January 22, 2025 • 4 minute read
Author: Phyllis Jackson, Senior Manager, US Marketing, UPS

Key Points

• Prioritizing supply chain visibility can improve relationships and give wholesalers greater control over demand data and interpretation.
• Including point-of-sale (POS) data instead of relying solely on historical-based data models can improve forecasting accuracy by 11.2%.¹
• Scenario planning can reduce forecasting errors by 20%, leading to more accurate inventory levels.²

Demand-Driven Supply Chains Offer Opportunities for Wholesalers

Wholesale businesses are entering a new era. The days of forecasting product needs based on limited data are giving way to a new age in which wholesalers have access to demand signals they can use to optimize their operations and better collaborate with supply chain partners.

A demand-driven supply chain is an industry strategy focused on flexing around indications of shifts in customer interest, which are referred to as demand signals. When the supply chain reacts to these signals, it can become more agile and responsive to real-time fluctuations. This dynamic approach can lead to greater efficiency, less product waste and, ultimately, higher customer satisfaction rather than solely relying on limited historic sales data.

For example, let’s say a viral commercial from a clothing brand is boosting sales of winter sweaters. The wholesaler that interprets this social media demand signal can work with retailers and manufacturers to ensure the inventory is optimized to meet demand — and beat out competitors. Whereas in the past, manufacturers would only have previous sales figures of what was actually sold to make demand decisions.

By adopting ways through experts and/or technology platforms to interpret and use demand signals, wholesalers become data translators. They can use this information to improve business outcomes and strengthen their relationships with manufacturers and retailers, says Charles Cawthorn, Strategic Lead, UPS.

“When wholesalers, manufacturers and retailers are informed with the same data signals, they can coordinate action and potentially minimize the impact of costly disruptions and unpredictable developments,” Cawthorn says.

Essentially, data translation is a way to show demand prediction in a more digestible way for business partners to understand.

Supply Chain Signals Every Wholesaler Should Know

Less than a decade ago, more than 70% of wholesalers relied on historical sales data and moving averages to project demand.³ Today, only relying on those data points represents a missed opportunity for more accurate forecasting.

And that can lead to stymied growth.

“Inefficiencies, overstocking, low inventory churn and dead inventory can impact a wholesale business’s competitive position,” Cawthorn explains.

As such, it’s important for wholesalers to look beyond traditional demand signals such as historical performance and consider other factors.

“We recommend that wholesalers broaden the demand data that they’re looking at to include internal and external analytics,” Cawthorn says. “This mix can help to align the wholesaler’s supply chain decisions with real-time demand patterns.”

Three demand signals integral to inventory demand planning are:

• Retail customer data (in-store and point-of-sale)
• Long-tail product performance
• Competitor benchmarks

Customer In-Store and Point-of-Sale (POS) Data

In-store and e-commerce customer POS data often provides a goldmine of information you can use to improve demand projections and optimize inventory for retail customers across all seasons.

POS demand signals include:

• Fast- and slow-selling products
• Cart abandonment rates
• Search queries
• Product and service reviews
• Peak buying times

These signals provide a real-time look into sales trends and encourage better and more informed decision-making about inventory stock levels and manufacturing orders. Better information typically means better results for everyone. In fact, using POS data over more traditional historical-based models can improve retail demand forecasting accuracy by 11.2%.¹

The Fruitfulness of Long-Tail Demand Data

What are long-tail products? They’re products that sell in low numbers individually but, as a group, encompass a large percentage of demand. They play an important role in sustaining retail sales. Gathering long-tail data can help improve forecasting and detect consumer trends in their early stages.

Understanding which long-tail products retailers rely on based on particular trends can help wholesalers better prepare. Getting this right can be fruitful because, by some estimates, long-tail retail sales data can help wholesalers achieve up to a 99.6% fill rate.⁴ And the better the fill rate, the better the chance of fewer stock-outs and backorders.

The Value of Benchmarking Competitors

Wholesalers can also gather valuable data through competitive analysis. The exercise can help highlight the business’s strengths, weaknesses and areas for improving efficiency. For example, if a company hasn’t completed a competitive analysis in the past five years, they’re likely missing out on how competitors adjusted to COVID-based supply chain issues, tariff impacts and changes in customer expectations that can help improve their operations.

