Industrial IoT Vision & Investment Guidelines

  /  Industrial IoT   /  Connected Industry   /  Industrial IoT Vision & Investment Guidelines
Industrial IoT Investment

Industrial IoT Vision & Investment Guidelines

A connected factory, a robotic arm, and a power drill walk into a bar. The bartender asks them for data. All three start yelling at the same time. The bartender walks out. The next day, his body is found in a trough of disillusionment outside the old Gartner place. The data wars had begun…

Today, the rush to obtain IoT data creates both crisis and opportunity. Manufacturers crave data for optimizing operations. Equipment providers seek to elevate customer relationships and improve service offerings. Everyone is looking for ways to collect and transform data into business value. Whether or not data is indeed “the new oil,” a battle is brewing among those seeking to control and refine it for a competitive advantage.

Industrial Data Layers

Inside a manufacturing facility are multi-layered systems capable of producing and acting on industrial data. For example, the power drill provides temperature, torque, cycle times, and other operational data. Often the drill is attached to a robot, which produces additional information about jobs being done. In turn, both the drill and the robot may be connected to an internal factory automation system. Data value comes from data analysis. Therefore, how data is collected, where it is stored, and who can access it are where battle lines are being drawn.

The Rise Of Data Componentization

By now, most hardware manufacturers are delivering connected product systems. Many projects are spurred by the need to protect and improve service offerings and parts businesses. Increasingly, facility operators simply stop buying un-sensorized “dumb” machines, increasing pressure on OEMs to transform more quickly. Moreover, many industries are reaching the limits of efficiency achieved through analog mechanical improvements alone. The next leap forward will be digital, powered by data analysis and machine learning.

Each component, from lasers to split things and welders to put them together, is becoming its own, self-improving connected system. For simple manufacturing operations, this can be instantly transformative.

Data Componentization Challenges

But what happens when multiple machines from several vendors are aggregated into more complex machinery, such as a robot with a drilling arm or a multi-phase packaging assembler? Furthermore, what happens when these systems are installed inside a complete manufacturing line?

Stakeholders at each layer may seek to control their data streams rather than allow them to flow freely into a central pool. A power drill providing data to the robot for analysis is at risk of becoming a replaceable commodity. Similarly, if the robot simply sends data from its own sensors along with drill data to a central automation system, it’s just a temporary aggregator with little value added. Each layer wants to be the provider of insights, not just information, to ensure its place in the value chain.

Sometimes facility owners are successful in extracting data from each component on the manufacturing line. However, they are hard-pressed to transform this torrent of raw data into actionable information themselves before it seeps beneath the surface of an expanding data swamp. While many aren’t so ambitious in their data demands, no factory operator wants to log into separate monitoring applications for each equipment vendor on the floor. Therefore, in many facilities, only a fraction of potential value is being created from the data produced.

The tools of industrial IoT are being used against the optimized future they were designed to create. As we’ll see later, this same competitiveness may also be the key to a prosperous future peace.

Realizing The Industrial IoT Vision

Perhaps unexpectedly, the answer is not to lay down your arms, but to stick to your guns – and improve your aim. For example, manufacturers should expand efforts toward improving interactions between components, and integration of production data with their enterprise systems (CRM, ERP, etc) to optimize overall business operations. Let the equipment providers themselves endure the burden of individual component performance. For manufacturers, contractual service level agreements (SLAs) with equipment providers for guaranteed uptime will increase productivity far more reliably than attempting to build a single master solution.

Connected equipment providers are well positioned for preventing unplanned downtime for each of their machines on the line. First, they are highly incentivized to deliver better results than competitors for fear of being replaced. Second, there are insights only they can cost-effectively produce. Consider predictive maintenance. Creating accurate algorithmic models requires large datasets reflecting many failures over time. While facility operators are limited to the data produced by machines inside their own buildings, equipment providers have data from every machine in the field and within their own test labs.

Uptime SLAs are a win-win. For equipment makers with well-architected connected product systems, they provide more business security and increased margins. For manufacturers, they grant the ability to focus on overall production issues and business outcomes.

Peace Through Prosperity

As shown, optimization doesn’t require a single IoT data value creation point. Greater overall value is obtained through embracing multiple optimization zones. Maximum benefits accrue to the entire system of production when each party is encouraged to pursue their own local maxima rather than fighting to dominate others.

Equipment makers should use IoT for producing more reliable, higher performing machines with lower operating costs to secure service contracts and parts businesses – not brawling over data dominance.

Manufacturers can achieve better results by improving overall production metrics and driving integration with their own business systems than by troubleshooting machinery. Similarly, component aggregators can ally their constituents into “better together” offerings with shared data agreements and unified service offerings.

Encouraging data diversity lets each party compete aggressively against competitors in their own layer – drill maker vs drill maker, robot vs robot, manufacturer vs manufacturer – rather than across layers in the same facility. These are “good fights” that produce stronger and higher quality components at all levels without restricting the capabilities of the system as a whole. The data must flow.

Industrial IoT Investment Guidelines

Whether you’re just getting started or replacing a system you’ve outgrown, pick your battles wisely. For equipment providers, there’s no need to build elaborate mobile applications for manufacturers who just want a raw data feed and uptime guarantees. However, custom dashboards and notifications may be necessary for your internal service and engineering teams to deliver on performance goals. Features should be prioritized and implemented according to validated user stories. Similarly, manufacturers can leverage vendor SLAs for uptime, and focus on data aggregation and integration rather than investing in attempts to capture total control over IoT data value.

Industrial IoT has initiated a new era of business warfare. The winners will be those who better understand what to defend, where to let go, and how to collaborate effectively. These choices will determine how successfully your business competes in a connected world.

For a review of your existing connected business and technology strategy or planning session for future projects, contact our team for a guided workshop and solution review.


marc phillipsThis article was written by Marc Phillips, the Director of Marketing at Bright Wolf, a leading IoT technology provider and system integrator helping Fortune 1000 companies design, develop, and deploy Enterprise IoT systems and connected product solutions.