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As AI has spread its innovative reach to every entity of businesses and organizations, it is now proving itself to be useful for workforce forecasting. For any company, its workforce is a crucial asset and it is quintessential to schedule and monitor their work and duties for better productivity. Workforce management plays a significant role in driving organizational success in the right direction. Understanding the peculiarity of this, AI techniques have been leveraged for accurate workforce forecasting.
As noted by Atoss, workforce forecasting provides the basis for efficient, agile workforce scheduling. Only utilizing professional workforce forecasting is it possible to predict which employees with what qualifications are to be deployed where, when and at what cost — calculated per day, per hour or ideally down to the minute.
Numerous modern and dynamic companies follow the just-in-time principle that offers economic benefits in a whole range of areas – not just production and logistics. After all, the following ultimately is true of efficient workforce deployment: if the staff is deployed when they are needed, it means less expensive downtime and less overtime. At the same time, there is an increase in productivity and service quality.
And here, AI is making a huge difference. Let's explore, How?
Among all others, call center operations seem to be the one industry most strongly driving sophisticated AI-based forecasting solutions. Clearly, many others can benefit ranging from retail to healthcare to any of the many gig-economy business models where too many workers mean unnecessary cost and too few means potentially unhappy customers.
According to Data Science Central, it's likely that call centers led this evolution because the platforms designed to help manage the process automatically collect a huge amount of granular data about demand and worker performance. The fact that this data is already present in electronic form facilitates creating the modeling features for different model types. But platforms that service broad industry types or even other industry-specific platforms are rapidly catching up.
Moreover, Gartner identifies the sophistication of staff scheduling and forecasting techniques as one of the most significant variables currently differentiating these platforms, with only a few offering a full range of forecasting algorithms.
One can easily find these in the broad-case HCM (human capital management) suites that seek to integrate all aspects of HR, or the more focused WFM (workforce management) applications that typically seek to at least integrate:
• Working time and absences.
• Creating efficient work schedules, some including shift swapping and planned absences, with the ability to ensure that necessary skills are evaluated.
• Ensuring compliance with external rules from federal and state labor laws, local union rules, and any other agreements or local policies regarding working time, pay or leave.
According to Data Magnum's President and Chief Data Scientist, "When you're evaluating platforms or even tasking your data science team to upgrade your forecasts there are several choices and tradeoffs to be considered. And while there are applications targeting companies with as few as 25 to 50 hourly scheduled workers, the real payoff will come when you have many more. Some platforms claim to be able to handle detailed forecasts for as many as 10,000 workers but a sweet spot seems to be in the hundreds to a few thousand. In this range, there will also be significant historical data to help make your forecasts more accurate."
Moreover, there are chances that organizations will need several different forecasts with different time frames depending on the business problem they are addressing. Each of these may be best served by different techniques.
Typically, but depending on the business, one might need:
1. Interval forecasts for durations as short as 15 to 60 minutes over a single day.
2. Daily forecasts.
3. Weekly forecasts.
4. Annual forecasts.
While all forecasts need to separately consider level, trend, and seasonality, the shortest term forecasts, interval, and daily forecasts must also consider special events or times of day which can be anticipated but don't follow a smooth pattern. These might arise from changes in a marketing promotion; a new product introduction; weather factors; special events (e.g. Super Bowl or flu outbreaks); or equipment failures.
These need to be understood and isolated in the historical data before putting them through the forecasting routine and anticipated as new events in the forward forecast.
When evaluating forecasting applications or working with different techniques, these four are currently most common though not all are available on all platforms.
Holt Winters has been the go-to technique for some years. The reference to 'triple' smoothing is its ability to separately consider level (last month's actuals), trend (short or long term growth), and seasonality (variations over a year based on seasonal factors) in a single calculation.
The advantage is that this can be performed on a spreadsheet. The disadvantage is that it is easy to overfit the data by selecting the wrong smoothing coefficient for each factor. Also, the resulting forecast is as the name implies 'smoothed' so that short term granular trends critical to short term forecasts may be lost.
ARIMA has proven accurate in many complex situations and requires a professional level understanding to select the right version. There are several including for example Double Seasonal ARIMA developed at Oxford University that allows user to layer in multiple seasonalities.
For example, user could set one seasonality to account for 30-minute intervals (48 time periods in 24 hours) and a second to weekly trends (336 time periods of 30 minutes).
As this illustration shows, ARMIA and its variants can produce sophisticated forecasts taking multiple patterns into account.
It's no secret that over the last few years multi-layered deep neural nets (DNNs) have been used to discover and predict complex patterns. Time series forecasting shares elements of the two most common applications of DNNs, that is CNNs (convolutional neural nets) typically used for image classification (considered static data even if video) and RNNs (recurrent neural nets and its several variants) used for text and speech recognition and response (considered time series data).
CNNs have also been used successfully in time series forecasting and have a speed advantage because they can be deployed in massive parallel processing (MPP) which RNNs cannot. Another factor is how much data one has as both types require large amounts of training data.
In short, DNNs can be an effective time series forecasting technique but require deep data science skills to execute.
At the leading edge of time series forecasting are evolving techniques that combine elements of different methods in a kind of ensemble method that allows simultaneous consideration of long and short term trends and even special events.
Consider for example this time series forecast impacted by seasonality, special events, weather, and underlying trend. The forward forecast is quite complex and considers all these elements.
This work is based on an R routine called MAPA – Multiple Aggregation Prediction Algorithm, which produces some promising forecasts. There is also another MTA algorithm called Thief. Some of the major analytic platforms like SAS have similar approaches.
*Based on the insights from Data Science Central
