Jane M Fraser

Source: https://rossetti.github.io/RossettiArenaBook/modeling-a-simple-discrete-event-dynamic-system.html  This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

What’s new?

In a 10 June 2021 posting on IISE Connect, Professor Manuel Rossetti of the University of Arkansas announced the availability of the third edition of his textbook Simulation Modeling and Arena as an open text. Previous editions were published in book form by Wiley, and Professor Rossetti decided to make this edition available for free, saying:

With technology constantly changing, I thought that it would be useful to get this book on-line so that I can more readily keep it up to date. It is my hope to provide updated versions when there are significant changes made within Arena.

What does it mean?

Humans are makers, including makers of tools. An important tool for humans and especially for humans who are engineers is a model, that is, a representation, sometimes conceptual or physical but often mathematical, of a real-world system.

Mathematical models can be analyzed using mathematics; that is, we can use mathematics to elicit results concerning the behavior of the model. For example, the differential equation that describes a pendulum swinging in small angles can be solved to give a mathematical description of the pendulum’s motion over time, but the differential equation is an approximation and does not hold if the pendulum swings in a large arc. Also, the more accurate differential equation that describes large swings cannot be solved to give an equation of motion over time, but rather must be solved using numerical methods, again giving an approximate solution.

The real-world systems of our organizations are complicated, so the models of them must be complicated. Also, unlike a pendulum whose motion can be predicted with certainty, we are inherently uncertain about what will happen in the future with many real-world systems. Professor Rossetti starts his book by describing the behavior of an emergency room in a hospital. Randomness abounds in such a system, so any model must use the mathematics of uncertainty. Probability is the field that enables us, indeed requires us, to talk precisely about our uncertainty.

Having good data makes your model a more accurate representation of the real-world system. But even without data, methods can elicit the knowledge of experts and express them in a way that can be used in a simulation.

Professor Rossetti uses the simulation computer program Arena throughout his book.  The graphical user interface of Arena supports a drag-and-drop approach to select model components from menus. If you want to get a feel for what kinds of systems can be modeled, what the necessary steps are, how Arena is programmed, and what the resulting model looks like, section 4.5 of the book has an extended example. Depending on your background, you may not understand all of what is shown there, but you will get a feel for simulation models and Arena. The book has other examples that will help you understand simulation more.

The book is not about the simulation of all systems. “This book primarily examines stochastic, dynamic, discrete systems.” “Stochastic” means that the system has randomness, “dynamic” means that it changes over time, and “discrete” means that changes in the system occur at specific times (for example, when a patient arrives at the emergency department) not continuously (for example, when water flows out of a reservoir). Many of our organizations have systems that can be modeled in such a way.

What does it mean for you?

As Professor Rossetti points out: “A simulation model can be used to predict future behavior through running what-if scenarios.” You can perform experiments in the simulation, experiments that you can’t perform on the real-world system. What if the number of people arriving at the emergency room spikes due to a pandemic? What if we added two more nurses at certain times of the day? You can ask – and answer – questions like these and determine the likely effects on important performance measures, such as the waiting time for patients, the number of patients in the hospital, and the health outcomes of the patients. The simulation operates, of course, much more rapidly than the real-world system so you can simulate years of operation in seconds. Thus, you can ask a lot of questions and a get a feel for how the simulation performs in many different scenarios. Section 8.1.1 shows the results of several what-if simulations of the model from section 4.5.

A key point to remember, however, is one of my favorite phrases, “it’s only a model.” A model gives you numerical predictions of the future, but because of the randomness in the real-world system and because a model does not represent all aspects of the real-world system, most people use simulations of models to gain insight rather than exact predictions. For example, what changes to the model have the biggest impact on performance measures?

If you haven’t considered using simulation in your organization, Professor Rossetti’s book is a place to start. I think you will get a good idea from the many examples in the book about whether this approach is worth trying for your organization.

Where can you learn more?

The major point of this post is that you can learn more in Professor Rossetti’s book – and it is free. His book has advice on how to continue your education in simulation.

A library of videos about Arena is available here, including videos showing the animation of Arena models, a very powerful feature for understanding a model.

This work is licensed under a Creative Commons Attribution 4.0 International License.

“Funky sculpture at the Dogfish Head Craft Brewery in Milton, Delaware.” Source: The Library of Congress, https://www.loc.gov/pictures/item/2018701329/, which says “No known restrictions on publication.”

What’s new?

I subscribe to the magazine American Craft. The Summer 2021 issue has articles on

• Sylvie Rosenthal who makes sculptures that combine animals and architecture,
• Detroit metal artist Tiff Massey,
• Artists whose works educate and advocate for ocean life,
• Research by Namita Gupta Wiggers on craft products as “social objects that are inscribed with histories and narratives that can tell us something about the world.”
• The artist in residency program at the Kohler Company in Wisconsin, and
• Textile florist Yi Hsuan Sung’s use of agar, a high-strength gel made from seaweed.

The theme of this issue is Flourish, about which the editor says:

A flourish is a bold, extravagant gesture. To flourish means to grow or develop in a vigorous way. Flourishing also speaks to having a strong sense of well-being and meaning. So, for this issue, we looked to the craft community to find stories about many ways of flourishing. One thing that became clear when we put this collection of stories together was that flourishing is deeply connected to community.

