This Halloween, if your house isn’t egged and your trees aren’t TP’d, thank your favorite candy producer. The rush to buy sweet treats every October is one of many seasonal swings, and if CPG supply chains aren’t ready, it can turn planning upside down and transform trick-or-treaters into plain ol’ tricksters.
But preparation isn’t as simple as boosting production before October. Making all those mini-sized bags and bars takes a monstrous planning effort, requiring companies to balance competing considerations across manufacturing, inventory, capacity, and transportation to keep costs low, shelves stocked, and consumers happy.
To stay on top of it all, leading CPG companies are using their own sweet mix of optimization, heuristics, and AI—including predictive AI for demand forecasting—to ensure trick-or-treaters celebrate Halloween with full bags.
The ultimate monster mash: Optimization + heuristics + AI
When it comes to seasonal planning, pure optimization is good on paper, but risky in reality. Optimization is invaluable for balancing tradeoffs and maximizing benefits, but the more detailed your model becomes, the longer it takes to solve, and the more its relevance and value diminish. If you’re a CPG manufacturer planning for spooky season, intricacy is unavoidable. And even with all the time in the world, last minute surprises like weather, regional shifts in trick-or-treat dates, and surprise trends like “summerween” can skew demand.
To truly unlock the full potential of your supply chain, you need the speed and accuracy that comes from combining optimization with heuristics and AI. With this approach, heuristics handle your end-to-end planning with speed and efficiency while optimization refines the critical, high-impact portions of it. Meanwhile, AI/ML continuously improves data quality and streamlines models based on past solves. Plus, when combined with capabilities like demand forecasting powered by predictive AI, your planning can go beyond historical trend assessment to anticipating future opportunities.
[Read more: A new approach to aligning supply chain optimization with business goals]
To understand how this approach beats pure optimization, consider some real-world scenarios.
Scenario 1: I Know What You Did Last Summer
It's June, and cocoa prices just spiked 30% due to extreme weather. You need to replan which chocolate products to prioritize and reformulate or substitute some items.
The challenge: You need to be able to factor in which products are strategically important beyond pure margin. Some lower-margin products are crucial for retail relationships. Some are featured in key holiday promotions already committed. Some use cocoa butter while others use cocoa powder.
The right approach: With combined capabilities, you can balance business-as-usual planning and re-prioritize chocolate products. You start by encoding knowledge like "always protect products in committed promotional programs," "maintain presence in value packs," and "prioritize products where we have strong brand loyalty over me-too offerings." You can then use optimization to determine the best production mix, whether to source more expensive cocoa immediately or adjust inventory drawdown rates, and how to reallocate capacity across products while respecting strategic priorities. When optimization suggests reducing production of certain SKUs, heuristics can help you quickly create plans for alternative products to boost.
The result: You navigate the supply constraint while protecting key strategic relationships and maintaining market position.
[Read more: Optimizing the consumer products supply chain]
Scenario 2: Invasion of the Demand Snatchers
It's mid-October, and real-time sales data shows surprising patterns: Parts of North America are tracking 20% above forecast (unseasonably warm weather means more outdoor activities and trick-or-treating).
The challenge: You've got inventory in the network, but it's not positioned ideally. You need to respond fast. Halloween waits for no one.
The right approach: Predictive AI, specifically ML demand forecasting, detects the deviation early and predicts it will continue through Halloween week based on weather forecasts and historical patterns. You instantly identify quick-wins, like inventory in nearby warehouses that can be redirected, stores within economical redistribution distance, and safety stock that can be tapped temporarily in lower-demand regions. You then use optimization to handle the complex redistribution challenges, like calculating the most cost-effective way to move inventory, considering transportation costs, handling costs, remaining demand forecasts, and the risk of stock-outs in different locations.
Result: You rebalance inventory to match actual demand patterns, minimize waste from overstock in some regions, and avoid lost sales from stock-outs in others—all executed in days rather than weeks.
Scenario 3: The Nightmare Before Christmas
It's early November, and while most of your Halloween candy moved as planned, some is still sitting in warehouses. Portions are Halloween-specific that can't be sold after October 31st. Others could be repositioned for Christmas or Valentine's Day, but every day this inventory sits costs money in storage fees, ties up working capital, and risks obsolescence.
The challenge: You need to make rapid decisions about multiple bad options—liquidate immediately at a steep discount, repackage for other holidays, donate for tax write-offs, or destroy product.
The right approach: You quickly segment the overstock into categories, like "Halloween-specific packaging = liquidate immediately," "generic chocolate = reposition for Christmas," etc. You then use a multi-objective optimization model to calculate the financial impact of different strategies, considering not just immediate costs but also downstream effects. For instance, flooding discount channels with excess product might save money short-term but hurt future full-price sales. Machine learning models can then help you analyze historical clearance patterns to forecast how quickly different products will move at various discount levels. Maybe 40% off moves product just as fast as 50% off for certain items—that 10-point margin difference could be worth millions across your entire overstock position.
Result: You minimize the financial damage from overstock while protecting brand equity and learning lessons that make next season's planning more resilient. More importantly, the process reveals the value of building in flexibility from the start.
A happy Halloween starts with the right mix
The beauty of blending optimization, heuristics, and AI is that it's not a one-time trick. It will keep your supply chain on track, no matter your seasonal scenario. The same approaches that keep CPG supply chains Halloween-ready in October also ensure candy hearts are ready for Valentine’s, champagne flows for New Year's, and sunscreen is plentiful on Memorial Day weekend. It also, unfortunately, works for in-demand Halloween tricks, like shaving cream and silly string, too. But hopefully, with the perfectly optimized candy mix, nobody will need them.
Learn more about how Kinaxis is helping supply chains deploy the right mix of optimization, heuristics, and AI to identify impactful business opportunities.
