McKinsey Study Shows Big Data is Affecting Numerous Industries Including Manufacturing

In the article How McKinsey’s 2016 Analytics Study Defines the Future of Machine Learning, the investigates a McKinsey study released in January of this year that shows how far big data and analytics have come in the previous 5 years as well as what to expect in the very near future.  The main insights gleaned show that the use of big data is growing in numerous industries, and that the key realizing the value of big data is integration and adopting from the top down.  For manufacturing specifically, machine learning leading to preventative maintenance and predictive analytics is one of the most powerful advantages that has emerged.  Unfortunately, just about every industry examined in the paper has still has not captured more than 50% of the potential value from data and analytics.

The original paper released by McKinsey released in 2011 speculated that manufacturing would find the greatest potential value in data and analytics from possible drops of 50% in product development cost, 25% lower operating cost, and 30% gross margin increases.  As of 2017, the new McKinsey paper shows that manufacturing has only captured 20-30% of this total value with the main issues being siloed data in legacy IT systems along with skeptical and sometimes unsupportive leadership.  With regard to one of big data’s most powerful uses, machine learning, manufacturing stands to realize significant value from machine learning.  In the areas of real-time optimization, predictive maintenance, and forecasting, there is definitely some value to be had in regard to machine learning.  In the areas of strategic optimization, predictive analytics, and understanding data trends, there is substantial value to be had.

Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.  Unfortunately, manufacturing as a whole has lagged in adoption with a few advanced players capturing a significant amount of the value.  Digging deeper to see exactly where the money has been saved, analytics has benefited the supply chain and production aspects of manufacturers, but only the small groups that have adopted the technology.  McKinsey estimates that only 5-10% of manufacturers have used analytics for financial savings in the supply chain and production aspects.  In the supply chain, the cost savings has come in the form of 10-15% reduction in product cost, and in production side, the cost savings has come in the form of 10-15% reduction in operating costs.  Overall, the main hindrances to more big data implementation is senior management involvement and designing and implementing the appropriate organization structure to support data and analytics.

Do you believe big data analytics adoption accelerate over the coming years?

Do you believe the results of the study are completely accurate?

How far away are we from a time where if manufacturers are not using analytics, they will not be able to compete in the market?

http://www.cloudcomputing-news.net/news/2017/jan/05/mckinseys-2016-analytics-study-defines-the-future-of-machine-learning/

How Analytic is transforming Supply Chain

The article (Forbes, Bernard Marr, April 22, 2016, http://www.forbes.com/sites/bernardmarr/2016/04/22/how-big-data-and-analytics-are-transforming-supply-chain-management/#11afda964c2d) talks about how analytic is transforming supply chain management.

There are many applications can be developed for supply chain utilizing analytic. To manage inventory effectively unstructured data can be analyzed with technology such as digital camera analytic to monitor stock level. Forecasting can be benefited by machine learning algorithm with predictive modeling. Distribution center and warehouse can be operated with minimum human intervention.

How visual analytic will change supply chain across manufacturing? How analytic will change supply chain in the transportation industry? How analytic will change supply chain in the retail industry?

Looking Forward at Smart Manufacturing and Manufacturing Industry Changes

Manufacturing has navigated a roller coaster of a landscape over the past 10 years, and the future appears to be ever-changing.  Whether it be the election of a new president in the US who promises to increase manufacturing jobs or the increasingly important technologies such as the IIoTs or big data analytics that make up the Fourth Industrial Revolution, manufacturing is transforming.  This transformation is from legacy, antiquated manufacturing techniques to what is called smart manufacturing.  Smart manufacturing is making data more and more readily available and thus the entire manufacturing process is benefiting.  And as many other articles have stated, any manufacturers that do not invest in this upcoming technological revolution risk being overtaken by competitors that do.