What does it mean?

The word “craft” is loaded with different meanings and uses. Craft can invite women in (arts and crafts) but can also shut them out (craft workers). Is a creation art or craft? Craft is generally low brow, not high brow. Craft may emphasize function while art emphasizes decoration.

Another article in the Summer 2021 issues,  titled “The Art of the Flourish,” points out that function and decoration can merge. Vintage radiators are designed with fins to maximize surface for radiation of heat to the room, and the result is pleasing to the eye. My partner and I have visited Shaker Village at Pleasant Hill several times and delight in the spare, functional designs there (Shaker Mother Ann Lee said, “Put your hands to work and give your hearts to God”).

Engineers say that their unique function is design, but I have had many delightful conversations with art professors about how the concept of design unites engineering and art, with various combinations of knowledge, skill, and theory required to design. When does someone transition from being an artist to an artisan to a manufacturer? Craft usually involves a handmade object, but all crafters use tools and many use machines.

Why do people craft? Rather than calling humans homo sapiens some prefer homo faber with making, not thinking, as the defining function of modern humans. Humans emerged with the first chip flaked off a stone 2.6 million years ago.  That act was the root of craft, art, and engineering.

What does it mean for you?

For me, one of the unifying concepts in craft is process – how is something made. Design matters, but then the design in the maker’s head has to be made. I enjoy reading American Craft to see what people have crafted but also to see how they crafted it. Another unifying concepts is materials. The article on agar has details on the properties of that material.  I enjoy reading about the materials the crafters use.

For you, I suggest that inspiration can come from many sources. You should be constantly scanning the horizon, in your trade and business publications, for innovations and ideas that will have impact in your industry, but publications far from your field can be inspiring too.

For example, the work of Namita Gupta Wiggers that I mentioned earlier includes her invented word “craftscape” to emphasize the cultural connections represented in a craft object. Her work may illuminate manufacturing work through her ideas about how labor and raw materials are transformed into usable objects. If you seek to create a corporate culture, aspects of her work may spark ideas for you about the deeper meanings of objects for the people in your organization. Culture is not just ideas but also objects.

For another example, the article on the Kohler Company mentions the not uncommon use of their bathtubs as shrines: upended and half buried to enclose a religious figure. Do you know how your products are actually being used?

Print magazines are not, of course, the only way for you to find inspiration. Certainly a deep dive in the rabbit holes of the Internet will uncover much good – and much that is a complete waste of your time. For me, magazines have the advantage of being curated carefully and of being in print. I  linger with a magazine – and coffee – knowing that people spent time thinking, planning, photographing, writing, rewriting, editing, and formatting. American Craft is a well crafted magazine.

Where can you learn more?

American Craft magazine is published by the American Craft Council.

You can search and browse among magazines at Magazines.com, Magazine.store, or Magzter.  The top 10 magazines in the US by circulation include one I had never heard of:  Game Informer Magazine, published by game retailer GameStop. Your local library probably has many magazines for you to dip into; I found American Craft through my library and read it there until I decided I had to have my own copies.

What magazines do you subscribe to? I subscribe to Make, New Scientist, and Smithsonian, among others, in addition to receiving magazines and journals from my professional societies, ASEE (American Society for Engineering Education),  IISE  (Institute of Industrial and Systems Engineers) and ASQ (American Society for Quality). I also receive several publications from genealogical societies, to support my genealogical hobby.

Diagram of a volcanic eruption. Source: Wikimedia. This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

What’s new?

In the 22 May 2021 issue of New Scientist, Michael Roberts of the Cambridge Image Analysis group at the University of Cambridge reported on a study of attempts to use machine-learning (an artificial intelligence technique) to diagnose COVID-19 and to predict how patients would fare with the disease. He and his colleagues examined over 300 papers published between 1 January and 3 October 2020 and found that none had produced a useful tool.

What does it mean?

I have written about artificial intelligence (AI) before in this blog. On 15 May 2020, I noted that the inability of many artificial intelligence based techniques to explain in human terms how they reached a conclusion limits their usefulness in circumstances where an explanation is needed as part of the decision making process. On 13 June 2020 I wrote that the computer program Watson had been a great success on the TV game show Jeopardy but had failed to be useful in the medical field.  On 2 Jan 2021, I cautioned against the hype concerning AI, in this case regarding a study meant to increase our understanding of whale calls. In my recent (8 May 2021) review of my year of blogging, I noted that AI was the one technology about which I was not generally positive. In this blog post, I am again going to caution about AI hype and about the need for a model that humans can understand.

