Category Archives: Analytics

Analytics Maturity

A young child begins by crawling, learns to walk, and eventually gains sufficient mastery to run. An analogous process is proposed by advocates of analytics maturity models: organizations must pass through three stages — descriptive, predictive, prescriptive — as they gain ‘analytics maturity’. (See for example the INFORMS Analytics Maturity Model and the TDWI Analytics Maturity Model.) Inherent in these models is the notion that ‘analytics’ is all about processing and analyzing data in a progression of increasingly sophisticated ways. When I see such ideas proposed, I find myself imagining the following discussion taking place in a corporate boardroom…

“Margret, I’m getting worried. We’re losing market share every month.”

“I know it Frank. Our supply chain is a mess, and our competitors are eating our lunch by offering much faster service. We need to optimize our supply chain.”

“That’s exactly what we need to do, but unfortunately, we’re just not mature enough! I’m afraid that by the time we are, we’ll be out of business. You know, it may be time for us to pull the cords on our golden parachutes! ha, ha, ha!”

Do you think such conversations are taking place? Does the notion of ‘analytics maturity’ make any sense? Let’s take a look.

Descriptive Analytics

We are in the realm of ‘big data’: unearthing insights from massive datasets using exotic software technologies such as HIVE, KAFKA, STORM and PIG. (Who names these things?) No one in the organization, except a few IT specialists, understands how this stuff works.

Predictive Analytics

Data scientists extract the maximum amount of information from datasets — often the same kind of datasets that people have been using for 50 years — to make predictions. They use advanced ‘machine learning’ and ‘deep learning’ methods, methods that no one else in the organization understands. Sometimes, even the data scientists themselves cannot explain how these methods work.

Prescriptive Analytics

Operations research analysts build mathematical models of real systems or processes in order to improve, optimize, or simulate them. Often the amount of data required is quite modest. These models, and the methods used to solve them, can be sophisticated, but since they are based on the relationships of entities existing in the real world, it is possible to explain — at least on a high level — how they work.

Do the above listed ‘stages’ represent the natural progression of steps in a single process? Should you wait years before you can use operations research to solve problems or improve operations?

Rather than using a model to determine your level of ‘analytics maturity’, let me offer you a simple test to determine whether you are ready for operations research:

If you are conversant with the concepts of more or less, and better or worse, you are ready.

Analytics Defined

For this post, I provide a link to my article entitled ‘Analytics Defined: A Conceptual Framework’ that was published in the June, 2016 issue of ‘ORMS Today’. A PDF copy of the article has been placed on the Indiana University – Purdue University Fort Wayne website. The link follows:

The core ideas in this article were first presented in four posts in this blog:

These ideas were further developed for a panel discussion on 11/1/15 ( [bottom of page]) and an invited presentation on 11/3/15 ( at the INFORMS National Meeting in Philadelphia.

Data Driven

We see a lot written about data-driven decisions. For example, an article on the Harvard Business Review website begins: “Not a week goes by without us publishing something here at HBR about the value of data in business. Big data, small data, internal, external, experimental, observational — everywhere we look, information is being captured, quantified, and used to make business decisions.” And, in an article on,  the first of ten ‘truths’ about data-driven decisions is: “If you’re not using data to make decisions, you’re flying blind.”

It would seem that everyone is busy gathering, cleaning, and crunching large amounts of data prior to making a decision. Should you follow their lead? Perhaps an episode from my past can shed some light on this question.

A True Story

A number of years ago I was working for a small consulting company, when a client requested help analyzing the performance of a voice response system that was being developed for a new internet based telephone service. The client was the director of a department in a large telecommunications company. The project, which today might be called descriptive analytics, involved writing a SAS program to analyze the call transaction data from the voice response system. I was not enthusiastic about working on this project, but I was available, and we didn’t want to disappoint an important client.

