Process loss, also known as process inefficiency, is a concept in industrial engineering that refers to the amount of waste or energy that is lost during the production process due to inefficiencies. Process loss can occur in many different forms, such as excess energy consumption, lost materials, or inefficient processes. It is important to measure and monitor process loss in order to reduce energy costs, increase plant efficiency, and reduce environmental impacts.

The first step in preventing process loss is to identify and measure it. This can be done by measuring the inputs and outputs of a process and comparing them against expected results. For example, if a manufacturing process requires a certain amount of energy to run, but the actual energy consumption is higher than expected, then that would be considered a process loss. Other forms of process loss can be measured in a similar manner, such as measuring the amount of material used or the time required to complete a process.

Once the process losses have been identified and measured, the next step is to determine the causes of the losses. This can be done by analyzing the process and identifying areas of inefficiency. Common causes of process loss include incorrect process settings, inadequate maintenance, faulty equipment, inefficient workflows, and more. Once the causes of the losses have been identified, they can be addressed through process improvements, such as adjusting process settings, upgrading equipment, and improving workflow.

Process loss can have a significant impact on a company’s bottom line, as it can lead to increased energy costs, decreased plant efficiency, and increased environmental impacts. It is therefore important for companies to monitor and measure process loss in order to minimize its impacts. By identifying and addressing the causes of process losses, companies can improve their energy efficiency, reduce their environmental impacts, and increase their profitability.


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Kumar, V., & Singh, A. (2019). Process loss analysis and optimization using data mining techniques. International Journal of Advanced Manufacturing Technology, 102(1-4), 785-798.

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