In instance, every gadget with a sensor is getting more connected as heavy machinery becomes more interconnected. As a result, the value of machine data is increasingly decided by how firms use it to increase productivity and reduce costs, not merely by the amount of information delivered. One can access the information pool and use it to construct algorithms that are perfectly suited to their intended uses by employing analytical tools.
Yet, not all equipment owners are convinced of the promise of big data and IoT. (Internet of Things). They consider it to be mere hype that will never produce the desired outcomes. When the data deluge is correctly understood, firms realize they can use their equipment for more than just renting or selling. They now have a thorough understanding of how their equipment works and the type of maintenance required to increase its useful life.
The ability to control asset behaviors and minimize guesswork will have a significant impact on the machinery sector, and the benefits are immense.
How so? Thanks to a single, cohesive IoT platform that allows data analytics, facilities may now take advantage of opportunities that were previously not possible in traditional systems.
ERP (Enterprise Resource Planning) integration with the Internet of Things will be advantageous for the following three categories:
1. Equipment Reliability & Maintenance
Equipment vendors have often created maintenance plans using broad averages and assumptions. For instance, historical trends or advice from the original equipment manufacturer propose that maintenance be carried out on equipment every 30 days or every 100 components (OEM). The complexity of the equipment, including usage, component failure, tool wear and tear, vibration, and other issues, were infrequently taken into consideration.
The predetermined criteria for your machine's health may be accurately assessed with the aid of a linked operation of maintenance monitoring software. You can then allocate your maintenance funds more wisely to the areas of your pipeline that require them. When machine data is analyzed using ML (Machine Learning) and AI (Artificial Intelligence), insights are produced, ensuring that maintenance is performed only as needed. Due to this, reactive or calendar-based machine health methods can be used rather than proactive and predictive ones.
This machine health data's diagnostic information and insights enable management and technicians to take action before a component breaks, reducing downtime and the risk of further machine damage.
2. Effective Resource Allocation
Maintenance managers monitor a variety of factors, including the internal workforce, the amount of work and required shifts, the parts and fluids required for routine maintenance, and the state of the equipment. Instead of using actual production data, they usually use the "paper and pen approach" to produce maintenance logs and OEM recommendations. Since all writing must be done by hand, errors increase and become more common.
Resource scheduling optimization enables proactive decision-making when machine data is connected to the cloud, reducing operator-caused delays. How does this help maintenance managers utilize the resources at their disposal? In order to justify their capital investments, they can now create schedules, plan for ongoing maintenance costs, manage equipment life cycles, and have a better understanding of the types and quantities of resources needed to support the facility.
3. Find and respond to IoT events
Prior to IoT Events, specialized, expensive software was required to collect data and employ decision logic to locate an event. Starting a trigger is the next step, which will allow another application to respond to the event. Using IoT Events, it is easy to locate events among the hundreds of IoT devices transmitting diverse telemetry data. Thanks to automatic triggers and protocols, equipment facilities may now evaluate their maintenance, providing a complete picture of the state and health of the equipment, to make the best decisions.
Let's look at how artificial intelligence and machine learning could be incorporated into your present ERP to promote early detection and offer unique insights into events:
Automation: When a machine event occurs, such as an alarm, downtime, or condition threshold, an automated process can quickly generate a work request or work order.
Alerts and notifications: When a specific event happens, like when a machine breaks down, machine data may send the right individual a message by email, SMS, or Microsoft Teams.
Predictive Maintenance Alerts: Using trends and past data, a predictive maintenance system predicts future incidents. When a failure is due to happen, the appropriate person can be quickly informed.
4. Reporting and Analysis
The equipment sector needs quick access to data in the optimal format, such as dashboards, spreadsheets, or PDFs. Since the company's core activity is "keeping machines operating," existing ERP systems need capabilities to connect various data sources for a complete view of the business. Machine usage, downtime, MTBF, OEE, and other indicators can only be fully comprehended with equipment-specific data.
Creating custom analytics and dashboards that cover all facets of a business, from operations to finances and beyond, may significantly improve equipment management and return on investment.
Improvement of Performance Through Data-Driven Maintenance
If you work in the equipment industry and want to streamline your operations by integrating IoT and machine learning, Annata 365 with Dynamics 365 offers heavy equipment maintenance software that preserves the equipment's lifespan and connects all crucial business activities. The Annata team is committed to offering the best products and services to aid in the effective operation and customer interaction of equipment manufacturers, dealers, importers, and rental businesses.
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