ProcessMiner is a decision automation platform that uses real-time artificial intelligence

We help reduce energy consumption, raw materials and chemicals usage, using the steps below.

1

Monitor

2

Predict

3

Recommend

4

Optimize

5

Learn

1

Monitor

Collecting clean, relevant and actionable data.

The first step in the optimization process is to access the historical data and analyze it. The analysis will determine if the data collected is “clean” or whether any adjustments need to be made.

The right sensors must be placed in the right locations, transmitting their data and relaying the information back to a server where a database stores the records. Some manufacturers may collect data from hundreds of sensors but they use only a portion of the information.

For example, the data assessment may uncover the need to replace a faulty sensor, or discover a gap in the data collection that needs to be filled. The data scientists at ProcessMiner™ help to translate the analysis of the historical data and determine the information is useful and trust-worthy.

2

Predict

Collecting clean, relevant and actionable data.

The first step in the optimization process is to access the historical data and analyze it. The analysis will determine if the data collected is “clean” or whether any adjustments need to be made.

The right sensors must be placed in the right locations, transmitting their data and relaying the information back to a server where a database stores the records. Some manufacturers may collect data from hundreds of sensors but they use only a portion of the information.

For example, the data assessment may uncover the need to replace a faulty sensor, or discover a gap in the data collection that needs to be filled. The data scientists at ProcessMiner™ help to translate the analysis of the historical data and determine the information is useful and trust-worthy.

3

Recommend

Collecting clean, relevant and actionable data.

The first step in the optimization process is to access the historical data and analyze it. The analysis will determine if the data collected is “clean” or whether any adjustments need to be made.

The right sensors must be placed in the right locations, transmitting their data and relaying the information back to a server where a database stores the records. Some manufacturers may collect data from hundreds of sensors but they use only a portion of the information.

For example, the data assessment may uncover the need to replace a faulty sensor, or discover a gap in the data collection that needs to be filled. The data scientists at ProcessMiner™ help to translate the analysis of the historical data and determine the information is useful and trust-worthy.

4

Optimize

Collecting clean, relevant and actionable data.

The first step in the optimization process is to access the historical data and analyze it. The analysis will determine if the data collected is “clean” or whether any adjustments need to be made.

The right sensors must be placed in the right locations, transmitting their data and relaying the information back to a server where a database stores the records. Some manufacturers may collect data from hundreds of sensors but they use only a portion of the information.

For example, the data assessment may uncover the need to replace a faulty sensor, or discover a gap in the data collection that needs to be filled. The data scientists at ProcessMiner™ help to translate the analysis of the historical data and determine the information is useful and trust-worthy.

5

Learn

Collecting clean, relevant and actionable data.

The first step in the optimization process is to access the historical data and analyze it. The analysis will determine if the data collected is “clean” or whether any adjustments need to be made.

The right sensors must be placed in the right locations, transmitting their data and relaying the information back to a server where a database stores the records. Some manufacturers may collect data from hundreds of sensors but they use only a portion of the information.

For example, the data assessment may uncover the need to replace a faulty sensor, or discover a gap in the data collection that needs to be filled. The data scientists at ProcessMiner™ help to translate the analysis of the historical data and determine the information is useful and trust-worthy.

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