There’s little doubt that industrial businesses are hungry for connected digital solutions that can enhance their operations. That appetite has created a global Industrial IoT (IIOT) market that’s currently growing at a phenomenal 23% CAGR.
Through IIoT, organisations can access an entirely new level of industrial intelligence.
This is reducing machine downtime and increasing productivity, enabling better safety solutions and helping to improve efficiency. Such is the pace of growth in this field, the global IIoT market is expected to grow from $264B in 2021 to $1.1T in 2028.
As we watch this trend drive the Fourth Industrial Revolution, huge opportunities are emerging – especially for equipment manufacturers supplying the sensors, instruments and platforms that are capturing all of this industrial data.
In addition to the equipment itself, manufacturers are also able to create entirely new streams of revenue by providing services, via apps and dashboards, that can monitor data in real time, analyse the information and produce intelligence reports – even raising the alarm if abnormalities are detected. As well as providing additional income, this is helping businesses to serve their customers better and build lasting relationships, with stronger brand loyalty.
Before industrial equipment manufacturers can capitalise on this potential, however, they need to overcome numerous technical challenges and embrace new spheres of technology such as Edge computing, IoT connectivity and cloud computing.
Following this path will present dilemmas that will force decision makers to question their traditional ways of thinking. For example, the standard approach to product design has been to optimise wherever possible and create the smallest footprint to minimise costs. But when it comes to IIoT products that may not be the smartest move.
Cost vs computation
When devices are connected via IIoT, businesses can generally add new functionality after the product has been deployed. If manufacturers are not creating products with sufficient computational capacity, however, they may leave themselves unable to make changes at a later date.
This is a crucial consideration as industrial devices may be left in situ for five to ten years – and it’s highly likely that they will want the flexibility to introduce new features or carry out security upgrades over that period of time.
We’re also moving into an era where companies are slowly starting to introduce machine learning to devices within industrial settings. This is allowing organisations to deploy solutions such as predictive maintenance – something that could save manufacturers up to £180B a year in the UK alone.
If manufacturers underpower industrial equipment they will limit their ability to deploy machine learning at the Edge. Instead, they should consider over-powering products with computational capabilities, such as enhanced processing, memory and connectivity.
Investing in additional computational power may challenge traditional cost-conscious business practices, but it might just be the best plan for the long-term viability of the products and services that could be sold to customers further down the line.
This dilemma is just one of many that companies will need to resolve as they find the right balance for their own business use case. If you want to understand the other factors that businesses should consider, please take a look at our comprehensive guidance paper Industrial IoT: Seizing a golden opportunity for manufacturers, which includes commentary from several experts in the field.
Here we highlight the real-life scenarios that manufacturers are currently facing as they make the transition into IIoT products.
To find out more about how Mobica can help you understand and seize a golden opportunity for manufacturers, explore our latest Mobica White Paper: Industrial IoT.