Hi everyone, I’m Nicki from Empowered Analysts and and I'm really excited to be involved with this great initiative by 365 Data Science – Data Use Cases.
There are plenty of great use cases for data, but one of my favorite over the past few years is employee productivity.
We also made a video on the topic that you can check out below (or just scroll down to continue reading).
Data Science, Employee Productivity, and Work Efficiency
Imagine you work in a factory which employs hundreds of people to pack products for customers. Those employees working hard is crucially important for a few reasons. Firstly, making sure that customers get their product on time. So, they need to be efficient in what they do.
Why Is Employee Performance Important?
We also want to know that all those employees are working for the whole shift. If they work hard for one hour, but not doing much the rest of the time, our business will be affected. Customers won't get what they want by the time we promised it, and we might stop getting orders if people no longer use us. So that's where employee performance data comes in. We can query data stored in a transactional database using SQL to manipulate the output for reporting. We may well store it in a data warehouse to help speed up performance and have the output stored so that it can simply be queried on by analysts at a later point. That way we can focus on the insight rather than the data manipulation.
Employee Productivity Tracking: Key Measures
So, there are a few key measures here that are really important.
Firstly, being able to track which employees are due to work a shift and how long they get the breaks. That way we can identify the total time they're available for work. So, let's call that available hours.
Secondly, in productivity measuring, we then want to know what they've been working on during that time. So, we want some kind of logging system that monitors what tasks they're doing and how long it takes to do those tasks. We can call that logged time.
How to Measure Productivity?
Utilization
Our first measure is going to be employee utilization.
Let me explain.
Imagine Jane is an employee at the factory and she's going to be on a shift for 10 hours. So, 10 available hours. These are the number of hours she'll work after her breaks. Then, Jane also logs time for those whole 10 hours. That means she's been utilized 100 percent of her shift. And that is great. Exactly what we want. But suppose you have another employee, Samantha, who is also available for 10 hours, but only logs eight hours, so she only 80 percent utilized, not so great. We really want to start understanding why there's a difference in performance and what we can do to improve employees performance.
Efficiency
The next measure we'd want to look at is employee efficiency. So, this is the expected time it takes to do a task against how long it actually takes. So, if Jane took two hours to do a task and we expect it to take two hours, she'd be 100 percent efficient. If she did that to our task in one hour, she would be 200 percent efficient. But if she did that two-hour task in four hours, she would only be 50 percent efficient. This calculation will contribute to how much product the factory will distribute, how quickly their customers will get their products and, ultimately, massively impact the future products if customers don't come back to purchase more.
How Can You Monitor Work Performance?
Tools like Power BI and Tableau can then be used to summarize data for management to monitor. It can be presented at regional insight levels, but also be set to allow managers to drill down to teams or individuals so they can drive the behaviors that improving employees performance and productivity in their factories.
Interested in Ways to Improve Work Performance?
Productivity in the workplace is an infinite topic. If you want to expand your knowledge on employees performance and learn the best methods for managing a team’s workflow, check out the 365 Data Science Course in Product Management for AI and Data Science.
