The first mistake that large companies tend to make here (especially technologically oriented companies) is approaching this phase by developing and producing their own IoT hardware. They feel that if they have the production capabilities that it’s better to produce their material in-house.
But taking this approach increases the cost in many ways.
What they should be thinking about is how fast they can offer the benefits of IoT technology to their customers. And, by hiring IoT experts to do the job means that everything will be done in record time. With the Zerynth platform, you can go from prototyping to having mass production in less than 6 months.
When we look at software, we need to make the distinction between: firmware, middleware, the cloud, and the application.
Finally, there are service implementation costs to consider. IoT projects and solutions are not like other products. You can’t transition to IoT-enabled systems and solutions in your company with a “pay once and you’re done” approach. IoT solutions require monitoring, future adaptation and training in order for them to achieve an optimum configuration.
You also need to consider that the IoT technology needs to be integrated with both modern and legacy industrial machinery as well as with IT systems existing in the company (such as MES, BI tools, ERP). This kind of integration enables easier industrial process analysis and extraction of important data. The Zerynth Platform offers a simple and effective way to enable industrial process monitoring and optimization for your company. The platform interfaces with Industrial machines easily in a non-invasive way, while our hardware communicates with the Zerynth Cloud using Wifi/Ethernet streaming real time data and insights to the user. This way your company can receive real-time insights and powerful reports to monitor industrial processes using the integrated Zerynth Dashboard and Alert Engine.
As you realize that IoT is an evolving technology, there is always a need to further educate your team. To give you a good rule-of-thumb tip, start calculating those post-implementation service fees into your project budget right from the start. This is the right approach in order to calculate Total Cost of Ownership (TCO) of the technology rather than just the purchase cost.
Using one platform and one team for all of these moving parts is the most cost-effective way to handle your IoT implementation.
Data management and maintenance
After the entire solution is implemented, and up-and-running, it’s the data management and maintenance costs that can give the company management difficulties.
Proper data management helps you understand what the upsides and downsides of your product are, what your customers like the most, and what you can improve. In industrial production facilities, good data management can help detect errors, predict malfunctions, and improve the quality of your production processes on the one hand.
On the other hand, poor data management and poor analyses of it means that you have wasted the implementation of your IoT system. In the land, where information is king, you can not ignore proper data management.
This is why Zerynth Dashboard and Storage keeps track of all your KPIs from a single dashboard. With it you can easily share important data with your team, make customized dashboards and collaborate effectively.
Limited bandwidth is often a problem that starts popping up after the implementation of an IoT solution is finished. For example, if your connectivity depends on cellular networks and you have a lot of devices connected to your network, and you continue to add new ones, this can get pretty expensive.
To handle connectivity, you need a high-capacity network solution that is capable of handling all the hourly and daily transmissions, without compromising the network deliverability.
Even if organizations should face many challenges, the opportunities offered by IoT technologies are becoming clear. Companies can immediately benefit from more accurate data and insights improving the efficiency of industrial processes, reducing operational costs and allowing data-driven decisions.