Industrial Internet Of Things: End Up On The Winning Side


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Operations and technology executives take notice when experts such as McKinsey project $11.1 trillion in economic value by 2025 as a result of linking the physical and digital worlds.  That’s a tremendous amount of economic value in a very short time, even if the experts might be a little off in their estimates.

The impact of the internet over the last 25 years certainly supports predictions of disruption and promise as the Internet of things (IoT) and Industrial IoT (IIoT) continue to connect the physical and the digital.  Organizations that transform themselves using IIoT can become giants; those who lag or fail in their execution may become mere memories. How do you ensure you and your organization land on the right side of this disruption?

Operational data is not a new phenomenon

Mentions of IIoT pepper nearly every operations- or technology-related conference these days.  Many traditional control system vendors are relabeling their offerings as part of the IIoT movement.  While industrial control systems remain critical components of operations in many industries, simply rebranding existing systems is certainly not going to going to deliver the trillions in economic value that McKinsey and others predict.  That magnitude of value creation comes only from truly transformative changes to how companies and industries operate.

Inherent risks in embarking on transformative change

Any large organization that can greatly benefit from the promise of an IIoT world has a number of existing critical assets, control systems, IT systems, processes, and skilled people that are essential to their operation.  Many industries have equipment and systems that have been acquired over several decades. Displacing all of these existing operational assets with a sparkly new, end-to-end IIoT-enabled operation is risky and typically not economically practical. Mergers, acquisitions, large IT projects and other attempts at transformative change fail at an astonishing rate. Estimated failure rates range from 30% according to the optimists to 70% from the pessimists.

If you are trying to create transformative change while relying on existing systems, processes systems, and people, you inevitably will face execution risks related to:

  • Lack of interoperability and openness of existing control and IT systems
  • Poor data quality in dependent systems
  • Lack of scalability, both technically and economically, of these systems
  • Insufficient internal talent, expertise, and bandwidth to manage a large project that touches the operations and IT sides of the business
  • Security exposures as you open up systems that have traditionally been on a closed loop system
  • Poorly defined objectives and accountability
  • Striking the wrong balance between building versus buying IIoT systems, ending up with either a solution that isn’t fit for purpose or a solution that exceeds cost and timeline estimates and doesn’t scale.
  • Difficulty maintaining balance of schedule, cost, ROI and executive support

How to end up on the winning side of IIoT

The risks and complexity make getting started with an IIoT initiative seem daunting.  But with this sort of disruptive change, playing the laggard is not an option. How do improve your odds for success?  Here are a few recommendations:

Think big but start small – Think big about how your organization can use new data sources and analytics to improve their operations and service uptime, but start small by first tackling a discrete, well-defined problem. Deliver value quickly and then consider another problem to tackle or extending the first solution to solve other related problems.

Clarify the problem, solution and accountability – Ensure the problem, solution requirements and dependencies are clearly understood.  Appoint a clear, accountable owner for the project who has organizational support.

Prioritize vendors that have “skin in the game” – Many software, hardware and communications vendors will happily sell you the parts of an IIoT solution–platform access, software licenses, sensors, access points, gateways, network access, and servers–but leave you to sort how to assemble these parts into something that solves your problem and provides value. Prioritize vendors who provide ongoing service with lower up-front costs.  This enables you to ensure the service delivers on its promised value before you have committed too much funding.

Challenge traditional thinking in your organization – What got you here won’t get you there! Clearly for many industries existing levels of security, reliability and regulatory compliance must not be compromised. However, that shouldn’t mean that new approaches such as cloud computing, internet connectivity, open source software, and commoditized hardware should be dismissed.  These will be required in many cases to realize the potential value of IIoT solutions.  Many companies use these technology solutions successfully today while balancing the associated risks.

Get Started – Don’t get stuck in analysis paralysis – Obviously it is important to ensure a problem, the solution and potential value for solving it are well understood.  It is also critical to assess risks and get necessary organizational buy-in.  Once you have done that, get started, learn and improve.  The opportunity is immense and those who lead with successful IIoT solutions will have tremendous efficiency advantages in their respective industries.

Allan McNichol is the former CEO of GOFACTORY and Managing Director for Intelligent Energy

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Are You Analytically Driven?


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“Ultimately, the analytically driven enterprise is one where analytics is seamlessly woven into the fabric of the entire business.”

This statement introduces the 2016 Dun & Bradstreet Enterprise Analytics Study. In the study, nearly three-quarters of respondents considered their organizations as “analytically driven.” Yet a majority of respondents:

  • Did not possess a corporate strategy for how analytics are used across the organization
  • Did not share analytics models and results outside of their departments
  • Did not consider their company skilled in visualization tools for analytics
  • Did not have the analytics resources in-house to identify new product or market opportunities

Clearly there’s a gap between how broadly people perceive analytics influencing their job and how sophisticated the role analytics actually plays in their job.

The majority of respondents in this study work in the manufacturing, financial services, and high tech industries. These industries are not unique in their slow adoption of analytics. Many industries are just starting on the analytics learning and maturity curves.

