How Retailers Use AI to Cut Waste: Lessons Inspired by Costco

Retail is a game of tight margins, fluctuating demand, and unforgiving customers. As costs rise and competition intensifies, leading chains are turning to artificial intelligence to trim waste and protect profits. Recent earnings from major warehouse and big-box retailers highlight how AI is quietly reshaping inventory, pricing, and operations. This article unpacks the key ways retailers are deploying AI to reduce waste and strengthen their businesses.

Why AI Has Become a Strategic Priority for Retailers

Retailers operate on thin margins, and a disproportionate share of profit can be lost to waste: unsold inventory, food spoilage, markdowns, logistical inefficiencies, and even energy overuse. At the same time, consumers expect full shelves, fresh products, and stable prices. Artificial intelligence (AI) has emerged as a critical tool to balance these competing pressures.

Warehouse clubs and large retailers, similar to Costco, sit on massive volumes of transaction and operations data. When this data is fed into AI models, it can reveal patterns that human planners and traditional spreadsheets simply cannot see. The result is smarter decisions across the entire value chain—from how much to buy and where to store it, to how and when to sell it.

Investor discussions around earnings from big retailers now routinely include questions about AI investments. While the financial disclosures are often high level, the underlying theme is clear: AI is no longer a side experiment; it is a core capability for cutting waste and strengthening earnings quality.

Where Waste Happens in Retail – And How AI Targets It

To understand how AI helps, it is useful to break down the main types of waste retailers face. Each of these has a corresponding AI application that can mitigate the problem.

1. Inventory Waste: Overstock and Stockouts

Ordering too much leads to markdowns and spoilage; ordering too little results in empty shelves and lost sales. Traditional forecasting methods rely heavily on historical averages and manual judgment. They often struggle with seasonality, promotions, and sudden demand shifts.

AI-based demand forecasting uses far richer data and more sophisticated models to predict how much inventory is needed at each location and time period, significantly reducing both overstock and stockouts.

2. Operational Waste: Labor, Processes, and Time

Manual checks, paper-based processes, and disconnected systems create invisible waste—slow replenishment, misallocated staff, and inefficient store layouts. AI can streamline these operations, recommending better schedules, routes, and workflows.

3. Product Waste: Spoilage and Shrink

In grocery-heavy retailers, perishable goods are a constant source of loss. Food that expires before sale and items that go missing through theft or mistakes both erode margins. AI is increasingly used to detect anomalies, predict spoilage risk, and optimize markdown timing so that more inventory sells before expiration.

4. Financial Waste: Pricing and Promotions

Poorly designed promotions can trigger either unnecessary discounting or disappointing traffic. AI pricing systems can test and optimize prices at scale, improving both revenue and customer value while limiting margin erosion.

How AI Demand Forecasting Reduces Waste

Among all AI applications in retail, demand forecasting is often the most impactful. Retailers with a model similar to Costco’s—high volume, limited assortment, and recurring member purchases—are especially well positioned to benefit.

From Simple Averages to Sophisticated Models

Legacy forecasting systems typically use moving averages and simple seasonality adjustments. AI models, by contrast, can account for:

• Weather patterns and local events that affect foot traffic
• Promotions, holidays, and pay cycles
• Regional preferences and store-specific behavior
• Macroeconomic signals like inflation and fuel prices
• Online search trends and website/app browsing data

By understanding dozens or even hundreds of variables, AI systems can predict demand much more precisely. For a large retailer, even a small percentage improvement in forecast accuracy can translate into millions of dollars saved in reduced waste and higher sales.

Case-Style Example: Warehouse Clubs and Member Data

Warehouse-style retailers tend to operate with membership models, which provide a rich source of repeat customer data. When customers scan a membership card, every purchase becomes a datapoint in a longitudinal history. AI can then model individual and group purchasing behavior, including how members react to price changes or new product introductions.

On a practical level, this might look like:

• Predicting bulk purchases before major holidays or events
• Identifying early signals that a product is becoming a “staple” versus a passing trend
• Adjusting orders dynamically for regions that respond differently to promotions

By aligning inventory more closely with expected demand, retailers reduce the amount of product that needs to be discounted or disposed of.

