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AI can help factory operators connect machine, sensor, application and control-system data to spot bottlenecks and detect signs of trouble earlier. In an interview with The Economic Times, Tata Communications IoT business unit vice president Praveen Arora described how the company’s Factory 360 approach brings those inputs together. His account explains a potential use of AI in manufacturing—not independently verified evidence of results across Indian factories.
What does AI actually do inside a factory?
In the approach Arora described, AI analyzes data from equipment and production systems to help operators understand how a plant is performing and where problems may be developing. The aim is not simply to add an AI tool to a production line: it is to give people a more complete, timely view of operations so they can make better-informed decisions.
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Factories may already collect information through sensors, machines, applications and production processes, but those sources can remain separate. The Economic Times describes Tata Communications’ Factory 360 offering as bringing data from sensors, applications and factory control systems together. In Arora’s words, the goal is to work “with the business outcome in mind.” The Economic Times interview presents this as the company’s approach, not an independent product evaluation.
How connected data can reveal a bottleneck
Once information from different parts of a plant is visible together, operators can compare installed capacity with actual output. A gap between what equipment should be able to produce and what the line is making can help point to a constraint that deserves investigation. Arora described the benefit as visibility into “where is the bottleneck” and the difference between installed capacity and the gap in output.
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That view can help teams ask more targeted questions: Is one machine limiting the line? Is a process step slowing production? Are the available systems recording a change that coincides with lower output? The interview does not provide a technical architecture, customer case study or method for measuring these effects, so the value should be understood as the capability described by Arora rather than a guaranteed result.
From alarms to earlier anomaly signals
Conventional alarms typically alert staff after a threshold is crossed or an obvious fault appears. Arora contrasted that response with AI analysis that can identify earlier signals associated with an anomaly and feed them back into operations. If staff can investigate and act in time, the system may help prevent a developing equipment problem from becoming a failure or line stoppage.
That is a potential early-warning use, not a promise that AI will predict every failure. The interview does not specify which signals Factory 360 analyzes, how accurately it detects anomalies, how much warning time it provides, or how false alarms are managed. Those details matter when a manufacturer evaluates whether an alert is actionable and how it fits into maintenance procedures.
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Manufacturers do not necessarily have to replace every older machine to make it part of a connected operation. Arora said machines that are 20 or 30 years old can be included by converting analogue signals into digital information. The interview does not identify specific machines, interfaces or compatibility requirements, so the practical work will depend on the equipment and the signals it exposes.
A deployment can involve networks, devices, sensors and applications. Arora said the choice between processing data near equipment and processing it in the cloud depends on the application and overall operating cost. The interview supplies no latency figures, security evaluation or deployment benchmark to establish which arrangement is preferable in a particular factory.
- Processing near equipment: The interview identifies this as an option, but does not give performance or cost figures for it.
- Cloud processing: Also presented as an option; the decision should be tied to the use case and total operating cost rather than assumed to be right for every plant.
How manufacturers might start—and what the pilot claim means
For a smaller manufacturer, Arora suggested beginning with a business goal such as improving efficiency or managing cost, then working with data that existing systems already collect. The reporting says he described checking security gaps, conducting analytics and running a pilot using existing-system data. He also said a proof of concept could be demonstrated in less than a week.
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That timing is Arora’s statement in the interview, not a published implementation benchmark. The article does not identify a customer, test protocol, scope of work or independent case study behind it. A proof of concept should therefore be treated as an initial demonstration, not evidence that a production-ready deployment, integration or measurable operational improvement can be completed on the same schedule.
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- Choose the operational outcome. Define whether the priority is a bottleneck, a developing equipment problem, efficiency, energy use or another concrete production concern.
- Inventory available data. Identify what sensors, machines, applications and control systems already record, including whether older equipment exposes analogue signals that could be digitized.
- Review integration and security needs. Determine what must connect, what information will move through the system, and what security questions need to be addressed. The interview does not specify Factory 360 security controls or certifications.
- Decide where processing belongs. Assess near-equipment and cloud processing against the application and its overall operating cost; the interview does not offer a universal configuration.
- Define how a pilot will be judged. Agree on the data, time period and operational measures that would demonstrate whether the system helps. The interview does not publish such a test protocol or independent results.
What the reported throughput figure does—and does not—show
The Economic Times reports Arora citing throughput improvements of 30% to 200% in some situations using the same resources. The article does not describe the underlying cases, baseline, measurement method or independent validation. The figure is therefore an attributed claim about some situations, not a typical expected gain or a verified result for Indian manufacturing generally.
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Worker safety, privacy and human control
Arora linked worker safety with production continuity: fewer incidents can mean fewer interruptions, while information shared across teams may help them identify and resolve problems sooner. He described connected technology as a way to equip employees with more information for decision-making.
The accompanying ET Now video summary says the discussion also covered wearables, video analysis, worker privacy, manual overrides and training employees to use AI. It does not establish what privacy controls, override designs or worker-consultation practices are used in a particular deployment. Manufacturers considering these technologies need to assess those issues in their own operating context rather than assume a specific safeguard is part of the offering. The video coverage does not provide implementation details for those topics.
What is established about the broader Indian market?
An earlier Tata Communications blog by Arora said IoT investment in India was close to USD 5 billion in 2019 and forecast a 17.1% compound annual growth rate. Those are figures and a forecast published in a 2022 company blog, with the investment amount referring to 2019; they do not establish today’s market size or current growth rate. The blog provides historical context, not a current market measurement.
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The central idea is practical: connect data that is otherwise separated, use it to make production constraints and emerging anomalies more visible, and give workers useful information for action. For any factory considering such a system, the interview supports asking how existing equipment can be connected, where processing should happen, what the operating cost will be, and how alerts and worker-data concerns will be handled. It does not establish Factory 360’s specifications, availability, security certification or independently measured performance, nor does it demonstrate industry-wide outcomes.
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