Posted under Blog by Kristin Smith, Content Marketing Specialist
The beginning of a new year often brings excitement. It is a time where new goals can be set, and hope for change is alive.
In business, you’ll find that the beginning of a new year means time for new initiatives or a new roadmap. It is an opportunity for organizations to re-evaluate their systems and processes and make changes for the better. Organizations can develop a list of priority items that they want to accomplish by years end.
Trends in the maintenance world have been steadily headed towards more advanced technology and 2019 shows no signs of changing direction. Discussions centered around how machine learning impacts maintenance is dominating the industry.
So the question stands, what’s in your 2019 roadmap?
As discussed in our previous blog: The Future of Maintenance is Now: How Machine Learning is Advancing the Industry, Machine learning is the current application of artificial intelligence (AI) where machines are granted access to data and automatically learn and improve from experience without being explicitly programmed.
The Gartner, Inc. 2019 CIO Survey shows that the number of enterprises implementing AI and machine learning initiatives grew 270% in the past four years, and tripled in the past year alone. Gartner, Inc. predicts that by 2021, 70% of organizations will assist their employees’ productivity by implementing AI and machine learning in their workplace.
AI is increasingly making its way into various workplaces, in all types of industries. For maintenance, this can mean a major shift in how work is detected, scheduled, executed and analyzed.
Machine learning has the potential to offer maintenance organizations with an advanced method to transform their maintenance processes from reactive to predictive, and thereby can increase asset efficiency, reduce downtime and ultimately save money. Machine learning offers a new, smarter route to meet maintenance goals.
According to PWC’s report Predictive Maintenance 4.0 – Predict the unpredictable, there are four levels of predictive maintenance.
Level 1: Visual Inspections – periodic physical inspections; conclusions are based solely on an inspector’s expertise.
Level 2: Instrument inspections – periodic inspections; conclusions are based on a combination of inspector’s expertise and instrument read-outs.
Level 3: Real-time condition monitoring – continuous real-time monitoring of assets, with alerts given based on pre-established rules or critical levels
Level 4: Predictive Maintenance 4.0 (PdM 4.0) – continuous real-time monitoring of assets, with alerts sent based on predictive techniques, such as regression analysis.
PWC goes on to define PdM 4.0 as
“harnessing the power of artificial intelligence to create insights and detect patterns and anomalies that escape detection by the cognitive powers of even the most gifted humans. PdM 4.0 gives you a chance of predicting what was previously unpredictable”
In other words, PdM 4.0 utilizes machine learning to predict, detect and resolve maintenance issues.
Machine learning solutions make handling tremendous amounts of sensor data a much easier task. It allows organizations to detect and respond to failures before they take place. More significantly, the data gathered can be used to further optimize maintenance processes and performance.
One major challenge for organizations hoping to implement a machine learning strategy is figuring out where exactly to start. The prospect of machine learning offers so much potential that it can be overwhelming to review and try to decide where the priority lies.
Maintenance work scheduling is at the heart of any asset maintenance strategy. Few people have as much influence on maintenance and reliability as the planners and schedulers. They are responsible for establishing work priorities and ensuring maintenance workflow is controlled so that asset utilization and uptime can be improved. Managing and controlling workflow with the right resources will reduce maintenance costs, optimize asset utilization/uptime, and mitigate risk.
Machine learning offers organizations with the potential to advance their scheduling processes with predictive algorithms.
Machine learning capabilities can be applied to various sections of the planning and scheduling process to improve the quality of work orders and optimize problem areas.
These algorithms can improve planning processes by focusing on key implications of planning and scheduling including asset criticality, estimated hours, material required and available, work order priority and more. Machine learning can analyze these areas in order to make better maintenance planning decisions.
Similarly, machine learning algorithms can improve maintenance scheduling by completing complex analysis on common problem areas and building schedule scenarios based on optimization of areas such as cost, duration, assets pms, asset priority and more.
Machine learning gives organizations the ability to make superior schedules. These schedules take complex details into consideration to produce a smarter and more optimized process than ever before.
Machine learning offers organizations the ability to leverage predictive algorithms to optimize maintenance processes, reduce downtime and boost production. In other words, machine learning offers maintenance organization the ability to gain a deeper understanding of the ins and outs of their physical assets.
Machine learning is the new frontier, but is it in your 2019 roadmap?
To learn more about how the VIZIYA WorkAlign® Suite is utilizing machine learning, contact one of our reps now: