The Goldilocks Principle: Retail Inventory Optimization Through Predictive Demand Forecasting

5 min read

How to use machine learning to set your stock levels just right — avoiding stockouts, reducing excess inventory, and building retail agility

As the age of supply chain disruption-as-a-rule continues unabated, retailers can't afford to have their operating capital tied up in inventory. Unfortunately, many still find themselves saddled with excessive stock unaligned with current demand. The reason? Faced with a lack of robust demand forecasting and fearing revenue losses due to stockouts, many retailers end up with inventory mismatches.

To avoid resource misallocation, it’s crucial to get inventory levels just right — high enough to satisfy consumer demand, but not so high that unsold items occupy shelf space in stores or accrue storage fees in warehouses. Increasingly complex consumption dynamics spread over multiple channels make determining optimal stock levels a tall order. But what if you could boost the accuracy of your demand forecasting by up to 20% — enabling precise stock management and reducing inventory costs?

Up to 20% more accurate demand forecasting with predictive machine learning

That’s now eminently achievable, thanks to advances in machine learning (ML). Built using the data you already collect and enriched with external sources, ML algorithms deliver the robust demand predictions you need to safely avoid overstocking, free up working capital, and grow your retail operation’s resilience.

Read on to learn more about how one of SoftServe's clients used ML-driven demand forecasting to substantially reduce overhead without sacrificing sales.

Overstocking: The lesser of two evils?

Flexibility is paramount to enterprise resilience, and pivoting requires the ability to redirect funds. Yet many retailers still find themselves burdened by overstocked warehouses, severely restricting their working capital and weighing them down with pricey storage fees. The reason is that, in the tight confines of retail stores, space is a premium, which leads to a dependency on warehouses for surplus stock. Without the ability to precisely predict the what, where, and when of consumer demand, retail stores can struggle to maintain ideal stock levels.

Resilient retail supply chains require agility, which necessitates efficient resource allocation

Considering the potential for revenue losses due to unavailable products, overstocking warehouses is often seen as the lesser of two evils. Stockouts and unsatisfied consumers are, naturally, well worth avoiding. But that caution often leads to the consequences of overstocking — wasted products, slashed prices to move unsold items, or resilience-killing working capital lockups.

Missed revenue and excessive carrying costs: A product of outdated demand forecasting

So, how can retail companies reduce inventory and free up their capital while also guaranteeing in-stock products and satisfying consumer demand?

Consider the example of an American hardware retailer. Operating across several regions, the company had to regularly re-stock its stores from distribution centers. Their replenishment strategy relied on store managers’ planning and outdated demand forecasting using static statistical models to determine the necessary inventory levels.

The result? Systematic overestimation of the minimum stock levels, tying up $100 million of the company’s capital to manage 40,000+ stock keeping units (SKU) that significantly vary in demand depending on location.

Optimized inventory with predictive demand forecasting

In the face of overstocked products locking up cash and floor space, the hardware retailer partnered with SoftServe to build a better method of forecasting demand for their most important product categories. The result was a predictive, ML-driven demand forecasting algorithm.

Based on a data pipeline including the company’s internal data from historical sales, inventory, and purchase orders, the algorithm was enhanced by external factors such as local events, alongside analytics accounting for periodic and seasonal variations.

Reduce inventory requirements by up to 12.5% without sacrificing sales

In a further step, the algorithm proposes a reorder level and a minimum reorder quantity based on batch size and expected delivery. Using its more advanced demand forecasting, the hardware company is poised to free up over $10 million annually thanks to:

12.5% reduction of inventory levels without sacrificing revenue
20% greater forecast accuracy compared with statistical models
Three-month maximum forecasting horizon for reliable predictions

This approach not only averts the risk of lost sales due to stockouts but also keeps warehouse costs in check, fostering a more financially agile and customer-responsive retail operation. That balances the need to meet consumer demand without the burden of excessive inventory.

ML-driven demand forecasting: The key to an increasingly complex retail environment

Conventional forecasting methods were created for a simpler time. In an uncertain environment dominated by supply chain instability and increasingly complex customer demands and buying patterns, they are not up to the task. Armed with ML-driven predictive demand forecasting drawing from internal and external data sources, retailers are positioned to optimize stock management, free up working capital, and secure profits by satisfying consumer demand.

With a wealth of experience in deploying AI/ML solutions for retail across all major cloud providers or on-premises, SoftServe is the technology partner that can help you quickly deploy a robust, intelligent, data-driven demand forecasting solution for your business. That builds resilience and agility into your operations.

Don’t hesitate to learn more about SoftServe’s offering from our demand forecasting demo page.

Or, if you are interested in starting your own intelligent demand forecasting solution — Let’s talk!

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