Penn State Beginning $1.5 Million Smart Manufacturing Project


In the article Researchers to Begin $1.5 Million Project for Manufacturing Industry, the author explains how a teams from Penn State, Case Western Reserve University, GE Global Research Center, and Microsoft are collaborating on a smart manufacturing research project.  The project aims to help machines internally recognize signs and wear and fault conditions through the use of cloud-based wireless sensing and prognostic system for monitoring machinery health conditions.  The overall goal is for the machines themselves to make intelligent decisions about fault detection and proactive maintenance scheduling, hence smart machines.  The use of the IoTs, cloud computing big data analytics, and machine learning allows this smart ecosystem to exist.

The project brings experts from across academia and industry together.  On the academic side there are experts from the fields of industrial and manufacturing engineering and mechanical and aerospace engineering.  On the industry side there are experts from GE’s industrial internet of things global research center and senior managers from Microsoft.  Penn State will be hosting the project, but the project is 50% funded by Digital Manufacturing and Design Innovation Institute (DMDII).  DMDII, which was founded in 2014, is funded by the US department of Defense along with number of other entities with the purpose of digitally transforming American manufacturing.

The plan is to begin in February of 2017 and run the project for 18 months.  The actual workings of the project will be done in FAME (Factory for Advanced Manufacturing Education) which is a 10,000-square- foot integrated high-bay laboratory for teaching and research.  Senior Research Associate Wu stated the overall purpose of the project as “The overall goal of this research is to establish a generic framework for real-time process monitoring, diagnosis and prognosis for smart manufacturing using cloud computing and big data analytics.”  It’s easy to see that with lofty goals as described above, this smart manufacturing project could help transform the industry.  Only time will tell how successful the project becomes, and possibly even more important, how the results are viewed and accepted by the greater manufacturing industry.


Do you believe the team will be successful at creating a generic framework that can be used by a majority of the manufacturing industry?

Do you think the project will stay within budget and finish on time?

What are your thoughts on the fact the Department of Defense is funding part of this project?


The Edible Elephant in the Room – Big Data Analytics


In the article Big Data Analytics: Eating An Elephant, the author explores what big data currently means to supply chain management and some of the ambiguity big data brings to business.  The metaphor the author first introduces is that big data is like an elephant – it can’t be swallowed in one bite, and no matter where you start, you will be eating for a while.  In this sense, big data solutions can be daunting to begin, and once started, must be continuously evaluated and updated to be efficient.  A recent survey of executives found that 81% thought big data was “disruptive and important,” which is a curious way to describe big data.  As seems to be the trend through many articles and current opinions about big data, many think it’s useful, some are willing to try it, and a few are actually reeking the benefits from it.

One common assumption in the supply chain world is that big data has the potential to solve complicated problems.  An example illustrated in the article is that of Dell’s customer support function.  Dell notes that service information for one customer can encompass 25,000 log files and up to 400 GB of data.  Through identifying the important variables and the use of big data analytics, Dell has seen an 84% improvement is helping solve customer issues faster.  Its noted that Dell had a specific purpose in mind as well as a good understanding of the data it already had.  Dell wasn’t fishing for answers it wasn’t sure existed.  This lesson in big data is simple: have a problem and purpose in mind when analyzing the data.

The author further drives this point home as the article goes on.  As he points out, there’s no reason to create a giant data repository and then hire individuals to analyze the data, looking for hidden trends.  It’s about asking the right questions to begin with.  An example of this type of corporate behavior can be found at Nestle.  Nestle has created a hierarchy for its use of big data to understand demand.  First, baselines are generated, then casual modeling is done, then advanced modeling for decision making.  Each successive step uses more data and more modeling.  This structured approach is allowing Nestle to create value from the big data is has.


Do you think the hindrance many companies see to using big data is that they believe big data analytics involves data scientists examines huge data sets?

Based on the feeling that big data is “disruptive and important,” do you think there is some frustration with the digital revolution?

When will we or are we already seeing the lack of adoption of big data techniques hurting companies?

US Manufacturers are Falling Behind in The Digital Revolution


In the article, US Manufacturers, Too Slow to Adopt Industry 4.0: BCG Study, the author is investigating a Boston Consulting Group Study that has shown US Manufacturers are falling behind the digital revolution and not fully experiencing the benefits of Industry 4.0.  90% of manufacturing companies survey believed that Industry 4.0 technologies, including big data analytics, can improve productivity, but manufacturers overall are only adopting the digital processes one at a time or slowly.  The entirety of Industry 4.0 needs to be implemented over time to realize the strong productivity gains, and but it appears that manufacturers lack the understanding to start proper implementation.  Without a clear goal or starting point, the technology is not being adopted nearly as quickly as it should be, and thus savings are not being experienced as they should be.

