Once we have a calculation of demand1 it becomes a baseline to forecast the need for the future workforce. This process ‘provides one of the greatest benefits in workforce planning because it offers the chance for an [organisation] to reexamine its purpose and the direction of its programs in light of changes that are taking place in the external environment’.2
Drivers of Change
The difference between the current demand and the forecast is impacted by only one thing: change. These changes will either be internal or external.
- Internal changes are driven by your organisation. Is there a goal to increase revenue, which may increase the need for workers? Does your business plan to sell new products, which might mean a need for new capabilities? Conversely, is there an ambition to automate processes that might reduce the need for people on the production line?
- External changes are driven by factors outside your organisation. These are the political, economic, social, technological, legal and environmental (PESTLE) factors that present a risk or opportunity to your business. For example, is there an upcoming election cycle or a period of political uncertainty, that might impact consumer demand? If we plan to increase our revenues by 10% year-on-year, how much does that need to outpace the level of regional economic growth?
Forecasts based on reference class are just one of a number of quantitative methods that can be used to forecast the derived demand for the workforce.
• Extrapolation and Interpolation are perhaps the quickest and easiest methods of forecasting. Extrapolation involves taking the historical trends and projecting them forward, which is useful for understanding likely change based on external or internal factors. Interpolation involves taking a target state (eg 20% revenue increase by 2025) and working backwards to the current state.
• Moving Averages are an effective method of forecasting using time-series data. The simple moving average (SMA) is an unweighted mean of the time series data. IMAGE. If, for example, quarterly output for a process is 300 units, then the 3-month moving average would be 100 units.
• Error Correction Model is a much more complex, but highly effective, method of forecasting workforce demand based on time series data. It corrects for the short-term deviation from the long-term equilibrium that can exist in moving average models. One example was able to establish a long-term relationship between economic variables and the derived demand for labour within the construction industry3.
Qualitative approaches are some of the simplest to use when conducting demand forecasting and are useful where there is an absence of high-quality quantitative data. Some examples of this are:
• Delphi Method might involve the managing director, finance director, operations director and HR director each creating an independent and anonymous forecast of revenue change over the next 5 years. These are consolidated and shared so that those same people can revise their forecasts. This is repeated until there is a consensus of forecasts.
• Nominal Group Technique might involve the managing director, finance director, operations director and HR director each creating an independent forecast of revenue change over the next 5 years, then coming together to discuss and vote on the forecasts. This is repeated until there is a consensus of forecasts.
Taking quantitative or qualitative approaches will enable you to understand the impact of internal and external factors on your current level of demand, to provide a forecast of your future workforce requirements.
- Gibson, A (2021) Calculating Demand
- Perez, M B (2013) Strategic Workforce Planning in the Federal Government: A work in progress, in Ward, D L & Tripp, R (ed) Positioned: Strategic workforce planning the gets the right person in the right job, AMACOM, New York
- Wong, J M W et al (2007) Forecasting construction manpower demand: A vector error correction model. Building and Environment, 42 (8), pp 3030–3041.