I am heavily influenced in my opinion about AI by the results of a PhD dissertation I advised at Ohio State in 2002, titled “Quantitative measurement of loyalty under principal-agent relationship.” Keiko (Kay) Yamakawa attempted to detect disloyal insurance agents for a large insurance company, that is, insurance agents who issued policies from several companies and whose behavior indicated they may be failing to recommend products from this particular insurance company. Dr Yamakawa had little success with AI approaches (such as a hidden Markov model) but found that a more traditional approach using control charts was successful. The former approach, hidden Markov models, is based on a search for a statistical model that reproduces the patterns in the data and, even if successful, is unable to be used to generate an explanation of what it did. The latter approach, control charts, is based on classical hypothesis testing (with all its benefits and faults) to detect if a process that has been behaving with statistical regularity has moved out of that state of statistical control; the method includes charts that visually display an explanation of the result. Indeed I was dismayed that it took Kay and me so long to decide to try control charts since the formulation of the problem was clearly the detection of a process that had moved out of control.

Nineteen years later AI techniques and computer capabilities for handling huge data bases have advanced greatly, but I believe that the general findings of that dissertation still apply. AI techniques are mesmerizing in their promise to detect patterns and apply them to practical situations without the need to understand the domain. Other techniques require more domain knowledge and more understanding of the particular problem being attacked. A tension always exists between those who have powerful techniques and seek to apply them in disparate areas, sometimes in areas where they have little domain knowledge, and those who have the domain knowledge and watch the masters of technique flounder.

Paul Lingenfelter cites political scientist Donald Stokes as describing the statistical technique of factor analysis as “seizing your data by the throat and demanding: Speak to me!” Researchers must always guard against uncovering spurious patterns that occur just by chance (for example, by reserving some data to test findings). Increasing access to big data sets and computing power and increasing use of sophisticated data analysis techniques have created many successes but have also created failures such as this one in COVID diagnosis.

In 1947, economist Tjallings Koopmans wrote a cautionary article titled “Measurement Without Theory.” In reviewing a book on business cycles, Koopmans lamented the authors’ attempt to measure and analyze data without the use of theory. Koopmans wrote:

Measurable effects of economic actions are scrutinized, to all appearance, in almost complete detachment from any knowledge we may have of the motives of such actions. The movements of economic variables are studied as if they were the eruptions of a mysterious volcano whose boiling caldron can never be penetrated. There is no explicit discussion at all of the problem of prediction, its possibilities and limitations, with or without structural change, although surely the history of the volcano is important primarily as a key to its future activities. There is no discussion whatever as to what bearing the methods used, and the provisional results reached, may have on questions of economic policy.

Almost 75 years later, those sentences still bite.

What does it mean for you?

In my 11 July 2020 blog on the topic of models, I cited the quote “the purpose of modeling is insight, not numbers.” In creating models of real world systems and in analyzing data, I urge you to focus on understanding the system, not just on finding empirical patterns.  A black box that takes input and gives output is less useful in the long run than a transparent model that promotes understanding. You should consciously be building models – mental or mathematical – of your organization and the environment in which it functions.

Where can you learn more?

Koopmans’s article was published in The Review of Economic Statistics, volume 29, number 3, August 1947, pages 161-172.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Source: US Library of Congress. This image is in the public domain. “Astra, Red Cross health fairy, brings gifts of health principles from the milky way to the juniors of the Prince School, Boston. The health fairy, who serves the Boston Metropolitan Chapter, is at the right, standing. Pupils of the school, members of the Junior Red Cross, who assisted her in a health play program given recently at the school, as they appear in the picture.” February 1922.

What’s new?

Inspired by Ray Dalio’s 2017 bestselling book Principles, performance coach Brad Stulberg put together his own list of principles as “a foundation for a better you.” Pocket brought it to my attention.

What does it mean?

A 2013 review of Jessica Lamb Shapiro’s book on the self-help industry in the United States, Promise Land, cites “an Ancient Egyptian genre called ‘Sebayt,’ an instructional literature on life (‘Sebayt’ means ‘teaching’)” as the “progenitor of self-help books.” I haven’t been in a bookstore for over a year, but I am sure that the self-help section at my local Barnes and Noble store still stretches over many shelves.

I haven’t read any of these books by Lamb Shapiro, Dalio, or Stulberg. For a description of the book by Ray Dalio, founder of investment firm Bridgewater Associates, I am relying heavily on this article by Elle McFarlane. She reviews the book’s contents and Dalio’s five principles, including “Use the 5-step process to get what you want out of life.” That process has five stages:

1. Having clear goals
2. Identifying the problems that prevent you from achieving these goals
3. Getting to the root cause of these problems
4. Designing plans to help you overcome these root causes
5. Enforcing these plans to get your desired results .

Stulberg, inspired by Dalio, created a list of eight principles, including: Focus on the Process, Not Results (“Research shows that concentrating on the process is best for both performance and mental health”); Take Small, Consistent Steps to Achieve Big Gains (“Small and consistent victories compound over time, leading to massive gains”), and Make the Hard Thing Easier (“Rather than relying completely on self-control, intentionally design your environment to make the hard thing easier”).

When I taught the course Introduction to Industrial and Systems Engineering at Colorado State University-Pueblo for many years, I required students to read and report on a book, chosen from a list I provided or approved by me if not on the list. One of those books was The 7 Habits of Highly Effective People, by Stephen R. Covey. His seven habits include Habit 2: Begin With the End in Mind. “Habit 2 is based on imagination–the ability to envision in your mind what you cannot at present see with your eyes. It is based on the principle that all things are created twice. There is a mental (first) creation, and a physical (second) creation.”

