Process Mining – Big Data Working for Manufacturers

In the article Why Manufacturers Need Process Mining – A New Type of Data Analytics, the author is extolling the benefits of what he calls a new type of data analytics – process mining.  Process mining can be used to reduce inventory costs, identify production bottlenecks, improve on-time delivery, and optimize logistics between production sites, distribution centers, and end clients.  It’s hard to justify  that process mining is actually a new type of data analytics that can be used for manufacturers, but process mining does follow the a few of the key rules for successful big data usage.  First, as the name implies, it concentrates on one process.  Implementation of successful big data projects normally requires concentration on one area for improvement.  The specificity of big data projects usually allows them to be successful, and the article looks at why process mining, and big data analytics on the whole, can be successful in saving manufacturers money.

Process mining specifically looks at a very important factor in manufacturer’s processes, KPIs or key performance indicators.  KPIs are exactly what they sound like, the main factors that measure performance and the overall successfulness of process or project.  Process mining’s value lies in the fact that it initially makes one look at KPIs of a process.  It also challenges the manufacturer to validate if they are measuring the right KPIs and understand if the data they are gathering can be related back to KPIs.  Process mining, as all big data does, uses software do the difficult work of visualizing the processes and highlighting specific variances impacting KPIs.  One example used in the article is the examination of throughput times and the ability to identify that specific vendors are not meeting their lead-time commitments.  These types of analyses and results is exactly what big data projects are meant to do and achieve.

Another point that the article points out is that process mining encourages manufacturers to identify inefficiencies and problems within the process.  It encourages companies to embrace their issues.  This ideology is absolutely necessary for continuous improvement, and it is a key to any big data project.  Embracing issues can be difficult, especially for older, engrained processes, but it is the only way to eliminate them.  It certainly is not the easiest thing to do, and requires a humble mindset entering an improvement project.  Process mining has all the key factors of a successful big data software, and it could be very useful for manufacturers that want to embrace the big data revolution.


Do you believe addressing KPIs first is the best way to approach a big data project?

Do you think process mining is actually a new or different type of big data analytics or just a rebranding of basic big data?

Do you think that some manufacturers are reluctant to implement big data projects because they do not want to know their inefficiencies?

Looking for the Signal in the Noise

In the article A Signal in the Noise: How to Best Manage Big Data, the author advocates some ways to improve the use of data for manufacturers as well as his idea for the best way to approach the use of big data analytics and data management.  The author equivocates big data as sometimes “a needle in the haystack” when it comes to finding and using it in the manufacturing environment.  With so much data produced by even small firms, the management, organization, and analyzation of the data can become overwhelming if not impossible.  Industrial Internet of Things-based systems are estimated to create $4 trillion to $11 trillion in new economic value for manufacturers by 2025, according to McKinsey Global Institute.  With such large value to be had, ignoring or underutilizing data could be a catastrophic mistake for manufacturers.

In a study of manufacturers conducted by the MPI Group, 76% of respondents reported plans to increase their use of smart devices in the next two years while 66% plan to increase their investment in IIoT-enabled products over the same time period.  With many firms taking the necessary steps to collect and use data, a strategic approach is necessary.  The author suggests a number of steps to jump-start the transformation to using data analytics.  Recognizing human limits and the burden of isolation is the first suggested step.  Here the author is advocating that firms understand that human teams are just not capable getting the insights that technology is capable of.  The next step is forgetting the traditional supply chain cycle, and embrace the complexity of modern supply chains.  With the IIoT, data can be captured all along the supply chain which can offer useful insights not previously possible.

Finally, the author advocates understanding four different measures before taking on new data management and analytics initiatives.  Find the actual problem, the “signal in the noise” is the first and foremost issue at hand.  Unless there is direction to the project, the project will almost certainly fail.  Next is understanding the business case.  One has to understand the strategic advantage behind any data management implementation.  Lastly, finding where the optimization is most desperately needed and identifying the experts that are best suited to handle the data being generated is key.  Overall, the biggest pitfall in bringing on new technology is the belief that good things will just happen.  An understanding of the problem, the right people, and a tailored process will allow the technology to do the work it is supposed to.

Do you believe that understanding big data is really about finding the proverbial needle in a haystack?

Do you believe the increase in smart device usage automatically translates to more useful big data, or just more data in general?

As with most strategic IT plays, projects start off to gain a strategic advantage but quickly become part of the IT infrastructure.  Do you think we are in the middle stage between big data being a strategic advantage and a necessary IT infrastructure requirement for manufacturers?

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?

Manufacturing Recognizes the value in Big Data Analytics, Lack Implementation

In the article Big data Analytics is Transforming Manufacturing, the author reviews a study done by Alan Turing Institute and Warwick Analytics regarding how big data analytics is finding its way into manufacturing.  The main points of the study indicate that many manufactures understand the usefulness of big data analytics, but not many are actually not using big data to its full potential.  The study indicates 7 key points where big data could offer benefits to manufacturers, and it also reveals some interesting findings.