I started by reviewing the system flowcharts. The system was designed to handle both customer service and inbound sales calls. Callers were first asked if they were existing customers. If they answered no, they were asked if they had seen the website; then they were asked if they had received a mail advertisement; and finally they were asked if they had a promotion code. If they answered yes to this last question, they were asked to enter the code. If they didn’t answer within a short time, or they entered an inappropriate number, they were again asked to enter the number. None of the preceding questions could be bypassed, and only after they had been completed would a potential customer be connected to a representative.

After looking at the flowcharts, I went over to talk to Jim, the person at the client site that I was working for. I told him that I thought that the system was badly designed since many customers would get frustrated by the difficulty of getting through to a representative, that they would hang-up, and sales would be lost. He replied that the project team was very keen on gathering data on their marketing efforts, and in any case, we hadn’t been asked to evaluate the system, only analyze the data.

I didn’t argue. I wrote the SAS program, and in due course, the voice response system went live. Our first report, which showed that 35% of callers were hanging-up, prompted a panicked response from the project team. As a result, Jim suggested that maybe, we should, make some recommendations to the project team. So I put together a presentation, and several days later Jim and I met with the project team in a large conference room.

I pointed out, as gently as I could, that it was not a good idea to make it difficult for potential customers to get through to your sales representatives, and that each new question that the system asked had the effect of providing an opportunity to hang-up. I further pointed out that the potential for lost sales could easily be 10 times the value of any cost savings generated by the system. My words got through, and after I finished, there was complete agreement that changes should be made. It was decided that we would meet again to put together a plan to revise the system.

I was feeling a lot better about the project; it was getting a lot more interesting, and I might actually make a difference.

However, before we could meet again, word came down, that because of the poor financial performance of the new service, senior executives had eliminated the systems budget; there would be no changes to the system; the service would be allowed to die.

The Consequences Of Obscurity

In a recent blog post, Polly Mitchell-Guthrie, when referring to an operations research project at UPS, wrote: “Does it really matter what we call it, if people value what was done and want to share the story? If it leads to the expansion of OR I don’t care if its called analytics.” In a 2011 blog post Professor Michael Trick went even further, stating: “The lines between operations research and business analytics are undoubtedly blurred and further blurring is an admirable goal.”

The desire for an association with the very popular, and wildly hyped terms, analytics and business analytics, is perhaps, understandable. Unfortunately, these terms are associated, not with the problem-centric paradigm of operations research, but with the data-centric world of IT/big data. I have been told by people who would know — an entrepreneur in the analytics space and a leader of a data science team — that when executives and IT leaders talk about analytics, they do not include operations research.

The terms analytics and business analytics are strongly associated with the word data: gathering it, cleaning it, mining it, analyzing it, presenting it, and attempting to gain insights from it. These activities in turn, are closely associated with disciplines such as statistics, data science, computer science, and information technology. As a result, the analytics universe is diverse, and much larger than the operations research community. (Interestingly, Professor Trick, in the above mentioned post, acknowledges that:  “We are part of the business analytics story, but we are not the whole story, and I don’t think we are a particularly big part of the story.”)

Blurring the distinction between operations research and analytics would obscure the distinctive approach, and unique capabilities, of operations research, creating a situation in which operations research no longer has a unique identity, and becomes lost in a larger data-centric universe that is characterized by extreme, data-focused publicity. Were this to occur, you should consider how the following questions would be answered:

  • Will students decide to spend years of their lives studying operations research? Will they even know that such a discipline exists?
  • Will universities continue to offer programs in operations research? Will they continue to require MBA students to take operations research courses? Will they continue to hire professors who specialize in operations research?
  • Will companies form new operations research groups, or maintain existing ones? Will IT leaders decide to add operations research analysts to their data science teams? Will jobs and consulting assignments exist for operations research analysts?

I am afraid that obscurity will not lead to the “expansion of OR”; it will lead OR into oblivion.

Not Your Father’s Society

[Virtual interview June 6, 2037 between Steve Smith, executive editor of ‘Quantum Systems Review’, and Jack Roberts president of the ‘American Association of Certified Analytics Professionals’ (AACAP).]

“Jack, I understand that you have an announcement.”