What does it mean for you?

I am sure you have already noted that some of these personal principles echo strongly organizational principles. What works well for self-improvement also works for group-improvement.

I am an industrial engineer. My elevator speech to answer the question “what is industrial engineering?” is that industrial engineers are about efficiency, quality, and safety. We design the workplace so that ordinary people can achieve extraordinary results. Chapter 2 of the textbook I wrote for my introductory course lists “Big ideas you will hear frequently” and some of these ideas about industrial engineering could fit comfortably into any self-help list (“Small incremental improvements of a process add up, but more radical reengineering may sometimes be needed”).

Dalio’s five-step process is very similar to Six Sigma’s improvement cycle (Define, Measure, Analyze, Improve, and Control). Stulberg’s focus on the process is one of the core ideas of industrial engineering (in my book, I wrote: “The process for doing a task makes a big difference in how efficiently, well, and safely the task is done”). Taking small consistent steps is another way to describe continue improvement. Making Hard Things Easier is poke yoke or error-proofing. Many of these principles also take a systems view; for example, Dalio’s first principle is to understand reality.

What works well for self-improvement also works for group-improvement. Rather than relying on one of these gurus to provide you with a list of principles for how you want to act in your personal life and in your organization, I challenge you to learn from them (and many others) to create your own set of principles that you use to improve your personal life and your organization. What are your guiding principles for improvement of self and improvement of your organization?

Where can you learn more?

Of course there are many web pages that will give you advice on creating your principles or your core values and even lists of principles you can select from (101 Timeless Principles to Guide You to Your Best Life). For some reason, most lists of principles have an odd number of items (5, 7, 101), but Stulberg has 8. Make of that what you will.

Of course actions must reflect principles. As Patrick Lencioni wrote in 2002: “Enron—although an extreme case—is hardly the only company with a hollow set of values.”

Of course there is a contrary view: Why You Shouldn’t Be A Person Of Principle. Moral particularism points out that any set of ethical principles may seem fine. “But then you run into that odd, unexpected situation where following your rulebook doesn’t seem so neat and tidy. This new case is special, unique, and unanticipated by your ethical system. In fact, it just feels wrong to follow the rules here in this instance. Do you go with your rulebook, or your current intuition?” One of my guiding principles is: know the rules, choose which ones to follow, and live with the consequences.

Source: The US Library of Congress, 1942 July. This image is in the public domain.

“Women in war. Machine gun production. Intent on the important job at hand, Elsie M. Terry uses a precision snap gauge on the machine gun part she has milled. One of 2,000 women employed by a Midwest plant, converted from spark plugs to machine gun manufacture, Mrs. Terry typified the American woman war worker. Serious, skilled and reliable, she is making an invaluable contribution to the war effort. A.C. Spark Plugs”

What’s new?

A December 2020 article from Modern Machine Shop, which somehow just caught my attention, says that a company acquisition has to focus on the human aspects to be successful.

What does it mean?

The article, by Christina M Fuges of MoldMaking Technology reports on the company B-Square Precision Group, founded by two individuals (Mark Beck and Tony Butler) with the plan to acquire a portfolio of companies in high precision manufacturing. The article touches on many trends, including the impending retirement of many owners of smaller shops, manufacturing approaches such as lean and ISO certification, and strategies for portfolio construction, such as combining companies that can cross sell each other’s capabilities. The central idea of the article is the need to focus on people.

With its goal of acquiring other companies, B-Square has many people issues to pay attention to, since the retiring managers and the continuing employees of acquired companies often legitimately fear that the company will be broken up, that cost cutting measure will be implemented and will degrade work enjoyment, and that any existing company culture will be brushed aside.  

The B-Square approach includes the importance of training employees, initially and on an ongoing basis, putting safety first in the list of five metrics to be tracked, improvements to pay and benefits, improvements to shop conditions, and increasing collaboration within the company.

What does it mean for you?

Precision manufacturing requires high end machines and highly skilled workers, so one could argue that the focus on the humans in B-Square is necessary to retain employees and to maintain the necessarily high level of skill, but I argue that all companies could benefit from treating their workers as highly skilled and as valuable. I have never worked in restaurants (my partner Mark has) but I know that high levels of skill in the kitchen and on the floor result in a much better customer experience. On the other end, as a highly skilled professional, I have been appalled to realize several times in my career that my employer viewed me as simply another professor, easily replaced and not really needing to be nurtured.

Management advice often focuses on how to treat workers, with emphasis on teams, incentives, and more. The risk, I think, is platitudes. An encouraging feature of this article is a quote from an employee: “Mark and Tony stress that it’s not about them. It’s not about me. It’s not about management. It’s about the team,” suggesting that the management in this case is acting, not just talking.

Engineering has a long history of recognizing the importance of humans in systems. My field, industrial and systems engineering is a leader, with specialties in human factors, cognitive engineering, and ergonomics. The electrical engineering society IEEE has a division called Systems, Man, and Cybernetics. While the second and third words in that trio have not aged well, the name lives on.  

