Introduction

Jagdish Upadhyay, Director – Lumada Product Marketing, Hitachi Insight Group, elaborates on a business case that justifies the journey towards a scalable IoT solution.

  • Client

    Lumada Product Marketing, Hitachi Insight Group

  • Services

    IoT enabled solutions

  • Technologies

    IoT Ecosystem

  • Dates

    13/09/2017

Description

Manufacturing companies today are challenged with rapidly changing variables in their industries, such as requirements for shorter lead times, mass customisation, the increasing number of product variations and there is an ongoing shift in overall consumers’ mindsets as more people move away from product ownership-based consumption in favor of as-a-service business models. Additionally, conventional models of production planning by experts are no longer efficient due to unforeseen disruptions and changing business environments.

When you speak to any factory operators and plant managers across Industries, some of things you hear are still very basic, e.g., how do I get more visibility in to my operation? How do I plan for what I don’t know? How do I minimise impact of downtime? How do I plan for spares ahead of time to minimise the impact of a breakdown? And at executive levels, you would often hear about higher level objectives that impact share of market, competitive advantage and customer experiences.

The bottom line is overall operations rarely go as planned. This problem gets further amplified with aging equipment and legacy processes which cannot be replaced without significant investments and impact on the ongoing production. No manufacturer is willing to stop producing and replace the entire setup just to gain more operational efficiency.

Internet of Things is expected to address the unique challenge of dealing with legacy assets and processes while optimising for efficiencies and help business innovate. Enterprises are looking for technology solution to deliver product and/or services to its customers in the most predictable and cost effective way without compromising on the overall quality goals and customer experience. Predictable outcome and costs are directly linked to how assets and operations are managed.

Across all Industries whether it is manufacturing, energy, transportation or health care, there are assets that need to be managed smarter. These assets could be a robotic arm in manufacturing, a windmill in energy, a train in transportation or a MRI scanner in health care. Availability and performance of these assets are not only just the need but also a business imperative. Internet of Things offers a potential economic impact of $4 trillion to $ 11 trillion a year in 2025 as per McKinsey report on Unlocking the Potential of the Internet of Things. When you double click on this data point, you realise one of the key industry that’s contributed majorly to this trend is Manufacturing where the impact from operations management to predictive maintenance is around $1 to $3 trillion as per the same report.

The very first step in the IoT journey is to connect factory assets through a gateway or an add-on device that enables flow of sensor data to either an edge and fog systems near asset analytics and prediction or cloud based solution for more powerful analytics and ML driven outcomes. Internet of Things architecture elements leverage the data streams from PLC, sensors, relays, switches or sometime externally mounted instrumentation and blend with data from IT systems and other sources such as weather, demography, manufacturer own data sets, etc., to make predictions that enable use cases such as remaining useful life, prevent equipment failure, reduce maintenance costs, degrading performance indicators and asset safety.

So there is enough business case that justifies the journey towards a scalable IoT solution that not only looks at a specific problem the manufacturer face but also provides future proofing of the investment. Eventually the vision for the manufacture should be moving towards an autonomous economy that senses and responds to demands on a real-time basis.

 

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