“That’s right Steve; we are announcing a name expansion.”

“Name expansion?”

“Yes, we are simply adding the word accounting between analytics and professionals. Our new acronym will be AACAAP; so you just have to stretch-out the last ‘a’ sound.”


“You know: AAC-a-a-a-a-a-a-a-a-P.”

“Why the change?”

“Well, with the introduction of stat-chips, and the collapse of big-data, the market for analytics professionals dried-up, so we decided to pivot.”


“Ever since the financial crisis of 2031 was caused by sixth generation derivatives, the large banks have been desperate to find people who could understand them, and as a result, the analytics accounting market has become red-hot. Since we weren’t getting the growth we were looking for with analytics, we decided to jump on the analytics accounting band-wagon.”

“But, what about your members?”

“What about them? We have moved on; they should too. Listen Steve, we’re taking a leadership position in the non-profit space: we believe lifetime membership in a professional society, like lifetime employment, is passe′.”

“I’m not sure I…”

“It’s been great chatting with you Steve, but I have to run. I have a meeting with Professor Teresa Laporte, the well known author of ‘Winning With Analytics Accounting’; She has agreed to serve on our certification board, and I can’t be late.”

“And remember, the acronym is AAC-a-a-a-a-a-a-a-a-P.”

Analytics And String Theory

The notion has arisen that analytics is an emerging field that represents a convergence of the quantitative decision sciences. For example, in a 2015 paper in Interfaces, the authors state: “…the emerging definition of analytics as a field of expertise that subsumes OR.”. If true, such a convergence would be a surprising and remarkable development, as it would represent a dramatic reversal of the trend in human history toward specialization. It is worthwhile therefore, to examine this idea, and consider its logical consequences.

A convergence of the decision sciences into a single field would imply that this new field would contain all the knowledge and methods currently included in the decision sciences, and would lead to one of four possibilities being true.

A new unifying theory is developed. Currently, hundreds of theoretical physicists are working on the development of string theory, which they hope will mathematically unify quantum mechanics, particle physics, and gravity. Unlike string theory, there is no history of an analytics theory going back to the 1960’s, no founders of an analytics theory, no seminal papers introducing an analytics theory, and no conferences where an analytics theory is discussed.

In fact, in a 2014 paper in the European Journal of Operational Research, the authors found only 15 articles in theory oriented journals that were listed in the International Abstracts in Operations Research database with the term analytics in the title or abstract. Moreover, the types of analysis that analytics thought leaders offer as examples of analytics, such as advanced statistical analysis, econometrics, and optimization, are actually examples of existing methods from existing disciplines. (See for example, ‘Competing on Analytics’.) So, while string theory may provide a unifying ‘theory of everything’ for physics, there is no evidence that a unifying theory of the decision sciences exists, or any reason to believe that one could be developed.

Analytics practitioners must master all the knowledge and methods of the decision sciences. Without a new simplifying mathematical theory, mastering the knowledge and methods of all the decision sciences would require five or six Ph.D.s and 80 to 100 years of experience. Since the human life span is insufficient to accomplish this, we can reject this possibility.

Analytics practitioners produce simplistic or superficial work. Lacking a new simplifying mathematical theory, or a sufficient life span, analytics generalists would be unable to perform at a level comparable to current experts in the decision sciences. I will assume that those who believe that analytics is an emerging field, those who would employ analytics practitioners, and everyone else, will view this outcome as a highly negative development, and therefore, will not accept it.

Analytics is practiced by individuals specializing in different areas. Since the other possibilities are impossible or undesirable, we must conclude that specialization is necessary.

Allow me to list these specialties for you: statistics, computer science, operations research, industrial engineering, economics…

What Is Analytics?

There is a surprising admission in an article entitled ‘What Is Analytics?’:

“It’s not likely that we’ll ever arrive at a conclusive definition of analytics…”

In an article entitled ‘Operational research from Taylorism to Terabytes: A research agenda for the analytics age’ the authors state:

“…may be the lack of any clear consensus about analytics’ precise definition, and how it differs from related concepts.”

