I co-founded Scandit in 2009 together with Samuel Mueller and Christof Roduner, whom I met at ETH Zurich. Our ambition was (and still is) to transform the daily lives of customers, workers, and businesses through software that can change the way we interact with everyday objects by blending the physical and digital worlds.<\/span> <\/span><\/p>\n\n\n\n <\/p>\n\n\n <\/p>\n\n\n\n Originally, we thought that RFID technology was the way to do that. However, it soon became apparent that there was a better way: extending the Internet of Things (IoT) paradigm to everyday objects by developing a highly specialized computer vision platform for camera-equipped smart devices.<\/span> <\/span><\/p>\n\n\n\n At Scandit, our priority right now is shifting from scanning to acting. With the release of our Scandit SDK 8.0, we\u2019re embedding intelligence directly into the data-capture workflow: adaptive barcode, text, and object recognition; augmented reality (AR)-guided tasks; and edge-based decision-making so frontline teams in manufacturing, warehousing, and logistics can act in the moment.<\/span> <\/span><\/p>\n\n\n\n In parallel, we\u2019re constantly improving Scandit Express – a fast-track implementation offering designed for manufacturers who want to deploy smart data capture with minimal custom code and time to value. It\u2019s a great turnkey solution for businesses that have existing hard-to-modify apps or that simply don\u2019t have enough development resources to integrate an SDK.<\/span> <\/span><\/p>\n\n\n\n At the same time, we\u2019re investing heavily in our AI-powered shelf-intelligence solution, ShelfView. While it\u2019s currently designed with grocery retail in mind, even serving CPG manufacturers with shelf-level information (e.g., share of shelf, number of facings), some of the underlying capabilities may also prove valuable in certain factory or warehouse settings over time.<\/span> <\/span><\/p>\n\n\n\n Most manufacturers don\u2019t suffer from a lack of data – they suffer from a lack of reliable data captured at the frontline. We still see fragmented systems, manual workarounds, and legacy hardware that create blind spots in traceability. And whenever workers are keying in serial or batch codes manually, accuracy drops, and productivity is low. For example, 60 percent of SKUs are affected by inventory record inaccuracies. The result is a disconnect between what\u2019s physically happening on the frontline and what the system thinks is happening. That gap is the root cause of many downstream issues.<\/span> <\/span><\/p>\n\n\n\n Smart data capture transforms the simple act of pointing a camera at an object into a real-time intelligence layer. Manufacturers get end-to-end traceability, higher accuracy, and dramatically faster workflows thanks to automation; often with 30-50 percent efficiency gains. Solutions like multi-scanning enable workers to capture multiple items in one scan, and automated label scanning allows workers to capture barcodes and printed text simultaneously. Likewise, AR overlays not only speed up tasks but also create a more intuitive support experience for frontline workers.<\/span> <\/span><\/p>\n\n\n\n Because it\u2019s software-driven, smart data capture runs on devices manufacturers already have – Scandit alone supports more than 20,000 smart device models – making it easy to scale.<\/span> <\/span><\/p>\n\n\n\n <\/p>\n\n\n <\/p>\n\n\n\n Three categories stand out for me:\u00a0<\/span><\/p>\n\n\n\n Label and document data beyond the barcode<\/strong>: A lot of valuable information is printed on labels – serial numbers, batch IDs, expiry dates, text instructions. Historically, capturing that meant either manual typing or separate OCR systems, so it was often skipped. With solutions like Smart Label Capture, manufacturers can capture both barcodes and text in one pass and feed it directly into MES, QMS, or ERP systems.<\/span> <\/span><\/p>\n\n\n\n Context at the edge<\/strong>: Manufacturers are good at recording what was produced and when, but they often miss the surrounding context that makes data actionable – things like where on the line an event occurred or what conditions were present at the time. Smart data capture can enable manufacturers to pair that information with relevant process data in their existing systems. This turns routine scans into more complete and actionable operational insights without adding steps for workers.<\/span> <\/span><\/p>\n\n\n\n Exception and quality data<\/strong>: Quality checks, rework, small stoppages, and manual interventions are frequently logged on paper or not logged at all. Smart data capture lets workers scan and tag exceptions in the workflow – for example, flagging a mis-pick, recording a defect with a quick photo and barcode scan, or capturing a deviation from a work instruction. Over time, this builds a much richer dataset for continuous improvement and predictive maintenance.<\/span> <\/span><\/p>\n\n\n\n We see automation and human judgment as complementary. Automation should handle the repetitive, error-prone parts of the workflow, while humans stay in control of interpretation and handling exceptions. Our approach is to build a \u2018co-pilot\u2019 experience – the software guides the worker through the process, validates critical steps according to pre-set rules, and highlights anomalies, empowering the human to make the final call.<\/span> <\/span><\/p>\n\n\n\n Toyota in Japan uses smart data capture on tablets to ensure every part on the production line is traceable. Operators scan QR codes on components as they are installed; the system checks against digital work instructions and records which part went into which vehicle. This reduces on-site workload, cuts costs, and gives full traceability for safety and recall scenarios.