Two industrial IoT value chains

Bob Emmerson

The IIoT is predicated on a simple concept:  acquire device data and leverage its value. That is the basic business model. Vendors package the various hardware, software and service components into bottom up solutions that go from networked devices through to enterprise environments.

That is the value chain they “sell”, although most times it is marketed as a service. The value chain that the business community “buys” is top down: It leverages the intrinsic value of the data and exploits IoT’s potential, e.g. the creation of predictive maintenance programs, says Bob Emmerson, freelance writer and telecoms industry observer.

Unfortunately although the difference between selling and buying would appear to be obvious, a combination of IoT hype and excessive coverage of new technologies is introducing confusion and a confused market doesn’t buy, it waits. Will LoRa turn out to be the optimum LPWAN technology or will it be NB-IoT? What about augmented reality, machine learning and artificial intelligence? Where do they come in? This is industry insider stuff and at times it gets confusing, even to insiders.

The market isn’t looking for answers to those questions, but when evaluating the offers of different vendors it’s almost impossible to avoid the new technologies and their conflicting OTT claims.

At first sight the “Solution as a Service” model sidesteps the regular evaluation process.  As the term implies, it delivers complete solutions that deliver all aspects of cloud computing, integrated as a single offer that has been optimised for a specific department or industry.

The solution is then delivered through a “pay-as-you-go” model.  While this model does eliminate the need to hire a system integrator, or undertake a complex in-house cloud development project, the ability to deliver the requisite functionality has to be proven via a business-centric evaluation process.

The evaluation process should examine the:

    • Ability to align performance with key strategy objectives and the related key performance indicators
    • Architecture, which should be flexible, able to accommodate future known and unknown needs. It should also be technology agnostic and be based on open standards
    • Need to rethink ROIs. For example, cost savings are often indirect, reducing downtime with predictive maintenance is hard to quantify
    • Ecosystem partners and their track record (a single provider cannot deliver end-to-end solutions).

There are more aspects that need to be examined, but the evaluation process should be preceded by a clearly defined IoT strategy. That may appear to be obvious, but many deployments fail because the myriad implications are not thought through. A recent report by Cisco indicated that 75% of IoT projects are failing.

Implementing IoT technology in order to optimise business processes or make and market intelligent connected products will involve a number of significant changes in the way companies conduct their business. Therefore it is important to start with a formalised analysis of the impact IoT could have on the company, its products, services, resources and capabilities, as well as those of the customers and the competition.

In a nutshell, these tasks are needed in order to define the buyer’s value chain that will enable companies to realise improved manufacturing processes, customer lock-ins, customisation and regular updates, enhanced customer experiences, insights into customer behaviour, improved pricing management through price sensitivity analysis, data as a revenue source and so on.

Therefore it’s hard to over-emphasize the importance of starting with a clear, defined strategy and that is clearly something that has to come from C-level management: it is not a task that can be delegated to the IT department.

The author of this blog is Bob Emmerson, freelance writer and telecoms industry observer

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