Unplanned downtime is the largest source of revenue loss in many companies. When machines in a manufacturing plant fail to operate, overhead costs continue to increase, causing a company to lose vast amounts of money.

In the last three years, 82% of companies in the US experienced downtimes that lasted for an average of four hours, costing the companies about $260,000 per hour.

This whopping amount has a significant effect on the company’s cash flow. There are 4 Benefits of Advanced Pattern Recognition Software Plant managers are always on the lookout for technological solutions that can prevent these incidents from happening. One innovation that has helped many institutions achieve operational excellence is the advanced pattern recognition software.

What is Advanced Pattern Recognition?

Advanced Pattern Recognition or APR is the process of detecting and analyzing patterns and trends in operational processes, data, and asset conditions. It aids in improving the production and maintenance procedures of manufacturing plants.

APR is a breakthrough solution for the challenges that many production plants face. With the massive volume of data they receive and process every day, they need to sort through all the noise to discover actionable intelligence. The Industrial Internet of Things (IIoT) makes data processing more complex, thus the need for Advance Pattern Recognition.

How Can a Manufacturing Plant Benefit from APR?

The following benefits highlight why advanced pattern recognition is a must-have for manufacturing plants and production sites.

  1. Process Optimization

With the deluge of data that process engineers need to deal with daily, identifying patterns in production and operational systems can be quite a challenge. APR can significantly optimize disruptive and cumulative operational systems. It can hasten the identification of signal data patterns and simplify the recognition of process conditions.

2. For an Improved Search Process

Advanced Pattern Recognition uses an existing historical database to create a critical base layer for the new systems’ technology. Machine learning algorithms, together with APR, enables users to search for trends in processes within specific events. They are also helpful in detecting process anomalies. This technology is specially designed for simple installation, without disrupting the existing historian infrastructure.

3. To Save on Production Costs

The APR software provides an early diagnosis of equipment malfunction days or even months before it breaks down. It helps asset-driven organizations prevent downtime, boost performance, and increase equipment reliability.

When production equipment runs smoothly without significant failure, operation costs, and maintenance costs are reduced. Production equipment issues can be dealt with even before it blows into a considerable disruption. The software will immediately notify the process engineers so they can provide concrete solutions and adjust the schedule and human resources accordingly.

4. For Comprehensive Data Analysis

The APR software provides advanced model-based and statistical business tools that allow users to monitor potential production issues effectively. It serves as a platform to enable the viewing of raw data and the results of the model. It also allows users to compare the performances of similar assets and analyze the effects of specific alerts. Data is effectively interpreted using statistical applications, allowing data scientists and engineers to focus on other tasks.

By determining the production patterns, engineers can quickly identify the sources and causes of failure so that they can avoid them in the future.

The power generation and manufacturing plant industry have essential roles in the economic development of the country. Their optimal performance and efficient services can drive not only the company’s growth but the progress of the nation as well. When you go with Advanced Pattern Recognition Technology, you enable the company to reach its maximum potential, which will surely boost the economy.

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