Companies across retail, CPG, and consumer electronics are in the process of completely reimagining the traditional forecasting methods to better keep pace with shifting consumer behaviors, rapid product launches, and evolving omnichannel sales.
For many companies, the impact of the COVID pandemic and market uncertainties, along with evolving customer trends and rapid product launches are forcing brands to rethink their traditional demand planning strategies.
Innovative demand planning strategies that rely on artificial intelligence and machine learning (AI/ML) algorithms are helping brands unlock greater business potential by helping planners connect and understand diverse demand signals that go beyond historical sales, bring in more data to facilitate improved decision-making, and ultimately anticipate what, where, and how often consumers are going to buy a brand’s products.
:o9 Solutions and Google are both creating AI/ML-driven forecasting platforms that help consumer-facing brands face the current challenges in demand forecasting. :o9 Vice President of Industry Strategy Vikram Murthi and Google’s Head of Customer Engineering for Retail at Google Cloud Vish Ganapathy share insights on how both the :o9 Digital Brain platform and the Google Vertex AI Forecast work together to bring in a very accurate, granular forecast that equips brands to deal with evolving retail and CPG landscapes.
Vertex AI Forecast is an AI platform from Google Cloud that leverages machine learning models to create high-accuracy forecasts through a platform that supports large datasets and faster forecast results. When integrated into the :o9 Digital Brain platform, the Vertex AI Forecast can use proprietary Google models to develop a forecast, based on brand/product data, which is then integrated into the :o9 Digital Brain platform.
“What we’re able to do from the online platform is actually call out to the Vertex AI Forecast service, so that we bring in a very granular, accurate forecast and then use that to drive business decisions to ensure better outcomes such as an increase in in-stocks and sending more reliable procurement signals to suppliers, driven by more accurate forecasting,” says Vikram.
The challenges faced in demand forecasting
For many retailers and CPG brands, the past few years have been an environment rife with volatility, uncertainty, complexity, and ambiguity. Many organizations are also challenged with the need to cleanse and harmonize data, scalability, working in silos, and data accuracy.
But one of the most significant challenges may be dealing with digital disruptions and supply chains that are becoming more complex. Brands that continue to base their forecast predominantly on historical data are putting themselves at a disadvantage. Vish gives an example of a U.K.-based fast-fashion retailer that relied on historical data to purchase cooler weather clothing experiencing a major sales slump due to an unseasonably warm fall that planners didn’t anticipate. “It’s a great example of how simply executing forecasts against past historical consumer demand can cause issues,” Vish says. “The biggest challenges we see is that data continues to be a challenge, silos continue to be a challenge. But interestingly, there are a lot of new ways to solve for it.”
How the Vertex AI Forecast is a differentiator
As more and more external data points become accessible to brands to help inform their forecasting strategies, AI/ML-based platforms will be essential to help build effective forecasts efficiently. Vertex AI Forecast allows users to choose between pre-built models for forecast planning that leverage the machine learning techniques that Google has honed over time or organizations can create their own models based on specific requirements. When connected to the :o9 Enterprise Knowledge Graph (EKG) users can tease out more hidden patterns and run multiple models to deduce the best model based on the parameters an organization wants to optimize for. Vertex AI Forecast also assists with model drift management. If a model is affected by data and situation changes, model drifts are detected automatically, allowing planners to recalibrate.
Overall, platforms that utilize AI/ML algorithms enable retailers and CPG companies to forecast demand more accurately when compared to traditional models. Next-generation platforms like Vertex AI and the :o9 EGK allow teams to work in a collaborative, holistic manner that creates a single source of information that helps teams build consensus in creating a demand plan that is visible across all necessary teams.
“The benefit of that integration with [Vertex AI and] the :o9 Digital Brain for retail and consumer goods is the time to value but also that this addresses a lot of the issues brought up by working across silos, meaning you have the data scientists with demand planners with the inventory planners, a bringing them all onto a single platform and the ML forecast and… it scales to retail volumes and then it gets brought into your retail inventory and demand planning process. So that’s really the benefit of both [platforms].”
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