Getting Seasonal Adjustment Right in Your Inventory Purchasing – TheRetailAcademy
Author: Rod Daugherty
From soup to nuts, seasonal profile adjustment is an important – but often overlooked – part of inventory forecasting. Are your organization’s forecasting models keeping up?
For inventory managers, predicting sales for products with seasonal, slow or otherwise intermittent demand is notoriously challenging. In a recent survey of wholesale suppliers, nearly half (45%) called seasonal profile management a pain point for their businesses. From freezer case to OTC, seasonal items make up a significant portion of SKUs, which means accurate forecasting is critical. Yet many retailers are falling short, relying on outdated methods that don’t take seasonal dips and spikes into account.
As customer demand becomes increasingly complex in today’s flexible fulfillment environment, forward-thinking organizations are turning to new tools to help solve these challenges. By embracing a data-driven approach to inventory management, retailers can predict what they once considered unpredictable and manage their entire portfolio more effectively.
While new supply chain tools can help dramatically improve forecasting, many retailers haven’t yet moved beyond manual forecasting methods – or even old-fashioned gut instinct. According to the same survey noted above, half of suppliers haven’t implemented machine learning in their forecasting yet. By depending on outdated methods, retailers face challenges like:
For grocers, inaccurately forecasting seasonal items means getting stuck with excess holiday baking supplies in January or Easter hams in May. Stockpiling is a common tactic for organizations, the survey found, with more than 60% of wholesalers holding more than one month of inventory. Those high inventory levels don’t translate into higher service levels, however, with more than a quarter of organizations missing at least 4% of sales in 2019. Rather than ramping up inventory across the board, accurate forecasting requires optimizing levels by product – a practice that’s even more important for seasonal or other unpredictable items.
Finding the right model. Many businesses rely on a classic exponential smoothing model to predict demand, which smooths sales activity throughout the year into a holistic forecast. But what happens if an item doesn’t have any sales for six months out of the year? Rather than smoothing periods of no sales into the forecast, which doesn’t produce the most accurate results, use advanced forecasting techniques for SKUs with intermittent demand. For example, aggregating sales to a higher level, such as category or location, can help provide a more precise forecast.