Recent data from the Department of Energy (DOE) indicates that approximately 60% of electric utility workers will be eligible for retirement in the next five years. The impending loss of the most skilled and knowledgeable workers in the industry has put many utility companies on red alert. This potential workforce crisis means that companies have just a few years to transfer what’s often referred to as, “Tribal Knowledge” from those retiring to those who will have to fill their shoes.
Many utility workers hold positions within their companies for nearly 30 years, so one can easily imagine the amount of intangible knowledge and varying experiences that each worker has. What companies really need is a way to tap into the experienced worker’s knowledge while traveling in the field. Better yet, they would like to find a way to limit travel in the field; traveling hundreds of miles to analyze and solve problems poses its own set of risks, from driving hazards to on the job injuries. When something goes wrong on the grid, lives are at risk.
Challenges in the Field
Much of the information and knowledge needed by today’s utility worker is stored in a computer or in the cloud, and is not accessible in the field by the organization’s workers. Because of this, institutional knowledge and expertise decreases. Many companies today are purchasing collaboration solutions, including web meeting software, video and Internet-based phone systems. This package is intended to upgrade collaboration across business units, address the issues of lost tribal knowledge, increase workforce effectiveness and manage company priorities arising from their aging workforce. Challenges faced include a slow, sub-optimal rollout plan, forced by lower company revenues. Analysis points to automation of capture and storage of knowledge, utilities could take advantage of their expert employee’s skills and leverage their knowledge for less experienced workers in the field, thereby giving a good return on investment for early deployment of mobile collaboration.
There are three immediate problems to resolve: access to Tribal Knowledge, better utilization of experts for training, and improving safety for the mobile workforce traveling in the field. Companies remind us on a regular basis that training a utility lineman can take 10 years or more – and the average age of the current power lineman workforce is 47-years-old. Utilities are also focusing on retention of younger employees, who are generally more technologically savvy and who expect work access to tools they use outside the workplace, like smart phones and other wireless technologies. In fact, new or younger workers prefer to work in an area with new technology. Mobile devices such as smart phones and ruggedized tablets can be especially useful in the field where workers can get access to advice from experts in real-time or even start a meeting – all to create increase access to institutional knowledge.
One component of Cisco’s solution is expert locator software. Employees such as line workers and technicians would have the ability to be connected anytime or anywhere via a five-product Enterprise Collaboration solution: Expert Locator, IP call control with video IP phones, web meeting (Webex), an immersive video solution (TelePresence) and ruggedized mobile video (Librestream Onsight).
New technology can change the way utilities conduct business
Workers would use mobile video in the field to show details of problems to experts throughout the company, senior workers could provide advice and support for repair of damaged equipment in the field without having to travel to the field. Experts can also quickly convene and escalate meetings to resolve a problem via the web and Telepresence. If a worker is on-site and there are challenges with a device, the worker can start a meeting, have the ability to share and give/get advice in real-time. Because of this, repair times go down while safety goes up.
How can Cisco help your organization support new collaboration and create a mobile workforce? Find out more by visiting our solutions page and share your thoughts in the comments section below.
Curious about OpenStack? Don’t miss this episode of Engineers Unplugged, where Kenneth Hui (@hui_kenneth) and Gabriel Chapman (@bacon_is_king) explain the difference between OpenStack the project, product, and service using bacon as an analogy. Don’t watch hungry.
Cue the unicorns.
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Episodes will publish weekly (or as close to it as we can manage)
You know the Internet of Everything (IoE) is gaining traction when you hear about it from the guy changing your oil. Earlier this month I was dropping off my car for its regular service when the technician began asking me how the Internet of Everything will change automobile maintenance and repair. Twenty minutes later – after we had discussed how quickly cars are becoming smarter and connected – I was on my way home with yet another example of just how fast the Internet of Everything is coming our way.
IoE — the networked connection of people, process, data, and things — is spawning business opportunities in just about every walk of life. However, the proliferation of traditional and new data sources and the movement of data to the cloud are making it harder for businesses to access all their data assets. Research shows that through 2017, a whopping 90 percent of the information assets from big data analytic efforts will be limited to specific project siloes and — more importantly — unleverageable across multiple business processes. [Source: Gartner “Predicts 2014: Big Data”] Read More »
The theme of this year’s Cyber Security Awareness Month is “Our Shared Responsibility.” At Cisco, security is everyone’s responsibility – from our trustworthy development processes, to innovation enabling our customers and partners to address threats on end points, networks, and in the cloud. That is why Cisco is setting the industry standard for meeting the security needs demanded by the Internet of Everything (IoE).
Over the next six years, the number of devices connected to the Internet is going to reach 50 billion, creating some pretty unique opportunities and dilemmas as companies and industries are connecting people and devices to one another in ways we’ve never seen before, changing the way we work and live.
As the number of connected devices in the “Internet of Things” increases exponentially, organizations must keep security top of mind as the number and type of attack vectors increases alongside the quantity of data IoE creates. This shift is creating a daunting challenge for companies and those responsible to defend the infrastructure.
I recently did a video blog on the IoE from the security perspective. Take a look and let me know what you think in the comments.
The recently concluded Predictive Analytics World 2014 in Boston, Massachusetts, was chock full of insights from organizations who have successfully implemented analytics in a variety of settings. A few points stood out and I will attempt to capture them here:
1. Focus on ROI measures: This is spoken of very often, but frequently in an attempt to develop the “right” or “perfect” model, the focus on ROI sometimes begins to waver. Being driven by ROI implies understanding which variables are controllable by the business, which data observations are of real interest and sometimes making adjustments to accomplish that. This may mean considering variables that are otherwise not significant, or oversampling certain data observations and so forth. But a relentless focus on ROI will yield the desired results.
2. Eschew Complexity: Seek simpler models, fewer variables, and explanations that make sense. Given results, the human mind will find ways to explain it – so do not rely on interpretability as a defense of your models. But let the sheer simplicity of models tell their own story.
3. Ensure algorithmic Data Preparation: As all practitioners know, Data Cleansing and Preparation is 80% of the effort – but what is sometimes forgotten is that Data Preparation is not a one time effort, but is subject to the algorithms being considered. Understand not only the assumptions, but the limitations of the algorithms being considered – and do for the algorithms, what the algorithm cannot do for itself.
4. Consider Ensemble Techniques: Ensemble methods such as Random Forest, Gradient Boosting and others have proven repeatedly to provide stable and usable results. Master these techniques and more.
5. Simulations are often a good Communication technique – All practitioners understand that the ultimate success or failure of their efforts depends upon successful communication of their findings. Leverage simulation of your results, and the likelihood of success or failure of your model in real situations to help further communicate and define your findings.
And incidentally, if you wish to understand the analytics maturity of your organization, visit this link at the INFORMS website!