The Internet of Things (IoT) is, like the universe, expanding. This past summer, IPv4 addressed, the 32-bit protocol that underpins the internet address system ran out. If you have paid any attention to this potentially impending doom then you know there is no real impending doom. The savior IPv6 is already here to save the day, or at least is around the corner as there’s some work to be done infrastructure-wise for the 128-bit protocol to take flight. Actually, the American Registry for Internet Numbers (ARIN) among other Regional Internet Registry (RIR) bodies were executed on their exhaustion protocols limiting blocks of addresses available. There’s a plan and our world will not grind to a halt. In fact, as far as the internet is concerned it will grow unchecked as we have an unlimited supply of addresses with IPv6. This is key to leveraging and growing the potential 30 billion smart-connected devices and the estimated $1.7 trillion market prediction for 2020. A risk well managed some might say.
Managing Risk is Inherently Subjective
Risk is a pervasive topic in mainstream quality management currently, but for many it has been part of their toolkit for decades in the form of FMEA or PFMEA or as part of their certification and/or regulatory compliance activities based on ISO 14971 for medical devices (ISO 134985). Identifying hazards or potential failures is particularly challenging as one must have the right people involved at the right time, document the hazard/threat/opportunity etc., and then assess these for likelihood of occurrence and consequence/benefit and then revisit the cycle regularly to adjust based on new and updated intelligence. Significant challenges regarding how to manage the body of information exist, like how to compile and present the risk landscape, and maintain as a living body of knowledge to benefit stakeholders and underpin sustainable performance.
One main concern voiced by professionals that specialize in risk is that some subjectivity in the identification and assessment of hazards is inevitable and unavoidable. Given that the very principle is to anticipate an event and prevent it from occurring in the first instance, this is not surprising. Often a scientific best-educated-guess, evaluated for probability and severity is as close as one can get. Tracking the actual hazard or event out in the real world is difficult, even with strong processes and good value-chain spanning communications in place. The type of mechanism an enterprise quality, environmental, health, and safety solution might offer as an example. These solutions allow for some intelligence to be mapped. Non-conformances, safety and or environmental incidents, complaints, safety near misses are typically tracked and, in the best implementations of these enterprise systems, are linked to all and any hazards or threats in the risk register.
Objective Evidence Also Known as Facts
Ideally, we would have some way to validate that our risk management approach is effective or at least on the right track. Something that’s hard to achieve without continuous monitoring and a technology-driven solution is often disproportionate in cost or simply impractical.
This is where IoT begins to open the door as the technology becomes cheaper, more pervasive, and in time more standardized. It strips out as much human involvement or intervention as possible in monitoring and use the intelligence from the device/product/item or persons monitored; and it feeds into the hazard identification, risk assessment and controls monitoring process.
Potential Applications of IoT in Risk Management
In order for IoT in quality management to flourish long-term, there are some vital ingredients, and unfortunately still a modest element of subjectivity. Objective evidence from appropriate cost effective sensors is already a reality due to the IoT. Aircraft engine performance and servicing is one example, train performance is another area where objective smart-connected devices are driving efficiency. The question is, how does this potentially improve risk management?
The subjectivity in the equation is what parameters (to monitor) are useful for application in a risk-intelligence scenario. It seems we have a chicken and egg situation. However, we have to start somewhere and leveraging sensors that serve standard functions have a use, like temperature for example. One example that came to the fore at a recent vendor conference after a number of medical device related suggestions (many of which were very good), was that of the monitoring of time to temperature, maintaining temperature and efficiency of industrial ovens in the food industry. This data has relevance to multiple stakeholders in an organization that potentially has many thousands of outlets where baked goods are part or all of their product. Imagine this feeding:
- Environmental aspects registers,
- Food safety field risk profiles,
- Safety risk assessments
- Engineering reliability assessments
This data on a global scale fed to the cloud has the potential to yield intelligence that feeds efficiency, risk and other strategic and tactical decision making. Notwithstanding the obvious (and initially more likely) service and warranty management of the equipment.
LNS Research has discussed wearables in a safety context before and to some extent how this could feed risk assessment. Another end-user suggestion witnessed recently was of the proximity sensor. It’s especially important around large equipment in mining and aggregates operations, but also applicable to the use of forklifts and automated/unmanned vehicles. This technology has an obvious immediate vicinity potential to help prevent an incident by virtue of an alarm. It has the potential to log every single breach of proximity, unauthorized use, and every proximity related near miss without question or effort on the employee or supervisor’s part.
Don’t Fail to Plan
The challenge of managing risk for quality and EHS leadership comprises of the formal process, ongoing assessment and verification of controls, and either validating or refuting assumptions as to the validity of the nature of the hazard or threat in the first place. Any and all evidence-based intelligence to support more robust risk practices no doubt will be welcomed and time will tell whether risk management will be a beneficiary of the IoT. The foundations are in, the technology is being delivered and improvements made all the time. Perhaps organizations should attempt to connect some of their risk-related needs to the IoT strategy discussion they are having sooner rather than later.
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