<\/span> <\/span><\/p>\n\n\n\n At Artivion, a global manufacturer of cardiac and vascular medical devices, teams use smartphone-based scanning to accurately capture product barcodes and perform fast, reliable consignment inventory counts. By replacing manual entry with high-performance scanning, they\u2019ve strengthened traceability, reduced errors, and ensured high-value, highly regulated devices are always accounted for throughout production, storage, and clinical handling.<\/span> <\/span><\/p>\n\n\n\n Elektro-Material in Switzerland integrated smart data capture into their mobile ordering and stock-management workflows, replacing an open-source scanner. The result was five times faster scanning and more than 95,000 scans in the first three months – significantly accelerating replenishment and improving technician productivity.<\/span> <\/span><\/p>\n\n\n\n Security is built into our platform from the ground up. Most scanning and image processing happen directly on the device rather than in the cloud. That alone removes a major security risk.<\/span> <\/span><\/p>\n\n\n\n When customers do choose to transmit data, for example, into their ERP or analytics systems, it\u2019s encrypted in transit. Scandit is ISO 27001:2022 certified, follows a secure development lifecycle, and complies with all major privacy regulations, including GDPR and CCPA.<\/span> <\/span><\/p>\n\n\n\n The bottom line is that manufacturers stay in full control of what data is captured and where it flows. Smart data capture strengthens operational visibility without compromising security.<\/span> <\/span><\/p>\n\n\n\n Regulations like the EU\u2019s CSRD require manufacturers to provide far more granular, auditable information on waste, material flows, and supply-chain impacts. You can only do that with accurate, real-time data from the frontline, and many manufacturers are discovering that their existing capture methods simply aren\u2019t robust enough.<\/span> <\/span><\/p>\n\n\n\n Smart data capture helps close that gap. It reduces waste by making expiry dates, lot codes, and condition data visible and actionable, and it strengthens traceability at the item and batch level, which is essential for circularity and credible Scope 3 reporting. Just as importantly, capturing information in a structured, machine-readable format at the point of work makes ESG reporting itself faster and more reliable, replacing manual spreadsheets with data you can trust.<\/span> <\/span><\/p>\n\n\n\n We\u2019re entering a new phase where data capture becomes far more intelligent, multimodal, and context aware. The sector is moving from simply reading barcodes to understanding the wider physical environment – text, objects, packaging, conditions, and anomalies – all in real time and all on the devices people already use. As AI matures, more intelligence will shift directly onto edge devices, enabling frontline workers and autonomous systems to make accurate, instant decisions without specialized hardware.<\/span> <\/span><\/p>\n\n\n\n We\u2019ll also see data capture become tightly integrated with workflow automation. Instead of capturing data and acting later, the act of scanning will increasingly drive the next step, such as triggering validations, checks, or instructions immediately. This is part of a broader shift toward what we see as \u2018Physical AI\u2019: AI that senses and acts in the real world, not just in digital spaces. The future is software-defined, AI-assisted, and increasingly ubiquitous – where anything with a camera becomes a source of high-quality operational intelligence.<\/span> <\/span><\/p>\n\n\n\n In a world shaped by AI, your output is only as good as your input. You can\u2019t automate, optimize, or predict anything reliably unless the data coming from the frontline is accurate, timely, and consistent, and workers are empowered with tools that make its capture seamless. Smart data capture is the foundation that makes AI useful in real operations – it turns every interaction with the physical world into trusted, actionable intelligence while also significantly improving the employee experience. Manufacturers who get that right will move faster and operate with far greater confidence than those relying on incomplete or error-prone data.<\/span> <\/span><\/p>\n\n\n\n![\"an](\"https:\/\/manufacturing-today.com\/wp-content\/uploads\/sites\/4\/2026\/02\/Main-Interview-244-body-image-1.jpg\")
<\/figure>\n<\/div>\n\n\nWhat is currently at the top of the agenda for\u00a0Scandit\u00a0in the smart data capture arena?\u00a0<\/h2>\n\n\n\n
What are the most common data capture challenges you see manufacturers facing today?\u00a0<\/h2>\n\n\n\n
Talk about the main benefits that smart data capture offers manufacturers?\u00a0<\/h2>\n\n\n\n
What types of data are manufacturers most often overlooking or underutilizing?\u00a0<\/h2>\n\n\n\n
![\"a](\"https:\/\/manufacturing-today.com\/wp-content\/uploads\/sites\/4\/2026\/02\/Main-Interview-244-body-image-2.jpg\")
<\/figure>\n<\/div>\n\n\nHow do you balance automation with human oversight in data capture systems?\u00a0<\/h2>\n\n\n\n
Have you got any specific examples\/case studies from manufacturing that\u00a0demonstrate\u00a0this in action?\u00a0<\/h2>\n\n\n\n
For any manufacturers concerned about the cybersecurity side of smart data capture, what protection does\u00a0Scandit\u00a0have in place?\u00a0<\/h2>\n\n\n\n
How is sustainability or ESG reporting driving demand for better data capture?\u00a0<\/h2>\n\n\n\n
Looking ahead but more generally, with your\u00a0expertise, what do you think is next for the data capture sector?\u00a0<\/h2>\n\n\n\n
If there was one critical\u00a0message\u00a0you\u2019d\u00a0like a reader to take from this piece, what would it be?\u00a0<\/h2>\n\n\n\n
Are there any other areas that you consider important for the sector and that\u00a0you\u2019d\u00a0like to include?\u00a0<\/h2>\n\n\n\n