Having a consistent understanding of the role that data play or should play in your business is hard, yet absolutely essential to stay ahead as this new digital economy gets fully embraced. The sensors of an IoT solution can provide tons of data. Because of this, making sure that this data is linked to clear business objectives becomes elementary and the first stage to plan before making any movement.
Among potential business goals you might perhaps be aiming at reducing operational costs, ensuring maximum quality, improving customer service and satisfaction or supporting new data-driven business models. The alternatives are many, but your investment must be beautifully backed up by a clearly defined goal. From that point on, you will be then able to run the numbers and properly evaluate the return of your investment when adopting a new tech solution.
For such a daring purpose, we have come up with an evolution of the traditional ROI formula, named ROII (Return on IoT Investment) and wrapped in the so-called IoT Technology Adoption Model. A twelve month period plus a brief Proof of Concept interval has been chosen as the time frame of reference, given the nature of the solutions considered.
If the ROII value is net positive, the investment in a given solution will be worthwhile. When compared with a set of alternatives, we will usually go for the one with higher ROII assuming that all the elements of the model have been properly evaluated. The result is expressed as a ratio. If for example the result is ‘2’, a ratio of 2:1, for every $1 invested in the solution 2 additional dollars are expected to be generated.
To better illustrate the model, notice that we are talking about IoT technology adoption and no development. It means that solutions under analysis are pretty much ready to be implemented out of the box, with a brief PoC carried out to fine tune users requirements and needs, the solution’s value generation potential, as well as costs associated right before full adoption. Providing some additional context, we have previously seen in Connected Sight that an end-to-end IoT solution is made up of a set of elements. From number one, connected to your assets, to number four, extracting value out of the data gathered to directly impact your business processes.
Now, there are two main formats that are gaining more and more popularity in which these solutions get delivered to end users. Hardware components can be directly bought, while connectivity, storage and data platforms take the form of monthly or yearly subscriptions. On the other hand, the whole solution can take the form of a subscription model with all its components , including hardware, delivered “as a service”.
As introduced before, although these solutions can still allow for a certain level of customization, their quick adoption nature is why a 12 month period plus PoC time is considered here. Nevertheless, the model can be expanded as much as needed depending on the complexity of a given solution and the expected operational timeframe.
Wrapped around a clearly defined IoT business strategy, the IoT Technology Adoption Model contains the following elements:
Proof of Concept (PoC): understood as a trial implementation for testing purposes, before committing to a full-fledged adoption of a specific solution. It might have an associated cost or not, depending on complexity and the terms agreed with your solution provider, but it will be fundamental to better evaluate the value generated (VG) component upon full adoption.
Value Generated (VG): the value expected to be created by the solution. This figure is defined under an umbrella that blends together strategic analysis and business objectives. Being the core metric behind the model, we will get into more details in the next section.
Initial Investment (II): amount required to put the solution to work at full scale within your company, considered as an initial and one-time expense. It will include hardware costs (N modules × C modules) if purchased, installation and consultation costs if any, as well as potential costs related to onboarding processes and employee training.
Operational Cost (OC): amount required to keep the solution running that will be usually billed on a monthly or yearly basis. Connectivity cost, data warehousing and cloud infrastructure, as well subscriptions to data platforms are the most common expenses in out-of-the-box IoT solutions. There might be cases where some of these components are developed in-house, those elements will then be part of the Initial Investment amount as a form of fixed assets.
t: it makes reference to the periods of time under which the return of investment is analyzed. In this case, the magnitude months is the one chosen for the reasons previously exposed, although it can easily be escalated to years.
The variables Initial Investment and Operational Cost will highly depend on the format in which the solution is delivered. If hardware components are purchased with the rest of the elements under a monthly subscription basis, the II amount will be consistently higher, while the OC will be lower. If opting for “as a service” format for the whole solution, II values will be much lower, but OC will obviously be higher, as the cost of the devices is proportionally passed into the monthly subscription fee. Each approach will have its pros and cons depending on the particular case considered.
Let’s move on now to the three phases of the model. A quick tour through them will help to get a clear picture of how everything fits together:
PoC phase: considering the quick adoption nature of the solutions that we are evaluating, this stage will commonly take around a month. It will be called ‘to’, right before adoption and the starting point when value begins to be generated.
Adoption phase: after evaluating PoC’s results and better defining needs and requirements, the company will be ready to implement and fully adopt the chosen solution. This is the phase when all the Initial Investment (II) related expenses will be faced, including device installation, onboarding and employee training. This process usually takes one or two weeks’ time. Once everything is in place, for the rest of the period until the first month is completed, the now implemented solution will start pouring value. It will be month1 or t1 share of Value Generated (VGt1).
Operational phase: after that first month with everything now running smoothly, the solution will begin to fully, or get closer to fully, deliver its expected Value Generated figures according to what has been forecasted. This phase will extend from month2 to month12, ending in month twelve for the purpose of the analysis. If everything goes well and results meet expectations, it will surely be extended beyond that first year. This consideration can and should be taken into account when building the model, if commitment periods are longer or have been previously defined in that way.
