Manufacturers are in a constant state of improvement, but increasing efficiency and reducing lead times has brought much attention to the amount of time machines are not in production. 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 machine downtime to ensure manufacturers are generating as much throughput as possible.
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. Additionally, over 80% of the companies lack the data or ability to accurately calculate the cost of their downtime. With recent trends in IIoT, machine connectivity, and monitoring solutions, manufacturers are starting to get answers to questions they did not even know they should have asked.
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.
Every manufacturing process has periods of time where equipment is unavailable due to setup, tooling changes, material changes, part changes, program changes, or any other changes to production that must be performed while equipment is stopped. However, many of these processes are highly inefficient due to a lack of measurement, analysis, and improvement. While it's highly important to track this time, most shops are unable to do so, and those who do more often than not attempt to do so manually which is inefficient, inaccurate, and the data is often difficult to compile, analyze, and derive insights from.
No one is perfect. Humans get tired, injured, forget, etc. Sometimes operators are overworked or are tending multiple machines. These reasons can lead to a machine going down for a significant time before being noticed. The skills gap is also resulting in a large portion of the workforce retiring, bringing with them deep tribal knowledge that may not be passed on to new hires.
With the cost associated with inventory, many manufacturers want to operate as lean as possible. Unfortunately, a lean inventory can increase downtime events when there are disruptions in the supply chain. Having insight into demand forecasting and materials supply can help to mitigate this problem.
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 is not 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, 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 is imperative to have accurate cycle time data to ensure expectations are realistic.
It is 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 is affecting production, and how you can work towards solving it. Not everything needs predictive analytics.
Manufacturers may talk about a fully autonomous facility, but you have to walk before you run. Using data as a foundation is the first step to learning what affects production and lead times the most. A machine monitoring solution like MachineMetrics provides accurate, real-time machine data and gives workers the ability to log and categorize the causes of downtime. All the information is automatically collected and standardized to be displayed in pre-built and customized real-time reports and dashboards to deliver critical visibility for managers. These insights identify gaps and opportunities for manufacturing leaders to drive improvements.
Here are some strategies to help reduce downtime:
Without enough accurate downtime data, it is 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 is 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.
Using automatic tracking technology provides real-time visibility to the shop floor, whether stakeholders are on the shop floor or at home. Downtime events are seen immediately on the dashboard. With real-time data and the right software, managers can address downtime as it happens. Additionally, automated notifications can be triggered based on downtime events. For example, if a pump alarm is triggered, MachineMetrics can send a notification directly to maintenance to streamline downtime response. If materials are running low, inventory control can receive a notification to refill or order more supply to ensure machines do not shut down while waiting for material.
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.
Goals give direction to staff and organizations. With accurate data and easy-to-follow dashboards, it is possible to track production between shifts, operators, and machines to establish baselines and set goals. Other benefits include the ability to:
Overall, goals and accurate data work together to improve overall communication. Employees that understand the connection between downtime and goals, or profit, help prioritize responsibilities and can increase their productivity which can reduce downtime.
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 that can predict when a machine needs servicing can prepare and plan maintenance when it will minimize disruption to production, and if there are any other parts that should be serviced during downtime to combine and reduce the overall amount of downtimes.
While a platform solution can reduce the amount of hardware needed, legacy machines may still need a way to connect. Simple I/O adapters or on/off monitoring is enough to start collecting downtime data. Most modern equipment will have the needed sensors or technology but may need a gateway to send data to a platform. MachineMetric offers hardware with multiple ways to connect for quick, easy integration.
With the adoption of new technology, the skills gap, and operator error being common causes of downtime, training is imperative. Training can not simply be a series of checklists and documents, but must also include a clear understanding of goals. A properly trained employee will reduce downtime by understanding their responsibilities and how they affect the team, production, and downtime It is important to enable operators with visibility into production so they can better understand where they stand when it comes to production goals on any given day. While training sounds self-explanatory, having performance data can improve training by identifying knowledge gaps and focusing on more likely challenges each employee might face.
Many manufacturers do not have accurate downtime data. They are 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.
With the high cost of production and lack of accurate data, managers can not make effective decisions to reduce downtime. Manufacturers must hone processes, reduce waste, and maximize machine utilization. MachineMetrics makes this possible without increasing already strained employees and resources.
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