Going Beyond a Judgment-Based Forecasting Process

Going Beyond a Judgment-Based Forecasting Process

Person using software to schedule workforce


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What does your scheduling process look like?

As the days lengthen and the season shifts to spring, the way you schedule your employees will begin to differ from the cold winter months. As a manager, you know that foot traffic through the door will increase as people enjoy the warmer days outside, so you adjust your upcoming schedule accordingly.

As you think of how to fulfill the demand for your future sales and services, what other factors do you consider? Promotions, online traffic, day of the week, overall economic trends? How do you decide if you have enough manpower to effectively fill upcoming demand or if you need to place a ‘Now Hiring’ sign on the door? What systems do you have in place to keep this all straight?

Judgment-based forecasting – good, but is it good enough?

Many business decision makers use a form of judgment-based forecasting. They use their own subjective knowledge to set work schedules based on experience, intuition, and past behavior. While this method can prove effective in some situations, there are typically busy or slow periods that don’t align with one’s intuition and seem unpredictable.

Fortunately, new technologies are available to take the guesswork out of scheduling. Software fueled by artificial intelligence can pinpoint the exact factors that affect demand, assess their interdependencies, and improve forecasting in a systematic, quantifiable way.

How does AI-based scheduling actually work?

AI-based scheduling software builds quantitative forecasting models to predict future demand, down to the type of demand expected. These systems can implement a forecasting process that organizes all demand drivers, internal and external, to understand their exact incremental and combined impact on demand.

Applied artificial intelligence solutions incorporate the factors that affect demand by analyzing historical data and reporting on the actual demand changes correlated with these external and internal factors. Not only can AI software report on the known drivers of demand, but it often also identifies factors important to an accurate sales forecast that decision makers may not explicitly be aware of. AI can expose gaps in a prediction model and allow users the chance to explore additional demand drivers that went unseen in the past.

While augmenting human judgment with AI-generated models takes the guesswork out of scheduling, AI is not meant to replace human decision makers. Rather, AI-based demand forecasting is a tool used to inform management of the expected demand, to quantify demand drivers, and to suggest a schedule that management can adjust as needed.

Will the investment be worth it?

The improved forecasting accuracy that comes with implementing AI-based forecast methods is proven to drive positive changes to the bottom line. Moving from judgment to data-driven analysis drastically improves the ability to schedule the workforce accurately. This means better customer service, happier employees, and lower labor costs all at the same time.

If you are relying on judgment-based forecasting alone, there is a better way!

Author: Anna Schultz

Anna is Marketing Coordinator at RXA, a Growth Marketing Intelligence company fueled by data science and applied artificial intelligence, and parent company of Weave Workforce, a workforce optimization software. In her role, Anna helps companies meaningfully activate the benefits of data analysis through RXA’s GMI platform and applications. In today’s world, all companies collect data – it’s what you do with it that makes a difference!

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Let us show you how Weave Workforce can take your labor scheduling to the next level and enhance the fundamental operations of your business.