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
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 of data analytics should consider the following:
- 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.
- 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.
- 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.
Tags: #MFG, Cisco, Internet of Everything, IoE, Manufacturing, operations, Real-Time Location System, rtls
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.
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.
Tags: #MFG, Cisco, Internet of Everything, IoE, Manufacturing, operations, Real-Time Location System, rtls
Our world is rapidly connecting people, process, data, and things in ways that were unimaginable just a few years ago. The Internet of Everything (IoE) is at the heart of this transformation.
As more dark assets are “lit up,” organizations will receive an influx of valuable data that can lead to insights, knowledge, and opportunities. However, much of the data generated will be just beyond reach, frequently referred to as “dark data.” Read More »
Tags: Big Data, dark assets, dark data, Internet of Everything, internet of things, IoE, IoT, Joseph Bradley
Today’s retailers face a hard truth: their customers have embraced digital technologies faster than they have.
But I believe that retailers have an opportunity to elevate the shopping experience in exciting new ways. By integrating the digital and the physical — in effect, merging clicks with bricks — retailers can capture new revenue, along with loyal, satisfied customers.
First, retailers need to understand a changed landscape. In only the past five years, mobility, analytics, e-commerce, and other technologies have had a profound effect on the entire shopping experience, putting the customer in charge. Traditional retailers must respond with highly relevant experiences that drive greater efficiency, savings, and engagement.
Recently, I shared some thoughts on this topic with Cisco, both for a new global study on retail trends and also in a podcast titled The Last Checkout Line. The U.S. and U.K. findings of Cisco’s study were released early this year and showed some surprising results. As Cisco’s paper emphasized, customers demand a hyper-relevant shopping experience, in which past shopping histories, current contexts, and future plans drive real-time interactions with the retailer, in-store or out.
Some retailers are already excelling in these areas. Sephora, the French cosmetics franchise, is a good example of a retailer that is offering digital and mobile experiences in-store, enabling customers to interact and discover products in new ways while also bridging a seamless connection with the online experience. Other retailers have leveraged analytics to ensure stock availability for individual customers, integrating with other store locations to ship products to the customer’s home or a more convenient store location.
I believe that all retailers will need to assess their current capabilities. The mobile experience in the store is essential, both to interact with customers on a deeper level and to empower in-store associates with real-time contextual information. This requires enabling Wi-Fi and expanding bandwidth to accommodate new digital experiences.
Analytics, of course, is critical to understanding customers, in-store and out. Retailers will need accurate information at all stages of the shopping journey. That includes accurate data on inventory and customer browsing habits; there is no faster way to disappoint a customer than not having the item he or she expects, or to make the customer wait.
But retailers will also need to be sensitive to how much information customers are willing to share. There’s a fine line between an appropriate “opt-in” incentive and one that is perceived to be intrusive. If retailers get it right, customers will see the clear benefits and value in sharing their data.
As Cisco’s retail paper stressed, technology has accelerated changes in customer behavior, and traditional assumptions around age demographics are outmoded. Gen Y can enjoy the store experience, for example, while older customers may be highly connected and mobile. Retailers will need flexible, future-proof infrastructures that enable them to respond to ever-shifting customer demands.
I see the winners in retail succeeding on three key fronts:
- They will provide breakout innovations that set market expectations for new kinds of customer interactions, new ways of sorting and tracking products, and new ways of fulfilling customer needs. These will be highly relevant and situationally aware; that is, aligned with customers’ current contexts.
- They will have flexible systems and architectures in place to support these new kinds of interactions, and adapt to changes in customer behavior.
- And they will ensure a consistent, seamless experience, whether the customer is engaging via email, call center, online, a mobile device, or with an in-store customer associate.
In the end, winning retailers will shift their focus from short-term profits to a customer-centric strategy. After all, the more relevant, streamlined, and seamless the customer experience, the more likely it is that those customers will return — again and again.
Tags: analytics, Cisco, Cisco Consulting Services, connected retail, data, digital, hyper-relevance, Internet of Everything, IoE, IoT, Leslie Hand, retail, shopping
“Let the buyer beware” is a sentiment that dates back well before consumer protection and truth-in-advertising laws. Yet, the issue of trust continues to permeate all areas of society today. A few weeks ago, I wrote about the “trust cliff” that affects the amount of information consumers are willing to share with retailers in order to have more relevant interactions.
Now, a new Cisco study on retail banking in 12 countries reveals a different kind of trust problem: consumers are getting less value than they expect from their banks, and this “value gap” is impacting customer trust.
The global financial crisis of 2007-2008 greatly damaged consumer trust in financial institutions, and brand equity has fallen along with it. In 2009, one year after the financial crisis, the world’s top 500 brands saw the value of their brands drop by 32 percent. For many banks, their brand value has yet to recover from pre-crisis levels.
But the roots of distrust go deeper than that. Our study shows that there is a fundamental disconnect between banks and their customers, and many customers no longer look to their banks to help them meet their financial goals. In fact:
- 43 percent of customers say their bank doesn’t understand their needs
- One in four would choose another provider for their next account or service
- Only 40 percent of respondents worldwide turn to a financial professional for advice, and of these, 28 percent believe the advice is ineffective
Meanwhile, a growing cadre of disruptive “non-bank” innovators is exploiting this value gap between banks and their customers. They range from technology companies such as Apple and Google, to retailers such as Amazon.com and Tesco, to mobile and digital-only banking services, payment companies, and automated investment services. A surprising 80 percent of consumers surveyed said they would trust a non-bank for their banking services. In eight out of the 12 countries surveyed, more consumers would actually trust a non-bank than their own bank.
Read More »
Tags: analytics, banking, CCS, Cisco, Cisco Consulting Services, data, digital, Financial Services, hyper-relevance, innovation, Internet of Everything, internet of things, IoE, Museum of Lasts, trust, value gap