Workforce planning has come a long way from the days when employees were recruited based on gaps in the company organization chart.
Successful organizations are making workforce planning a strategic process – one that is based the business strategy of the company and supports both short-term and long-term goals.
Strategic workforce planning is not something that can be done at the last minute or in your spare time. It involves the whole company. It’s also an ongoing process, not a one-time exercise.
One of the trickiest parts is building a workforce planning model that works for your company. The model should be able to assess your current workforce, analyze future business scenarios and map out their probable outcomes so you can determine your future staffing needs and take the necessary steps to achieve them.
Not only should your workforce planning model help to assure that you have the right people in the right jobs at the right time – it should help you adjust quickly to disruptions in the market, unforeseen shifts in talent demographics or any unexpected changes.
Clearly, there is a lot riding on the workforce planning model you use to assess, analyze and forecast the valuable human resources your company needs.
Think of your workforce planning model as the core of a complex exercise that requires careful data aggregation and planning.
While it sounds daunting, you don’t have to go it alone. It pays to use specialized tools developed specifically for smart workforce management.
In this post, we’ll quickly review the definition of workforce planning; the main steps that comprise workforce planning, and the elements that can go into a workforce planning model.
We’ll also take a look at AI-driven tools that assist the workforce planning process and help it become an integral part of your organization’s workforce management efforts.
Workforce Planning in a Nutshell
In short, workforce planning is having the right people in the right jobs at the right time. Successful workforce planning ensures that your company is neither understaffed nor overstaffed, but optimally staffed – with the necessary skills to achieve business goals.
The workforce planning process comprises four main steps in a rinse-and-repeat cycle:
Step 1: Strategic workforce planning begins with the business and organizational strategy of the company and flows from there. Once the company strategy is clear, workforce planning can commence.
Step 2: Analyze the current workforce in terms of size, quality, cost, agility, performance and future potential. Once you understand the workforce formation that you already have, you can begin to map out the future skillsets and roles that will be needed.
Step 3: Envision the future in multiple scenarios and prepare for probable outcomes. Since the future is never certain, strategic workforce planning creates multiple scenarios of the future and uses tangible examples to plot possible outcomes.
Step 4: Determine your future workforce and take the necessary steps to achieve it. This process necessarily occurs in stages.
What’s in a Workforce Planning Model?
Workforce planning models range from simple spreadsheets to complex systems of data aggregation and analysis. It depends on how many variables and scenarios you want or need to take into account in order to obtain an accurate forecast of workforce requirements.
Simple Workforce Planning Model
A simple workforce planning model, for example, could be based on sales forecasts over a 5-year period for one or more product lines. Each product line requires a certain number of sales and support personnel.
Based on the projected sales figures, HR will know the number of people and the types of skillsets they need to recruit. Sometimes, the model may point to retraining or retirement of employees if a product line is expected to diminish and phase out over time.
The profitability of each product line can be factored into the analysis – giving preference to workforce requirements in the more profitable lines.
Complex Workforce Planning Model
A complex workforce planning model, for example, would consider additional data in the assessment and analysis of the workforce requirements. Continuing our example from above, some roles are more critical than others to the success of the business.
The criticality of these roles and their skillsets can be factored into the model. The same is true for revenue per employee, internal and external mobility per employee, and retraining potential if you anticipate switching employees to different roles or product lines.
Complex models can also take business divergence into account. For example, a business whose support model is going digital after many years of in-person interaction must plan workforce changes to support that strategy.
Complex models can also factor in external elements such as competition and talent demographics. You may want to analyze the probable outcome of recruiting contingency and freelance resources from a global talent pool to work alongside or instead of local talent.
The future of your business is affected by numerous factors, both internal and external. The more of those factors you can put into your workforce planning model, the more useful it will be in helping you to assess, analyze, and determine the optimal workforce to achieve your goals.
Tools for Building a Workforce Planning Model
Today, workforce planning is blessed with tools driven by artificial intelligence and machine learning that replace the cumbersome mathematical models that only experts can manipulate.
A leading example is NICE WFM Enhanced Strategic Planner (ESP), which leverages AI and ML to provide a highly usable tool for accurate long-term workforce planning.
NICE Enhanced Strategic Planner (ESP) allows you visualize the impact of potential decisions and future workforce trends through AI-based forecasting that helps to:
- Automatically evaluate dozens of forecasting algorithms and determine the model with the greatest accuracy.
- Increase the accuracy of the workforce plan.
- Increase the operational efficiency of the workforce plan.
- Adapt to changing data patterns.
Article written by Richard Correia at NICE.
This blog post has been re-published by kind permission of NICE – View the original post
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