Early shopper waves, midnight snackers, holiday hoarders, and last-minute planners – the holiday frenzy has uncertain customer highs and lows. Tagging along the nuanced customer are the retailers – convenience and grocery alike – dealing with the holiday rush, promotions, inventory planning, and supplier-induced disruption. With trends in purchase and pricing fluctuating rapidly, retailers often find it hard to make the most of the holiday season with better profits, lesser wastage, and more efficient promotions.
Studies reveal that more than 76% of customers will make their Thanksgiving purchases at the stores. Also, a staggering 79% of shoppers are planning to keep their Thanksgiving purchases the same as every year despite the rise in prices. However, they are seeking better offers and bundled items to make up for them.
Outsmarting complex demand patterns and sales influencers is challenging with manual or traditional demand planning and replenishment solutions that plan only for set variables and have limited ability to adapt to real-time changes. On the other hand, AI and ML-driven inventory optimization solutions can not only adapt to real-time supply-side disruptions, but they can also plan for a diverse set of sales influencers at the product-location level while offering insights for promotions management as per demand lifts and shifts.
Here are five ways AI/ML-driven inventory optimization can help retailers unlock record holiday profits!
1. Accurate Demand Forecasting
Accurate demand forecasting is the heart of inventory optimization, and failing to capture accurate demand can derail replenishment planning significantly. Studies reveal that the cost of holding excess inventory in grocery retail has increased by as much as 30%, reducing the profit margins significantly.
Further, as many as 75% of customers have altered their purchase behavior owing to recent market changes, such as the economy, evolving priorities, etc.
AI-driven demand forecasting is dynamic, is done via intelligent ensemble algorithms that are tuned to retail industry nuances, and offers the option to customize the forecasting process according to different business requirements. Retailers can identify hidden demand patterns, demand shifts, and lifts during specific times of the year, consider historical sales data, add multiple sales influencers in the form of forecasting constraints, and still generate highly accurate forecasts within minutes.
2. Product-Location Dynamics
Retailers often grapple with location-induced inefficiencies and pain points, that can bleed the revenue. They are unable to find which products and categories are performing the best at which locations during which times and how the interplay of seasonal, community, or promotional factors affects the overall sales performance.
Let’s explore how this becomes crucial.
Recent research outlines that shopping preferences change not only across categories or products but also from one geography (store) to another. For instance, 80% of the Northeast US customers were more likely to purchase canned Whipped Cream than the national average, while 90% of West US shoppers and 50% of Midwest customers favored Heavy Cream. The trends fluctuate in core holiday categories and products, such as frozen and fresh turkey, cranberries, nuts, baked mixes, blended juices, and so on. AI/ML-driven inventory optimization solutions plan for product-location-level dynamics with hyperlocal precision to ensure that each location has the right products in the right pack sizes and at the right times. This effectively cuts down inventory holding costs, out-of-stock and overstock events, and boosts shelf availability, thereby increasing customer satisfaction.
3. Adapting to Supply-Side Disruptions
Supplier-induced challenges, such as increased lead times, reduced safety stock levels, or delayed deliveries, can negatively impact replenishment and can land retailers in massive capital lock-ins. This, in turn, snowballs into issues like obsolescence, lost sales opportunities, and overstocks and affects supplier relations.
While there is no reliable way to manage these disruptions in manually managed supplier-retailer ecosystems, traditional supplier management practices also fail because of siloed supplier and inventory data across multiple locations.
AI-driven inventory optimization solutions effortlessly overcome these challenges by integrating supply-side data and offering predictive analytics-based actionable insights for any disruption. Retailers can leverage self-learning models to optimize replenishment plans by modeling variations in lead times, fill rates, safety stock, and pending orders. This helps in safeguarding stores and warehouses from supply chain disruptions in an adaptive, robust, and scalable manner.
4. Managing Promotional Chaos
Promotions and sales events during holidays change every few days and even hours, depending on categories, products, locations, and platforms. And, the consumers want to make the most of these deals. During the Thanksgiving week, the trends are particularly fluctuating, with Thanksgiving baskets becoming one of the top-selling items and bundled discounts running steeper.
This leads to a complex interplay among different products, product sizes, brands, categories, etc., across different stores and locations, and can actually eat away as much as 17% of the projected revenue to be made from sales. Also termed in-store cannibalization or inter-SKU effect, this shift or lift in the demand can be managed seamlessly with precision across all stores and locations from a single integrated dashboard. Inventory optimization solutions plan for cannibalization well in advance to generate highly accurate order plans, and can also adapt these order plans for any real-time demand fluctuations.
Thus, retailers can keep their revenue intact, prevent profit dilution, and run successful promotions, to make the most of the holiday season.
5. Multi-Echelon Inventory Optimization
What makes holiday inventory optimization particularly challenging, is the fact that retailers are unable to optimize the replenishment plans across the entire value chain in a holistic manner. The inability to capture and manage Direct Store Orders, Stock Transfer Orders, and Warehouse Orders from a single interface can lead to inefficiencies and an increase in inventory holding costs.
AI/ML-driven inventory optimization solutions can support such orders while offering additional advanced capabilities such as accurate forecasting, predictive alerts for days of stock situations, excess stock, and expiring products, and can also facilitate easy new product introductions.
Such solutions can help retailers optimize their inventory in a scalable and intelligent manner and can improve shelf availability by 90%, reduce OOS by 75%, cut inventory costs by 10%, and wastage by up to 30%, thereby unlocking greater savings, and unparalleled efficiencies.