The truth is that all production systems are systems of technology and humans. You imperil the success of the system by underemphasis on either of those pieces, from the simple fact that people have to use the technology correctly to gain the benefits, through to more sophisticated ideas about using technology to augment what workers do (from decision support systems through heads up displays for pilots). If you want technology to work for you, you must have a high level of attention to the humans in the system. Technology works best when it is considered as part of the system of machines and humans.

Where can you learn more?

You can learn much about how to view systems of machines and people through many fields. Search for phrases such as socio technical systems (applied, for example, in healthcare), human factors (this blog post explains four approaches to that topic), and cognitive engineering. Recent developments have highlighted how automation and AI (artificial intelligence) should work together with humans; see, for example, new ideas on augmented workers.

Almost all approaches to systems thinking include humans in the system.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Source: Wikimedia. This image is in the public domain.

What’s new?

At Engineering360, technical writer Janeita Reid writes about the use of sensors throughout the electrical grid, from generation to use.

What does it mean?

My father was one of the chief architects of TASI, the multiplexing system used in the first transatlantic telephone cable system in 1956. The principles of Time Assignment Speech Interpolation had been known but could not be implemented with slow, bulky, over-heating vacuum tubes; the invention of the transistor in 1947 enabled the application of these principles.  But the invention of the transistor built upon and then required more engineering developments in order to lead to the mass manufacturer of transistors which supported developments such as TASI.  

The invention and development of modern electronics continues to enable more inventions and developments. Rarely if ever does one simple device appear in a flash of genius and lead immediately to new uses. Instead, a soup of swirling ideas and devices leads to constant improvement in the ability of devices to sense, compute, and control other devices. You can see these results in the small computer you use every day, your cell phone, with its amazing ability to help you to communicate, to navigate, and to find information.  

The Department of Engineering that I chaired at Colorado State University-Pueblo offers engineering degrees in two areas: industrial engineering and mechatronics. (To be clear, the two undergraduate degrees are the BS in industrial engineering and the BS in engineering with specialization in mechatronics). Industrial engineering is about designing systems to support efficiency, quality, and safety. Mechatronics combines mechanical and electrical engineering with computer programming to create useful devices. The two fields overlap in many ways; one is their overlap in the use of sensors to collect data, data that can be analyzed for long term systems improvement and for real time decision making.

The essence of mechatronics is the creation of devices that sense, compute, and control. The essence of industrial engineering is using information to improve the operation of systems. When developments that were originally a topic of advanced research in labs such as those at Bell Labs become embodied in undergraduate engineering degrees, you know that progress has been made.

One of Ms Reid’s opening sentences, “Advanced sensors are among transformative disruptors building the case of distributed energy resource systems paired with superior data-driven optimization capabilities,” supports the story I have told. Mechatronic devices, especially the sensors inside them, are the keys that enable better decision making, especially using the optimization techniques of operations research, a part of industrial engineering. She then describes the role of sensors and optimization in power generation (via wind, sun, biomass, and water), power transmission, and power use.

What does it mean for you?

The soup of swirling ideas and devices include sensors and optimization as well as much more. These ideas and devices are revolutionizing the provision of electric power and, as Ms. Reid concludes, enabling the transition to renewable energy. She also touches on the interesting dynamic between decentralization and centralization. Electrical generation can be increasingly less centralized, but sensors support remote control and management of those assets.

Whatever your organization, you should be watching for such trends in sensing, computation, and control to support better decision making. These trends enable you to have a better real time knowledge of what is happening throughout your organization and the system in which it operates. Your approach can start, for example, with Internet searches set up as alerts, to keep you aware of what is happening in your field. What other sensors can you set up for your organization?

Where can you learn more?

 Engineering360 has an impressive list of sensors here, with links to more information for each.

The website of the US Department of Energy is one good place to follow trends in energy, especially renewable energy and changes to the grid.

This work is licensed under a Creative Commons Attribution 4.0 International License.

What’s new?

I published the first issue of Make Technology Work for You one year ago, on 9 May 2020.

What does it mean?

I started this blog with several goals in mind, but mainly out of curiosity about what it feels like to write to self-imposed deadline every week. I was certainly familiar with the pressures of teaching a course that met two or three times a week, so I thought writing a blog might be similar, but I wasn’t sure if I would be able to maintain the blog as being retired grew on me. I am certainly active in my retirement, so I didn’t really need to have another activity, but writing was attractive.

Secondarily, I thought I had things to say that others might want to read. As the heading on this blog says, I have “40 years’ experience teaching engineering and a lifelong interest in technology.” I hoped that I could help make technology interesting and useful for others.

So what has happened in the last year with me and with this blog?

When I started, the week had a high level of panic. Once I identified a topic each week, the panic level went down and once I published on each Saturday morning, it disappeared. For about two hours. I have always been good at keeping up the pressure on myself and at worrying and the blog did become a cycle of worry. However, as with teaching, the worry (it is no longer panic) has become an old friend. Once I have identified a topic (often early in the week, even sometimes on Sunday, sometimes as late as Thursday), I enjoy thinking about what I will write. When I finally sit down to write, I find that I have whole paragraphs almost ready in my head.

