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The Internet of Everything Will Help Solve Problems That Lead To Recalls

Product recalls can be a headache for customers and consumers, but a financial nightmare for manufacturers.

Just look at the auto industry. An air-bag recall will cost one manufacture up to $235 million. While a gas pedal problem will hit another manufacture with upwards of $2 billion. Yes, billion.

But recalls aren’t isolated to the auto industry. Food. Toys. Tech. Virtually no industry goes untouched.

And it’s not just the size of a recall that matters. It’s the damage to your brand’s reputation. Plus, recalling a product is more complex than ever.

Here’s why. Read More »

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Manufacturing Customer IoE/IoT Globe Trotting: Part 1

From my home in North Carolina to San Diego, to Atlanta and all the way to Greater China—Shanghai, Shenzhen and Taipei—throughout April, I am presenting at several Manufacturing industry, Supply Chain executive, and Internet of Things (IoT) regional events, along with visiting all types of manufacturing customers. Earlier this month, I was at a customer advisory where we met with industrial thought leaders eager to share experiences (see Tony Shakib’s blog, “The Digital Factory: Real Solutions and Real Outcomes”). In the meantime, several of my colleagues exhibited Cisco industrial solutions this past week at Hannover Messe in Germany. Across the globe, manufacturers are wrestling with how to capture the opportunity and value associated with IoT and Internet of Everything (IoE) strategies. The good news is that the industry is thriving, alive and well and at the forefront of IoT adoption.

At the IoT Regional Forum in Atlanta last week, I had the opportunity to meet some manufacturing companies from the region and hear first-hand the challenges and address questions they had regarding automation and networking and the convergence of IT and OT, from technology to culture to organization. What I hear repeatedly are questions on how to tie together the various islands of automation and information that exist throughout most factories and across manufacturing enterprises. In addition, the lack of one integrated view results in delayed decision-making and responses to issues and problems that arise, and inhibit the introduction of new products and business models.

Often, we will assist our industrial customers with this IT/OT convergence by recommending a pilot or proof of concept approach to adopt wired-and-wireless networking architectures for use cases that demonstrate quick results and impact, and then more broadly adopt the technology across that and other plants within the enterprise. Interestingly, ARC analyst Greg Gorbach recently wrote up a blog proposing a “Let’s Just Try it” approach in profiling our customer Stanley Black and Decker.

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The Digital Factory: Real Solutions and Real Outcomes

I recently participated in a Cisco advisory board meeting attended by some of our leading manufacturing customers. There was a lot of discussion about the tough challenges the industry is facing. Flexibility, agility, and managing costs were hot tony shakib pic 1topics. Traditional manufacturing environments with manual processes, independent systems, and siloed data create a lack of visibility into real-time operations and result in delayed responses to quality issues and inventory waste. Many manufacturing organizations are starting to take their first steps towards becoming digital. Let’s take a look at what that means and why making the transformation to a digital factory is the next wave of evolution. Read More »

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IoT at Work: Connected Vineyards and More

The convergence of Operational Technology (OT) and Information Technology (IT) is becoming more important now than ever – and that sentiment was heard loud and clear at last week’s complementary Cisco Live Melbourne and Rockwell Automation ConnectED events. Held for the first time under the same roof, the two events provided a unique opportunity for end users to learn how to accelerate industrial business performance in a joint experience.

Attendees to both events alike enjoyed seeing examples of industrial technology in action such as the Connected Vineyard demo, which I had the pleasure of demonstrating to customers in the Cisco Live World of Solutions.

In the demo, we discussed how to add business value on top of sensor information. For example, the images below show sensor information in an easy-to-read dashboard that can help us troubleshoot potential issues before they affect the bottom line.

IoT at Work Image 1

IoT at Work Image 2

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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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