Current Supply Misconceptions By Matt Foust, DCMME Center Graduate Student Assistant

Current Supply Misconceptions

As a profession, those that work in supply chain are constantly on the lookout for initiatives that can make companies more efficient, cut cost, or incorporate new technology (usually to become more efficient or cut cost).  But not all of the newest hype is always worth an investment.  And some of the newest trends or prevailing knowledge don’t always save time and dollars.  In the article The Biggest Supply Chain Fallacies, we will look at some of the current misconceptions and over-hyped technology that currently are in the supply chain industry.

Regarding newer technologies, Blockchain is very popular just about everywhere.  Through its rise in cryptocurrency, many companies have taken notice and exploring where Blockchain might add value.  I’ve even wrote in recent blogs about Samsung is planning to implement Blockchain in its logistics.  But Blockchain isn’t without its issues, and the author of the article brings ups a very good point – Blockchain cannot overcome the issue of garbage in – garbage out.  Suppose an upstream supplier lies about what they are doing and enters the untrue information in the Blockchain.  This information will be regarded as true and is unable to be changed later on.  The need for certify and monitor suppliers is not solved by Blockchain, and since Blockchain’s records are unchangeable, the need to certify may actually be more important.

The next fallacy is that Corporate Social Responsibly (CSR) initiatives will assuredly drive better financial performance.  Unfortunately, this is just not true. CSR programs can reduce cost such as initiatives to reduce fuel consumption through better routing and more full truckloads, but CSR programs overall tend to be better looking from the outside.  A realistic view of CSR programs is that may reduce cost, they will most likely attract better talent, and they will attract positive attention to the firm.  Overall, CSR matters more in wealthy nations and to younger employees, and its important to be realistic about what can be achieved through their usage.

Finally, the last supply chain fallacy discussed is the apparent truck driver shortage.  This shortage, as claimed by the majority, is due to the fact that younger people do not want these jobs. But the basis of this issue is most likely just economics.  The average wage for the truck driver according to is a bit over $42,000/year.  At that wage, most young people do not want that job.  If wages went up, economically it would make sense that there would be more individuals that would want to drive trucks.  And in fact, that is apparently happening.  From 2013 – 2017, truck driver salaries increased between 15%-18%.  As wages increase, there will eventually be more drivers, and this shortage will be solved.  Interestingly, the author of the article brings up automation taking the place of drivers as wages rise.  Autonomous vehicles are a hot topic right now, but it’s highly unlikely that autonomous trucking fleets make their way on to our roads anytime soon.

As supply chain professionals, its important that we discern fact from fiction and over-hyped technology from value-adding technology.  Getting differing opinions, staying well read, and keeping an open mind appear to be the best ways to move forward, even in an ever-changing environment.



Do you believe these points to be true?

Are there other supply chain misconceptions not mentioned?

Where will blockchain’s utility be found or is it most likely not useful in a supply chain context?

Using the Cloud to Improve Warehouse Performance by Nick Wright, DCMME Center Graduate Student

Using the Cloud to Improve Warehouse Performance

Starting in May of 2018, Ametek Prestolite Power will begin offering Ametek Insight though their Wireless Battery Identification Devices (WBID). Ametek Insight, a cloud-based ZigBee software, is the newest intelligence solution provided by Ametek that, when paired with the WBID, aims to solve one of today’s warehouse challenges – fleet optimization.  The WBID allows users to continuously and remotely monitor an entire fleet of forklifts using real time data, transmitted and collected using Insight, by managing battery performance, changing settings, updating software/firmware, and more. The ability to use and apply this technology will allow companies to optimize fleet management to extend battery life, increase productivity, reduce costs and ultimately better serve customers. Ametek expects that Insight will eventually become a standard feature on a majority of its batter charger, not just offered through WBID. Can this technology be adapted to optimize other areas of the supply chain besides forklift operation? As the Internet of Things and real-time data analytics takes a more prominent role in supply chains, how will the job of a supply chain manager change?

