How Artificial Intelligence Improves Workforce Management

December 15, 2022

by Farshad Kheiri, PhD

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Legion helps businesses maximize labor efficiency and employee engagement simultaneously with workforce management software that’s intelligent, automated, and employee-centric. To maximize labor efficiency, we identify the precise amount of labor that’s required to meet demand. A company’s demand is defined as the number of customers, sales, transactions, etc.

To determine the required labor, we need to identify the tasks that hourly workers will perform. In almost all hourly jobs, the tasks are created based on demand. The hourly workers perform the tasks to serve the demand. Therefore, we must have a clear idea of the demand. Demand forecasting is the first step for any workforce management that has a one-to-one relationship with demand. An accurate demand forecast ensures the optimal published schedule.

Traditionally, in a retail store, for example, managers use the previous week’s demand to predict what will happen in the following weeks. This process is called “random walk”. (Different from the famous “random walk” as a random process in probability and statistics.)

More advanced store managers use statistical techniques, such as moving averages, e.g., next Monday’s traffic will be the average of the last six weeks of traffic on Mondays.

Other store managers use a more complex process and even more advanced techniques like ARIMA (Auto Regressive Integrated Moving Average) or SARIMA (Seasonal ARIMA).

In recent years, machine learning techniques have been used extensively for many problems requiring automation.

In demand forecasting, machine learning (ML) can be applied to enable managers to

  • capture more complex patterns over extended periods
  • use more input data from different sources
  • update as the data changes

As a result, ML techniques enable us to generate more accurate forecasting.

Sales, traffic, and transactions are driven by external inputs, such as the economy, weather, an event happening close by, etc. ML can consume these data points along with the demand at hand and generate more accurate forecasts because it can see how these events impacted the historical data. Legion WFM takes advantage of hundreds of models, and it selects the optimal forecasting ensemble model.

But what if a new location opens or management starts to gather data in an existing location? There are different techniques to generate synthetic data; depending on the application, each has benefits and drawbacks. Data scientists have studied data augmentation and synthetic data generation extensively and presented their findings in various literature. However, Legion developed its own patented algorithm that outperforms conventional techniques.

Data integrity plays an essential role in generating demand forecasts. In recent years, many workforce management vendors have focused on ML but haven’t focused on the data integrity and quality feeding these models (“garbage in, garbage out”).

To ensure ML works as expected, it’s critical to monitor the input data to ensure it is accurate. And that it hasn’t been tampered with randomly or systematically through an integration failure or clerical mistake.

Data integrity can be examined and tested using many different algorithms, which should be a major component in any ML platform.

One way to monitor data integrity is to check the input data distribution over time and track any changes or abnormalities.

Any consistent drop in ML model performance can be a good indicator of some data abnormalities, which could be a mistake or a real issue that needs to be addressed by re-adjusting (re-training) the models. Even when the models are scheduled to be re-trained periodically, a system should be in place to allow these re-trainings to happen as needed. Legion AI engine has an automated system to monitor the data integrity continuously.

Finally, although AI automates many repetitive and exhausting tasks, such as how the weather impacts traffic or sales and how a nearby sporting event can change demand, it should still allow human intervention. The AI system needs to be designed to consume feedback and apply it to improve the system. Human intelligence can be captured and added to AI power. Using techniques like online learning can help train the AI engine as it gets user feedback.

Even when the AI engine has captured a lot of external data about the business, it may have missed some unforeseen issues, such as an accident that caused a road closure or a health inspection that closed the store. The AI engine should capture these events and learn how they impact the forecast to improve future estimates. Any AI engine working closely with a human operator must gather feedback. When human intelligence enables artificial intelligence, forecasts are even more accurate. Legion AI engine empowers users to give feedback and rectify the results as they see fit. This feedback is weighted against what actually happens and weighted accordingly in the future.

These are some of the key aspects of Legion AI capabilities, which enable intelligent automation to improve business agility, accuracy, and labor efficiency – automatically. Demand forecasting is required to build an optimal schedule based on business needs and employee skills and preferences.

Learn more about Legion Demand Forecasting and sign up for a free pilot.