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Collaboration Makes the Future Bright for Healthcare

Well, New Year’s Day is long past. As I continue to plod along with my New Year’s resolutions through this brutally cold winter, I do so with varying degrees of success. I still start my day with an apple-ginger juice and try to eat my veggies.   Along the way I bolster my dedication with the incremental successes that a healthy lifestyle delivers.  I feel lucky to have my health and I’m trying my best to protect it! 

Sometimes, I get bummed when I read the healthcare headlines: There’s a new “super bug”… there is a shortage of doctors… antibiotics are nearing the end stage of their effectiveness…. The headlines can be scary!

But I know that headlines are meant to elicit emotions and capture attention. I recently looked behind the headlines at stories about how companies in the healthcare industry are using Cisco collaboration technology.  I came away feeling optimistic.  There are some cool things happening!

Technology is making a huge difference in doctor-patient care.  

Park Nicollet Healthcare is a nonprofit integrated healthcare system located near Minneapolis. The company was looking for ways to improve collaboration between the more than 1000 physicians on staff. Park Nicolette made huge strides when it added Jabber to its existing Cisco Unified Communications Manager implementation Read More »

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Deliver Services at Internet Speed

How quickly can your organization stand-up a new application or deploy new services?  Most customers tell me, “not fast enough!”   I am clearly hearing from them that the new standard expectation across the organization is to receive precise data center resources in “internet time,” easily and definitely on-demand.

But customers are not the only ones affected by these new expectation standards.  Application developers also expect to receive the resources they need to support their efforts within one hour — without a lot of process meetings and repetitive, slow paperwork.  They want what they want, when they need it, which is always now!  Can’t get it now?  Out comes the credit card and they go on a shopping spree to outside resources.

Developers don’t worry about security, governance or quality of service.  If you are in operations, or you’re a C-level executive, you care.  You need to meet compliance guidelines.  So how can you get everyone on the same team, working together so the organization can succeed, the old “win-win-win?”

At CiscoLive Milan in January, we introduced the Cisco ONE Enterprise Cloud Suite. Watch this replay of our live broadcast.

 

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Solving Manufacturing Complexities through Data Analytics: Part Two – Implementing Data Analytics

Data analytics has been an integral part of manufacturing management for most of its history. However, analytics has undergone both evolutionary and revolutionary changes over the decades with the advent of information technology and digital data gathering and analysis. In Part One of this series, I took a look at the evolution behind data analytics and applications in Manufacturing. Part Two provides insights into implementation of analytics in manufacturing.

Part Two: Implementing Data Analytics in Manufacturing

Acquiring Data

The first step for data analytics in manufacturing should be to implement solutions that connect manufacturing equipment, sensors and controllers to a converged network so data can be captured, moved and stored for analysis in an appropriate manner. While manual data entry is common and will probably continue to some extent, automation is critical to ensure that data is captured in real time, accurately and in the right format to enable analytics and decision making.

The amount of data available on the manufacturing plant floor has increased by many orders of magnitude over the past decade, however analysis and application of such data in decision making has not kept pace. It is this space of analytics that is now driving adoption of ‘Internet of Things’ (IoT) technologies such that IoT and analytics have now become intricately linked to each other.

Going beyond just analyzing data from IoT and expanding it to include the impact of this data on the people, skills, business processes and linking all of these disparate elements into a single business-focused system is referred to as the ‘Internet of Everything’ (IoE). Manufacturers are only now just starting to take this wider perspective to analytics and the application of analytics in manufacturing. As manufacturers begin to rely more on data for analysis into business processes, they must also consider some challenges that may arise during implementation.