The study itself gleaned its findings from views of 50 senior industrial executives, most of whom are with global manufacturing companies in the UK.  Its noted that the greatest value of big data analytics is in its predictive powers, giving companies the ability to extract meaningful insights on processes, production, and maintenance, to name a few of the important manufacturing functions.  Three of the seven key areas addressed in the study were improving yield, predicating and/or preventing maintenance, and improving supply chain operations – all areas where manufacturers could extract value through big data analytics.

The findings of the study paint an interesting picture of big data at this current point in time.  Just over 40% of businesses are in the experimental stages of using big data analytics, but surprisingly, 50% didn’t know the difference between big data and previous business intelligence tools.  With a knowledge gap present, it’s not surprising that more widespread adoption of big data analytics isn’t happening.  Without an understanding of how big data will improve a corporation, it’s hard to get big data programs sponsored and rolled out.  Though these issues are certainly present, there are few absolutes.  One of them is that the data is being collected, in huge amounts, and that this data is normally being used to some extent.  The issue lies in that the proper analytics are not begin done so the possible benefits are not being realized.


What will it take for more manufacturers sponsor big data programs?

What are the differences between big data analytics and previous business intelligence?

A reoccurring question seems to be – are we collecting too much data?  If so, how do we begin to collect only the pertinent, necessary data?

Changing the Perception of Manufacturing

The article “See How 3D Printing In Manufacturing Could Help Close The Skills Gap”(  reviews the US’s current manufacturing industry decline, as well as 3D printing’s potential impact for growth. With the manufacturing industry in the US at a steady decline, and a “rate of lost manufacturing jobs between 2000-2010 exceeding that of the Great Depression”, there is obvious room for concern. Per the article, “China produces 80 percent of the world’s air conditioners, 70 percent of its mobile phones, and 60 percent of its shoes”. Perhaps more damaging than the hold China has on the manufacturing industry is the misconception that “manufacturing is a “dirty” industry” which has driven away the younger workforce.

The article goes over a few ways this perception is being changed. Schools are doing tours of local companies to show the technical expertise and skill required. The article states “Companies are automating like crazy because, as a society, we realized we had to work smarter, not harder. If young people show focus in the skills needed to operate these machines, there seems to be an unlimited opportunity for jobs.”

A key component of this new “perception” is 3D printing. It not only is more efficient than traditional manufacturing production chains, but is more appealing to the young workforce because of the expertise needed as well as the innovative ideas behind 3D printing and its future growth. In conclusion, “Advantages like low labor costs overseas or large established facilities matter less, and the type of technology being utilized in these smaller manufacturing houses means the industry can attract more talent”.

Collaborative production and shared Economy

In a previous article, (, I discussed the need for sharing infrastructure to make it accessible for small and medium size companies for implementing Smart Manufacturing. A presentation by Helge Spindler of Fraunhofer IAO on sharing economy in urban environment ( dtd Sept 25, 2014) hints at the idea of a company as a shareable platform. This would mean common resources can be used for manufacturing by different parties. Emergence of smart manufacturing can enable this sort of a structure which could lead to a lot of cost cutting, machine and labor utilization and overhead reduction. Would companies join hands for such a co-ordination? If yes, who would be the central party leading or coordinating such a group? How would potential IP conflicts be resolved? And are our accounting and financial institutions strong enough to accommodate such type of changes?


7 Things to Know about the Internet of Things and Industry 4.0

The article published by Modern Machine Shop ( is about Internet of Things and the facts related to it. Internet of Things (IoT) is the intelligent connectivity of smart devices by which objects can sense one another and communicate thus changing how, where, and by whom decisions about our physical world are made. The seven points provide an introduction and background to the Industrial IoT and Industry 4.0 for metalworking companies and machine shops.

1. Key standards are creating the pathway: The ability to connect manufacturing equipment to a Web-based network and derive substantial value from these connections is more practical and compelling than ever.

2. Better, faster decisions are coming to the shop floor: When devices are connected, the data they generate can flow into software applications that create the information individuals can use to make choices that are timely and effective. Better decisions mean fewer mistakes and less waste.

3. People empowerment is essential: The individual human being will continue to play an active, engaging role in manufacturing.

4. Cybersecurity is a major issue: Cyber threats to the Industrial IoT are real, global and growing.

5. A new generation of sensors is coming:  Sensors can process or analyze this data, and they can transmit this data or make it available for collection across a network for use in a software application.

6. Machine tools will be regarded as cyber physical systems: The definition of a cyber physical system describes it as a system in which embedded computers monitor and control physical processes through a feedback loop in a networked environment.

7. Cloud computing and Big Data will play vital roles: The capacity of the cloud to store and process data is virtually unlimited and is generally more economical, flexible and secure than on-site alternatives.

Let’s simply end with a summary that includes at least three main imperatives:

Take heed. The Industrial IoT is real and taking shape here and now. It is also happening “there and then” in the sense that your global competitors are implementing it too and may be ahead with prior implementations.

Keep your eye on the prize. Better decision-making is the main benefit of creating a connected factory in which machines and people are smarter.

Start small, but plan big. Whether it is machine monitoring or cloud-based CAM programming, the initial steps have to be manageable, transparent and respectful of the individual.