be the extent a manufacturer knows about the problem. A 2017 report titled "What is the True Downtime Cost (TDC)" stated that most companies significantly underestimate their true downtime cost. With recent trends in IIoT, machine connectivity, and monitoring solutions, manufacturers are starting to get answers to questions they didn’t even know they should’ve asked.
Carolina Precision was able to increase shop productivity by 20% and save $1.5 million by identifying the top causes of downtime using MachineMetrics. Read the full story.
Identifying the top causes of downtime is a good place to start looking for improvement. The most egregious downtime culprits often reveal seemingly obvious inefficiencies and areas for improvement. Below are some of the most common reasons for machine failures and causes of downtime in manufacturing.
Too little or too much maintenance can lead to failures. Another great benefit of connected devices is reducing the amount of preventive maintenance. With accurate machine data, manufacturers can better predict when maintenance is necessary. In the “Roadmap to Digital Maintenance Automation," we discuss how manufacturers can move toward a more effective, cost-efficient maintenance strategy to reduce costs while increasing equipment uptime.
However, it isn’t all about predictive analytics. In one example, a manufacturer documented over 14,000 hours for machine setup. After adopting MachineMetrics to automatically track setup times, the company discovered the true time for machine setup was closer to 1,000 hours. Being able to reduce planned downtime can free resources and make it easier to spot the size and scope of unplanned downtime. It isn’t necessarily about planned versus unplanned downtime but rather the largest problems affecting production and capacity.
Manufacturers are under stress. Letting this stress reach the operators can make them feel like they don’t have time to breathe, fix mistakes, and perform routine cleaning or maintenance. A stressful culture of constantly operating at maximum speed can lead to increased operator error and machine maintenance. This is why it’s imperative to have accurate cycle time data to ensure expectations are realistic.
It’s imperative to follow the analytics journey using data as a foundation. Before automation can be adopted and successful, data is needed to bring visibility to the problem and drive the decision-making that can eventually lead to automation. This visibility will show where the problem exists, the extent to which it’s affecting production, and how you can work towards solving it. Not everything needs predictive analytics.
Here are some strategies to help reduce unplanned downtime:
Without enough accurate downtime data, it’s difficult to prioritize improvement actions. Switching from manual to automated machine tracking is essential for not only understanding the total amount of downtime a shop is experiencing, but it’s also helpful for tracking a variety of KPIs, such as machine utilization and OEE. However, operators are still important to downtime data collection. They can provide the "why" behind downtimes by quickly documenting the reason for the event.
With MachineMetrics, operators can easily categorize downtime events on tablets placed at machines. All of this data is collected and propagated in pre-built and customized reports.
For a better view of the shop floor, MachineMetrics developed software with the operator in mind. If the machine is down or is down longer than expected, features appear on tablets placed at the machine, which lets the operator categorize and add reasoning for the downtime through the Operator view. Between automatic machine tracking and features that let operators log reasonings for downtime directly on the machine’s tablet, MachineMetrics gives both operators and managers the information they need to make better operational decisions and work to reduce downtime.
Real-time data also streamlines analysis and reports. The faster raw data is contextualized to be consumed (in reports and dashboards), the faster decision-makers can find and attack the largest causes of downtime. MachineMetrics uses connected technology and advanced cloud computing to deliver accurate, fast downtime reports with interactive Pareto bar charts that highlight the top reasons for your downtime.
Downtime Pareto charts easily identify the most common and costly downtime reasons.
Start an accountability or incentive program
Add value to staff evaluations
Find any training gaps or retraining needs
Finding the sweet spot for maintenance is possible with the right tools. Tracking machine performance can help adjust maintenance schedules or even predict when maintenance is needed. With the ability to reduce or eliminate unplanned downtime, predictive maintenance is another driver of connected technology. Managers who can predict when a machine needs servicing can prepare and plan maintenance to minimize disruption to production and if any other parts should be serviced during downtime to combine and reduce the overall amount of downtime.
Detect process variations
Identify trends and patterns
Make comparisons between actual performance and desired performance
Predict problems before they happen
Identifying key employees
Developing a strong bench of backup staff
Ensuring there is good communication between departments
Start with the basics
Break down the checklist into categories
Group related items together
Identify specific areas to inspect
Use pictures or diagrams to help illustrate the areas you’re inspecting
Machine safety features
Electrical wiring and components
Spare parts inventory
There are a number of ways to make backups. One common and reliable method is to make backups using cloud-based services that securely store your files online. MachineMetrics offers this service through a cloud-based manufacturing software that ensures data reliability through backup and recovery solutions, meaning your data will always be protected from data loss.
Many manufacturers don’t have accurate downtime data. They’re unsure of the exact reasons or how much downtime is costing the company. Many manufacturers are experiencing a skills gap and stressed resources. Adding manual data collection, analysis, and reporting only strains employees and resources further. This is why enabling a downtime tracking solution is so important.
With the high cost of production and lack of accurate data, managers can’t make effective decisions to reduce unplanned downtime. Manufacturers must hone processes, reduce waste, and maximize machine utilization. MachineMetrics makes this possible without increasing already strained employees and resources. Our production monitoring software can give you insight into your downtime issues to improve processes and reduce unplanned downtime.
47 Pleasant St, Suite 2-S, Northampton, MA 01060
For Machine Builders and Distributors