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How Much Money Can Predictive Maintenance Save You?
In the event of planned downtime, there are unavoidable costs. These costs consist of labor expenses, including necessary overtime, in-house maintenance teams and outside contractors. There are production costs, such as reduced operation capacity. Maintenance planning, replacement parts, inspections, cleanings, machine and system restarts can also be included.
However, these costs can be compounded when there is unexpected downtime. There can be added costs for obtaining the necessary replacement components in an emergency. In many cases, such as with wind turbine repair, expensive and logistically complicated disassembly is necessary to determine the cause of the breakdown and to correct it. In the commercial aviation industry, having to keep an aircraft grounded because of unexpected repairs can cost billions in lost revenue.
Ineffective maintenance programs, such as those that rely too heavily on rigid time-based maintenance measures, result in unplanned downtimes that lead to inflated maintenance and operation costs. Condition-based monitoring and predictive maintenance provide organizations with the insights needed to reduce these costs while ensuring their machines are properly maintained. Plainly speaking, it is simply less expensive to fix a machine before it is broken.
Predictive Maintenance is a Cost-Saving Strategy
Maintenance cost reduction through predictive techniques begins with data. The data regarding the condition and usage of a machine, its maintenance history, environmental data and more are funneled into AI-powered algorithms that can learn the normal operations of a machine. Using a smart, IoT-enabled condition monitoring tool to help gather machine data is an important aspect of predictive maintenance and enabling the appropriate automated task, such as the scheduling of an immediate inspection.
For instance, when data that is collected during remote condition monitoring with Odysight®.ai’s visualization sensor solution indicate anomalies in the performance of wind turbine gearbox, notifications can be automatically issued. Maintenance teams can then begin planning the appropriate maintenance measures.
Condition-based monitoring and predictive maintenance enable maintenance teams to detect a failure in a machine early enough for them to take immediate action. Because of this early detection, the amount of time required to repair the failing is typically much shorter. But more importantly, it prevents the significant expenses that would have resulted from the failure of critical equipment.
This proactive approach to maintenance makes it easier to create maintenance budgets and to avoid going over the budget when breakdowns do occur. This means that Industry 4.0 organizations in the aviation, transportation, UAVs, wind turbine and energy markets can control their maintenance costs.
How Predictive Maintenance Saves Money
Being able to identify wasted maintenance resources, reduce or eliminate inefficiencies and determine the root cause of machine unreliability are advantages that organizations can use to mitigate downtime predictive maintenance cost savings of 25% in maintenance expenditures. Condition-based monitoring and predictive maintenance create cost savings by:
Unexpected downtime is expensive. In fact, it can be one of the largest money drains in Industry 4.0 organizations. For example, the unplanned downtime of machines costs industrial manufacturers $50 billion annually. Predictive maintenance helps eliminate unplanned downtime and allows maintenance teams to keep necessary, planned downtime to a minimum and at a more convenient, less disruptive schedule.
The autonomy over planned downtime is critical for operators in the railway or aviation industry, where busy schedules provide smaller windows of opportunity for maintenance and for which planned downtime sometimes has to be scheduled months or even years in advance.
Making Maintenance More Tailored
The maintenance of machines has never been one-size-fits-all. There are factors like improper usage, localized environmental conditions,etc., that inform what is necessary to keep machines working at optimal performance.
Predictive maintenance allows organizations to take those factors and directly address the exact conditions of the machines. This results in maintenance that ensures the only components replaced are those that have to be replaced.
Enabling Better Inventory Management
Being able to forecast a failure means that maintenance teams can take steps to obtain all the components necessary to prevent a breakdown.
This reduces or eliminates the last-minute purchases of components and having to pay expedited shipping costs. Organizations also save on the costs that come with overstocking inventory, including components that have been stored for so long that they have a shortened working life. It ensures that the replacement components that are stored onsite are service-ready when they are needed for a repair.
Improving Operation and Productivity
Machines that are in optimal working condition perform better and for longer than those that are not. With a predictive maintenance program, a machine’s uptime is increased because maintenance teams do not have to contend with excessive, insufficient or unscheduled maintenance tasks that do little or nothing to attend to the actual condition of the machine. Maintenance measures are applied only when they are absolutely warranted.
Odysight.ai Helps Industry 4.0 Save Maintenance Costs
Industry 4.0 organizations can achieve maintenance cost reduction through predictive techniques. With condition-based monitoring and predictive maintenance tools like Odysight.ai’s Camera-as-a-Sensor™ technology, organizations can obtain critical data from the areas of machines that are difficult or impossible to access by humans.
To learn how Odysight.ai can help you make maintenance a truly controllable expense for your organization, get in touch with us.