When it comes to workforce management forecasting, I've always lived by a simple principle: start basic, then build up. Just got reminded of this while reviewing Philip Stubbs weWFM podcast episode: "I would always advise to start simple. And only add complexity to your forecasting model if you can demonstrate that it will give you extra accuracy."
In case you missed it Philip Stubbs's full episode can be found:
This advice by Philip Stubbs resonates deeply with my experience working with contact centres across Europe and the UK. Many organisations fall into the complexity trap, thinking more variables equal better predictions. Not always true.
Some key thoughts on this:
The best forecast is still a guess, but an educated guess beats shooting in the dark
Every layer of complexity you add should earn its keep with measurable accuracy improvements. The Forecast Value Added (FVA) method for this, you can read more about FVA in this blog post: https://lnkd.in/em64eQdd
Your sophisticated model becomes worthless if your team can't understand or maintain it
What's often overlooked:
Complex models typically have diminishing returns... the effort to achieve that extra 1% accuracy might not justify the investment
Simpler models allow for faster adaptation when business conditions change (as they inevitably do)
I've seen countless operations teams struggle with forecasting models that were architectural marvels but operational nightmares. The most successful implementations I've guided start with the fundamentals, prove their value, then evolve thoughtfully.
Your workforce planning approach should match your operational maturity. Better to master the basics than stumble with advanced techniques.
What's your approach to forecasting complexity? Have you found the sweet spot between simplicity and accuracy?
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