Mika Aalto / Devoca Oy
From “traditional voice” to a combined workflow
Voice picking used to mean fixed word lists, special headsets and handhelds, and rigid processes. Not anymore. Today, speech works together with a screen and simple tools, so pickers can choose the best input at each step. This combined model reliably lifts speed, accuracy and user comfort. Because order picking is a major share of warehouse operations and costs, even small gains add up quickly.
Built to work in noise — and on the move
Reliability starts with a clear exchange between the user and the app. Speech recognition has improved a lot in recent years and stays accurate even in noisy areas. That matters on a busy floor with forklifts, fans and people moving.
Cost effective on common devices
Modern voice picking is not tied to special hardware. It runs on standard phones and tablets and connects easily to scanners and wearables. Headsets are optional for many use cases. This keeps costs low and makes life easier for users and IT. Where helpful, you can pilot options before scaling.
Easy to learn (simple on purpose)
Training should take minutes, not days. A small set of clear voice commands plus a visual fallback (on‑screen prompts, keypad) keeps work moving when speech alone is not ideal, for example, in extreme noise or with unusual item IDs. Using more than one input method also reduces mental effort and helps people stay “in flow.”
Comfort, reliability and UX = steady performance
Hands‑free, eyes‑up work only delivers if it stays comfortable for a full shift. Good ergonomics, lightweight gear, clear feedback and simple steps, lead to fewer errors, a steadier pace and happier teams. That’s how you get lasting results, not just a short spike. It also helps retain staff.
Why the combined model wins: voice + visual + simple tools
This is not a “voice vs. screen” competition. It is both, plus a quick action on a scanner or a button when needed. Voice keeps hands free and speeds up confirmations. Visuals show quantities, images and exceptions. Simple tools provide a quick fallback, with key data always available on the go. The result is a setup that adapts to different tasks, user preferences and real‑life changes on the floor.
What independent studies and reviews highlight?
- Picking drives a large share of warehouse cost, so even small improvements matter.
- Speech recognition now handles noise far better than older systems, especially when paired with clear on‑screen cues.
- Ergonomics keeps the gains: better human fit → more consistent performance across shifts.
- Test before you scale: try the options against your own assortment and process.
Bottom line
Voice picking has moved on. The best pattern today is a combined, device‑flexible setup that works in noise, is easy to learn and gives people the right way to interact at the right time. In Devoca’s case, the solution is affordable for warehouses of any size. Do that, and you get fast, accurate work that stays comfortable all shift and savings that compound at scale.
Neutral studies and lab resources (recent) to futher research
Order picking cost & operations
- Trends in order picking: a 2007–2022 review (2023, Taylor & Francis) – consolidates research and notes that order picking often makes up >55% of warehouse operating cost. Trends in order picking: a 2007–2022 review of the literature
- DC Measures (2024, Warehousing Education & Research Council) – annual neutral benchmarking of DC metrics (industry‑standard reference; paid report). DC Measures
Speech recognition in noise (background)
- Noise‑robust automatic speech recognition: review (2023, Springer) – methods for handling noise in ASR. Noise robust automatic speech recognition: review and analysis | International Journal of Speech Technology
- Audio‑visual speech recognition survey (2023, MDPI) – overview of AVSR techniques that improve robustness in noisy environments. A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition
Ergonomics & human factors
- Ergonomics in warehouse design and operations: systematic review (2024, Springer) – collates findings on human factors in picking. Ergonomics in warehouse design and operations: a systematic literature review | Operational Research
- Ergonomic workload assessment of order picking (2025, Elsevier proceedings) -machine‑learning‑based assessment using sEMG and task features. Ergonomic Workload Assessment of Order Picking Operations Based on Machine Learning with sEMG Signals – ScienceDirect
Comparing picking methods
- Comparative study of order‑picking methods (2025, MDPI Sensors) – lab comparison of pick‑by‑paper, pick‑by‑light and pick‑by‑point. Comparative Study of Selected Order-Picking Methods: Efficiency, Ergonomics, and Adaptation Rate of New Employees
Neutral labs & testbeds
- Fraunhofer IML Picking Lab – overview – hands‑on evaluation of pick‑by‑voice, smart glasses, and scanners in a standardised environment. Picking Lab – A hands-on approach to order picking – Fraunhofer IML
- Fraunhofer IML Picking Lab – institute page. Efficient picking: Your partner in the Picking Lab – Fraunhofer IML