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Staying Ahead of Customer Needs with the Help of CNC Technology, By Nick Wright, DCMME Graduate Student

Staying Ahead of Customer Needs with the Help of CNC Technology

Bridge Tool & Die is a major player in the Carbide tooling market since 2005 and has been transitioning from the use of manual grinders, the tooling industry norm, to the use of automated CNC grinders to increase productivity, capacity, and quality in the hopes of being able to better serve its customers who are continually looking for better and cheaper solutions. This transition began back in December of 2015 when Bridge Tool & Die implanted a Three-Pronged Strategy to enhance their manufacturing processes by reducing machining time and increasing consistency. The three prongs were: retrofitting their existing manual grinders with CNC, setting up multi grinder work stations, and purchasing high-end CNC machines. Using this approach, an operator would theoretically be able to operate three machines at once – one manual, one CNC, and one semi-automatic grinder. Most recently, in 2017 Bridge Tool &Die invested in a Studer CT960 CNC multi-axis grinder to further improve quality and capabilities, boasting of grinding parts in a third of the time, halving the polishing time required, and achieving tolerances of 0.0001” in an industry where the market normal is tolerances of 0.0004”. As well, the new CNC machine is lowering costs for Bridge Tool & Die, as it is expected to require two fewer operators to run, reducing labor costs for the company. Glenn Bridgeman, the owner of Bridge Tool & Die, states that “The need for increased technology was not driven by reducing operators in our shop . . . Rather, it offers us the ability to keep all of our experienced operators, and address capacity versus technology-allowing us to grow over 15% per year.” Will Bridge Tool & Die continue in its trend towards a more automated grinding process? How will industry competitors react to their ability to achieve above normal tolerances? How soon before automation with CNC becomes the new norm and all manual grinding, both at Bridge Tool & Die and elsewhere, becomes obsolete? How will Bridge Tool & Die continue to improve its processes beyond the use of CNC grinders?






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?

Caterpillar is Saving Big Money using Big Data and the IoT

In the article IoT And Big Data At Caterpillar: How Predictive Maintenance Saves Millions Of Dollars, the author examines an interesting case of the company Caterpillar saving significant amounts of money using big data and the IoT.  The best part of this case study is that Caterpillar is seeing a very quick ROI on their big data investment, which is not something that can be said for most companies.  As a Caterpillar manager put it, you don’t have to look for a “grand slam” with big data; sometimes you just need multiple smaller applications of big data to experience significant savings.  In this instance, gathering as much data as possible seems to be the best approach, and utilizing experts in the processes and in the data to analyze and understand the insights gleaned helps realize real value.

Caterpillar utilized big data on in its Marine Division, mainly to analyze fuel consumption for its customers as it most affects the bottom line.  Sensors on the ship monitored everything from generators, engines, GPS, refrigeration, and fuel meters, and Caterpillar utilized Pentaho’s data and analytics platform.  Insights gained have been a correlation between fuel meters and amount of power used by the refrigeration containers, and also that running more generators at lower power instead of maxing out a few was more efficient.  The cost savings here added up to $650,000+/year.  Another insight was to the optimization of a ship’s hull cleaning schedule.  Through the collection of data of cleaned and uncleaned ships, the data showed that cleanings should be performed once every 6 months instead of once every two years.  The savings associated with this optimization was $400,000/ship.

In the grand scheme of big data, predictive maintenance analytics seems to be the most powerful tool consistently being used.  With data being generated just about anywhere you could imagine via the IIoT, understanding trends becomes easier and easier.  Interestingly and contrary to previous articles, Caterpillar believes that you can’t collect too much data.  They point out how data storage is very cheap.  In the words of a Caterpillar manager, you can’t see “relationships about relationships” in the data if you don’t collect it.  Although a more is better approach is definitely not what some companies have ascribed to, it seems to be working well for Caterpillar’s marine division as they continue to pull value of out of big data and analytics.

Quick returns on big data investments seems to be rare, so do you think that companies just aren’t utilizing the big data correctly?

Do you believe in the more is better approach with regard to collecting data?

Do you believe Caterpillar is more likely to invest in big data projects in other parts of their company due to the success in the marine division?

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?

How Analytic is transforming Supply Chain

The article (Forbes, Bernard Marr, April 22, 2016, 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?