So, is your organization analytically driven? How would you answer these questions from the survey?

  • Does your organization have a strategy for using analytics across the enterprise, and not just within departments?
  • If your department generates and applies analytics models and results, are you sharing those with other departments?
  • What data visualization tools and skills do you use on a regular basis to communicate across your organization?
  • Can you point to new products or new markets that you identified and exploited using analytics?
  • What percentage of your organization’s analysis is performed by external researchers and consultants?

If you struggled to give specific, positive answers to these questions, know that you’re not alone. And, as they say, the first step is admitting you have a problem. With the amount of data and devices pouring into the world in 2016, you’ll quickly encounter problems if you don’t have an organizational analytics approach–especially if your competitors do.

This white paper contains an easy approach to evaluating your organization’s analytics maturity and models to consider for your organizational analytics strategy. It’s a good first step toward becoming truly analytics driven.

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Pipeline Analytics Lower Natural Gas Risk


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Pipeline accidents in Allentown, Pennsylvania in 2011 and Sissonville, West Virginia in 2012 destroyed homes, caused death and injury, and reminded us how critical careful gas transmission and distribution really is. We are accustomed to natural gas reliably powering our homes and businesses, but even those responsible for getting it there can take that system for granted.

Safety and aging infrastructure are top concerns of natural gas executives surveyed by Black & Veatch in 2015, and with good cause. Replace just five percent of a room’s air with natural gas and the atmosphere becomes explosive. According to a U.S. Department of Transportation report, nearly one-third of natural gas distribution pipelines in the U.S. were built before 1970.  More than 50,000 miles of these older pipes were welded together with outdated techniques that are prone to failure.

Despite these known conditions and obvious hazards, one-third of respondents to the Black & Veatch survey did not have a resilience plan in place for their natural gas operations four years after the Pennsylvania and West Virginia accidents. Fifty-four percent of respondents agreed with the statement that “a formal risk-based planning approach has not yet been undertaken to my knowledge.”

Why should so many utilities executives be without a risk-based plan, when the consequences of risk are so high?

Much of our natural gas infrastructure is hidden underground, which means it is often out of sight and thus out of mind, contributing to our becoming inured to problems. Gas utilities have developed clever methods such as pigging, hydro testing, and cathodic inspection for measuring and maintaining the health of pipes. Those clever methods generate multiple, disparate sources of data related about assets. Today, many utilities are flooded with data but no closer to fresh and useful insights based on that data.

Situational intelligence offers a powerful approach to analyzing those data sources and quantifying the risk present in natural gas assets. By correlating, analyzing and visualizing data related to an asset’s age, condition, location, network relationships, and operating history, situational intelligence provides a method for making decisions based on the likelihood of asset failure and the consequences should failure occur.

With this specific understanding of risk, natural gas managers and executives can prioritize maintenance, repair, refurbishment and replacement work to focus first on the most critical assets. This approach drives down risk faster than following a time-based or even condition-based approach to asset planning and operations.

We can’t fully eliminate risk, but we now have the analytics approaches to understand, quantify and lower risk to help prevent future pipeline accidents.

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Three Ways Analytics Improves Emergency Response


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In previous posts about safety, I’ve talked about how predictive analytics help prevent accidents before they happen. But, analytics are also useful for improving the speed and effectiveness of response when accident do happen.

Here are three ways to apply analytics to emergency response:

Analytics help you identify the real problem

In today’s IoT and Big Data world, you may have multiple systems creating alarms in response to a single accident or event. How do you know what the real problem is? Analytics that bring together alarms from across your enterprise help identify and respond to the real problem.
For instance, your building management system gives you an alarm that sprinklers have turned on in one building of your corporate campus. Does this mean that there is actually a fire, or that someone created a false alarm, or that the sprinkler system has somehow malfunctioned? Without analytics that correlate the sprinkler alarm with other data such as smoke alarms and security video, it’s hard to know.

Analytics help you triage effectively

Alarms tell you that something happened, but by themselves don’t tell you the consequences of what happened. Analytics that include a criticality score for locations, equipment and inventory quickly give you the true magnitude of an accident or event. Knowing the magnitude of consequences also allows you to prioritize your response to multiple simultaneous events.
Continuing with our building fire example, let’s say that a correlated alarm system tells you that there is, indeed, a fire. You immediately want to know the potential consequences of the fire. How many people work in the area? What sort of equipment or inventory is nearby? Are there any guests in the building? Without analytics that rate the consequences of the fire, it’s hard to fully assess the situation.

Analytics help you respond efficiently

Once an accident or event has been properly identified and assessed, there’s no time to waste with inefficient or ineffective response. Analytics that correlate the type of incident with the specific qualifications of first responders and their respective current location means that people arrive on the scene ready to act, instead of ready to assess. The same approach applies to vehicles and equipment that first responders may need to address the situation.
If our building fire is a chemical fire in an inventory warehouse, that situation requires a different type of response compared to an electrical fire in an office building. Without analytics that correlate people, equipment, locations and events, you risk having the wrong people respond with the wrong equipment to handle the situation.

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