Virtualizing the Data

Today’s manufacturers need the ability to integrate all data from various departments/locations, which has proven to be difficult in the past. The old approach consisted of building a data warehouse where data was extracted from multiple sources, transformed (normalized, processed, condensed) and loaded on a periodic basis into a central data warehouse. Today’s manufacturers need data that can be used in real-time to make decisions, not data stored in a warehouse for historical analysis. A steep increase in the use of cloud storage for such data warehouses has led to data being stored across different clouds (mix of public and private) on different platforms. Bringing all of this together to yield meaningful results without moving all the data physically into one data warehouse has been a challenge. Data virtualization solutions now enable accessing data that is physically in different databases and geographic locations as if it were physically in a single data warehouse. This has becomes even more critical with the large volumes of big that are typically unstructured and not easily amenable to traditional data warehousing approaches.

Integrating analytics into business processes

Data analytics cannot be a standalone activity done in a data center by a team of experts. It has to be integrated into the key business processes such that analytics are focused only in areas that provide business value and are available to decision makers at the right time in the right place. Important questions to be considered when implementing analytics solutions are:

  • How will the data be used?
  • Who will use it and how often?
  • What kind of analysis is needed?

Responses to these questions will define your strategy and dictate how analytics are integrated into the business. Implementation models could include

  • Data acquisition from sensors and analytics at the ‘Edge’ to feed-back to control system or human operator. The data is acquired and moved to a computing platform on the switch (in the manufacturing cell network) or to a data center in the manufacturing plant where it is processed and the result is used to drive the manufacturing process through control signals or visual / audio signals through the Human Machine Interface (HMI). Example would be a high definition camera taking 3D images of the product and comparing it to standards to identify quality defects in real time to eject the defective product or stop the machine or just sound an alarm via the HMI for the operator to take action.
  • Data capture from sensors and equipment for periodic reporting. The data is acquired, moved to a data center and analysis / reporting is done in conjunction with other databases on a periodic basis. Application would be machine uptime and speed data acquired in real time and used to report Overall Equipment Effectiveness (OEE) in conjunction with data like product mix, raw material / packaging source etc to identify performance issues and improve OEE.
  • Adhoc analysis of data acquired from sensors, done offline, after data has been normalized and moved to a data center. Typical use case would be analysis in support of six sigma/quality improvement projects where data gathered from the machine / production system is analyzed to support (or reject) hypotheses for problem resolution by shop floor employees.
  • Data capture and streaming out to equipment vendor in real time (machine as a service) where the machine vendor monitors performance of the machine parts and is able to take remote corrective action or schedule predictive maintenance or bring in appropriate spares just-in-time to ensure machine up-time and performance per contractually agreed levels. In such cases, security becomes a key issue too.

Implementation challenges

Implementation of data analytics should consider the following:

  1. Appropriate manufacturing cell and zone network to ensure high speed, quality of service and reliability. This is absolutely critical and is a huge challenge for manufacturers give the proliferation of standards and protocols in use on the shop floor and the lack of convergence of the networks.
  1. Moving and storage of data and location of the data center. This becomes very critical when handling big data in large volumes and high velocity and the decision on whether data center should be co-located in the manufacturing plant or remote/cloud can drive performance and cost of the solution.
  1. A comprehensive strategy and implementation approach focused on the entire data chain and not just on the final analytics and visualization. Typically analytics is seen as using algorithms on data and developing reports/visualization with little focus on acquisition, movement, storage and organization of the data. What appears in the user interface is the most visible but not necessarily the most important or most challenging aspect of implementation.

How can Cisco help your manufacturing organization improve efficiencies and gain valuable insight through data? Visit our solutions page to find out more and share your thoughts with us in the comments section below. Stayed tuned for Part Three of this series where I will share experiences in implementation and detail how analytics and IoT are working together to bring results in manufacturing.

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Making sense of Service Provider Virtualization

nehib-1Guest blog by Greg Nehib, SP Product and Solutions Marketing

I like to think of virtualization as an expanded networking toolkit, providing us with additional options to get the job done. It’s almost like when cordless tools entered the consumer tool market. You could take the cordless tools anywhere and use them in new and exciting applications. But there was a key drawback that I’m sure you remember. The early cordless tools had a limited effective power range. Over the next decade or two, battery technology improved and there were fewer power related drawbacks to going cordless.

