How in the world are retailers and suppliers supposed to come up with accurate demand forecasts if they aren’t sharing the same data in the same manner at the same time?
COVID-19 is rightly blamed for many of the problems that plagued consumer products supply chains throughout 2020. But shortages of toilet paper, bottled water and other essentials were made even worse by the misalignment of demand data strategies between suppliers and buyers. That’s the conclusion of a recent report by software engineering company CI&T.
“The retailer-supplier relationship isn’t arranged in the way most conducive to confident demand forecasting,” the report states rather politely. But the consequences are serious, presenting the retail supply chain with a host of issues stemming from the failure to accurately predict demand.
The two sides can’t even agree on the nature of the problem. Suppliers say their number-one challenge in forecasting demand is visibility and access to data. Retailers say it’s scaling the data platform. “Strategically, how they’re thinking about demand forecasting is really different,” says Melissa Minkow, retail industry lead with CI&T and co-author of the report.
The disconnect starts with the basic approach to forecasting by the two sets of nominal partners. The majority of suppliers say they’re likely to break down relevant data by geography, while retailers do it by channel, such as e-commerce and in-store sales.
In addition, most suppliers compare sales data for a given month with the same period of the prior year in order to predict the following month’s sales, while retailers compare the figures to those of the previous month. The two methods can yield vastly different conclusions about how much product to make, store and shelve for the next 30 days.
The parties do agree on the type of information they consider to be of the greatest value in formulating a demand forecast: detailed consumer data such as gender, age and household size. But they can’t seem to manage to exchange that intelligence in real time.
There’s a nagging hesitancy to sharing sensitive and proprietary data, “which means it’s a struggle for them to work together,” says Minkow. That lack of transparency is at least partly responsible for the divergent approaches taken to interpreting the data.
In a time when disruptions such as COVID-19 are threatening the stability of consumer-products supply chains, it’s never been more urgent to view key data through a single lens. But retailers and suppliers appear to be falling short of that goal. “There’s been a lot of complacency” over the last 15 months, Minkow says. “Both parties are playing the whole game very close to the vest. And it’s only going to get worse in the next six months, with the holidays coming up.”
To be sure, many of the issues plaguing supply chains today are beyond the control of retailers and suppliers alike. But the two sides can do more to mitigate the impact of external crises by harmonizing their approach to data analysis and demand forecasting. In uncertain times, resilience is key. “There’s a lot of room for data to be shared,” Minkow says.
The CI&T report urges a new framework for creating demand forecasts, one that doesn’t develop them in isolation. At the moment, Minkow says, “they don’t even bother to look at each other’s demand forecasts. If they did, they would see where they can learn from each other.”
In the age of social media, more data is available to the retail supply chain than ever before. That’s both a blessing and a curse — properly analyzed and shared, the information can be used to pinpoint customer needs and tailor products accordingly. But the sheer volume of it threatens to overwhelm providers that lack the ability to make sense of it, and coordinate its flow between partners.
Minkow says retailers and suppliers aren’t making full use of the data that’s available to them. In addition to consumer-level intelligence, they should also be drawing on sales or basket data for both e-commerce and in-store purchases, seasonal trends, weather patterns, holidays and the pricing strategies of competitors.
CI&T proposes that the retailer own the data-sharing relationship, then determine which types of customized data each supplier should be privy to in an “uber-forecasting” system. “We would put more power into retailers’ hands, but we’re also open to suppliers being first movers,” Minkow says. “The idea is to be in the middle, where all the data sets exist. Then you can tweak the relationship as you see fit.”
Failure to coordinate the crucial relationship between retailers and suppliers for purposes of demand forecasting is costly to all parties, but especially those at the end of the supply chain. “If there’s not an optimized data-sharing strategy, consumers are the ones who will lose,” says Minkow. “And when the consumer loses, everybody loses.”