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Backed by AI and machine learning, key business decision-makers can independently leverage accurate, future-facing insights. Intelligent planning demystifies AI and ML forecasting, empowering both planners and decision makers to model multiple scenarios and shape future business outcomes—with a full understanding of commercial, operational, and financial implications of their decisions across any line of business. ML-based forecasts are able to incorporate a wide range of both historical and external data that helps deliver a finely tuned prediction, whereas traditional forecasting typically leverage just a slice of internal historical data, which can yield inaccurate predictions. In our second blog, we’ll take a deeper look at some of the challenges that business leaders face when adopting an AI and ML-based forecasting solution, and share what types of capabilities are most important to help enterprises easily adopt intelligent forecasting.

True, logistics did mostly keep up as Amazon continued to push the envelope on shipping speeds and Black Fridays gave ground to Cyber Mondays, or as the early shocks of the pandemic shifted more buying to e-commerce channels. That perception of resilience changed in the 2020 holiday season when the big shipping companies didn’t have enough capacity to accommodate e-commerce demand, and many retailers were left scrambling to adapt. With e-commerce demand at an all-time high, logistics had a tough time keeping up on the supply side, slowing delivery times to such an extent that many customers who ordered after and even on Black Friday were left without their packages by December 25. And serving an omnichannel customer base demands a consolidated view of inventory in both warehouses and stores, flexible workforce planning to support sudden shifts in demand and capacity, and an ability to capture and synthesize real-time data across all channels.

With that information, they can develop a catalog of risks and opportunities that facilitate faster decision-making in a way that’s less like a single plan and more like a series of playbooks.3) Expand your data sets to better account for volatilityIt’s important to understand the key drivers for achieving your operational plan, know which of those drivers are going to be most volatile and uncertain, and make sure you have your fingers on the pulse of those drivers.“Forecasts are traditionally managed around organizational or account structure, such as a business unit or geography. That is really critical to understanding the impact and how that then translates to your finance and operational metrics.”AI can help with building in those new data dimensions and levels of detail.“There’s no such thing as too much data (for AI-based predictive analysis). In other words, you need to look at both very granular, detailed data from various sources such as ERP updates on manufacturing output or customer profiles from a CRM system (the “bottom-up”) part, as well as high-level historical data for the functions you’re trying to predict (the “top-down” She has deep expertise in partnering with and enabling organizations’ use of Anaplan’s connected planning platform to forecast, plan, prepare for, and make decisions on some of the most complex business scenarios that companies face.

Instead of making projections just once a year for the next 12 months (which means that by the end of the year you’re only looking ahead a month or two), the rolling forecast always extends at least 12 months out from the current date, getting an update every month or quarter. But instead of planning around a calendar or fiscal year, it combines the detail view of the traditional annual plan with the multi-year outlook and wide-reaching goals of the strategic plan – while being updated throughout the year. Moreover, instead of concentrating the planning efforts into 2-5 months of intensive effort at the end of the fiscal year, often involving long hours and late nights, the new approach spreads the planning work more evenly throughout all four quarters. A business that depends more on major capital investments (let’s say a telecom planning a 5G rollout involving lots of installations) may prefer a 24-month or greater rolling forecast to accommodate long-term plans.