The failure to construct a single definition that encompasses the meaning of analytics is not surprising: the word analytics is used in three different ways, with three separate meanings, and therefore, analytics requires three separate definitions:

  • analytics is used as a synonym for statistics or metrics. Examples are website analytics (how many views or clicks) or scoring analytics (number of points scored per 100 possessions).
  • analytics is used as a synonym for data science. Examples are data analytics, predictive analytics, or operations research and advanced analytics [the preceding phrase refers to two separate things: operations research and data science(advanced analytics)].
  • analytics is used to represent all of the quantitative decision sciences. This is the Davenport ‘Competing on Analytics’ usage.

Once it is recognized that three definitions are needed, it becomes possible to answer questions about analytics that previously caused problems. For example:

Question – Is analytics a discipline?

Answer – no, yes, no

The answer depends on which meaning of analytics we are referring to:

  • analytics = statistics/metrics. No. This is a type of measurement, is context sensitive, and essentially involves counting.
  • analytics = data science. Yes. Data science can be considered to be a discipline that combines elements of statistics and computer science.
  • analytics = all quantitative decision sciences. No. Analytics represents disciplines, but is not itself a discipline. (See Confusion Over Analytics)

So, not only can we arrive at a conclusive definition of analytics, we can (and must) arrive at three conclusive definitions of analytics!

Hidden In Plain Sight

I do not usually subscribe to conspiracy theories. However, I find myself wondering if operations research could have reached its current state of obscurity by chance. Consider what you might do, if you were tasked with hiding operations research. You would probably realize that it would be impossible to keep it completely secret; so you might come up with an alternative approach: make it very difficult to get information about operations research and its benefits, and use disinformation to confuse those who might be interested in it.

To implement this strategy, you might take the following steps:

  • Encourage operations research journals to publish papers that almost no one (except for a few specialists) can understand;
  • Do not require operations research journal articles to relate to real world situations or problems;
  • Make sure that the papers in the practice journal, that describe successful operations research projects, are only available to a few subscribers and research libraries, and never publicize their existence;
  • Bury the videos describing world class operations research projects deep inside a single website, and make some of them available only as a membership benefit;
  • Stop using the name operations research, and replace it with the ambiguous term analytics;
  • Do not promote operations research, and instead use all resources to promote analytics, big data, and data science.

These steps happen to correspond exactly with the current approach to ‘promoting’ operations research in the United States. And, this approach has had the expected result: very few people understand operations research or its benefits.

Recently however, there has been a surprising development: the editor of the journal ‘Manufacturing & Service Operations Management’ has created a review blog, where authors can present a non-technical summery of their articles. He is also, encouraging authors to publicize their articles through social media.

While this is a small step at a single journal, it represents a new approach to promoting operations research. Imagine what might happen, if instead of restricting access to information about operations research and its value, we took advantage of the internet, social media, and thousands of operations research professionals to publicize its value:

  • Imagine if all operations research journals began to encourage research designed to solve real problems, and then publicized that research;
  • Imagine if thousands of operations research professionals began to tweet, share, and blog about their research and the value of operations research;
  • Imagine if large numbers of videos describing successful operations research projects were placed on YouTube and promoted on social media;
  • Imagine if free online journals describing successful operations research projects were created and widely distributed.

Now, imagine a future in which the practice of operations research is ubiquitous.

Should We Re-Brand Operations Research?

There are some in the operations research community who want to re-brand operations research. They would like to be called analytics professionals. The reasoning behind this appears to be the following:

Operations research is not that popular;

Analytics is very popular;

They would like to be popular, so;

They will call themselves analytics professionals, and then;

They will be popular.

Here, I will not dwell on the flawed premises, or faulty logic embodied in this reasoning. Instead, I will focus on the consequences of a successful re-branding. When considering these consequences, we should keep the following points in mind:

  • While those promoting analytics have trouble defining it, they are in agreement that it encompasses many different disciplines (see Confusion Over Analytics), such as statistics, computer science, data science, big data, business intelligence and operations research.
  • Operations research represents a tiny fraction of the IT/analytics universe.
  • The existence of generic analytics professionals would imply that there is no longer a meaningful distinction to be made between the ‘former’ disciplines of statistics, computer science and operations research.