“So many things have changed in the supply chain since COVID,” Cawthorn says. “Wholesalers that run a competitor analysis can see where they stand in their corner of the industry and which solutions they might be missing, including those that can help improve efficiencies and lower costs.”

How Wholesalers Can Use Data Translation Tools

Finding the right tools to convert data into actionable steps can help streamline operations — and also strengthen critical connections with manufacturers and retailers.

“Becoming a data translator builds trust,” Cawthorn says. “I think it gives everyone in the relationship — manufacturer, wholesaler, retailer and consumer — a better chance at success.”

Leverage Advanced Analytics and AI

AI and advanced analytics have become critical tools for wholesalers because they provide the ability to compile, interpret and generate actionable insights from demand data with tremendous efficiency. Those insights can help you calibrate inventory, reducing costly excess stock-keeping units (SKUs) while maintaining enough stock to adapt to retailers’ needs.

For example, wholesalers can use AI to integrate a wide range of data — historical figures from manufacturers and retailers, in-house historical data, weather patterns, social media trends, and geopolitical impacts — into demand translation, giving wholesalers and their partners a sophisticated look into future demand.

Set Up Customer Segmentation

Wholesalers that segment customers into audiences based on certain characteristics are more likely to find nuances that give them an edge over competitors that view their customers as a uniform group.

A wholesaler could segment its retailers by:

• Region/location
• Business characteristics, such as organizational structure, performance, industry, revenue and size
• Purchase history and buying behaviors to hone product inventory

For example, a textile wholesaler may segment its customers into how much product they buy seasonally or even annually. This level of segmentation can help define special pricing or new product promotion. Insights gleaned through segmentation can help you understand how demand could fluctuate over time and how to tailor your inventory strategy to meet that demand.

The result? Better forecasting that helps eliminate costly inventory mistakes. Wholesalers that use segmentation have improved inventory forecasting by up to 15%, leading to a 2% increase in profit margin.⁶

Stay Proactive and Prepared with Scenario Planning

Demand can shift for many reasons:

• Seasonal cycles
• Products going viral on social media
• Geopolitical activity
• Tariffs
• Weather and other factors

Scenario planning helps to model the ways that events like these can impact demand. Scenario planning empowers wholesalers to explore the movement of goods, how to adapt and potential costs.

For example, with scenario planning, a clothing wholesaler could run a simulation to see how an unexpected cold snap could impact existing inventory, and whether current stock would meet retailers’ needs. The information would either verify that there’s enough inventory to meet surprise demand or be a signal for the wholesalers to order more products from manufacturers based on real-time and historical demand data.

Using scenario planning as a demand translator can reduce a wholesaler’s forecasting errors by 20%, leading to more accurate inventory levels.²

Outpace the Competition With UPS

Wholesalers that prioritize supply chain visibility can become demand translators, leveraging actionable insights to provide more value to their supply chain partners — making them more likely to outpace their competitors.

However, playing the role of demand translator requires a level of technology and expertise that many wholesalers want but are looking for more talent to help achieve. For example, McKinsey reports that 95% of distributors are exploring using AI in their operations, but only 30% of them have the talent needed to scale their AI efforts.⁵

For wholesalers that want to grow into a demand translator role, the right logistics partner can provide supply chain value. UPS can analyze your current operations and outline recommendations to connect you with the right resources, including UPS’s network of vetted third-party partners.

² “AI Case Study: Danone reduces forecast error and lost sales by 20 and 30 percent respectively and achieves a 10 point ROI improvement in promotions with machine learning,” Best Practice Artificial Intelligence, accessed September 12, 2025.
³ “Wholesalers struggle to adapt to new consumer demand patterns,” Supply Chain Dive, accessed September 11, 2025.
⁴ “Forecasting the Long Tail and Intermittent Demand,” ToolsGroup, accessed September 11, 2025.
⁵ “Harnessing the power of AI in distribution operations,” McKinsey & Company, November 15, 2024.
⁶ “Demand Segmentation: One Size Does Not Fit All!” Optimact/Xeleos, accessed September 11, 2025.

Individual results and options will vary. UPS makes no promises of any specific outcome in this document but instead provides only example outcomes based on certain UPS customer experiences.