Identifying a topic has become harder, much to my surprise. Some reliable sources (newsletters) have fewer interesting articles; some new sources have emerged and others have faded. I structure the blog around a current news article, so I am restricted to topics that have been mentioned in some news source in the previous week, but that is not a great limitation, of course. Am I running out of things to say? I don’t think so, but I am puzzled about why I am having more trouble identifying topics.

I worry that I have become repetitive and that I wrote only on the same topics over and over (additive manufacturing, for example). Since I love data, my obvious approach was to make a database and analyze the data. I reread each blog post and coded the contents into categories, with multiple categories allowed for a single post. This table shows some results.

My top topic, certainly not to my surprise, is Systems. Yes, that is my top topic; that is how I think, how I analyze, and how I view the world. You should have that as your top topic, too. As an industrial engineer and as someone active in the maker community, I was also not surprised that my second topic was Manufacturing/makers/making.  The topics that all got 5 to 8 entries were also no surprises, although I sometimes feel that I am obsessed with additive manufacturing and writing too much about it. I am pleased to find that all the topics in this table, save one, are topics about which I write positively; I am in favor of the world doing more of all of these. The exception is Artificial intelligence, where my postings were sometimes positive but sometimes cautious about hype.

What have I learned and how will I proceed in the future of this blog?

I remain committed to finding a good image to head each blog and to using only images in the public domain or licensed for public use. Wikimedia Commons and the Library of Congress continue to be my reliable source of excellent images. They have almost never failed me. I have also used my own photos occasionally. Only once did I omit an image. On 9 January, 2021, I wrote about the crashes of the Boeing 737 Max and was unable to find a publicly licensed image of any of the crashes. I really like the animated images I have been able to use a few times, most recently comparing vertical axis and horizontal axis wind turbines.

I remain committed to never writing about military technology, a decision I made before my first post. A former colleague once said that military technology is not engineering since engineering must be for the benefit of mankind; war is never of such benefit. Since this colleague comes from a country that was bombed by the United States (that statement does not narrow down his country of origin much), I respect and value his opinion.

I remain committed to posting a typo-free blog and have done fairly well. In rereading all the articles, I did find a few typos I missed, but not many.

I remain committed to taking stances and giving my opinion. I am not just giving information about technology, although I always want to educate. I am also evaluating that technology for its usefulness for you and for its usefulness to society.

I find I am losing my commitment to publishing by 7 am on Saturday; I have failed to meet that goal several times (including today) and it doesn’t bother me. I apologize if it bothers you.

Finally, I remain committed to writing a weekly blog. I enjoy writing this blog, even when I am having trouble finding a topic.  In the last year, I missed only one day (25 July 2020) during the week I was attending virtually the annual meeting of the Engineering Accreditation Commission (ABET-EAC). For some reason that I don’t remember, I have two postings dated 9 May 2020, my first day of blogging. Thus, I have posted 52 posts in the last year. I also remain committed to using the structure I established in my first post: What’s new? What does it mean? What does it mean for you? And Where can you learn more?

I am still musing about building readership, advertising my blog, and monetizing my blog. I care most, I think, about having my words be meaningful and helpful to others, so I will think more about how to accomplish that goal.

What does it mean for you?

Only you can tell me that. Please leave a comment below or email me at janemfraserphd@gmail.com. I look forward to hearing from you.

Where can you learn more?

I use WordPress with the WEN Business Pro theme. I had to learn how to use WordPress and I like it.

Optinmonster publishes this list of blogging statistics.  Blogging Wizard has more blogging facts.

3 October 2021. Ritta Blens suggested another good resource about blogs and blogging. Thank you, Ritta.

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Source: Wikimedia. This figure is in the public domain.

What’s new?

The Engineer reported that a recent article in the journal Renewable Energy used computer simulation to conclude that vertical axis wind turbines can be clustered together to increase total energy output, unlike traditional horizontal axis wind turbines.

What does it mean?

The two basic types of wind turbines differ by how the turbine rotates when the wind is blowing. In the example in the center of the figure at the top of this blog, the three blades rotate around a horizontal axis, that is, an axis perpendicular to the figure and parallel to the ground; it is a horizontal axis wind turbine (HAWT). In the two others, the rotation is around a a vertical axis, that an axis that is parallel to the figure and perpendicular to the ground; each of these two examples is a vertical axis wind turbine (VAWT).

HAWTs are increasingly used to generate electricity, but a well known problem is that, in an array of such turbines, the turbines first struck by the wind generate turbulence that reduces the energy able to be captured by trailing turbines. Previous research had shown that VAWTs seem to have the opposite effect, in which the capture of energy by trailing turbines is actually enhanced by the earlier turbines. Note that, of course, no combination of turbines can capture more energy than the total energy contained in the wind.

Computers have enabled many wonderful accomplishments for us (my latest is the Merlin bird identification app on my phone). For engineers, computer simulation is a wonderful tool. Computer simulation enables us to create a mathematical model of a real world system, described in computer code, and then to perform experiments on that model.  A crucial part of simulation is to validate the model, that is, to compare its output with data from the actual system in order to confirm that it faithfully models the real world in the crucial measurements. Depending on how realistic the underlying model is, we can then make predictions about how actual devices will perform in the real world.

