While both planning and forecasting software are valuable, planning tools typically only place emphasis on optimizing certain aspects of the business, such as inventory levels, capacity. However, they do not take market- and macroeconomic factors into consideration.
We break down what you should consider.
Can it provide a consolidated view of both internal data (like historical demand and sales figures) and external data (market trends, macroeconomic factors)? Economic fluctuations, political changes, technological advancements, and other external factors can significantly impact forecasts.
In certain industries, regulatory changes can significantly impact market dynamics. You want to be able to both preempt it and hedge for it. Being able to conduct a scenario analysis arms you to understand and prepare for potential outcomes.
Achieving a 100% forecast accuracy is a pipe dream, but working with as small of a confidence interval as possible is the next best thing. Does the forecasting tool deliver this consistently? This can be undoubtedly challenging to ascertain without usage of the tool.
By conducting a proof of concept, it can provide you with a clearer scope of how it performs on exactly your data. Access to past forecasts performance will also be useful towards showing you how its predictive capacities have historically performed.
Does the software employ correlation to identify your relevant market drivers?
The fallacy with correlation is well-known and it essentially robs you of identifying new drivers that could be more effective at predicting your sales. Only using univariate models to forecast? All, if not most, forecasting models built into planning tools (even the newest AI-based planning solutions) are solely based on or restricted to using straightforward univariate models.
While these univariate methods are valuable, they don't account for external factors that might influence the variable being forecasted. In modern business environments, relying solely on univariate methods might not be enough for comprehensive forecasting, especially when external factors play a significant role. It's often beneficial to combine these methods with multivariate approaches or to use them in specific contexts where they are most appropriate.
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