Fix Fashion Inventory Missteps with the Right Tech

It’s no secret that overstock is the Achilles heel of fashion.

Retailer inventories rose by over 12%, reaching USD 740 billion over the course of 2022 in the US. Similarly, in the UK fashion manufacturers are holding on to an average of USD 107,600 worth of inventory. This overstock is not only slowing down the businesses’ attempt to free up cash flow, but is also taking up valuable warehouse space.

While the inventory problem is not a particularly new one, the issue has become more pronounced following the COVID-era induced supply chain disruptions—the effects of which we continue to feel. Last year, retailers had hoped to clear some of the excess inventory during the Black Friday sales. Many retailers slashed prices with hopes of rebalancing their stocks of merchandise ahead of the new season. However, despite the markdowns, many businesses found themselves holding onto ‘aged inventory’ due to high inflation and dampened consumer spending. While consumer spending has picked up this year compared to the last, there is also a shift in consumer spending habits so the outlook for spending remains sluggish. So how can the fashion industry break free from its loop of missteps? This blog explores how fashion manufacturers and retailers can leverage the right combination of strategies and technologies to manage the inventory problem.

Having had to grapple with low stocks due to supply chain disruptions during the pandemic, businesses sought to build a buffer against unexpected events. However, this soon proved to be an even bigger problem. As the world emerged from the pandemic, businesses found that consumer spending slowed and the demand for non-essentials dropped. This was partly in response to rising inflation and partly due to consumers’ shift in mindset, which saw many opting for longer-lasting, higher-quality, sustainably produced goods. Going into the However, fashion’s inventory problem is hardly a new one. Overproduction has plagued the industry for decades, and in the UK alone, an estimated USD 180 million worth of clothing enters landfills every year.

Excess inventory is expensive. While you might imagine that the loss ends at the unit cost of each deadstock item, the truth is that the hidden costs of unsold inventory can be prohibitive. Not only does the non-working capital tied up in excess stock diminish profit margins and revenue, but it also takes up valuable shelf and warehouse space. It doesn’t end there – cost of storage, insurance on the warehouse space and products, and the need to make space for new product lines can drive costs up further. Faced with mountains of inventory, businesses often find themselves facing a conundrum – discount or dispose. While some brands have chosen to recoup losses from depreciating items by slashing prices, others have sent overstock to landfills or incinerated them to retain the brand’s perceived exclusivity and consumer brand perceptions.

The answer to fashion’s inventory problem is both simple and complicated. The solution lies in a combination of strategies including, agile supply chains, intelligent demand forecasting and the ability to identify and manage slow moving stock. This sentiment is echoed in the BoF-McKinsey State of Fashion survey which explored how fashion executives expect to address the issue of overstock – 56% are looking to implement an agile supply chain to address the root causes of this problem. While others believe that analytics will prove to be the biggest differentiator that separates the success from failure – 60% of fashion executives hope to leverage analytics to better understand their consumers, while 47% are tapping into advanced analytics to improve their assortment planning. The bottom line? Technology is a powerful enabler for businesses looking to build resilient and agile supply chains. Take for instance, an ERP like Infor M3 and CloudSuite that has built in industry-specific capabilities to tackle the fashion industry’s unique challenges. This can help drive end-to-end supply chain visibility, ensuring that the flow of products from suppliers to store shelves is fast and efficient. It also improves supplier visibility, helping executives spot early indicators of supply disruptions that may affect their inventory levels. Real-time visibility of inventory means that businesses can streamline production and work towards accurate demand forecasts, which in turn can help prevent stockouts and overstock.

In the age of the perpetually picky and impulsive consumer, forecasting can seem like a lost cause. There is a fundamental disconnect between demand forecasting, actual market demand, and production floor realities. This was quite apparent over the past couple of years – businesses first struggled with stock scarcity issues during the pandemic amid supply chain disruptions. As lockdowns lifted and supply chain flows normalized, the same businesses were caught off guard when their orders surged in, driving their inventory levels high. As discussed earlier, this disconnect is, however, not a new issue – Over 60% of brands would agree that overproduction had has been a significant issue
for their business even before the pandemic. Steep markdowns, brands failing to replenish best-sellers fast enough, and waste is commonplace in the fashion industry.

However, these problems are not without solutions. The answer lies in taking a granular, data-based approach to managing demand. Businesses looking to tackle the issue of overstock must invest in data analytics capabilities to effectively forecast, plan, and deploy inventory. From tapping into AI to better monitor their stock levels via real-time inventory tracking to analyzing sales data and trends to adjust inventory levels and production schedules, business leaders can predict demand more accurately by capturing the right data. Brands can also run this data through ML algorithms to identify popular products and forecast demand patterns. These insights can be used to optimize production processes and reduce waste. Drawing from past sales trends and comparing it against variations in the forecasted sales numbers, brands can spot imminent inventory imbalances before they happen and intervene as appropriate. AI forecasting tools can also aid in re-deploying excess inventory. However, underpinning all of these ambitious plans is data. To accurately managed inventory, executives need a full picture of the business’ end-to-end supply chains. Data related to sales, inventory levels and customer behavior is generated across multiple internal and external sources, including ERPs, CRMs, point-of-sale (POS) systems, market trends and social media sentiment. A unified analytics platform can integrate data from all of these disparate sources, allows business to generate comprehensive insights and optimize their supply chain effectively.