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To Compete in the Retail Revolution, Mobility and Analytics Are Critical

Today, mobile devices are everywhere — and vying for the attention of just about everyone. On a train, in a café, or in the park, people are gaming, connecting with far-away friends, and watching TV shows.

Increasingly, they are also researching, browsing, and buying products.

Such tech-savvy mobile shoppers are driving a retail revolution that has left many brick-and-mortar retailers scrambling to catch up. In fact, mobility and apps have created an industry disruption similar in scope to what we saw with e-commerce in the late 1990s and early 2000s.

For many traditional retailers, the stakes are high and the challenges daunting. However, I see tremendous opportunities. Read More »

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The Center for Digital Business Transformation: Helping Our Customers Thrive in a Digital World

Powerful technology trends including, social, mobile, cloud, and Big Data are converging, creating unprecedented “digital disruption.” We are in a unique period of time where business and technology leaders have the opportunity to create new value and win market share by leveraging the advantages of a hyper-connected world.

Agile competitors with better business models seemingly emerge overnight. Ingrained ways of thinking and working make changing to an innovative culture painfully slow. Needed talent and resources lie outside the four walls of the organization in a wider ecosystem of capabilities. And while technology challenges abound as we confront the future, people and process changes are even more vexing for most organizations.

So how do executives keep their companies from being added to the growing heap of once venerable brands that didn’t transform fast enough?

It’s not easy.

According to Gartner research, by 2020, 75 percent of companies will be a digital business or will be preparing to become one, yet only 30 percent of these efforts will be successful. The number one reason companies fail to transform is because they don’t re-imagine and reinvent the business from top to bottom before they begin.

Read More »

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#CiscoChat Explores Mobile Data as the New Currency for Today’s Retailers

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The Internet of Everything (IoE) is driving remarkable change and opportunities across nearly all industries. But few are as visible — and rapid — as the upheavals affecting retail. Today, retailers aren’t just competing with the store across the parking lot. Industry leaders face an expanding universe of mobile and virtual shopping possibilities vying for the attention of their customers.

Recent Cisco retail research shows that mobile commerce grew forty-seven percent in 2014 (Q2), far out-pacing e-commerce (ten percent) and total retail overall (three percent). And it’s not surprising, with nearly every customer using a mobile device of one type or another. Today, eighty percent of shoppers are now classified as “digital.”

Mobile devices — and rapidly evolving customer behaviors — are driving expectations for more fully optimized digital shopping experiences, in store and out. Yet traditional retailers have an exciting opportunity to meet this demand by offering hyper-relevant customer experiences that drive savings, efficiency, and engagement. In merging the best attributes of the physical store with the online experience, brick-and-mortar retailers can drive their own industry disruption. Read More »

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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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Why We Need More Tech Talent to Digitize the World

This blog was originally published on the Huffington Post ImpactX.

Digitization. This topic was top of mind for many of the 2,500 world business and government leaders at the recent World Economic Forum annual meeting in Davos, Switzerland. Digitization is the full-scale adoption of computer- and Internet-enabled technologies by consumers, businesses and governments; it is important because it can grow economies and create jobs.

In fact, according to the 2013 Global Information Technology Report, adoption of such information and communication technologies (ICT) provided a $193 billion boost to world economic output and created 6 million jobs in 2011. Read More »

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