By Shinichiro (SHIN) Nakamura, President of one to ONE Holdings
Most of the time, circuit board manufacturing bottlenecks, system failures, and disruptions are not direct consequences of a technological error but rather a communication breakdown. There is no shortage of data in most manufacturers’ organizations. What they do tend to lack, however, is the ability to ensure that data and information are exchanged promptly and efficiently.
It is time to rethink the very framework of knowledge sharing. That includes how it is conveyed so teams and staff, whether they are on the shop floor or overseeing backend operations in the office, are always up to speed and in the loop. Moreover, strong communications is more than making operations efficient: it gives manufacturers the much-needed resilience and agility to continue managing smooth operations at scale—particularly as a retiring workforce threatens to take knowledge out of the firm’s doors.
One lever that many manufacturers continue to overlook is voice communications, which has the power to bridge understanding and immediate, accurate action. A vocal format can transmit what a chatbot cannot, with the same speed and much more powerful contextualization. Here is why voice data should no longer remain an untouched asset as manufacturers look to strengthen their communication and operational capabilities.
Why good communication is about reading between the lines
Innovations like chatbots and sensor alerts have an important role in operations, but they do not always carry the same clarity as a voice message. These forms of communication do not give colleagues and managers the full insight they need to avoid a crisis or issue.
Take the example of health and safety and team management. A text-based AI tool that simply transcribes what is said, but not how it is expressed in terms of tone, can filter out important cues that the recipient should flag as potential issues.
For instance, a tired employee finishing their shift hands over required written notes to their manager. Basic, important operational details are logged, but not the fact that the employee is exhausted and approaching burnout, making them more susceptible to errors. On their next shift, they overlook a minor but vital detail, such as a misaligned component placement or a missed structural fault when checking a PCB, causing major disruption to safety and operations.
A voice-recorded note, or even a multi-modal AI tool with the capability to flag tone characteristics such as fatigue, would have alerted a well-trained supervisor to that risk. It gives the invaluable context and clarity that empowers teams to be proactive, attentive, and responsive to potential issues in both equipment and people.
Humans, by nature, are social creatures. Tone, inflection, pace, and pitch are important parts of reading each other and understanding more about what the other person is actually saying or experiencing. In many ways, voice is just as powerful a signal as facial expressions or body language. It can certainly be more explicit.
Blending voice with technology
Technology such as AI can, in fact, be used to hone the human side of communication. AI systems have evolved to include features that not only capture what is said, but also how it is expressed. Manufacturers must be aware of striking a balance between speed and efficiency in information and knowledge sharing, and the clarity and context behind every message or note sent.
This is a vital building block for achieving a communication infrastructure that also enhances trust, particularly from a supply chain management perspective, where circuit board manufacturers must work closely with a range of component suppliers, fabrication houses, and OEM partners on a constant basis.
And algorithms can be trained with voice to better understand focus levels in the bid to enhance safety and productivity throughout workflows. For example, over time, an algorithm capturing voice data may note that fatigue sets in towards the end of the shift. Accordingly, managers may restructure workflows to avoid intense work that requires a great deal of concentration as a worker’s shift comes to a close, or schedule proper timings for taking rests. This can help slash threats of accidents and errors that put teams in harm’s way and undermine production.
Putting voice data into practice
Manufacturers already sit on a huge amount of data. Introducing a new data source—voice—with increasingly advanced AI systems must be planned carefully, with well-governed and guided data management at the core of modernization strategies.
Safety outcomes should always be the top priority guiding these strategies. Start with the workflows where early human interventions carry high stakes, such as incident reporting and handovers, where the smallest change in tone can reveal a huge amount to managers and other team members.
These markers—higher-pitched tone and accelerated speech in moments of urgency—can also give a more complete picture of the purpose and intuition of a human worker in certain tasks and situations. That’s extremely important for training increasingly sophisticated AI systems such as robots.
Following safety, the cascading order of priorities should be quality and then productivity. Knowledge transfer is made easier with hands-free voice data capture, maintaining a standard level of quality across shifts, departments, and even systems. Instead of rushing to write down important notes, workers can more comfortably and effectively relay crucial information and instructions, particularly when sharing engineering change orders or updates on build-of-materials.
From a productivity standpoint, voice-captured data takes far less time than typing out and proofreading written notes after overcoming initial hesitation or a sense of unease. The automated ability to transcribe what is said, how it is expressed, as well as capture why an action or decision was undertaken, takes a fraction of the time when voice-recorded. That data can be recorded to update handbooks and instruction guides, as well as inform AI models that are behind automated optical inspection (AOI) systems, robots, sensors, and other IoT technologies.
This hierarchy of priorities is vital because, as factories become more sophisticated in terms of technology, human interactions become far more consequential. There is more at stake for manufacturers navigating massively complex data ecosystems studded with innovative technologies. Being as proactive as possible with communication is a must, and that is precisely why voice data should be leveraged.
Manufacturers’ technology stacks are evolving rapidly. As the industry looks to close gaps in knowledge sharing, data management, safety, productivity, and quality control, firms must not ignore the power of voice as the connecting layer to do so.