To help you envision a post re-branding period, I offer two scenarios. In both, an IT executive is speaking to the leader of what was once an operations research group, but is now an analytics group after being re-branded. Remember, operations research no longer exists!

Scenario A

“Alice, I am assigning you and your team to be part of our data quality initiative.”

“But, Sir.”

“No buts Alice, big data is our priority — we must have high quality data!”

Six months later….

“Well done Alice. You and your team have reduced the error rate by 6%. I’m going to make this assignment to data quality permanent.

Scenario B

“Tom, I am assigning you and your team to our text analytics initiative.”

“But Sir.”

“No buts Tom, our competitors are all heavily involved in this area — we will not be left behind!”

Six months later….

“Tom, you and your team don’t seem to be up to the task — all of your projects are months behind schedule. I’m going to have to let you and your team go. Report to human resources and pickup your termination package.”


So, in one case those who have re-branded survive, and in the other case they do not. In both cases, the practice of operations research ends.

Will Operations Research Survive?

There have been some troubling signs: a 2010 article in OR/MS Today suggested that analytics would subsume operations research; a 2013 LinkedIn discussion asked “Will Big Data end Operations Research?”; and ominously, even INFORMS seems to be distancing itself from operations research.

How should we react to this? Should we:

  • Take early retirement, move to Vermont, and open a bed and breakfast?
  • Claim to be analytics professionals, and hope no one asks us about Hadoop or NoSQL?
  • Return to school to study data science?

No! None of the above will be necessary. To understand why, it is necessary to go back to first principles.

The original meaning of the name operational research (what operations research is called in Great Britain, where it was invented) was literally, scientific research on operations. The name was meant to distinguish scientific research on operations, from scientific research on the underlying technology of some product, e.g. radar. In the late 1930‘s the British Government funded scientific research directed towards creating radar equipment with sufficient range and precision to locate attacking aircraft. They also initiated an operations research study to determine the most effective way to deploy the radar stations, and integrate them into an effective air defense system.

This type of scientific research, and the scientific method upon which it is based, is a problem solving paradigm. Operations research is the application of this problem solving paradigm to the solution of operational and management problems.

During the summer of 1940, this paradigm arguably saved Great Britain from defeat. Today, as the Edelman Competition routinely demonstrates, this paradigm creates benefits so great, that they transform entire organizations. And, it is because of this paradigm that operations research can create value that can be created in no other way. This value — lower costs, higher profits, military advantage, more efficiency, better service — was needed in 1940, is in evidence all around us today, and will be in demand for as long as human civilization persists.

So, there is no cause for alarm. Just continue ‘Doing Good with Good OR’.

Apache Helicopter

Certification Wars

“Jim, you look beat.”

“Oh, hi Mary, I was up late crunching the numbers.”


“You know, since The Alliance entered the analytics certification market, we’ve been losing market share.”

“I know. It’s not fair — our test is much better than their test.”

“It’s true, but unfortunately, no one can tell the difference.”

“Well, the board has just approved MAP 2 — we’ll meet the $99 price point and cut our test to 49 questions.”

“Yes, but have you heard the latest?”

“No, what?”

“Now, when you get certified by The Alliance, they send you a beautifully engraved brass plaque. Actually, I wouldn’t mind hav”


“Sorry. Listen Mary. I’ll tell you what’s keeping me up at night.”

“What’s that?”

“Well, it’s only a rumor, but, the way the story goes, The Alliance has gotten to some key California legislators, and they’re getting ready to push through an Alliance based licensing program for all analytics professionals in California.”

Licensing! But…but…but then, we’ll all be screened out!”

