For example, consider a retailer’s demand forecast for product A in region B within store C. Whereas predictive models rely exclusively on historical volumes to generate the forecast, there’s so many other factors to consider. Like what? How about the weather in region B? And what if store C built a new parking garage? Or what if a major competitor opens a location across the street? Don’t factors like these also impact product demand?
Of course they do. And machine learning models are designed to capture scenarios like the examples above.
But these machine-based forecast insights also come with a cost. What cost? Machine learning models are hard to scale across a sophisticated organization. In other words, there’s not one-button to click. Not to mention, machine learning also requires data scientists to work side by side with finance teams.
Bringing It All Together
Ultimately, it’s not a question of whether predictive analytics or machine learning is better or worse than the other. As I shared with the audience, the bigger and more important point for finance teams is to know what you’re trying to achieve with advanced analytics and then select the right technique.
Both predictive analytics and machine learning offer FP&A teams a new way to ask “why”. And there’s nothing bad about having an unbiased forecast scenario to help drive dialogue with business partners.
In fact, that’s part of unleashing finance!
For more on Guardian’s journey and to learn about the future of FP&A, click here to watch and listen to the replay of the webinar.
Re-posted with permission from Source