Leveling Up Inventory Counting: A Product Engineer’s Perspective

I’ve spent the better part of the last two decades designing and optimizing technology systems for inventory management. My background is in industrial systems engineering, and I started my career working on embedded systems for retail hardware. These days, I’m a senior product engineer at one of the world’s leading inventory counting solution providers. Over the years, I’ve had the opportunity to work alongside global retailers, big-box chains, high-volume warehouses, and even niche luxury brands—all of them facing the same fundamental challenge: inventory accuracy.

I’ve walked hundreds of floors, toured facilities across four continents, and sat in more than my fair share of war rooms during annual counts. From those experiences, I’ve seen what works, what fails, and what makes the biggest impact when it comes to leveling up inventory operations. Here are five key strategies that I recommend to every organization looking to modernize and optimize their inventory counting—each backed by real-world performance data and engineering insight.

1. Implement RFID for High-Speed, High-Accuracy Counts

RFID is no longer “future tech”—it’s here, and it’s transforming inventory operations in ways barcodes never could. I’ve engineered systems for major distribution centers that previously spent four to five days conducting a full physical count using barcode scanning. With passive UHF RFID, we cut that process down to under 12 hours—with 98%+ accuracy.

Here’s how it works from an engineering standpoint: each item is tagged with a unique RFID identifier, which doesn’t require line-of-sight like a barcode. Using handheld RFID readers or fixed overhead gateways, you can detect thousands of tags within a radius of several meters in seconds. In one recent implementation at a footwear retailer, we deployed a combination of fixed readers at exit points and mobile readers for back-of-house. They were able to reduce shrinkage by 18% year-over-year and eliminated 90% of their out-of-stock errors.

Key consideration: RFID works best in environments where items are not overly dense with metal or liquid, which can interfere with signal. It’s critical to conduct a feasibility study and properly map your RF environment before deployment.

2. Leverage Full-Service Supplemental Labor for Peak Counting Periods

Let’s face it—inventory counting is labor-intensive, especially during year-end audits, seasonal transitions, or remerchandising cycles. One of the most overlooked ways to improve accuracy and reduce internal burnout is by leveraging full-service supplemental labor teams.

As an engineer, I’ve designed mobile platforms and software systems to be scalable for third-party teams. What we’ve learned is that specialized inventory crews are far more efficient than general retail staff when it comes to counting. They’re trained to move quickly, know how to troubleshoot equipment, and understand variance protocols. In a recent deployment with a sporting goods chain across 200+ stores, using full-service labor cut their total inventory cycle time by 36%, with fewer post-count adjustments.

Tip: Look for partners who provide both hardware and labor. The biggest failures I’ve seen stem from disconnects between devices, software, and temp staff who weren’t properly trained. A single-source partner can streamline the entire workflow.

3. Integrate Your Inventory System with E-Commerce and POS in Real Time

This might sound obvious, but the devil’s in the details. Too many retailers still operate with inventory systems that sync once a day—or worse, once a week. In the age of e-commerce and omnichannel retail, real-time synchronization is non-negotiable.

From a system architecture perspective, we build APIs and data pipelines that continuously update inventory status across point-of-sale systems, online platforms, and back-end ERPs. When implemented properly, this enables features like dynamic reallocation (moving inventory between locations based on demand), instant restock alerts, and safety stock automation.

One of the most dramatic improvements I’ve seen came from a North American apparel brand. After implementing real-time syncing between their Shopify backend, Oracle ERP, and RFID-enabled warehouses, they improved inventory accuracy from 88% to 99.3% and reduced split-shipments by 42%.

4. Use Machine Learning for Predictive Cycle Counting

Manual cycle counting schedules are often based on tradition or gut feel—"we count this section every Tuesday," for instance. But with the volume of data we now have, we can be much smarter.

I’ve worked on machine learning models that analyze sales velocity, shrink history, item volatility, and seasonality to dynamically determine which SKUs should be counted and when. This is known as predictive cycle counting. In essence, the system learns which items are most likely to be wrong and flags them for verification.

One high-end electronics client saw a 3x increase in error detection by shifting from fixed schedules to predictive ones. They counted fewer items overall but caught more discrepancies—saving them both time and money.

These models get better with scale. If you’re running 10 or more locations and tracking SKU-level inventory movements, you’ve got enough data to make predictive cycle counting worthwhile.

5. Use IoT and Sensor-Based Tracking for Passive Monitoring

One of the fastest-growing areas in our space is the use of IoT devices and sensor-based systems for passive inventory tracking. Unlike RFID, which requires an active scan event, IoT sensors (like weight plates, smart bins, and shelf sensors) continuously collect data about stock levels and movements.

For example, I engineered a solution for a parts warehouse that stocked thousands of tiny resistors, capacitors, and microchips. Manual counting was nearly impossible. We used weight sensors embedded in the shelving units to monitor inventory levels in real time. The system could detect when stock was running low or when an anomaly occurred, like unexpected depletion.

In the CPG and grocery sectors, these sensors are often tied into environmental monitoring—like temperature, humidity, or light exposure—which can affect spoilage or shelf life. The key engineering hurdle is power management. Many of these sensors operate on low-energy protocols like Zigbee or LoRaWAN, and we design them to last years on a single battery.

Bottom line: IoT doesn’t replace inventory counts—it enhances them by providing continuous verification. Think of it like an always-on audit layer.

Final Thoughts

Inventory counting has come a long way since the days of clipboards and spreadsheets. As someone who's helped design and deploy inventory systems for some of the most demanding operations on the planet, I can tell you: the tools are here. Whether it’s RFID for speed, supplemental labor for scale, IoT for visibility, or machine learning for strategy—the challenge now is how companies choose to adopt and integrate them.

For me, it’s always about building systems that balance accuracy, efficiency, and human usability. Tech should never make things more complicated—it should empower your teams to do more with less, and do it better.

If you’re still struggling with inconsistent counts, too much shrink, or inefficient workflows, I’d recommend starting with one of these five areas. You don’t need a complete overhaul—just a smarter next step.

Let me know if you’d like help identifying where that step should be.