Evolved Programmable Network_SP

A few similarities exist in the network functions virtualization (NFV) space. I Read More »

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An Internet that spurs not strangles innovation

The world we live in today is one where people, process, data and – increasingly – things are connected as never before. The Internet of EveryThing (IoE), is driving the most dynamic area of innovation, creating new business models, economic, social and environmental sustainability and also has fantastic potential to improve our quality of life.

Just imagine: a blind man gaining independence because his once ordinary walking stick is able to communicate with his other senses through sensors, vibrations and GPS technology that guide him through the city maze. Imagine a connected car informed of traffic jams by analyzing traffic patterns and adjusting traffic light operations. Or think of smart manufacturing facilities that cut costs by reducing waste and energy consumption. And these are just the possibilities being realised today. Imagine what the future will look like in 5, 10 or 25 years from now.

We have barely begun to scratch the surface of what’s possible. We don’t know what applications and services will shape the Internet’s future. To continue innovating, we need the Internet to remain open, giving the most creative among us the chance to experiment with daring new ideas.

We also must be sure not to stifle the very innovation that we seek to encourage. If we do so, it could inhibit growth and new ideas alike. This is why today we should focus on putting in place the right policy principles that will further develop this new Internet of Everything.

In policy debates, net neutrality is often understood to mean that all bits should be treated equally, regardless of whether it’s a text, email, picture or video. While at first sight this may sound reasonable, the truth is that such a strict net neutrality principle would become an innovation straight-jacket. It would require us to re-design the Internet as we know it, doing away with tools that have become essential to its success.

Different Internet services have different requirements. It doesn’t really matter if an email arrives now or a second or two later. But if you’re dealing with real-time applications – such as video communication, or buying stocks or monitoring vital signs, delays can have an incredible impact on user experience and effectiveness.

So the truth is that you have to manage internet traffic to make sure that the data that has to get there immediately – does.   This short video explains what traffic management entails and why it is so important.

Reasonable traffic management is so deeply embedded in the Internet’s core structure that it could not operate smoothly without it. This is the case already with the traffic loads of today, let alone in the future. Because management and scheduling are a crucial part of the Internet, we are closely following European efforts to formulate new net neutrality legislation. Cisco believes such legislation has merit but it could also have sweeping implications for reasonable traffic management and new services that would ultimately stifle rather than encourage innovation on the Internet. These implications can and should be avoided.

Fortunately it seems there is an increasing realisation among some policy-makers that net neutrality legislation, necessary as it may be, shouldn’t eliminate reasonable traffic management altogether. That approach would undermine rather than improve the quality of users’ experience. One way to establish net neutrality rules that prevent bad behaviour while maintaining a role for traffic management is to pursue a two-thronged approach where a line is drawn between the types of bad behaviour we do not want to see in the Internet and the necessary and reasonable traffic management techniques that ensure the fast, reliable and scalable networks that we all rely on, and need as consumers.

Equally, there is an emerging consensus that we must avoid overly prescriptive attempts to cast into law lists enumerating or narrowly defining the types of services other than internet access services that we deem “deserving” of specific levels of quality. Such attempts are bound to get it wrong in many cases. Moreover, any such neutrality law would quickly be outpaced and overtaken by reality. Building a Procrustean bed for the Internet is not the way towards a more vibrant digital economy in Europe. It is not necessary to have these prescriptive definitions and conditions on innovation as long as we maintain strong and clear safeguards to ensure an open and reliable Internet.

As the debate on neutrality in Europe enters its final phase, with trialogue negotiations starting this week, we hope the European Parliament will take a fresh look at the issue and we achieve a balanced final outcome.

In essence, the legislation we need should be sturdy enough to hold things together, but flexible enough for Internet entrepreneurs to continue adding new applications and services.

Just think about what the Internet looked like 15 years ago: a handful of wires, noisy connections that would bump you off from time to time, and streaming would be as quick as a snail. We have made huge strides, and we can continue towards an Internet of Everything – a smarter, more productive and efficient way at approaching life. But to get there, striking the right balance in Europe’s regulatory framework is more crucial than ever before.

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