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How IoT is revolutionising manufacturing, transportation and logistics

When deciding what to write about in my next blog, the topic that came to mind is the fact that unforeseen downtime is probably the biggest headache for factories, warehouses and manufacturing plants. Why? Well, any stoppage in an industrial environment is likely to hit you hard where it hurts the most – your pocket.

In most supply chains, the output is dependent on a number of processes and – especially in the new age of Just In Time (JIT) manufacturing – a breakdown in that link of processes will inevitably result in measurable economic penalties for a company. Simply put, if production suddenly grinds to a halt, you could be facing a hefty bill. Aside from the cost of repairing what is broken, you need to take into consideration the loss of man-hours, raw materials and product.

Not a pretty picture. The answer to this problem is the Internet of Things (IoT), which is starting to gain traction in the South African market across various industries such as manufacturing, transportation, logistics, mining and even government sectors.

Impressive stuff

While the use cases for IoT are unique to each environment, the impact of this technology is collectively impressive, as it enables organisations to reduce downtime for maintenance and creates a smoother operation.

When added up, the costs of a breakdown far outstrip the investment required to deploy monitoring and management solutions, so IoT is ultimately what progressive companies are investing in to optimise their operational expenditure model.

Until recently, in all process-oriented industries, the de facto standard was to fix something once it’s broken, but through effective measuring and monitoring, you can now predict when something will fail. This is at the heart of what IoT tries to accomplish in the industrial space by providing a daily view to help organisations be proactive rather than reactive.

The name of the game is telemetry data and it enables IoT to drive predictive maintenance and proactively prevent downtime. Companies now can use IoT and real-time data analytics to detect wear and tear and respond immediately, acting proactively rather than retrospectively.

Many different applications

IoT is changing the world of transportation and logistics in many different ways. From warehouse management and freight tracking to self-driving cars, IoT-enabled trucks and the monitoring of rail infrastructure – this technology can be put to good use in many innovative ways.

Mining is another sector that can benefit from industrial IoT solutions. The optimisation of a mining operation often revolves around how you move material in and out of the mine. A small thing, like a detour around a pothole, can negatively affect output, so having IoT-enabled trucks gives mines a real-time view of changing road conditions, allowing them to redeploy trucks on a different route.

These small savings will add up to a lot in the end. It’s all about achieving that last bit of optimisation around the unforeseen side of your business, and that is where IoT enables visibility, leading to improved efficiencies, less downtime and streamed-lined operations.

For enterprises that are looking to deploy IoT technology, it is extremely important that they first define the critical processes and procedures in their environment, whether these are production processes, financial processes or sales.

You must have a solid understanding around the workflow for any particular process, as it will allow you to intelligently place devices to achieve that maximum visibility of that process. On the other hand, keep in mind that if you’re just making use of IoT to, for instance, monitor foot traffic without correlating that to sales or theft or customer retention data (preferably in real-time!), then it is not having a business impact and you’re simply making use of the technology for the sake of it.

Riaz Patel
Hewlett Packard Enterprise


About the Author


I am passionate about technology, and in-memory computing in particular. I am interested in how in-memory computing is driving analytics as well as the ability to make decisions in near real-time.

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