Industrial IoT Helps Deliver Seamless, Smarter, More Connected Factories

Industrial ioT Services | IIOT Services

The industrial net of things (IIoT) has arrived – and it’s driving manufacturers from the U.S. towards smarter, more joined factories. Companies that successfully integrate IIoT into their operations realize significant value from their efforts, such as better decision making, continuous process improvements, reduced cost – even opportunities for innovation and expansion. Accenture estimates that IIoT could add $14.2 trillion into the global market by 2030.

Recognizing the need for an IIoT strategy, major companies such as GE, IBM, Intel, Cisco and AT&T got together to form the Industrial Internet Consortium (IIC) in 2014 to accelerate the development, adoption and widespread use of IIoT and from that time to present, its membership has risen to over 500 companies.

But while some manufacturers benefit from IIoT, others struggle to capture the efficiency gains from their IoT-enabled assets. They wrestle with legacy environments and arcane procedures, and they lack the resources and skills to deploy a really effective solution.

To improve operations, optimize factories and enable big data applications such as predictive maintenance, manufacturers need an IIoT solution that not only connects everything from machines and manufacturing planning systems to supply chain management but also provides rich sensing solutions to unlock the real business potential of IIoT and big data analytics.

Guiding Factors Behind IIOT

With operational assets delivering the proper data in real-time, any organization can apply IIoT capabilities to uncover insights that drive continuous improvement and leaner operations. The following principles guide the vision of a smart, attached factory enabled through IIoT.

Legacy Technologies and Procedures Have to Be Linked

In today’s factories, 85 % of machines are not connected and thus can’t provide data or visibility in their health and optimal maintenance schedules which would have enhanced their operational value.

Multinetwork surroundings that have aging machines of many different types and software which don’t speak a common connectivity language pose large challenges for many IIoT solutions. Organizations need a complete IIoT solution that securely collects data from disparate machines to enable a complete transition to a hyper-connected atmosphere.

Industrial IoT is Much More Than Simply Collecting Data and Analyzing It

IIoT and large data analytics can offer effective insights into factory operations only if relevant data is captured and made available. Advanced sensing solutions that capture hidden data from shop floor environment with constantly mobile resources (machines, employees, and material) and digitize contextual information are critical for dynamic manufacturing environments.

To make IIoT more effective, both available as well as hidden data have to be captured and digitized which will provide accurate visibility across all manufacturing operations.

The Solution has to be Secure

Ubiquitous connectivity is the central pillar of IoT that promises to provide business value by linking myriad devices / assets that generate useful data. However, connectivity also exposes industrial apparatus to security attacks, that not merely disrupt entire systems but can also pose safety risks.

To minimize risks and keep operations protected from physical breaches and cyberattacks, an IIoT platform must provide security by tracking behavior of all data sources and alerting operators when anomalies are detected in the manufacturing environment. Securing end-to-end IIoT systems is critical so as to avoid unwanted financial and safety consequences.

The IIoT Deployment Must be Modular and Flexible

To integrate existing and future investments, the IIoT platform has to be modular and flexible so that it can co-exist using a partner/vendor ecosystem.

Furthermore, the IIoT solution must be able to orchestrate analytics anywhere across the whole IoT data chain — from Edge Analytics over close-to-assets to Cloud Analytics in a centralized system.

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