Frank Dandy: From Excel-Heavy Planning to Data-Driven Inventory Decisions with Madden Analytics

Frank Dandy: From Excel-Heavy Planning to Data-Driven Inventory Decisions with Madden Analytics

In a volatile retail market, where consumer demand shifts quickly and margins are under constant pressure, one thing has become increasingly clear for growing brands: relying on intuition and static spreadsheets is no longer enough. For Frank Dandy - a Swedish apparel brand founded in the early 2000s - the past few years have been about scaling growth while regaining control over inventory, capital tied up in stock, and operational complexity.

Frank Dandy was founded around 2003 and initially built its brand as a men’s underwear specialist, best known for its boxers and socks. Over time, the company has expanded product categories significantly. Today, the core remains men’s and women’s underwear, but the assortment has broadened to include swimwear and, in particular, leisurewear for both men and women - a category that has shown strong growth in recent years.

The rise of leisurewear reflects changing consumer behavior. Products are no longer used only at home; customers increasingly wear them on the way to the gym, around town, or as part of everyday outfits. For Frank Dandy, this shift has opened new growth opportunities but also increased assortment breadth and planning complexity.

A Shift in Business Model and Planning Requirements

Alongside its product expansion, Frank Dandy has also transformed its go-to-market strategy. Historically, the business was heavily wholesale-driven, working with several hundred retailers. Today, the majority of sales come from Frank Dandy’s own e-commerce channels and marketplaces, while the company continues to work with a smaller number of strategic wholesale partners.

The move toward direct-to-consumer has given Frank Dandy greater control over pricing, customer relationships, and speed to market. At the same time, it has shifted more risk onto the business - particularly when it comes to purchasing decisions, inventory levels, and capital binding. In a DTC model, there are no pre-orders to rely on; inventory must be bought ahead of demand on speculation, making forecasting accuracy critical.

A CFO Perspective Close to the Business

Johan Lövstrand joined Frank Dandy in 2022 after working as a consultant at Deloitte. As CFO, he is responsible for both operational and strategic financial management, including liquidity planning, cash-flow optimization, capital binding, budgeting, and forecasting. In recent years, his responsibilities have also expanded to include greater responsibility for purchasing and planning decisions.

“In a DTC model, you have much more control over the business - but you also carry more of the risk yourself,” says Johan Lövstrand, CFO at Frank Dandy. “That makes planning, forecasting, and inventory decisions significantly more important.”

In a challenging market environment, the company has continued to grow, surpassing SEK 100 million in revenue the previous year and expecting further growth. Frank Dandy sees particularly strong performance in its own e-commerce channel, not the least during the important fourth quarter of the year. 

Growth, however, brings new challenges. As assortment complexity increases and sales channels multiply, the cost of poor inventory decisions becomes higher. Avoiding both overstock and stock-outs has become a strategic priority.

The Challenge: Capital-Intensive Drops and Heavy Excel Workflows

One of Frank Dandy’s key focus areas has been reducing capital tied up in inventory. Historically, the company worked with large seasonal purchasing drops - typically one major spring order and one major autumn order for e-commerce. While straightforward operationally, this approach tied up large amounts of capital and increased risk if demand did not materialize as expected.

“The large drops made the business quite capital intensive,” Johan explains. “We want to move toward smaller, more frequent drops during the year, but that requires better planning and better data, and better coordination with suppliers and lead times.”

To do this effectively, the organization needed better forecasting support and more flexible planning tools.

At the same time, much of the internal reporting and planning work was heavily Excel-based. Sales data was extracted from the ERP system and combined with inventory data, often month by month because full-year datasets became too heavy to manage. Large Excel files with extensive VLOOKUPs and manual mappings were required just to gain a basic overview.

“I spent a lot of time just moving data around,” Johan says. “The files were heavy, not very dynamic, and if someone asked a new question you often had to rebuild the analysis from scratch.”

This manual setup also affected daily operations. Johan describes how he spent significant time each morning pulling the previous day’s sales data, mapping it in Excel, and distributing reports to sales and marketing teams.

“That alone could easily take half an hour every day,” he adds. “And that’s time better spent analyzing the business rather than preparing spreadsheets.”

Moving to Madden Analytics as a Shared Planning Platform

To support a more scalable planning approach, Frank Dandy gradually evolved how it uses Madden Analytics across the organization. One important step was simplifying the data foundation: instead of combining multiple data sources, the company now relies on a more streamlined data flow from its ERP system, creating a more consistent and reliable base for analysis.

Today, Madden Analytics is used daily across multiple functions. Finance, purchasing, marketing, and management all rely on the platform. Johan uses it every day, purchasing teams use it every day, and the CEO accesses it daily as well.

“What surprised us was how quickly Madden became a tool for the whole organization,” Johan notes. “It’s not just finance or purchasing — marketing and management use it daily to understand what’s actually happening in the business.”

Teams use the platform to analyze what sold yesterday or last week, identify slow-moving products, evaluate margins at SKU level, and decide on actions across marketing, purchasing, and assortment management. Because the data is readily available, teams can answer questions immediately instead of requesting custom reports.

For Johan personally, the efficiency gains are tangible. He estimates that he saves multiple hours per week simply by no longer having to manually prepare and distribute daily sales reports. Marketing teams now access the same information directly in Madden, while finance can focus more on analysis and decision support rather than data preparation.

Preventing Stock-Outs in Core Categories

One of the most important KPIs for Frank Dandy is out-of-stock time - particularly for never-out-of-stock (NOOS) products. Being out of stock on core items directly impacts revenue and customer experience.

“With Madden, it’s much easier to keep track of out-of-stock risk,” Johan says. “We have a completely different level of overview compared to before.”

Inventory levels and forward demand are reviewed on a regular basis, with structured weekly check-ins to assess whether any products are at risk of running out. Based on this, the team can decide whether to place new orders and in what quantities, taking lead times and minimum order quantities into account.

Enabling Smaller, More Frequent Purchasing Decisions

Ultimately, Frank Dandy’s journey is not just about reporting faster,  it is also about enabling a different way of working. Moving toward smaller, more frequent purchasing drops requires tools that can handle higher decision frequency and allow forecasts and plans to be updated continuously.

By reducing manual work, improving data accessibility, and lowering the risk of errors, Madden Analytics supports this shift. The result is an organization that can plan more precisely, react faster to changes in demand, and balance availability with capital efficiency.

Summing up the change, Johan puts it simply: “We spend far less time on manual work today. The data is faster, more accessible, and more reliable - which allows us to plan in a completely different way than before.”

About Madden Analytics

Madden Analytics is a cloud-based inventory planning platform for consumer brands, leveraging AI/ML technology to support buyers. With tools for forecasting, purchasing, store replenishment, and real-time analytics, Madden helps teams buy less, sell more, and stay in control of their inventory.