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There are multiple approaches to machine maintenance, each of which can be used strategically depending on the machinery in question and the predictability of its maintenance needs.

In many scenarios, more mature and complex maintenance strategies yield the most machine uptime and cost savings. Advanced maintenance strategies like predictive and prescriptive maintenance use machine monitoring technology to gather information about factory floor machinery which is then processed and used to generate insights about impending maintenance needs.

Data can be collected via Industrial IoT devices and sensors, as well as from operators at machines. This information can be gathered and analyzed in real-time and used for near-instant insights that can prevent major machine damage as well as boost floor worker safety. This type of data can also be used for longer-scale insights that can guide decisions such as how many spare parts to keep on hand at any given time.

One of the most effective ways to improve machine maintenance for most manufacturers is to focus on data collection and analysis. Data-driven maintenance saves resources and provides benefits in a variety of ways:

• Replacing parts only when necessary, instead of wasting still-usable parts
• Reducing downtime due to unexpected failures
• Reducing the time required by maintenance staff
• Reduced risk of machine and safety incidents caused by ill-maintained machinery
• Reduced storage space required to keep excess stores of replacement parts
• Increases predictability that can guide budgets, schedules, production expectancies, etc.

In order to collect accurate, real-time machine data, manufacturers can use machine monitoring software, such as MachineMetrics, to connect directly to the control of the machine. This machine data is contextualized for a variety of stakeholders to consume across the factory. And depending on the maturity of the manufacturer, the data can be used in increasingly valuable ways.