Forecasting

What is forecasting?

Forecasting uses mathematical models to calculate future demand, workload, and staffing requirements based on observed historical patterns. WFM is about having ‘the right number of people in the right places at the right times, doing the right things’ and forecasting is the first step in the process of determining ‘the right number of people'.

Sometimes, it simply isn’t possible to produce a forecast. For example, business process outsourcers (BPOs) are commonly provided by their clients with the forecast of workload or the required staffing. Organizations launching a new line of business will have no history with which to predict the future. Even in these cases, as soon as historical data begins to accumulate, the power of forecasting can be put to work.

Why does forecasting matter?

Forecasting is the foundation stone of workforce management. Without forecasting, it isn’t possible to balance the supply of employees with demand, because the demand is unknown. If demand is unknown, it’s impossible to schedule the workforce efficiently.

How does forecasting work?

Forecasting can be broken down into 4 steps:

1. Collect and analyze historical data

The main input to the forecasting process is the volume of demand or transactions occurring within specific intervals (e.g. per day or every 15, 30, or 60 minutes). Ideally, average processing time should be analyzed similarly. This is because workload depends on both volume and processing time, and processing time typically varies over time. In some industries, processing times can be longer in the evening than during the day, for example.

Long-term and short-term forecasting are both important. The more historical data that is available, the better you can detect seasonal patterns and growth trends in the data. It is important to collect data at short intervals. That’s because the goal is to match supply and demand across each working day, observing the peaks and troughs and avoiding under- and over- staffing. That isn’t possible If you only consider the total workload demand per day.

The data typically originates from the system that manages the processes for which the workforce is scheduled. When dealing with large datasets, an automated integration with this system significally saves time and minimizes errors.

Good forecasting practice includes analyzing the data to find anomalies such as gaps in history and one-off spikes in demand. These don’t belong in the forecast and must be removed. You should however keep a note of exceptions that you know will recur in the future, such as billing runs, advertising campaigns, or public holidays. We’ll come back to that in step 3, below.

2. Predict future demand

Once you have a clean set of data, you are ready to generate your forecast down to regular intervals throughout the day. Multiple forecasting methods (or algorithms) are available, including: 

• Moving weighted averages
• Triple exponential smoothing
• Auto-regressive integrated moving average
• Neural networks
• Multiple temporal aggregation

For the simpler algorithms, it’s possible to create a forecasting using a spreadsheet. The more powerful algorithms require a professional WFM application. injixo, for example, deploys multiple algorithms and uses artificial intelligence to constantly select and configure the algorithm that gives the best results with your data.

3. Apply business intelligence

Human intelligence is at least as important as artificial intelligence when it comes to forecast accuracy. No business experiences the same volume and pattern of demand 365 days a year, and frequently you will be aware of upcoming events that didn’t occur in the past. You have to deal with:

• Marketing campaigns and promotions
• Operational changes (e.g. billing, logistics, sales)
• Organizational crises (e.g. bad PR, competitive pressure)
• Corporate strategy and tactics (e.g. market development and expansion, new product launches, price changes, changes in customer base)
• Public holidays, some of which don’t happen on fixed dates
• Natural events (e.g. weather)

The impact of these exceptions must be factored into the forecast. This will be a manual process if you are using a spreadsheet, but if you are using a WFM application there should be a forecast calendar feature. Some of these drivers will have been revealed during the analysis in step 1. The rest need constant vigilance and good collaboration with colleagues in departments such as marketing. As with any process with a human element, there are several pitfalls to avoid.

4. Calculate the required number of employees

The final step is to convert the forecast of demand and average processing time into staffing requirements. Staffing requirement is the key input to the scheduling process. You need to determine the number of staff needed in each interval to handle a forecast workload while meeting a specific service standard.

In a contact center, for example, customer interactions can be handled via multiple channels: phone, web chat, email, etc. There isn’t a one-size-fits-all method of staffing calculation. 

The best-known method is Erlang, named after the Danish mathematician who invented it. Erlang is proven for inbound calls, but it’s no use for web-chat, because it doesn’t consider the fact that employees can typically handle more than one chat at a time. It’s no good for emails either, because emails don’t hang up and the goal is typically to handle them in a timescale measured in hours not seconds.

Sometimes, you need to create a constant staffing requirement for a period of time without an underlying forecast. This can happen when launching a completely new service, or for activities with such a low or volatile number of interactions that it is impractical to forecast. For example, in a retail store, time must be set aside for ‘facing up’, the process of sorting and arranging merchandise on shelves that previous customers have put out of order while browsing.

Staffing calculations need to take shrinkage into account. Shrinkage refers to the percentage of paid time that employees are not available to perform productive work. This includes unproductive at-work activities such as breaks, meetings, training sessions, and 1:1s, plus out-of-office time for vacations, sickness, lateness, and other unexplained absences. 30% shrinkage means that each employee contributes the effort of 70% of one full-time equivalent. That means that the staffing requirement must be inflated by (1 / 0.7 = +43%) to counteract the effect of shrinkage.

What impact does forecasting have?

Forecasting opens the door to the rest of the WFM process. The forecast calculates the number of heads needed in each time interval so you can schedule your employees accordingly. Without forecasting, scheduling is reduced to simple rostering, without regard to the underlying demand.

No forecast is ever completely accurate. This is why the workforce management process includes intraday management. Without a good forecast, ideally, one that is continuously updated with new data , the job of intraday management is considerably harder. Instead of taking infrequent, considered corrective actions, you are constantly fire-fighting.