Consumer goods companies rely on forecasts to support inventory planning and distribution across their sales channels. Building accurate demand forecasts requires more than just an understanding of the latest machine learning techniques; it also requires the right data and an understanding of the potential costs of incorrect estimates.Read Now >
When building demand forecasts for consumer goods, there’s a variety of algorithms you can use, from longstanding best practices to cutting-edge methodologies. While each have their pros and cons, at their core, every method is ultimately using historical data to try to predict future demand. The complexity, assumptions, and types of data inputs used in a given model type — and how they are weighted — will vary, but the basic ingredients are similar across the board.Read Now >
More accurate demand forecasts, powered by big data and machine learning, can generate millions in additional revenue for brands.
In our forecasting white paper, we shared the three principles of modern forecasting: use an integrated approach, keep the methodology transparent, and make results actionable. In this blog post, we’ll discuss why the second principle, keeping methodology transparent, is especially important as executives look to make decisions based on demand forecasts.Read Now >
In both weather and retail planning, inaccurate forecasts cause mild discomfort at best and catastrophic consequences at worst. It’s not fun to come home soaking wet after a day when sunshine was predicted, and it’s bad for business when excess inventory that didn’t sell has to be marked down. At Alloy, we can’t say that we’ve fully nailed meteorology, but we do have expertise in retail forecasting, and we spend a lot of time leveraging best practices to benefit our clients.Read Now >