New study shows fashion forecasting tech can decrease overproduction

Overproduction is a long-lasting, deep-rooted problem in the apparel industry.

15-45 billion items of clothing produced every year are wasted, mostly ending up in landfill or incinerated.

OC&C Strategy Consultants’ collaborative study with WGSN reveals how more accurate forecasting data can significantly improve margins and efficiency, and reduce wastage.

The research revealed two key findings:

• Buying more in line with customer demand can increase contribution margin 1-3 ppts by product category, as well as increase OTB at the same time as reducing wastage
• The reduction in overproduction is estimated to be 5-15 per cent by product category, removing a key source of unnecessary waste. This represents c.3 per cent reduction in carbon emissions

Fashion brands tend to use planning and buying models with an inherent risk of overbuying to reduce the likelihood of products running out of stock. Even with rising prices due to inflation, practices are yet to evolve.

However, agile, AI-informed buying processes are enabling brands to refine their range in line with consumer demand, operate more efficiently, and therefore drive significant value.

A case study (2022) reveals how a mass-market retailer could have improved margin by £1m-1.5m in its Women’s Skinny Jeans line if more accurate forecasting data was used.

Buying in line with the decline in market demand would have resulted in 10,000-40,000 fewer units of terminal stock.

In buying fewer units, the brand would have gained back £260k-600k in its Open-to-Buy plan, enabling it to invest more wisely in other categories and therefore deliver further topline growth.

Francesca Muston, VP Fashion of WGSN, commented: “A top priority for our clients revolves around reducing overproduction and creating a more sustainable business. While business practices can’t always evolve as quickly as brands would like, WGSN helps brands focus on improving their bottom line through AI-driven forecasts that influence accurate and effective product planning.”

Mairi Fairley at OC&C Strategy Consultants said: “We speak and work with fashion brands that are under significant pressure as the complexity of business models continues to increase, cost inflation rises, and there is a need to operate more sustainably. Evolving planning and buying to be more demand-led is a practical first step, enabling significant margins to be gained, and unnecessary waste and CO2 emissions to be reduced.”

Findings of the study also reveal five ways in which the fashion industry’s operating model is being disrupted:

1. The trends landscape is shifting, becoming increasingly complex: Consumers engage and transact across multiple channels from physical to digital to social (e.g., TikTok) and beyond. Understanding consumer influences in a more holistic way is crucial to ensuring stock aligns with customer demand.
2. Tech supports efficiency and decision-making: Inflating costs and complexity are driving the need for greater efficiency in operations from design / planning through to depot / supply chain. Technology is critical to unlocking ‘doing more with less’.
3. Shorter supply chains and stock aggregation are enabling flexibility: Greater flexibility to allocate stock in line with demand is becoming increasingly important to drive efficiencies across channels and markets.
4. Planning needs to be real-time: Shifting to shorter, more frequent buys more directly linked to demand allows better reaction to trend, management of waste, protection of cash flow and reduction in markdowns.
5. Circularity, rental, and resale: Completely different operating models are gaining traction as brands and consumers lean into sustainability. Resale represents c.7 per cent of market value and significant innovation in circularity taking place. Brands need to consider range requirements for resale in their planning.

In conducting the research, OC&C utilised WGSN TrendCurve+ data to model the potential for planning and buying improvements. TrendCurve+ is a product from WGSN that combines data sources across social, search, shelf, shows, and sentiment, with advanced machine learning that helps fashion brands invest in the most popular products with 90 per cent forecasting accuracy.