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Last year shattered the predictive capabilities of even the most sophisticated enterprises. Several of the world's largest and most advanced companies overbought or overproduced inventory by millions of dollars, primarily because consumer behavior deviated from expectations.

Planners cannot simply blame consumers for not aligning with forecasts, especially when many customers were responding to historic inflation in food, fuel, and housing costs.

“Demand planning went out the door for three years,” said Rick Jordon, senior managing director with FTI Consulting. “Now they’re picking it back up, because they have all this excess inventory everywhere. And they’ve forgotten how to do it, or they didn’t do it well before.”

After three years of pandemic disruption and economic volatility, sharpening demand forecasts and inventory planning across industries appears both as imperative and as challenging as ever. To achieve better outcomes, companies are turning to new data sets and advanced technological tools, including artificial intelligence, to analyze them.

From Spreadsheets to AI

An executive survey by McKinsey from early 2022 found that spreadsheets remain the primary planning tool for supply chain leaders, with 73% of respondents relying on them. However, 43% indicated plans to use artificial intelligence and machine learning for some planning activities, while another 17% said they intended to use these technologies for most activities at some point.

A survey this year from BlueYonder, a supply chain software company, found that demand forecasting and inventory optimization are among the top uses of AI and machine learning technologies by supply chain executives.

“We're moving well beyond Excel, and just moving to a more advanced tool set,” said Ron Scalzo, senior managing director with FTI Consulting.

In Jordon’s view, AI technologies will eventually help remove human bias from forecasting, lead to more systematic predictions, and enable running multiple predictive scenarios simultaneously. Jordon noted that AI is necessary to process through many data sets and make sense of a company’s own internal data. “Most organizations have a lot of data,” Jordon said. “Are they using that to the best of their ability, utilizing the best technology and ignoring that noise? That’s really where the next three or four years of development will happen.”

Finding the Right Data to Analyze the Right Way

At the most basic level, AI takes in data and produces predictions. To obtain the best predictions, the data fed into these systems might matter even more than the AI tools themselves.

“There are systems pulling in data which is lying inside the company already, from across their enterprise systems, POS systems, the inventory levels from their stores and all that,” said Meher Dinesh Naroju, director of AI services at Centific. “There are very few companies that actually go outside of this box and get data from sources that are completely unstructured.”

Such data sources include social media, which can provide clues about emerging market trends. Customers are “actively engaging and leaving opinions and clues about various things they intend to do in the market or a specific category,” Naroju said.

Even images from a retailer’s store cameras might house data relevant to customer behavior in the aggregate, including reactions to certain products, brands, and categories. “It’s a long shot, but it can help,” Naroju added.

Other sources, such as weather data, can provide early hints about supply chain timelines and inventory arrivals, he also noted.

Still, all these data sets must be placed in the context of local and regional cultures and other differentiating factors. “It is of the utmost importance to pick and choose what data you need and very clearly segment the data you want to analyze,” Naroju said.

Once a company has the best, most refined, and useful data it can obtain, that’s where AI tools become critical. The technology can derive insights from large pools of unstructured data that “no human being can do in so short a time,” Naroju said.

But even with the most creatively sourced data and advanced tools available, a prediction remains just a prediction. “If you crack the code on a perfect forecast with the consumer today, you're quickly going to be the richest person in the world,” said Jake Self, vice president of operations for third-party warehousing and logistics company Smart Warehousing.

‘So Much Noise’

Businesses for generations have relied on the past to anticipate the future. That approach may seem outdated after the pandemic and inflation scrambled usual patterns, with volatile cycles piggybacking on each other since 2020.

But for many, historical trends remain the most useful forecasting data. “It’s not especially sexy, but we’ve actually philosophically been using the perspective that history is the best predictor of the future,” Destination XL Group CEO Harvey Kanter told Supply Chain Dive in an April interview.

“I think there's been so much noise in the last three-plus years that the roller coaster we’ve been on is almost impossible to predict,” he added. “I don’t know that the greatest level of AI and ML can predict what the consumer is going to do.”

Kanter went on to note that the apparel retailer still looks at pre-pandemic revenue and historical revenue contributions by day, week, and month over the past 10 years, which he said has remained remarkably consistent.

Other businesses simply lack the budget to pay for or justify advanced technological tools. Smart Warehousing’s clients range from small brands operating out of garages to Fortune 500 companies. For some smaller companies seeking replenishment and warehousing help, their forecasting process amounts to “hopes and dreams,” Self said.

For those companies, historical data is by leaps and bounds better than no data. “We've spent a lot of time on our replenishment program, really putting an algorithm in place that learns and understands based on historicals where we're going to need to be,” Self said.

Smart Warehousing can do that having invested in forecasting and replenishment technologies for more than two decades, and with an entire team of specialists dedicated to that one skill set. An algorithm for cold replenishment operations out of Missouri “looked like something out of Good Will Hunting,” Self said, referring to the film featuring actor Matt Damon as an unknown mathematics prodigy.

But even with those resources, prediction remains tough in the current environment. “If you're not planning for an element of the unexpected, then you're probably not planning right,” Self concluded.