Why Failure to Accurately Right-Size Inventory Will Hurt Your Bottom Line

Why Failure to Accurately Right-Size Inventory Will Hurt Your Bottom Line

Dec 12

US businesses have $1.38 tied up in inventory for every $1 of sales, so it’s no wonder that exploring new initiatives for accurately right-sizing retail inventories is expected to be a top priority for CMOs in 2019. Whether you’re in apparel, footwear, FMCG, home products, or any other retail business, failure to predict quantities and assortments with high precision will take a big bite out of your bottom line.
Read more: Optimize your inventory using predictive machine learning
Many retailers understand the need to accurately forecast consumer demand. However, it is just as important to allocate the right quantities and assortments to the right DCs and stores. Otherwise, despite having ordered sufficient stock for your customers, that stock won’t be in the stores that actually have demand for those items! This will result in cash held in inventory, inventory management costs (including transfer orders and lost sales), eventual markdowns, poor customer loyalty, and low lifetime value.
The dollar value of the missed opportunities that these negative effects represent is staggering. So what are the retailers doing wrong, and how can they improve their demand forecasting accuracy?

Miscalculating seasonal impact

Retailers, especially in the Fashion & Apparel industry, grapple with high seasonal volatility due to holiday shopping, promotions, and sales. Miscalculating the number of units expected to be sold across different seasons will either result in stockouts and negative brand image or high working capital tied up in overstock.
Retailers need digital tools that offer advanced demand forecasting not only based on previous sales records and seasonal trends but also changes in business model, marketing spend, and strategies. This kind of retail BI solution must leverage the power of artificial intelligence and machine learning to quickly analyze a multitude of variables across different seasons, including customer preferences and other socio-economic factors.

Inability to estimate demand for upsell and cross-sell products

In addition to forecasting sales, organizations also have trouble determining the best products, pricing, and target segments for upsell and cross-sell opportunities. Older technologies provide limited functionality for identifying these opportunities, resulting in fewer high-ticket sales.
Retailers need modern AI-driven BI Solutions to predict exactly which market segments will buy specific cross-sell and upsell products, and in what quantities. These tools will also help ensure that marketing plans are made around high-value segments, and that production and sourcing budgets are adjusted to create more efficient order schedules.
Your fashion brand needs more than just basic operational/analytical reports to gain the agility required to succeed in fashion retail. Chat with us to learn how to reduce the amount of cash you have tied up in inventory and boost customer lifetime value through AI-driven forecasts.