Optimizing Special Events and Seasonality in Retail: Embracing High-Resolution Planning

Mastering inventory for special events and seasonality is a key ongoing challenge for retailers of every kind and size. The ebb and flow of sales during festive seasons, promotions, shopping days, and holidays can pose significant hurdles for inventory management. Retailers often find themselves grappling with excess inventory after a season or scrambling to meet the demand for best-selling products.

In this article, I delve into the intricacies of managing special events and seasonality in the retail domain. I explore the underlying complexities that make planning for these events so challenging and new technologies retailers are using to mitigate these challenges.

The Problem

Upstocking for the Season: A Double-Edged Sword

Retailers often face a conundrum while preparing for the upcoming season, and inventory mistakes have significant costs. The industry lost upwards of $1.8 trillion to stock errors like overstock, which is a frequent consequence of special events. Anticipating increased demand, retailers upstock their inventory, only to find themselves burdened with excessive stock once the season ends. This overstocking results in tied-up capital, increased carrying costs, and the risk of inventory obsolescence.

Shortages of Best Sellers

On the other side of the spectrum, retailers may encounter inventory shortages of their best-selling products during special events. Unanticipated demand surges for these items can lead to stock-outs, leaving consumers disappointed and potential sales lost. McKinsey reports that a majority of consumers — a whopping 71 percent — report switching brands or retailers to access out-of-stock items, with 13 percent of consumers waiting for items to be back in stock.

Key Planning Challenges

SKU and Location Variability

The key challenge in managing special events and seasonality is essentially a spatial one. Product behavior varies with location, the latter of which brings a range of dependencies on everything from starting price to consumer demographics. This means that while historical sales data might assist our understanding of ideal product types and quantities for special events, the real riddle — and lynchpin for success — is placement.

The need to factor the uniqueness of each SKU per location runs counter to today’s aggregate planning processes, which rely on forecasting and generic sales lift formulas (e.g., assuming a 30 percent markdown will yield a 30 percent lift) to calculate event needs and outcomes. Moreover, unique SKU behaviors by location can not be known in advance of any event, but rather reveal themselves only once items reach store shelves.

In this way, traditional planning methods lack the nuance and real-time data necessary to maximize sell-through via allocation. Without in-season intervention, retailers are bound to see items go to stores where they won’t sell, and miss opportunities to get the most out of that product in other locations.

Multitude of Impacting Parameters

One should never take for granted the influence of external factors on commerce in general and special events in particular. Just as weather can influence the outcome of elections, so too can it influence your event's bottom line. Other parameters like changing market trends, consumer preferences, macroeconomic conditions, and even social media trends interact in complex ways, making it difficult for retailers to accurately predict demand and allocate inventory accordingly.

The Solution: High-Resolution Planning With a Proximity Engine

To tackle the complexities of managing special events and seasonality, retailers need a revolutionary approach that empowers them with greater visibility and precision. This is where having the necessary data for high-resolution planning comes into play. Retail technology advisory IHL recently projected a 25 percent improvement in inventory distortion for retailers that employ artificial intelligence.

Granular Insights for Every SKU Location

The one-size-fits-all approach fails to consider the unique characteristics of each product in specific store locations. For best-selling items, the overall increase might not be sufficient to meet the surge in demand, resulting in stock-outs and missed sales opportunities. On the other hand, slower-moving items might end up with excessive stock, leading to unnecessary waste and tied-up capital.

High-resolution planning takes inventory management to a whole new level of precision. By treating each SKU and each store location differently, retailers can effectively train their proximity engine and achieve tailored inventory allocation that fits market realities.

Learning From Similar Products

With the incorporation of advanced AI algorithms, the proximity engine can learn from similar products and make intelligent adjustments to current trends. Even for new SKUs lacking historical data, the proximity engine leverages patterns from analogous products, offering more accurate demand forecasts.

Adapting to Multitude of Parameters

High-resolution planning acknowledges the impact of multiple parameters on actual demand. By factoring in market trends, consumer preferences, and external events, retailers gain a comprehensive understanding of the dynamic market conditions. This proactive approach enables more agile decision making in response to evolving situations.

Conclusion

Special events and seasonality will continue to be prominent features of the retail landscape. For retailers seeking to optimize their inventory management during these periods, high-resolution planning with the help of AI emerges as the ultimate solution. By embracing this cutting-edge approach, retailers can bid farewell to excessive stock, shortages of best sellers, and the uncertainty of assortment planning for new SKUs.

As the retail industry embraces the power of AI and data-driven solutions, high-resolution planning is a pivotal trend that can revolutionize inventory management.

Greg Arthur is the vice president of retail strategy at Onebeat, a company that specializes in adaptive inventory management at scale, item by item.