In this article, the engineers created a two-dimensional CFD (Computational Fluid Dynamics) model of a field of VAWTs, performed experiments by changing the layout of the turbines, and then predicted what will occur with real turbines. Obviously, they can perform many more experiments at much less cost than if they did the experiments with actual turbines.

Engineering improves products in three ways: design, manufacture, and use. In the design of a wind turbine, the engineers select a HAWT or VAWT, decide on the size and shape of the blades, determine the height of the tower, select materials for each part of the device, and so forth. In manufacture, engineers select and then continuously improve the processes for making each part, for assembling the device, and for installing it at its location. Finally, engineers make decisions about when the turbine will be operated, how its output will be used within the larger electric grid, select and implement a maintenance schedule, and eventually decide when to take the device out of service. The article in Renewable Energy is an example of improvements in the use of the turbines, that is, in their layout, but it also illustrates how design and use are interrelated. Renewable energy is coming on like gangbusters because of changes in design, manufacture, and use.

What does it mean for you?

Computer simulation is an amazing tool. The minute you ask any question starting with “what if …?” you should think about using a computer simulation. As an industrial engineer, I know about the use of stochastic simulations (ones that incorporate random events) for modeling production systems, enabling the asking and answering of “what if?” questions about inventory, equipment layout, scheduling, and more.

One of the most important facts about a computer simulation, which I have mentioned already, is that the results are only as good as the ability of the model to replicate the real world. I tell my students that they must practice saying, to themselves and to others, “it’s only a model,” said with a shrug of the shoulders. Engineers can all too easily fall into the trap of saying “the VAWT array functioned best in this layout,” when they really mean “the simulation of the VAWT array functioned best in this layout.” As George Box is often quoted as saying, “all models are wrong; some are useful.” You must be wary of engineers – and others – who aren’t careful in their language about predictions from models.

Renewable energy is coming on like gangbusters. Whether this progress and others will be fast enough and sufficient to save the world remains to be seen.

Where can you learn more?

This is my second blog post titled “It’s only a model.” The first one is here.

IISE (the Institute of Industrial & Systems Engineers) has a Division devoted to modeling and simulation. There are many useful computer packages: AnyLogic, Arena, Flexsim, Simio, Simul8, and more. The Winter Simulation Conference is a great source of current information about theory and application. Industrial engineering overlaps with many business areas and computer simulations can also be used, for example, in financial forecasting.

Engineering simulation can be used any time the mathematical equations describing a real world system are too complicated to be solved in general; they are instead solved numerically for the specific case being studied and are often solved using approximations. The applications and computer packages are too numerous to list.

This page from the US Department of Energy gives a good overview of developments in wind turbines.

If you aren’t seriously worried about global climate change, this page from NASA should do it for you.  

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Source: Wikimedia. This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

What’s new?

Interesting Engineering reported that the CEO of Turkish cryptocurrency exchange Thodex fled to Thailand with $2 billion of crypto assets, leaving 400,000 users of the exchange in the lurch.

What does it mean?

Blockchain is a computer technology that prevents changes from being made to a series of records; the most important features of blockchain are distributed storage and a type of internal consistency (one block of data is related numerically to the previous block, hence “block chain”). If someone wanted to change a record in a blockchain, they would have to change the record in many, many locations, and would have to change many, many records in order to maintain the internal consistency of the records. Blockchain thus can prevent certain types of fraud, that is, fraud in which records are altered. Blockchain creates unalterable accounting records. Blockchain is currently used for cryptocurrencies such as Bitcoin, that is, currencies created and maintained as computer records.

Most implementations of blockchain rely on proof of work to establish and maintain the records. When a new record is added, blockchain sites perform long and complicated calculations (following the internal consistency rules) to add the record; the first site to present proof of the completion of that work, called mining, is rewarded with additional cryptocurrency.

Blockchain protects against only certain types of fraud, that is, fraud involving the changing of accounting records. Blockchain will not prevent many other types of fraud. In fact, the whole area of cryptocurrency has a great deal of fraud; an Internet search on the words “blockchain fraud” or “blockchain scam” will turn up many examples.

Consider, as an example, an area of fraud I was concerned with for my 40 years as a professor of engineering: cheating by students. Blockchain could protect against recorded grades being changed fraudulently, a type of fraud that does occur. I am aware of several such cases that were detected and there are probably others that went undetected. But cheating by students takes many other forms, none of which would be prevented or detected by blockchain, for example, someone copying another’s work on homework or during a test.

Blockchain is also touted as useful for verifying someone’s identity and for establishing trust in business dealings with unknown partners, but I suspect that the actual usefulness is more limited than the hype and that other computer technologies can accomplish such goals. The mathematics of computational complexity, which I discussed two weeks ago in this blog post, underlie all these technologies for computer security.

What does it mean for you?

