In today's fast-paced world, organizations are seeking new ways to increase their efficiency and optimize their operations. One promising solution is predictive asset analytics, which can enable companies to proactively monitor, and assist in inferring the condition of their assets. If used correctly, decisions based on this information can lead to increased uptime, optimized maintenance schedules, and reduced costs. In this article, we will explore the meaning of predictive analytics, its role in asset management, and how it can be used in a larger decision-making architecture.
Predictive analytics is a data-driven approach that uses advanced analytics techniques to identify patterns, update understanding, and predict future outcomes. In the context of asset management, predictive analytics can use historical data and machine learning models to estimate the remaining life of a mode or assist in predicting when it will fail. However it doesn't need to be this complicated. Predictions can also be based of subject matter experts current understanding or thinking around the failure mechanisms. This knowledge enables companies to proactively schedule maintenance activities and replace parts before they fail, if it is cost beneficial to do so, thus reducing downtime and increasing asset availability.
To implement a predictive analytics system, it is important to first understand the current condition of the asset. This could involve collecting and classifying sensor data, as well as other asset information such as age and maintenance history. By analysing this data, a estimate of the current condition can be established, anomalies can be identified, and insights into potential issues can be derived.
Once the current condition has been established, the next step is to estimate the remaining life of the mode or asset. Again, this can be done using existing domain knowledge or increasingly by machine learning models, which can identify patterns in the data and make predictions about the future. By estimating the remaining life of the asset or mode, companies can proactively plan the appropriate maintenance activities, avoiding costly downtime.
Predictive asset analytics is just one piece of a larger puzzle. To make informed decisions, companies must also consider strategy, constraints (including budgetary and deliverability), objectives, and other measures of value. Only after considering these factors, can we generate future plans, budgets, and risk profiles that are aligned to the business needs. By combining predictive asset analytics with these other processes, companies can gain a more complete understanding of their operations and make better decisions.
Predictive asset analytics is a powerful tool for asset management, enabling companies to proactively monitor and maintain their critical assets. However, it is just one piece of a larger puzzle, and companies must also consider other factors such as strategy, constraints (including budgetary and deliverability), objectives, and other measures of value. As a leading provider of predictive architectures, Modla is well-positioned to help organizations implement and optimize their decision making architectures to include predictive asset analytics systems, enabling them to operate with the latest technology and information.