Automating product recognition: a foundation for a reliable and flexible Supply Chain
Between labour shortages, traceability requirements and pressure on costs, the slight input error can degrade the performance of an entire supply chain.
Automating product recognition (bar codes, QR codes, RFID, QR codes, RFID, RFID, computer vision, AI) makes it possible to make stocks more reliable, streamline logistics flows and support sustainable logistics, without burdening the daily lives of teams.
1. Input Errors: The Blind Spot in Supply Chain Performance
Manual entry remains very present in the management of operations: receipts, inventories, preparations, returns. However, studies show that a simple keyboard can generate an error every few hundred characters, while a bar code scanning system goes down to about 1 Error for Several Million Reads.
On a large scale, this results in:
- persistent stock discrepancies and deteriorated forecasts,
- preparation errors, delivery delays, returns,
- overconsumption of resources (time, transport, overstocks).
By automating recognition, each scan becomes a reliable event in the information system. This data is then fed into theInventory Optimization, the models of Forecast, the management of Logistic flows And the indicators of sustainable logistics.
The technologies ofAutomatic Identification and Data Capture (AIDC), already valued at several tens of billions of dollars and growing strongly by 2030, confirm this fundamental trend towards automation and real time.
2. The Key Building Blocks of Recognition Automation
Barcodes and QR codes: the basis
The bar code and the QR code remain the foundation:
- low cost, mass adoption,
- “standard” badge for the digitalization of operations,
- Simple integration with a Platform operations management.
Major use cases: reception control, guided preparation, rotating inventories, batch and serial number monitoring. In Many Warehouses, These Bricks Are Already Enough to Earn Several PointsInventory accuracy, sometimes up to +97%.
RFID: inventories and advanced traceability
RFID provides an additional layer:
- Reading without a line of sight, sometimes through porticoes,
- Complete inventories in a few minutes,
- followed by pallets, rolls, racks or reusable containers.
It is particularly relevant for high-turnover flows, products with high value or with strong regulatory constraints.
Computer Vision and AI: Recognizing Beyond the Label
Computer vision, coupled with AI, makes it possible to:
- automatically count packages and bins,
- check dimensions and conformity,
- Identify products even when the bar code is hidden or damaged,
- Automate some quality checks.
These bricks reinforce the Traceability and reduce errors in high-flow environments.
No-code platforms: orchestrate workflows
For these technologies to create value, they must be orchestrated by a Platform open, connected to ERP, WMS, TMS, etc.
The Approaches No-code /low-code allow you to:
- quickly set up the scan screens,
- Adjust the workflows (reception, transfer, picking, returns) without heavy development,
- Involve supply chain teams in defining business rules.
3. Three Priority Use Cases to Digitize Recognition
3.1 Reception: securing the entry point
Objective: to make the stock “fair” as soon as it is received.
- scanning the logistic units at the dock,
- automatic reconciliation with the order form,
- systematic capture of critical data (batch, DLUO/DLC, serial number),
- automatic assignment to the right locations.
The result: fewer disputes, better data quality for Forecast And theInventory Optimization.
3.2 Order preparation: making the “right product, right customer” reliable
- guiding the operator in picking order,
- mandatory product validation by scan,
- quantity control (UVC, carton, pallet) and FIFO/FEFO rules,
- Final packing check.
TEAAutomation Recognition in these workflows greatly reduces preparation errors, improves the service rate and secures e-commerce, retail or B2B logistics flows.
3.3 Rotating Inventories and Returns: Living and Traceable Stocks
Rotating inventories:
- List of locations to be checked generated by the platform,
- scan of the products present,
- Guided resolution of discrepancies.
Returns and revalorization:
- quick identification of the product and its history,
- orientation to the right workflow (restocking, repair, reconditioning, recycling),
- Consolidated Data for sustainable logistics and regulatory reports.
4. Succeeding with an Automation Project: A Pragmatic Approach
- Field diagnosis
- map flows,
- locate recurring errors and re-entries,
- Measure the impact on customer service and costs.
- Platform-oriented architecture
- Define the Platform operations management center,
- frame integrations via API,
- Predict scalability to RFID, computer vision, AI for forecasting.
- Measurable quick wins
- a pilot site, 1 to 2 use cases (reception, preparation, inventories),
- simple indicators: error rate, productivity, inventory accuracy, flexible flows,
- Quick iterations via tools No-code.
This logic is fully aligned with Google's latest Core updates, which prioritize content that is useful, reliable, well-structured, and grounded in a real experience, rather than artificially optimized text.
Conclusion: towards a more traceable, sustainable and flexible Supply Chain
Automating recognition (bar codes, RFID, AI) is not just an equipment project: it is a way to make operational data more reliable, to industrialize workflows And to increase the Flexibility of the Supply Chain.
Some useful questions to take action:
- Where do input or traceability errors generate the most hidden costs today?
- What flows would immediately benefit from being supported by scanning or automated recognition?
- Our architecture and our Platform Do they allow AI, no-code and new data sources to be added by now?
The answers to these questions form the basis of a digitalization roadmap focused on value, traceability and sustainable logistics.
FAQ — Automating recognition in the Supply Chain
1. Is Automating Recognition Only for Highly Automated Warehouses?
No Generalizing scanning at the reception, structuring guided preparation and setting up rotating inventories are already strong levers, accessible to experienced sites, as soon as they are integrated into an operations management platform.
2. Should we go directly to RFID or to computer vision?
Not necessarily. A gradual approach is often more effective:
- standardize bar codes and scan workflows,
- deploy RFID on high-value targeted areas,
- Add computer vision and AI when volumes or traceability constraints justify it.
3. What is the impact on operational teams?
TEAAutomation Reduces re-entry tasks, but reinforces the role of teams in supervision, anomaly resolution, and continuous improvement. Change support (training, screen ergonomics, involvement of operators in defining workflows) is a key success factor.
