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?


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?



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?



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?



Big Data Analytics Creates a Smart Supply Chain

In the article Benefits of a Smart Supply Chain, the author introduces the concept of big data being used advantageously in a manufacturer’s supply chain.  Big data analytics is widely accepted as a superior way for manufacturers to predict demand and understand customers, but big data analytics can also be used on the warehouse floor to save money.  The smart factory concept is one in which the entire manufacturing area is connected by sensors via the IoTs.    In the supply chain, big data is allowing manufacturers to predict bottlenecks, avoid machine failure, and reduce replacement part inventory via predictive analytics.  This Industry 4.0 holds the key to manufacturers staying competitive in a global marketplace.

The concept of Industry 4.0, run by smart factories, was actually introduced in Europe as recently as a few years ago.  To stay competitive in the global marketplace, manufacturers will have to adapt at least in some way to this new Industry 4.0.  Interestingly, a recent study indicated that 92% of manufacturers in the UK do not understand Industry 4.0 processes, but 59% of manufactures recognize the impact these new processes will have on the sector.  Using the UK as a representative sample, it certainly appears that the manufacturing industry as a whole needs to technologically transform and educated itself.  Those who stay ahead of the curve will reap the benefits of more efficient, smarter processes, while those who do not risk losing money.

The specifics of Industry 4.0 includes the big data analytics to design a smart supply chain.  A smart supply chain can avoid many of the traditional supply chain problems such as supply bottlenecks and machine downtime.  Bottlenecks can be avoided due to the fact that a connected factory shares data with other parts of the supply chain so production can be eased or intensified based on data from the factor combined with data from down the supply chain.  Furthermore, a smart supply chain can use predictive analytics to shutdown equipment and processes before the fail.  In this case, there is less downtime.  The sensors on these processes can be programmed to monitor equipment and order parts prior to equipment failure so that excess replacement inventory is not need thus saving money.  With all of these advantages, the smart supply chain managers will invest in the smart supply chain to keep their manufacturing processes ahead of the curve an competitive in a global environment.

What will need to happen to educate those in power at manufacturing companies so that the transition to smart processes happens?

Will these smart processes create or destroy jobs?

Will they transformation to a smart factory decrease or reverse the decay in the manufacturing industry as a whole?