Blockchain, as with many new technologies is the subject of much hype, some of which is misleading and even incorrect. For example, this article at Forbes says:  “Were the expensive free-range eggs we purchased really created at a free-range farm?  Was the gold ring I bought online really made with 24K gold? Companies can combat fraud with blockchain by verifying the legitimacy of every part of the supply chain process, helping both the buyer and manufacturer. You’ll never have to question that organic produce and those free-range eggs.”

I disagree. Nothing in blockchain can prevent someone from, at any point in the supply chain, substituting eggs from caged chickens for eggs from free-range chickens, just as nothing in blockchain can prevent a student from looking over the shoulder of another student during a test.

Blockchain does have important uses. The immutable nature of blockchain records is an important feature in maintaining security. But most hacking episodes involve stealing private records, not altering such records.

I am not addressing here the huge amount of electricity required for the proof of work aspect of blockchain (see, for example, “Bitcoin consumes ‘more electricity than Argentina’”) because, I am told by my local blockchain expert, other methods of blockchain do not rely on proof of work. I am also not addressing the independence of blockchain from regulations or governments (as part of crypto anarchism, for example), which others cite as an attractive feature; one upshot is that your recourse in the case of fraud and scam are limited. And, whatever you do, don’t lose your password; if you do, you lose your assets.

Where can you learn more?

This 2018 article “Blockchain is not a silver bullet for fraud prevention” is still very useful. Here is another article cautioning about the hype. This December 2020 article in Finance Magnates blames the lack of a killer application outside of cryptocurrencies for the failure of blockchain to achieve its promises. This December 2019 article uses a Gartner diagram of the phases of hype to speculate that blockchain will be useful five to ten years from now. This piece in TechBeacon lays out in detail some of the pitfalls of blockchain.

Some argue that blockchain eliminates the need to rely on trust  in business transactions, but this article by noted cryptographer Bruce Schneir points out that trust is always needed. He asks “Would you rather trust a human legal system or the details of some computer code you don’t have the expertise to audit?” He includes this image tweeted by Internet pioneer Vinton Cerf:

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Source: I used a portion of this image at Wikimedia. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.

What’s new?

An article from GlobalSpec Engineering360 combines some of my favorite topics: new materials, the element carbon, and additive manufacturing. This article was just one of many I read this week on additive manufacturing.

What does it mean?

Plastics and carbon nanotubes were combined in a new internal configuration to create a material with improved strength, toughness and stiffness, and lighter weight. Such a material could have significant application in replacing metals in vehicles.

One useful piece of technology in the Star Trek science fiction series was the replicator, used to create food, including Captain Picard’s Earl Grey tea. This article taught me that it was also used to create spare parts and items for consumption on the Holodeck simulation and that “By virtually eliminating material scarcity, replicator technology plays an important role in the moneyless human economy within the Star Trek universe.” This article expands even more about how it was used. The physical explanation (“matter-energy conversion”) is suitable for science fiction but not for science.

Regular readers of this blog know that I am interested in additive manufacturing. My interests include the technical aspects of the new materials and include the technical aspects of how the new materials are created, but also include the potential for changing supply chains, manufacturing, and our economy. While the abundance enabled by the Star Trek replicator is still science fiction, the future may involve using a limited number of feedstocks to create consumer products on demand, close to the final consumer. Your local big box store will be a manufacturing facility, turning carbon (and other materials) into products.

What does it mean for you?

Manufacturers should be excited about the potential for additive manufacturing to change their processes as well as the processes of their suppliers. The technology is changing the economics of additive manufacturing enough that it can now be used for small batches and larger batches as well, enabling customization in mass manufacturing.

But manufacturers should also be cautious: parts made with additive manufacturing are different. The article I cited above points out that the new material creates objects with different strength, toughness, stiffness, and weight. Careful thought must be given to the implications of these changes in use: for example, decreased weight may be a benefit for shipping, but may create issues in the ability of an object to remain stationary in wind. Is lighter weight lawn furniture always desirable? In addition, additive manufactured parts are usually created in layers and thus can tend to delaminate, with implications for durability.

Also, replacing conventionally made parts with those made by additive manufacturing can have other implications. Because additive manufacturing can create parts with shapes that were difficult to make by other manufacturing processes, an assembly of parts may possibly be made as one part, as explained here, with implications for the manufacturing work flow and for the workforce.

I often note that fasteners are a sometimes overlooked part of engineering design. This article explores how traditional fasteners (screws, for example) work with parts made with additive manufacturing and this article explains that the fastener may need to be selected to add strength to thinner parts made by additive manufacturing.

I hope that you share my excitement about additive manufacturing, but I also hope that you share my caution.

Where can you learn more?

While I subscribe to some email lists that tend toward coverage of a wide range of additive manufacturing, generally still in the research stage, applications of additive manufacturing that are actually being put into practice are probably more likely to be found in conferences and publications for that industry, such as the Food Automation and Manufacturing Conference and Expo. Food Technology Magazine had this 2020 article on how 3-D printing and other technologies may change the production of food.

Some sites that cover additive manufacturing, for example Additive Manufacturing Media, do have good articles on specific industries, such as this recent one on 3-D printing furniture.

I tend to use the phrase “additive manufacturing” as being more descriptive of the technology, but an Internet search should also try the term “3-D printing” since it is widely used.

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