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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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Solving the Manufacturing Workforce Crisis of 2030

Sir James Dyson, British inventor, industrial designer and founder of the Dyson Company once said, “Manufacturing is more than just putting parts together. It’s coming up with ideas, testing principles and perfecting the engineering, as well as final assembly.” He’s absolutely right; manufacturing is more than just manual labor on a shop floor somewhere. Today’s manufacturing jobs require a new wave of skilled employees, but where are they?

It amazes me to think about how far manufacturing in the U.S. has come since the days of the industrial revolution and all the way up through the 1950’s. Fast forward to today and you’ll see a manufacturing industry that now relies on advances in technology to drive production and help fuel a global economy. In fact, my colleague Chet Namboodri in his blog ‘Manufacturing Predictions for 2015’ mentions that advancements and adoption of industrial robotics will rapidly advance across all manufacturing segments. However, the longstanding perception of manufacturing has been one of harsh work environments, something that is no longer the case in many manufacturing plants. This outdated perception must be laid to rest and changed amongst a new, younger generation of tech-savvy workers because it’s discouraging qualified candidates from pursuing lucrative careers in manufacturing and directly impacting production in the U.S., a trend that could cause a largely diminished manufacturing workforce by 2030.

The New Manufacturing Environment

Overall, the manufacturing industry is more productive, efficient, and poised for new technological advances made possible by the Internet of Things (IoT). In the 1950s, long, tedious business and production processes created a labor-intensive manufacturing industry. Employees worked in difficult and hazardous environments every day. But as technology advanced, so did manufacturing. A lot of manufacturing jobs are no longer traditional assembly line roles and an industry once driven by manual labor is now moving forward at a much faster pace thanks to machine automation, information technology, and increased plant floor communications. Operators now require advanced knowledge of computers, software, science, and math to program machines that control manufacturing processes.

The manufacturing industry in the U.S. faces a workforce crisis as a widening skills gap is created as many workers reach the age of retirement. If current trends continue, U.S. manufacturers will be unable to fill 2 million manufacturing jobs by 2025, due to a worsening shortage of required skills, according to a report by the Manufacturing Institute and Deloitte. Today, there are really good, well-paying positions that need to be filled across the manufacturing industry. Many students and new graduates fail to consider manufacturing on their quest to find a career path – something that must change. Manufacturers must begin engaging local high schools and trade schools to enhance pipelines of Science, Technology, Engineering and Mathematics (STEM) trained graduates and developing strategies to attract qualified candidates as they enter the workforce.

Girls For IoT Innovation

Attracting the Next Generation of Manufacturers

The next generation of workers expects to always be connected. They have multiple mobile devices and interact with peers in new ways all the time. This inherent skillset can be a great asset to the manufacturing industry and with the advance of IoT, there will be a strong need for a STEM ready workforce. To generate interest in STEM and perhaps a career in manufacturing, educators must start early. Starting in elementary school, up through high school and college, career relevant math, science and computer instruction should be made available to a wider audience of students across age groups, demographics and geographies.

Not only are more skilled and tech-savvy workers needed put part of the manufacturing skills gap is the result of a lack of women in manufacturing. In fact, women have become an underutilized resource in STEM careers in general – something else that also must change. Pa. Women make up half of the U.S. workforce, but less than a quarter of manufacturing (STEM) jobs are held by women. How can manufacturers attract women to the industry and fill the current skills and gender gaps?

It starts with education. We need to educate young women about what a career in manufacturing is actually about, without continuing the negative perception of work environments. We can do this by supporting STEM education with programs that give kids practical hands-on experience. This is best accomplished when manufacturing industry leaders and organizations reach out to students and new grads, and encourage government leaders to invest in the right kind of training experiences in school curriculum.

IoT World Forum Young Women’s Innovation Grand Challenge

Cisco is helping to educate young women about STEM careers through the IoT World Forum Young Women’s Innovation Grand Challenge . The initiative is a global innovation challenge open to young women between the ages of 13-18. The aim of the challenge is to recognize, promote, and reward young innovators as they come up with new uses for Internet of Things technologies and is open now through May 18th, 2015. You can learn more about the IoT World Forum Young Women’s Innovation Grand Challenge here.

Whether next-generation workers seek a traditional college experience or vocational schooling, students must be exposed to the various options and training opportunities that are available in the manufacturing industry. Organizations should position themselves as go-to resources for prospects looking for jobs in manufacturing. They should offer internships and be able to connect future employees to employers. Hosting workshops, seminars, and conferences are also good forums to make connections.

Through these types of experiences, we can allow students and educational professionals to build passion for the manufacturing industry. In turn, the necessary skillsets will follow. The next-generation techniques and technologies on the plant floor will entice the new age of tech-savvy students. We need solutions now for the workforce of tomorrow and we are the advocates of manufacturing’s next generation workforce. Let me know your ideas in the comments below on how we can all make a difference on this issue.

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Drinking to the Future – Family Owned Beer Distributor Embraces IoE to Improve Operations

The warehouse today isn’t the same as 15 years ago. Cisco’s Internet of Everything (IoE), connecting people, processes, data and things, has revolutionized manufacturing, distributing and day-to-day operations. Del Papa Distributing, is a family-owned beer and beverage distributor based in Texas City, TX. The connected devices, sensors and nodes, and the data they generate, have helped transform our business giving us greater insight into our warehouses, buildings and delivery vehicles. Read More »

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