The cost side of the equation is relatively easy to measure and quantify, but the bread and butter of the whole model revolves around the study and forecast of the Value Generated (VG) component. This is so because a set of strategic and market oriented factors must come on the scene to paint a well-rounded picture upon which to evaluate the expected performance of the investment.
To properly assess the potential value to be created by the adoption of a new IoT solution we have to start with the strategic goal to be achieved with it. Whether it is reducing operational costs, ensuring total quality and minimizing call backs, refining customer service and satisfaction for higher retention or supporting new data-driven business models, clearly define it. The chosen solution and the new data flows that are going to be formed must be perfectly linked to that strategic goal, responding to specific business objectives and KPIs.
To help with the quantification of the Value Generated component, the PoC phase will be of great help, especially if there are not much data on the expected performance of this type of solution in your industry yet. Even if small, running a PoC will provide down to earth figures on what to expect out of the solution upon full adoption. With these numbers, combined with more general statistics and figures available from previous use cases in the field, we can now come up with a more realistic monthly Value Generated (VG) forecast.
The PoC phase can give as result one or more investment alternatives to be evaluated. Whether considering only one supplier, whose solution allows for different configurations, or considering and testing several potential vendors, in both cases more than one investment alternative might be there for evaluation in the ROII formula. Each alternative will be evaluated separately as a different option.
Lastly, to truly fine tune the estimated impact of the investment and expected Value Generated, we will factor key business and market elements into our forecast, helping to better quantify risk and performance through a wider picture.
How is our company going to better compete in the marketplace with these improved capabilities or new business model allowed by IoT technology adoption? That is the fundamental question to answer.
These insights will be then used to refine our assessment of the expected Value Generated (VG) component of the model. After putting everything together, we will be able to evaluate the investment with a proper mixture of quantitative (PoC) and qualitative (business and market factors) insights before making a decision. Handly come here the words of Alan Lakein, “failing to plan is planning to fail”.
Finally, with the solution running, we will also be able to compare ‘expected’ versus ‘real’ monthly Value Generated figures, to learn and improve the accuracy of our ROII modeling skills for future projects’ evaluation. New solutions, extension of current ones or additional new features, it will all become easier to assess as experience piles up.
Let’s get a little dirty here. Imagine a waste management company serving a municipality. Up until now, the company has been operating through a fixed route passing through every single container location regardless if they need to be emptied or not. After reading about how IoT is disrupting their industry, they decided to give it a try. They went for a solution that monitors waste levels inside their containers in real-time through sensors, combined with a data management platform that allows for intelligent route planning depending on container state. The solution is also able to differentiate between organic and non-organic waste, as the second allows for more flexible pick up requirements.
They carried out a small PoC costing 20 monetary units. Being quite satisfied with the result and insights gathered, they ran their numbers and strategic analysis, arriving to the conclusion that they would be able to reduce their operational costs by 100 m.u. per month after full adoption and fine tuning. It will also allow them to reinforce their position in the market against competitors in an upcoming tender, one that they have to win to keep the contract in their hands. The initial investment, including sensors and personnel training, will be 60 m.u. in the first month, with a 30 m.u. recurring monthly cost to cover sensor connectivity, data warehousing and the usage of the data management platform. Running the ROII model they see how this investment lands a 2,03 result.
With this 1 : 2,03 ratio, for every $1 invested in the solution 2,03 additional dollars are expected to be generated, as new value added to their current operations and business model. We can agree on the attractiveness of this investment in new tech adoption, which at the same time establishes a sustainable market advantage and builds effective barriers to entry against existing and future competitors. That is the beauty of the model, incorporating an external perspective of market position and competitiveness, and an inside perspective of innovation and growth, essential success factors mentioned in the book “The Outcome Economy”.
Bonus tip: you might be thinking about the financial impact of time in the model. In a twelve month period it might not be particularly differential, but for longer periods of analysis, calculating the net present value of the expected returns per period will become more relevant. For such purpose, here is a quick introduction to the NPV formula.
Closing up, the Return on IoT Investment model tries to help in the decision making process within the noisy IoT landscape, where practically everyone is promising astonishing results upon the implementation of their solutions. However, these statements are not always that properly backed up.
When evaluating your IoT technology provider, things like the possibility of combining out-of-the-box solutions with flexible design house capabilities, so that they can be fine tuned for your company’s specific needs, can make a big difference. It directly translates into tailored, highly relevant and quick PoC phases, allowing your solution to start generating more value in less time. As result, increased total ROII and minimized underperformance risk, while maintaining costs well under control.
This is the sweet spot that we at HC Technologies have been pursuing and polishing over the years. Recent witnesses of the same have been brands like Bimbo or L'Oréal. As these words get written, we keep perfectioning this approach, so that companies of any size and technology background can genuinely benefit from the Internet of Things revolution.
Let’s keep the conversation going on LinkedIn.
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