AI for Smarter Replenishment and Store Operations

Forecasting is only the first step. Retailers must then turn predictions into action—deciding when to reorder, which distribution centers to ship from, and how to stock shelves. This is where AI-driven replenishment and operations planning come into play.

Automated Replenishment Engines

AI-based replenishment engines continuously review sales data, inventory levels, lead times, and supplier performance. They then propose or automatically place purchase orders to keep stores within target stock ranges.

This reduces waste by:

• Avoiding panic orders that arrive too late or in excessive quantities
• Smoothing out demand on suppliers and transportation networks
• Limiting manual errors in reorder quantities and timing

Optimized Store Labor and Task Management

Labor is a major cost center in retail. AI can help managers create smarter schedules by predicting busy periods and task loads. This reduces overstaffing during slow times and understaffing when demand spikes.

Common AI-supported decisions include:

1. Forecasting customer traffic per hour, per store.
2. Mapping tasks (stocking, cleaning, checkout, online order picking) to those traffic patterns.
3. Generating optimized staff rosters with the right skills at the right times.
4. Continuously learning from actual conditions and adjusting future schedules.

Better labor allocation does not just cut cost; it can also reduce operational waste like missed replenishment cycles, neglected fresh departments, and long checkout lines.

Cutting Product Waste: From Spoilage to Shrink

For retailers with substantial fresh and frozen assortments, product waste is a constant pressure on margins. AI tools are particularly effective here because they can process data at a level of detail that would otherwise be impossible.

Predicting Spoilage in Fresh Categories

Perishable goods such as meat, produce, and dairy have short shelf lives, and demand is volatile. AI models can combine:

• Historical sales by day, product, and store
• Expiration dates and delivery schedules
• Local weather and event data
• Store-specific handling performance (e.g., known high-waste locations)

With this, systems can predict where and when inventory is at risk of not selling in time. Retailers can respond by:

• Triggering earlier, targeted markdowns for at-risk items
• Reallocating stock between nearby stores if feasible
• Adjusting future orders for products that chronically overstock

Loss Prevention and Shrink Analytics

Shrink—loss due to theft, errors, and damage—is another source of waste. Traditional loss prevention relies heavily on audits and cameras. AI adds a new layer by scanning transaction logs, inventory movements, and camera feeds for anomalies.

Examples include:

• Flagging unusual return patterns tied to specific items or customers
• Spotting discrepancies between scanned and expected items at self-checkout
• Identifying stores with statistically abnormal shrink in certain categories

These insights help retailers tighten controls, refine training, and ultimately reduce avoidable product loss.

Dynamic Pricing and Markdown Optimization

Price is a powerful lever, but also a risky one. Change prices too aggressively and customers may push back; move too slowly and inventory piles up. AI and machine learning can help retailers calibrate this balance more precisely.

Everyday Pricing vs. Markdown Strategy

Retailers often differentiate between everyday prices (base prices that hold most of the time) and markdowns (temporary reductions to boost sales or clear stock). AI assists in both areas:

• Everyday pricing: Evaluating price elasticity—how sensitive customers are to price changes—by product, region, and channel.
• Markdown optimization: Determining the timing and depth of discounts needed to clear inventory before a deadline such as season end or expiry.

How AI Improves Pricing Decisions

AI can simulate how different price points would affect demand and margin, using historical transaction data and controlled tests. It can also account for competitive movements when data is available. For waste reduction, the key benefit is that markdowns become targeted and data-driven rather than broad and reactive.

Supply Chain and Logistics: AI Beyond the Store

Waste reduction starts long before products reach the shelf. AI enhances efficiency throughout the supply chain, helping retailers ship the right goods to the right place at the right time.

Network and Route Optimization

Large retailers operate complex networks of suppliers, distribution centers, and stores. AI can evaluate this network holistically to reduce duplication, mileage, and emissions. Typical use cases include:

• Optimizing which distribution center should serve each store for specific products
• Planning truck routes that balance speed, cost, and capacity utilization
• Simulating the impact of disruptions (e.g., a closed route or supplier delay)

Every incremental gain—fewer half-empty trucks, better consolidation of loads—translates into lower logistics waste and often better product freshness.