Though the switch to the digital data transformation isn’t quite do or die just yet, it certainly will be soon.  A report from the Conference Board of Canada from earlier this year found that some manufacturers are working with 20-year-old software.  It would appear that some in the industry do not feel that making that adapting to the newest technology is necessary which could be disastrous.  There is a golden opportunity for small to medium size manufacturers to experiment with smart manufacturing solutions as firms of these size have nimbleness and ability to make digital changes quickly.  Furthermore, applying digital technology can be as simple as smart tags for better real-time tracking of inventory or cloud technology to aggregate and transfer information across the supply chain.

Some other simpler but advantageous smart technologies that small to medium sized firms can implement are mobile technologies such as smart phones, smart tablets, and smart watches as well as IIoTs.  Mobile technologies are relatively easy to implement and now affordable, and they allow managers to record and share real-time data easily.  The IIoTs is also catching on quickly with 40% of firms surveyed saying they already have some form of the IoTs in place.  The IIoTs can be used to optimize processes as well the key benefit of preventative maintenance.  Despite these advantages, nearly one-third of declining manufacturers expect to decrease their IT expenditures in the next year, which means firms in this category will be hitting serious roadblocks in the very near future.

 

Do you think the lack of investment in digital technologies could be due to management’s fear that these technologies might actually lead to a decrease in the need for actual human workers?

Does an decrease in IT spending absolutely mean that a company will not be investing in any smart technologies?

Do you think that another holdup with regard to implementing even simple digital technologies is the lack of ability to quantify the cost savings?

http://www.manufacturingglobal.com/technology/1043/Smart-manufacturing-will-push-the-industry-forward

Big Data Analytics is Becoming Increasingly Important to Manufacturers

In the article Big Data Analytics and its Impact on the Manufacturing Center, the author examines the increasingly important role big data analytics is having on manufacturing.  Big data services are projected to jump to $187 billion in 2019 from the $122 billion in 2015.  With such rapid growth, manufacturing stands to greatly benefit from big data in ways such as increased productivity and preventative maintenance.  Big data analytics really comes down to understanding the data and having a set goal in mind when analyzing it.  Those companies that fail to take advantage of this growing industry could be a serious disadvantage in the very near future.

Big data analytics can be simplified a bit by understanding exactly how the data is initially organized and analyzed.  Initial run throughs of large data sets usually are to identify any obvious trends using mean and standard deviation.  The data can be visually represented to better observe these trends.  Further examining the data for core determinants and correlation analysis can lead to deeper understanding.  After these initial methods, more powerful statistical methods such as artificial neural networks and significance testing.  Although there are certainly more methods that can be used, the four mentioned are arguably the most popular and can yield deep insights into large data sets.  From the results obtained using the above methods, process understanding and improvements are almost a guarantee.

The article further goes into some particular avenues that big data analytics has already helped manufacturers.  Cost savings is always a hot topic and manufacturers have experienced cost savings in production and packaging as well as warehousing and inventory.  Understanding workforce efficiency is another aspect of the process where big data analytics has been useful.  All the workforce data can be summarized and analyzed to see where and how employees work most efficiently.  Finally, the most powerful way big data has been helping manufacturers is in its ability to help collaboration between departments.  The flow of information between such places as engineering, management, and the production floor allows a much greater understanding of the entire manufacturing process thus allowing improvements not previously thought possible.

 

Do you know any reason other an unfamiliarity that a manufacturer wouldn’t invest in big data analytics?

Do you think there is a chance that big data analytics services will grow even faster than projected?

With the growth of big data analytics, do you think big data analytics jobs will be some of the most important in the future?

 

http://www.dexlabanalytics.com/blog/big-data-analytics-and-its-impact-on-manufacturing-sector

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?

 

http://news.psu.edu/story/442316/2016/12/15/research/researchers-begin-15-million-project-manufacturing-industry

 

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?

http://www.forbes.com/sites/kevinomarah/2016/12/22/big-data-analytics-eating-an-elephant/#46170817eb8d

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?

http://www.industryweek.com/technology/us-manufacturers-too-slow-adopt-industry-40-bcg-study