What’s Inside
Semiconductor manufacturing is a data goldmine—every tool, process, and schedule generates rich, complex information. In today’s world, collecting data is no longer the challenge but, rather, making sense of it quickly seems to be the real hurdle. Industrial engineers in particular don’t have it easy —they are managing dozens, sometimes hundreds, of topics each day. From analyzing schedules to evaluating capacity plans, the sheer volume of data and decisions can quickly become overwhelming. Current tools and systems obviously support their data analysis efforts, but often the relentless pace and constant demands of a dynamic fab environment leave them scrambling to keep up.
Imagine if industrial engineers could simply chat with their data, take actions directly through conversation, and then make their processes more autonomous. Conversational assistance powered by large language models could redefine how industrial engineers interact with their data.
Chatting with data
At times, industrial engineers find themselves pulled into urgent situations where they must quickly resolve unexpected issues. The issues could also vary on any given day – from equipment failures, production bottlenecks, or supply chain disruptions, to quality control problems. This reactive mode, commonly referred to as ‘firefighting,’ can consume a lot of time and energy. While reports are available to support their efforts, it can sometimes feel like there are reports just to explain other reports. And despite all the data and dashboards, what industrial engineers often need is simply a direct answer to a direct question. To do this they don’t want to write a query, build an analytic, or worry about misinterpreting the data. They just want a quick answer to a question such as, “How many tools are in the dataset?” or “Show me the lot starts per day?”
Chatting with data for better understanding —essentially doing Exploratory Data Analysis (EDA) through natural conversation —is a very simple concept. Yet, there is actually a lot to be gained in efficiency by simply asking clear, direct questions.
Furthermore, it doesn’t have to stop with simple questions. Industrial engineers might want to validate assumptions or check data quality, too. For example, consider the following inquiry: “Are there any negative processing times in the data?” Ensuring clean, reliable data is a constant challenge and standardizing validation procedures is difficult. For different situations, experts typically know what to look for, whether it’s outliers, missing values, or inconsistencies that could signal deeper issues. Streamlining data quality checks is critical to ensuring efficient decision-making.
Take actions
While conversational assistance is a powerful way to interact with data, chat alone might not be enough. Industrial engineers don’t just want answers, they want action. They might want to move beyond dialogue and drive outcomes directly through conversation.
Imagine updating data with a simple prompt like, “Change UID: X to UID: Y,” or effortlessly generating insights with, “Create a bar chart showing certifications per equipment.” Switching views instantly can be added to the commands, “Show this data in a table instead.” Even consider how conducting scenarios could become easier: “Push preventative maintenance for area X to date Y.”
Instead of relying on deep software knowledge or remembering specific UI steps, industrial engineers can simply talk to a system. The value of conversational AI moves beyond convenience into true operational impact when dialogue leads to action.
Autonomous insight
Now, what if AI didn’t just respond to questions and commands but actually anticipated daily needs? For example, imagine an industrial engineer starting their shift and already knowing exactly what needs to be done—and by when—not because of a checklist, but because an AI automated system proactively surfaced the tasks that matter most.
Instead of asking “What are the most important tools to be recovered today?” the AI simply and automatically tells you: “Here are the most critical tools to recover.”
Industrial engineers could again take this even further where they are looping back to the chatting with data concept. They could ask, “Why are these tools considered critical?” and receive a clear explanation. Here priorities are not only surfaced, but the reasoning behind the output is explained; industrial engineers can understand the why behind the what. This level of transparency is essential. It builds trust in an automated AI-driven system. Beyond trust, it also plays a vital role in effective change management as industrial engineers can confidently embrace a new way of working.