Supplier Collaboration and Lead Time Prediction

Suppliers rarely deliver in exactly the same timeframe every time. AI models can learn typical lead times and variability per supplier, lane, and product. Retailers can then factor this into their replenishment and inventory strategies.

For example, if AI predicts that a supplier is likely to be late on a high-demand item, the system can recommend earlier ordering or spreading volume across alternative suppliers. This helps avoid both emergency orders and excess safety stock—two hidden sources of cost and waste.

Benefits and Risks of AI-Driven Waste Reduction

AI offers compelling advantages, but it is not a magic switch. Understanding the trade-offs helps retailers set realistic expectations and design better programs.

Key Benefits

• Lower direct waste costs: Less spoilage, fewer unsold units, and reduced markdown depth.
• Higher sales and availability: Better stock accuracy increases on-shelf availability, improving customer satisfaction.
• More stable margins: Smarter pricing and inventory planning support more predictable profitability.
• Operational efficiency: Optimized labor, logistics, and store routines free up staff to focus on service.
• Sustainability gains: Cutting product and energy waste supports ESG and environmental goals.

Key Challenges and Risks

• Data quality and integration: Incomplete, inconsistent, or siloed data can undermine model accuracy.
• Change management: Store managers and planners must trust and adopt AI recommendations, not ignore them.
• Over-automation: Blindly following algorithms without human oversight can create new types of errors.
• Customer perception: Aggressive pricing algorithms, if misused, can damage brand trust.
• Regulatory and ethical concerns: Use of customer-level data must be compliant and transparent.

Practical Steps for Retailers Starting with AI

Retailers inspired by the success of major warehouse clubs and big-box chains can approach AI adoption methodically. The aim is to reduce risk while demonstrating early value.

Step-by-Step Implementation Approach

1. Clarify the business problem: Define a specific waste-related goal, such as reducing fresh spoilage by a set percentage in one region.
2. Audit your data: Assess what transactional, inventory, and operations data you have, how clean it is, and where it resides.
3. Select a focused use case: Start with a high-impact, measurable area like demand forecasting for limited SKUs or markdown timing for perishables.
4. Choose your technology path: Decide between building in-house models, partnering with vendors, or using cloud-based retail AI services.
5. Run a controlled pilot: Test in a small number of stores or categories, with clear before-and-after KPIs such as waste cost, availability, and margin.
6. Engage front-line teams: Train store and supply chain staff on how to interpret and use AI recommendations, and gather feedback.
7. Scale and refine: Gradually expand the program, adjusting models and processes as real-world results come in.

The Investor Angle: Why AI Matters in Earnings Discussions

When investors review earnings from large retailers, they increasingly listen for indications of how AI is being used across operations. The questions typically center on whether AI investments:

• Are translating into measurable margin improvement or cost savings
• Help offset inflationary pressures in labor, logistics, and goods
• Make earnings more resilient against demand volatility
• Support long-term differentiation versus competitors

While companies may not disclose granular AI metrics, commentary around improved inventory turns, lower waste in fresh categories, or better labor productivity often signals that data and AI initiatives are taking hold. For investors, this can provide context for why certain retailers maintain strong performance even in challenging environments.

Final Thoughts

AI has moved from theory to practice in retail, especially among large players with warehouse-style formats and significant data footprints. By attacking waste on multiple fronts—inventory, operations, pricing, and supply chain—AI supports more resilient, efficient, and sustainable businesses.

Retailers that treat AI as a disciplined, data-driven capability rather than a buzzword are already seeing the benefits in reduced waste and steadier margins. For executives and investors alike, the key takeaway is that AI is no longer optional for large-scale retail: it is becoming a foundational component of competitive advantage and long-term profitability.

Editorial note: This article is an independent analysis inspired by public discussions about how major retailers use AI to improve efficiency and reduce waste. For original reporting and broader market coverage, visit BNN Bloomberg.