How AI is changing warehouse logistics: 15% reduction in costs and zero errors by order pickers

Imagine: an order picker receives an order for 15 items, takes a cart, and begins a marathon through an 8,000 m² warehouse. An hour later, he returns with 14 items — he couldn’t find one item and mixed up two others. The customer receives the order in three days instead of one, and the company loses money on reordering.

Scenes like this happen in thousands of warehouses every day. According to McKinsey, companies that have implemented AI in logistics reduce operating costs by 15% and increase service levels by 65% compared to their competitors. But so far, only 14% of logistics companies worldwide are using such solutions.

Let’s take a look at how artificial intelligence is turning the chaos of warehouse operations into a well-oiled machine.

What tasks does AI solve in warehouse logistics?

Demand forecasting and inventory management
AI analyzes sales, seasonality, and external factors to generate accurate demand forecasts. The algorithms even take the weather into account—before a rainy weekend, they automatically increase umbrella orders.

Delivery route optimization
The courier receives 15 addresses, and AI calculates the optimal route in seconds, taking into account traffic jams, recipients’ working hours, and parcel weight. The result is a 15% reduction in mileage and fuel consumption.

Warehouse operations automation
AI in logistics transforms the warehouse into a “smart” system. An employee scans the goods, and the system determines the optimal storage location. When picking orders, the application calculates the shortest route between items.

Quality control and predictive analytics
AI cameras check goods at the entrance and exit, and the system predicts equipment breakdowns several days in advance and plans maintenance.

Specific benefits for businesses

According to McKinsey, companies that use AI-based analytics in logistics achieve:

• A reduction in operating costs of up to 15% through process optimization
• A 65% increase in service levels compared to competitors without AI

Comparison table: before and after the introduction of technologies

⭐ NOMIUM case

A company that manages a warehouse of doors and accessories approached us at Nomium. Goods were stored chaotically, without a clear system. When assembling orders, employees spent a lot of time searching for the right items among hundreds of items.

Problems: inaccurate accounting of receipts, long search for goods, errors in picking.

Our solution: a WMS-light system consisting of a web platform and a mobile application.

• The web system displays the current state of the warehouse: availability of goods, quantity, exact location. It synchronizes with accounting via delivery notes and is updated with every movement of goods.
• Waybill scanning: an employee scans a QR code and receives a list of goods for picking with their location indicated.
• Computer vision: the system analyzes the video stream, recognizes the labeling, and visually highlights the desired goods directly on the device screen.
• Warehouse navigation: the application builds the optimal route and guides the employee step by step to the required locations.

Result: search and picking time has been reduced significantly, errors have been minimized, and the company has gained transparency in the movement of goods without heavy WMS systems.

Who needs AI implementation?

Medium and large retailers
Chains with 10 or more stores and warehouse turnover exceeding 1,000 items per day.
Typical problems: frequent errors in order picking, time-consuming searches for goods among thousands of SKUs, inefficient placement of seasonal goods. AI helps automate supply planning and reduce order picking time.

Logistics companies
3PL operators with a customer base of 50 companies or more, courier services with a volume of 500 shipments per day or more. Key benefits: optimizing courier routes saves up to 20% in fuel, automatic planning of warehouse loading for different customers, predicting peak loads for personnel planning.

E-commerce
Online stores with a volume of 10,000 orders per month.
AI is particularly effective in e-commerce: it processes many small orders, optimizes packaging to save on shipping, and automatically groups orders by region to consolidate shipments.

Stages of AI implementation

1. Process audit (1–2 weeks)
We time the operations of pickers, analyze the error rate by product category, and check the quality of data in the accounting system. We identify the top three problems that cause the greatest loss of time and money.

2. MVP development (4 weeks)
We create a basic mobile application with warehouse navigation and product scanning. We program route optimization algorithms based on audit data. We configure integration with the accounting system via API.

3. Pilot testing (1–2 months)
We launch the system in one area of the warehouse—for example, for category A goods. We train 3–5 key employees. We collect daily performance metrics and feedback and eliminate technical shortcomings.

4. Scaling (2–6 months)
We roll out the system to the entire warehouse, train all employees, and add advanced features: predictive demand analytics, computer vision, and integration with delivery planning. We adjust business processes to the new system.

Result: savings, speed, accuracy

AI in warehouse logistics is no longer a technology of the future, but a competitive advantage today. Companies using AI reduce operating costs by up to 15%, speed up order processing by 2-3 times, and virtually eliminate human error.

➡️ Want to assess the potential of AI for your company’s warehouse? Fill out the form below, and we will conduct a free audit of your current processes and calculate the ROI of implementation within 48 hours.