The survey conducted by BCG included 380 US-based manufacturing executives and managers, and the main results were insightful.  Overall, 53% of respondents said transition to Industry 4.0 is a priority, and interestingly, that number jumps to 80% of respondents in cost sensitive industries such as oil and gas and semiconductors.  When it comes to productivity, almost all respondents believe Industry 4.0 practices can improve productivity (89%), but few believe it can increase revenue, only 28%.  Implementation of industry 4.0 is all over the map, with cybersecurity being on the top at 65% and additive manufacturing being one of the lowest at 34%.  Finally, lack of adoption seems to be a two-fold problem.  The biggest issue to beginning implementation, per the survey, is lack of strategy.  Also, 40% of respondents said changing culture was a roadblock to implementation.

BCG appears to understand the plight of manufacturers as they have launched what is called the Innovation Center for Operations (ICO).  The ICO includes model factories in the US and Europe where BCG’s clients can observe and understand Industry 4.0 processes.  The hope is that through the ICO initiative, manufacturers will gain the necessary insights to begin the digital transformation, and understand the strategic aspects.  It seems that the only manufacturers that will don’t benefit from Industry 4.0 will be the ones that intentionally ignore it or fail to take advantage of the learning opportunities available.


Do you believe it will take new talent being hired to fully implement industry 4.0?

Do you think part of the problem with implementing industry 4.0 is not being to quantify the savings?

Do you like the idea of BCG’s ICO?

Big Data is Useful for Lean Manufacturers




In the article Lean and Big Data: How Manufacturing Is Getting Even Leaner, the author is examining case studies of lean manufacturers using big data to become leaner and more efficient.  According to McKinsey & Co., big data could be worth tens of billions of dollars for Lean manufacturers in the automotive, chemical, FMCG and pharmaceutical industries.  According to the article, a Manufacturing Execution System can generate big data on accuracy of downtime, waste, and WIP which is data that is traditionally used in lean systems.  The overall goal of any lean manufacturer is improved processes specifically through reduced waste and increased efficiencies.  It’s beginning to look like big data can be a great asset in this lean journey.

The cases described outline how big data has become so useful to manufacturers.  One instance was a two-billion-dollar company that uses big data analytics to analyze and understand the habits of repeat customers.  Another instance is that of Intel using predictive analytics to reduce the number of quality control tests on each processor, saving them 3 million dollars in manufacturing costs on that line of processors.  One final case is that of a steel manufacturer using the Monte Carlo simulation with historical data to identify previously unknown bottlenecks.  After the subsequent process improvements, the steel manufacturer saw a 20% increase in throughput.

Finally, big data can help manufactures form answers to two of their most complex questions –

Who is buying what, when, and at what price?

How can we connect what consumers hear, read and view to what they buy and consume?

It all boils down to customer loyalty and sales.  The managers helping make this transformation see the opportunity for lean data to improve their business, and they are taking advantage of it.  They know what data to use and how to use it, which is translating to significant cost savings and separation from the competition.  Those who don’t employ big data are seriously becoming at risk of being left behind.


Can you think of a reason why a lean manufacturer wouldn’t want to employ big data?

Do you think the biggest obstacle in using big data is know what data to actually use?

Do you know of any cases where useless big data was used poor decisions were made?

Study Shows Manufacturing Executives Well Aware of Big Data Benefits

In the article, Big Data Analytics Can Benefit Manufacturers, the author uses data from a recent Honeywell study to show how manufacturing companies are responding to and using big data analytics to enhance their processes.  The Industrial Internet of things is providing significant amounts of data that many manufacturers are taking advantage of.  In particular, manufacturers are using analytics to prevent unscheduled downtime and equipment breakdowns.  Surprisingly, 33% of respondents of the 200-executive survey said they do not plan to invest in analytics, mostly due to lack of funds or lack of  understanding of how the analytics can benefit their company.

One of the most surprising results of this Honeywell survey was that 42% of respondents admitted to running their equipment harder than they should.  Consequently, big data analytics to prevent machine breakdowns is very important, and 70% of respondents said analytics can prevent breakdowns.  Without the proper analytics, manufacturers run the risk of falling behind competitors.  With so much on the line, it is not surprising that 80% of senior executives surveyed in a different executive recruiting firm poll said that investment in the digital transformation is critical.

