How a US Apparel Brand Reduced Inventory Costs by 28% With AI-Powered Demand Forecasting

The client was a growing US-based mid-market apparel brand with $25–50M in annual revenue, selling seasonal outerwear, dresses, casualwear, and other fashion products across Shopify, Amazon, and wholesale channels, with roughly 2,000 active SKUs and two fulfillment centers.

The company's buying team relied heavily on spreadsheets for demand planning: sales exports were consolidated in Excel, compared against historical sales, adjusted manually by the senior buyer, and turned into purchase order recommendations. This worked when the business was smaller, but as it grew toward the $25–50M range, the number of variables — SKU count, sales channels, seasonality, promotions, product lifecycles, weather-sensitive categories, and new products with limited history — made the process difficult to manage consistently. This pattern is common across e-commerce and retail brands juggling DTC, marketplace, and wholesale demand at once.

The cost of getting demand wrong showed up on both sides of the business. During one holiday season, a cold-weather outerwear collection was planned around the prior year's demand pattern, but an early cold spell pulled demand forward by about three weeks and the collection sold through in roughly 11 days, leaving the brand under-stocked during a high-demand period. At the same time, a summer product line underperformed and accumulated approximately $350K in aged inventory, with some products eventually marked down by around 35% to clear.

Demand planning was managed by one senior buyer and two merchandising coordinators, with no dedicated data science or demand planning analytics team. The team was spending approximately 20 hours per week exporting data, combining spreadsheets, updating formulas, and preparing purchase recommendations — time the business wanted redirected toward assortment strategy and supplier decisions.

The goal was not to fully automate purchasing, but to build a better forecasting and decision-support process. Six objectives guided the project: improve demand forecast accuracy by generating more reliable SKU-level forecasts using multiple data signals; reduce stockouts by identifying potential demand increases earlier; reduce excess inventory by avoiding purchases beyond likely sell-through; reduce markdown pressure through better purchasing decisions and less aged inventory; reduce manual forecasting so buyers had more time for assortment strategy and supplier decisions; and work within the team's existing systems rather than forcing adoption of a completely new planning platform.

Cor Advance Solutions began with a detailed review of approximately three years of historical business data — sales, inventory levels, SKU information, product history, promotional activity, seasonal patterns, stockout history, fulfillment data, regional demand, and weather-sensitive categories — to understand which signals could actually improve forecasting before any model development started.

The data revealed that demand behavior varied significantly between products: some established products had relatively stable demand, others were highly seasonal, some responded strongly to promotions, weather had a noticeable impact on certain categories (particularly cold-weather products), and new products were harder to forecast because they lacked sufficient historical sales data. A single forecasting method would not be equally useful for every product, so the solution needed to account for these different demand patterns.

The data audit surfaced a critical issue: several historical SKU codes had been reused across different seasons, meaning some sales records were tied to product versions that weren't truly comparable. Rather than train on this data as-is, the team worked with the client's operations group to rebuild the historical SKU mapping — identifying duplicate identifiers, separating seasonal product versions, and cleaning inconsistent records. This added roughly two weeks to the timeline but gave the forecasting model a clean foundation. The key lesson: better AI starts with better data, and a more sophisticated model would not have fixed the underlying data problem.

The forecasting system combined historical sales, inventory, promotions, seasonality, product history, and relevant external signals using gradient-boosted machine learning models for SKU-level demand prediction, with a seasonal decomposition layer to separate long-term trends, recurring seasonal patterns, promotional effects, and short-term demand changes — the same class of AI and machine learning modeling we apply across other demand-forecasting engagements. Weather data was applied selectively — more heavily for cold-weather outerwear, for example — rather than treated as universally important across every product.

Gradient-boosted models were chosen based on the type of data available: numerical sales history, inventory signals, promotional variables, product attributes, seasonal patterns, and external signals. This class of model is well suited to structured business data and can capture nonlinear relationships between multiple variables. The goal was not to use the most complex model available, but a model that was effective, practical, explainable enough for business users, and suitable for the client's data.

The system was built as a decision-support layer rather than a replacement for the buying team. AI generated the demand forecasts and recommendations; buyers retained final approval and could factor in context the historical data couldn't capture — a new product launch, an upcoming promotion, a large wholesale order, a supplier delay, a planned product discontinuation, or a change in merchandising strategy. The AI provided the forecast; the buyer provided the business context.

Rather than introduce another standalone forecasting tool, the workflow was integrated directly with the client's existing Shopify sales environment and NetSuite operational and purchasing environment, so forecasts appeared inside the existing purchase planning process instead of requiring exports, downloads, and manual spreadsheet updates.

Within two quarters of the full rollout, the business saw measurable improvements across inventory cost, stockouts, forecast accuracy, and forecasting time:

• Inventory costs: 28% lower, an estimated $410K in annualized savings, driven by lower aged inventory, fewer markdowns, and better purchasing decisions
• Stockout rate: fell from approximately 18% of tracked SKUs to approximately 9%, roughly a 50% reduction
• Forecast accuracy (MAPE): improved from approximately 35% to approximately 14%, though established products consistently outperformed new products with limited history
• Forecasting time: fell from approximately 20 hours per week to approximately 6 hours per week, freeing about 14 hours weekly for assortment planning, supplier discussions, and other commercial decisions

The workflow itself changed shape. Before AI forecasting, the process ran from historical sales through Excel consolidation, manual forecast adjustments, and buyer judgment straight to a purchase order, with stockout or excess inventory risk baked in at the end. After AI forecasting, sales, inventory, promotions, seasonality, and relevant external signals feed an AI demand forecast, which the buyer reviews before a purchase decision is made and the result is continuously monitored — a more proactive approach to inventory planning.

The project delivered improvements across three areas. Financially, approximately 28% lower inventory costs and roughly $410K in annualized savings, since less capital was tied up in aged and excess inventory. Operationally, roughly 50% lower stockouts, giving the buying team better visibility into potential demand changes. On productivity, about 14 hours saved per week as buyers spent less time preparing forecasts and more time on business decisions.

The buying process shifted from starting with "what did this product sell last year?" to starting with what the combined sales, inventory, promotion, seasonality, and external data suggested about future demand — with buyers reviewing and approving every recommendation rather than the system purchasing autonomously. The AI forecast became another source of information in the buying process; it did not replace buyer experience, it made that experience more data-driven.

Four lessons stood out. First, data quality is a business requirement, not a side task — the SKU remediation work was as important as the model itself. Second, one forecasting strategy does not fit every SKU, since established products, new products, seasonal products, and weather-sensitive products behave differently. Third, AI should support business experts rather than replace them, since buyers understood supplier relationships and commercial context that historical data alone couldn't capture. Fourth, demand forecasting needs ongoing monitoring rather than a one-time deployment, since customer behavior, promotions, and product mix keep changing.

The project moved the apparel brand from a primarily spreadsheet-driven forecasting process toward a more data-driven inventory planning workflow. Within two quarters, the business had achieved 28% lower inventory costs, roughly 50% lower stockout rate, about 60% improvement in forecast MAPE, close to 70% less manual forecasting time, and an estimated $410K in annualized inventory savings.

The most important result wasn't any single metric — it was a more consistent, data-driven way for the buying team to use sales, inventory, promotional, seasonal, and external signals when making purchasing decisions, echoing the same shift toward predictive, self-adjusting operations covered in AI in Logistics: From Manual Operations to Intelligent Workflows.