Heavy equipment failures always seem to occur at the most important stages of the manufacturing process. They can put vital projects in jeopardy, causing big losses to both men and machinery. Such failures can cost an enterprise a fortune and bring the entire process to a standstill.
Lack of preventive maintenance and reliability often leads to heavy equipment failures. Regular maintenance of equipment reduces the chances of accident during a production process. This is where predictive analytics can bridge the gap to combat such issues in the long run. Enterprises can leverage predictive analytics that makes predictions using techniques from data mining, modelling, artificial intelligence, etc.
Moreover, predictive maintenance is also a crucial activity to inspect or test for the presence of warning conditions that indicate the life-span of an equipment.
Subsequently, a corrective maintenance action plan is scheduled to replace, repair or overhaul the object before it breaks down.
Nowadays, various data analytics firms are speeding up to create holistic solutions for combating the issue of heavy equipment failures. For instance, ACSG Corp, a firm offering critical infrastructure protection solutions is capable of processing large amounts of existing equipment data to spot anomalies in the behavior of devices to predict the health of machinery. It reduces the chances of production loss significantly and ensures optimum use of the resources, while safeguarding both men and machinery.
The company uses data reconciliation process to draw correlations and validate the information pertaining to fault detection from large data sets. It smoothens the complex process variables, such as flow rates, concentrations and temperatures. The gross errors found in the measurement are either corrected or discarded.
Likewise, Tableau, a data analytics firm, determines the likelihood of future outcomes using techniques like statistics, artificial intelligence and machine learning, to name a few. It interprets an organization’s historical data to make predictions about the future. Predictive analytics can serve the purpose when the objective is well-defined, which is certainly the case in heavy equipment failures.
Heavy equipment and machinery accidents are common in construction, manufacturing, warehousing and other similar industries. Such failures can result from numerous factors, most of which can be prevented by predictive analytics. This technique can not only prevent equipment failures, but also help improve equipment lifestyle and reduce idle time.
Predictive analytics allows organizations to be more proactive, helping them make informed decisions. It can reduce the negative consequences and ensure fulltime production monitoring, which is a key function to ensure smooth completion of projects across various industries.