With a majority of senior level executives understanding that the investment in the digital transformation is so important, the next question is how will a company manage this transformation.  Many companies do not have a Chief Digital Officer, or CDO, so the responsibility moves to the rest of the C-level board to oversee the transformation.  This strategy can bring about issues since the transformation is happening so fast that high level managers already tasked with significant decision making will not put adequate time toward the company’s digital needs.  An ideal situation would be for a company to have a CDO, and preferably a CDO with experience in IT and/or sales.


Do you believe companies should invest in a CDO?

How bad will the consequences be for companies that don’t invest in big data analytics?

Which college graduates will be most in demand now that this digital transformation is happening?

The Global Manufacturing Industry is Changing Due to Big Data



In the article Exploring the new face of manufacturing – Industry 4.0, the author explores how the manufacturing industry is quickly approaching a significant change, coined Industry 4.0.  Industry 4.0 involves big data, improved data analytics, machine-to-machine communication, advanced robotics and 3-D printing.  These changes are happening across the world from Europe to Asia, and there is no sign of letting up.  But with these changes comes a serious amount of data, 4.4 zettabytes by 2020, and the only way to analyze this vast amount of data is big data analytics through cloud computing.

This unique IT infrastructure required to keep and analyze this data have made cloud computing an invaluable tool.  Manufactures need a system that is rapidly scalable to make use of the data pouring in from various channels: sensors in their factories, inventories, raw materials and other segments along the supply chain.  Cloud computing allows manufactures to analyze real-time data to understand product status and quality thus providing a product with less defects.  Cloud computing has taken away the hassle of having to buy physical storage devices while also allowing manufacturers to use the cloud’s different availabilities, capacities, and functionalities.  The data analytics is becoming simpler and more useful quickly which is allowing manufacturers to learn more from the data then ever thought possible.

An interesting separation of cloud computing is the public and private cloud.  The public cloud allows remote manufacturing facilities to leverage modern IT and communications systems without having a large team onsite.  Public clouds also allow customers and partners along the supply chain to access information easily.  The private cloud is best for sensitive data and intellectual property.  In fact, the article speculates that hybrid clouds will emerge that allows manufacturers to have the best of both worlds.


Do you believe the cloud is secure?

Are cloud based companies doing enough to keep the cloud secure?

Will a hybrid cloud really offer enough security for sensitive information with regard to the outside world as well as employees?


Manufacturers Are Experiencing the Benefits of Big Data

In the article and exhibits presenting in How Software and Big Data are Changing Manufacturing in the United States, the author lays out a vast array of benefits manufacturers can experience when they incorporate big data analytics into their processes and decisions.  The manufacturing sector represents just 12% of the US GDP, but the actual dollar amount is $1.2 trillion of exported goods.  Furthermore, the US manufacturing sector has increased output 30% since the global financial crisis of 2009.  Any advantage manufacturers can gain going forward translates to incremental dollars in sales and cost savings.  Big Data is the key that is allowing manufacturers to save money, grow, and compete in an international marketplace.

Some of the distinct advantages big data gives manufacturers are in productivity, product development, and simulation based approach test new products.  The conclusions drawn by an Ohio University study suggest that manufactures could boost productivity by 30% by utilizing more flexible production techniques along with big data analytics.  Manufacturers could experience up to a 50% decrease in production development costs which translates to a 7% reduction in capital assets.  Another interesting path big data paving is a simulation based approach to testing new products.  An example of this is Volvo integrating customer data into its forecasts to understand if a new design will appeal to customers.

Finally, big data also creates value in the supply chain and is spurring change in the management of manufacturing.  In the supply chain, big data is predicted to be able to save 15-20% on transportation costs and help reduce inventory by 20%-30%.  Big data is changing the way manufacturing enterprises are managed as well.  Big data is creating a huge number of jobs, estimated at 1.5 million.  Managers now must understand what data is relevant and how to use this relevant data.  Not adapting to the big data world will result productivity decreases and manufacturers falling behind competitors who do utilize big data.  Its management’s job to become familiar with big data techniques and hire those who specialize in the data so that companies can stay ahead of the curve.


Will big data finally allow manufacturers to bring manufacturing jobs back to the US?

Do you think the future of big data is in the simulation side?

Do you think that current managers can differentiate between what data is useful and what is not?