Analytics: A Conceptual Framework

In the 12/17/14 INFORMS Today Podcast, Glenn Wegryn observes that analytics is divided into two distinct camps. He notes that they tend to come from different organizational backgrounds and he describes them in the following way:

  • Data Centric – use data to find interesting insights and information to predict or anticipate what might happen;
  • Decision Centric – understand the business problem, then determine the specific methodologies and information needed to solve the specific problem.

That analytics appears to be divided into distinct camps should not be surprising, since, as I explained in Confusion Over Analytics, analytics should be understood as a conceptual grouping of the quantitative decision sciences as a whole. Therefore, it is to be expected that disciplines within the quantitative decision sciences have distinctive backgrounds, methods and approaches.

The data centric/decision centric categorization can be a useful way to think about analytics, since two disciplines contained within the analytics conceptual grouping fit these categories perfectly: data science (data centric); operations research (decision/problem centric). Using this categorization, a framework can be constructed, within which, the various types of analytics, data science, and operations research can be  related to each other in a logically consistent way.

Diagram of an Analytics Framework

Analytics Framework

Both common uses of the term analytics appear in the preceding diagram: to represent statistics and computer science and to represent all the quantitative decision sciences. This conceptual framework highlights a promising area for collaboration between data science and operations research (prescriptive analytics), while recognizing that most prescriptive quantitative analysis does not require intensive data analysis.

Confusion Over Analytics

The term analytics emerged in November, 2005. In the chart shown below, the relative number of Google searches on the term analytics from 1/1/04 to the present are displayed.

Chart Showing Searches For Analytics
Searches For Analytics

In addition to the dramatic growth in the use of the term analytics, there has been a proliferation in the way it is used, with phrases such as text analytics and healthcare analytics common.

Numerous definitions of analytics have been proposed, but consensus and clarity have been elusive:

  • The lead article in the December, 2013 issue of OR/MS Today was entitled ‘The Evolution Of Analytics’. In the body of the article, the authors presented a 200 year history of statistics!
  • In recent surveys, operations research professionals have expressed widely differing views on the relationship between operations research and analytics.

However, there is one point on which there is agreement — analytics is related to many different disciplines:

  • Davenport, Cohen and Jackson, in the May 2005 research report ‘Competing on Analytics’ mention statistics, operations research, industrial engineering, econometrics, and mathematical modeling as examples of analytics.
  • Rahul Saxena, co-author of the December, 2012 book ‘Business Analytics’, on slide #5 of a slideshare presentation, lists 14 disciplines as being antecedents of analytics. The list includes Business Intelligence, Computer Science, Statistics, Operations Research, Industrial Engineering, and Finance Planning & Analysis.

A Simple Explanation

How can we explain the sudden appearance of the term analytics and its connection to so many different disciplines? Did a new meta-discipline, representing a new problem solving paradigm, suddenly emerge?

Let me offer a simpler, and more plausible explanation. In November, 2005 a new concept emerged, and went viral: the idea that it is useful to be able to group together several distinct disciplines, and refer to them collectively with a single term.

This concept makes it possible for statistics, computer science and operations research to be represented by the word analytics in the same way that biology, chemistry, and geology are represented by the word science. Organizations can now consider quantitative decision sciences collectively for purposes of planning and resource allocation.

In current practice, the term analytics is used to represent two different groupings of disciplines:

  • a base grouping (statistics and computer science) – e.g., data analytics;
  • an extended grouping (all quantitative decision sciences) – e.g., business analytics.


When analytics is understood to be a conceptual grouping of quantitative decision sciences, confusion disappears:

  • the sudden emergence of analytics and its relationship to other disciplines is explained simply and logically;
  • the varied usage of the term analytics becomes understandable;
  • it is not necessary to postulate the emergence of a new meta-discipline;
  • there is an additional way of thinking about the quantitative decision sciences.

