The conventional method used to measure productivity in manufacturing companies is output divided by input. This method is ideal for the manufacturing industry,

Since workers focus on one task for a specified number of hours, thus making it easier to set a benchmark for the measurement of employee input. With time, this method of measuring productivity made its way into the knowledge economy. Most companies focus only on capturing output (e.g., calls made, tasks completed), then try to improve that output based on the expected output or what “feels right.”

Although that technique worked well for companies in the manufacturing industry, using work output to measure productivity for knowledge workers proves inaccurate and ineffectual. Work in the two industries could not be more different. When compared with workers in the manufacturing industry, knowledge workers spend more time at work, have many different tasks that they must complete, are exposed to myriad distractions, and do not have a unit count at the end of the day.

A research paper by the U.S. Army titled “Evaluating Knowledge Worker Productivity” states that not all production can be quantified. Cases in which the final product is developed by a number of employees (such as software development or testing teams) present a particularly intractable problem. This has been attributed to difficulties in measuring the input of individual employees within the teams.

Workers in manufacturing companies also work in teams, but since each of them is performing a single operation, it is easier to measure the individual contribution of each team member. This is not the case in the knowledge economy since collaboration between each team is a major portion of the input effort. Measuring tasks is possible, but how does one quantify the collaboration efforts of knowledge workers, especially those in the software development industry?

The various methods of measuring employee productivity used in software development, IT services and the BPO industry all focus on output, relegating the time and effort put in by workers to the background. To achieve accurate measurements, more emphasis must be placed on obtaining, collating and analyzing employee input data. Only then will managers be able to not only measure true productivity but also to improve it.

To get a true measure of productivity for knowledge workers, managers must take into account the input (regarding the tasks and collaboration efforts) required to bring about a particular outcome. However, acquiring this input data is a major problem; it requires significant time and effort and even then is imperfect at best. Seemingly, obtaining a measure of productivity for work in the knowledge economy is all but impossible.

The key to solving this problem is to automatically capture employee input data, which can be done by deploying automated effort tracking tools within the workflow processes. The effort tracking tools give companies the ability to capture input data accurately and provide them with enlightened analytics designed to drive employee engagement and productivity. It provides the means for automated measurement of effort and utilization compared with unreliable estimates. Ultimately, it helps to improve employee engagement, optimize processes and increase productivity and overall profitability.