Everyone is talking about digital transformation, Industry 4.0 and artificial intelligence (AI). Nobody wants to be left behind. For industrial businesses, this interest often leads to business leaders directing their engineering teams to deliver an industrial Internet of Things (IIoT) solution. But despite all their accomplishments, engineering teams are shutting down IIoT projects at an alarming rate.
Here’s what often happens: Months or years after the IIoT assignment, the engineering team returns with bad news. They’ve done their best, but the system just can’t do what the sales, service and product teams are asking for. Data scientists don’t have all the information they need. Recommendations from analytics tools are consistently inconclusive. Finance and IT are up in arms over costs and operational issues. Worse, the solution cannot meet a rapidly expanding list of customer requests. And the engineering team proposes to shut it all down and start over.
What is going wrong?
Off to a Rough Start
Too often, engineering teams are given a vague or poorly defined mission and instructed to choose an industrial IoT platform and build a connected system. The team starts by studying technology options and asking vendors about features and pricing. What does your platform do? How does it do that? What development services do you provide? How much does it cost?
What. What. How. How. These are all important questions — but journeys that begin with them may be long marches to disaster.
Successful digital transformation projects begin differently; the key question is why the business needs an IIoT solution in the first place. What are the desired business outcomes and their value to your enterprise? How will IoT benefit customers? Furthermore, what changes will be required across the organization to fully commercialize the solution, both internally and externally?
It’s impossible for the engineering team to make a good decision on the right tools without a clear understanding of the job at hand. How can they know whether an industrial IoT platform has the right features without clear, prioritized user stories? How will they determine the appropriate price? That requires knowledge — or at least an initial estimation — of how much each scenario is worth. No engineering team can possibly guess how much is appropriate to invest in developing a connected system without a clear understanding of its potential value.
When companies fail to provide an explicit denominator of expected revenue increase (or savings) for the ROI equation, they send an implicit cost numerator of “as little as possible.” That’s a dangerous guideline.
Stumbling at the First Hurdle
In some of the ill-defined IIoT projects we’ve seen, engineers surmount early setbacks and kludge up a solution … and then the problems begin. The engineering team is flooded with requests for new features, more connected product lines and additional data types by various departments. Many need better integration with existing business workflows and CRM and ERP systems.
Then, there are the customers. They want to know how the new system will improve their bottom line. Nobody asked them what features they wanted or why they wanted them.
Typically, engineering teams make valiant attempts to meet these new requirements. They start adding patches and hacks they’re not proud of but that get the job done. Pretty soon, though, they hit a set of change requests that are nearly impossible without rearchitecting the whole thing.
Creating an IIoT Success Plan
For many industrial businesses today, industrial IoT is a product management problem. It’s a company culture problem. It’s an executive leadership problem. But it’s no longer a technology problem. Unless the business understands and clearly articulates to engineering why it wants to connect equipment, what scenarios must be enabled, and how success will be measured, the system will inevitably fail to create sustainable value for both the organization and customers.
The key, then, is to start with a different approach. Company leaders should begin with a clear definition of why a digital transformation is necessary, backed by metrics. From the beginning, there should be a clear theory of success in place and a plan for validating assumptions.
Second, the organization must build a broad coalition to ensure business and technical requirements are clear ahead of time. Roles, goals and responsibilities should be noted and understood.
Next, the business might even conspire with customers to identify game-changing opportunities — before tasking engineering with building anything at all. Not only would this dialog drive eventual customer demand, but it would also inform critical decisions about the architecture and design of the system itself.
The goal is to consistently deliver IIoT solutions that drive real, sustainable business value. The impressive engineering teams we see in enterprises are more than capable of this feat — when they are provided clear direction.
This blog was adapted from the original post that appeared on the Bright Wolf website (a Cognizant company).
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