At The Heart Of Analytics

“operational research – at the heart of analytics”. This phrase is the banner headline on the home page of the website of the British OR Society. ( The January 12, 2015 blog post of The OR Society begins with the following statement:

‘Part of the OR Society’s mission statement is that the Society “effectively promotes the use of OR”; and this is something we do extensively through our publications, our events, our training, our OR in schools initiative, our web sites and elsewhere.’ (

Clearly, The OR Society believes that they can use the interest in analytics to promote operations research. If we, in the United States, want to do the same thing, we should keep two basic marketing principles in mind:

1) If you want to market a product, you must tell your prospective customers, THE NAME OF YOUR PRODUCT. In our case, the name of our product is operations research. If we use an amorphous name such as analytics, or the name of a different discipline such as data science, our customers will be confused, and they won’t know what to buy.

2) It is usually helpful, if you explain to your prospective customers, THE BENEFITS OF YOUR PRODUCT. In our case, we could explain to people with an interest in ‘big data’, that operations research can be used to turn insights from a ‘big data’ analysis into an optimal marketing plan.

It is possible, that by positioning operations research ‘at the heart of analytics’, we can promote operations research more effectively. Perhaps, we could even persuade the British OR Society to let us borrow their slogan.

Going With The Flow

Somewhere in an alternate universe…

The INFORMS board members sat in the conference room and wrestled with a recurring question: what should they do about analytics? They had hoped that the problem might just fade away. After all, expert systems, neural networks, and most recently big data, had all come and gone. However, interest in analytics had continued to grow. They were frustrated, and so they decided to engage the prestigious consulting firm of McKinsey, Boston & Yoda to help them develop a strategic plan. They were thrilled when Professor Yoda, one of the firms managing partners, agreed to meet with them.

One month later…

After listening for awhile, as the INFORMS board members described their situation, Professor Yoda rose from his chair and strode toward the white board. There was great anticipation in the room: would he draw one of the matrix diagrams that his firm was famous for? Instead, he wrote two words on the white board. He replaced the marker in the tray; returned to his place at the table; closed his briefcase; and left the room. He didn’t return, and after a few minutes, he was seen driving away.

Everyone was stunned. What had happened? What did it mean? What should they do? They kept staring at the white board ……………………. and then ……………………. they understood! It was so simple really: all they had to do was to align their strategy with THE FORCE.

The way forward was now clear to them, but the INFORMS board members knew that it would not be easy. So they asked the membership for help. They were not disappointed. It turned out that INFORMS members had already created a lot of content that would be valuable to people interested in analytics. There were lectures, tutorials, white papers, podcasts and videos. This content was organized and presented on a special section of the INFORMS website. New content was created, and then promoted through social media. After a while, people began to notice these efforts.

Meanwhile, two years earlier, INFORMS had begun an initiative to encourage the submission of applied papers to its journals. These papers were now beginning to flow in. The INFORMS board members thought: why not summarize these papers and feature them on the INFORMS website? And that, is exactly what they did.

Eighteen months later…

Steve Jobs sat in his office and played with the iPad 6 prototype. He was trying to decide on the perfect shade of white for its case when he got a call from Tim Cook.

“Hi Tim.”

“Hi Steve. I think I have a solution to the problems we’ve been having on the iTV production line.”


“Yes, we have some people monitoring the INFORMS website; they came across a new article on the optimal sequencing of subassemblies. I think we can use that approach to improve our process.”

“That’s great Tim. Why don’t we just go ahead and buy INFORMS?”

“Ah…. Steve, I don’t know if we can do that. INFORMS is a non-profit professional society.”

“Oh. OK, then let’s hire the people who wrote the article.”

“I’m already on it. They’re coming in on Wednesday to meet with us.”

“Great. Listen Tim, how do you feel about antique eggshell white?”

Twelve months later…

The featured article in the journal Foreign Affairs is entitled “The On-Shoring Craze – Should Operations Research Get All The Credit?”.

Twenty months later…

Time Magazine names operations research discipline of the year.

Sixteen months later…

Each of the INFORMS board members was announced as they entered the East Room of the White House. When the president asked them how they had achieved such great success, the reply came back immediately: “Madam President, we owe all of our success to our belief in THE FORCE”. The audience applauded, cameras flashed, and the president smiled as she presented each of them with the Presidential Medal Of Freedom.

The End.