Tag Archives: analytics

We’re Hiring: Open-Rank, Tenure-Track

Open-Rank Tenure-Track Faculty Position in Management Science

The Management Science Department at the Miami Business School invites applications for an open-rank, tenure-track faculty position to begin in the fall of 2019. Applicants with research interests in all areas of Operations Research and Analytics will be considered.

Applicants should possess, or be close to completing, a Ph.D. in Operations Research, Operations Management, Industrial Engineering, or a related discipline by the start date of employment. The Management Science Department consists of a diverse group of faculty with expertise in Statistics and Operations Research. We seek candidates whose research and teaching interests complement and strengthen our existing departmental strengths. Responsibilities include research, which is expected to lead to top-tier publications, teaching at both the undergraduate and graduate levels, and service.

Applications should be submitted by e-mail to MASrecruiting@bus.miami.edu, and should include the following: a curriculum vitae, up to three representative publications, brief research and teaching statements, an official graduate transcript, information about teaching experience and evaluations (if available), and three letters of recommendation. All applications completed by December 1, 2018 will receive full consideration, but candidates are urged to submit all required material as soon as possible. Applications will be accepted until the position is filled. Candidates who are presenting at the INFORMS Annual Meeting in Phoenix are encouraged to include the details of their session in their cover letter. Our faculty attending the meeting will try to attend these presentations and may arrange for interviews whenever possible.

Salary is competitive and commensurate with qualifications and experience. This is a nine-month appointment and summer research support is anticipated from the Business School.

The University of Miami offers a comprehensive benefits package including medical and dental benefits, tuition remission, and much more. For additional information, visit https://www.hr.miami.edu/working-at-the-u/new-employee-total-rewards/index.html. The University is an equal opportunity employer and encourages candidates regardless of gender, race, color, ethnicity, age, disability status or sexual orientation to apply.

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How to Optimally Allocate “Bribe” Money

 As I sat through a 4-hour meeting the other day, I had an idea for a very useful and generic prescriptive analytics model to be used in conjunction with the results of a statistical learning study (e.g. a regression model). I can immediately see a large number of applications for this model, some of which I’ll illustrate in this post. I also believe this idea could be turned into an interesting case study (combining predictive and prescriptive analytics) for class discussion in a master’s level course (MBA or specialized MS), and I’d love to partner with someone to turn that into reality. Let me know if you’d like to pursue this! (Disclaimer: I don’t have much experience writing cases, but I want to get better at it.)

Without further ado…

You’re interested in gaining a better understanding of the likelihood, or probability p, that certain agents will perform a given action that benefits you. So you hire a group of business analytics consultants who create a statistical learning model M that predicts the value of p with high accuracy given an agent (and their attributes/characteristics) as input.

Those agents with high-enough probability of performing the action, say p \geq 50\% (a subjective choice), don’t concern you too much because they’re already on your side. Your focus is on those whose value of p is less than 50%. One of the outcomes of model M is a number for each agent (or group of similar agents) indicating how much of an effect giving that agent a financial incentive would have on the value of their action probability p. For example, this number could be 0.1 for a given agent, indicating that they’d be 10% more likely to perform the action for each $1000 of incentive they receive. So, assuming a linear model, if their originally predicted p were 35%, giving them $2000 would be enough to push them beyond your target 50% threshold. (Considering incentives are given in multiples of $1000.)

Before we go any further, let me make this more concrete with a few examples.

Example 1: You are the seller of a product. Agents are customers. The action is purchasing your product. The financial incentive is a discount. You have a budget for the overall discount you can give and you want to optimally allocate different discount amounts to different customers to bring as many of them as possible to the brink of buying your product (say, a 50% chance).

Example 2: You are a politician. Agents are voters. The action is voting for you. The financial incentive is a bribe. This happens in Brazil and likely in other countries as well. (Disclaimer: I’m not advocating that this is an ethical or moral thing to do. Like any superpower, however, mathematics can be used by the dark side as well.)

Example 3: You are a university. Agents are admitted students. The action is picking you to attend. The financial incentive is a scholarship.

I’m sure you can come up with other examples. Let me know in the comments!

So the big question is: How do we optimally allocate the limited pool of financial incentives? Optimization to the rescue! I’ll provide a link to an Excel spreadsheet below, but first let’s understand the math behind it.

Given n agents, for each agent i, let b_i be how much the agent’s action probability (before the incentive) is below my target threshold, and let c_i be how much agent i‘s action probability increases for each f dollars of financial incentive. In my example above, b_i=0.15 (50% minus 35%), c_i=0.1, and f=\$1000.

Let’s create two variables for each agent i. Variable x_i is an integer number indicating how many f-dollar incentives we decide to give to that agent. And variable y_i is a binary (yes/no) variable indicating whether or not we manage to bring agent i to our side (i.e. whether we raised his/her action probability beyond the threshold).

To indicate that our goal is to push as many agents beyond the action threshold as possible, we write

\displaystyle \max \sum_{i=1}^n y_i

If our total budget for financial incentives is B, we respect the budget with the following constraint (note that the sum of all x_i equals the total number off-dollar financial incentive packages given away):

\displaystyle f \sum_{i=1}^n x_i \leq B

Then, if we want y_i to be equal to 1 (meaning “yes”), we need x_i to be high enough for the increase in the agent’s action probability to exceed the threshold value. This can be accomplished with these constraints

\displaystyle b_i y_i \leq c_i x_i

In my earlier example, the above constraint would read

\displaystyle 0.15 y_i \leq 0.1 x_i

Therefore, unless x_i is at least 2, the value of y_i cannot be equal to 1.

That’s it! We are done with the math. Wasn’t that beautiful?

Here’s an Excel spreadsheet that implements this model for a random instance of this problem with 20 agents. It’s already set up with all you need to run the Solver add-in. Feel free to play with it and let me know if you have any questions.

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Filed under Analytics, Applications, Integer Programming, Mathematical Programming, Modeling, Motivation, Promoting OR, Teaching

Tenure-Track Position in Big Data Analytics, University of Miami, School of Business

I’m very happy to announce that the School of Business at the University of Miami is hiring in my department! Details below. This is an exciting time to be involved in Business Analytics!

Tenure-Track Faculty Position in Management Science (Big Data Analytics)

The Management Science Department at the University of Miami’s School of Business Administration invites applications for a tenure-track faculty position at the junior or advanced Assistant Professor level to begin in the Fall of 2015. Exceptional candidates at higher ranks will be considered subject to additional approval from the administration. Salaries are extremely competitive and commensurate with background and experience. This is a nine-month appointment but generous summer research support is anticipated from the School of Business.

Applicants with research interests in all areas of Analytics will be considered, although primary consideration will be given to those with expertise in Big Data Analytics and the computational challenges of dealing with large data sets. Expertise in, or experience with, one or more of the following is particularly welcome: MapReduce/Hadoop, Mahout, Cassandra, cloud computing, mobile/wearable technologies, social media analytics, recommendation systems, data mining and machine learning, and text mining. The Management Science Department is a diverse group of faculty with expertise in several areas within Operations Research and Analytics, including statistics and machine learning, optimization, simulation, and quality management. Duties will include research and teaching at the graduate and undergraduate levels.

Applicants should possess, or be close to completing, a PhD in computer science, operations research, statistics, or a related discipline by the start date of employment. Applications should be submitted by e-mail to facultyaffairs@bus.miami.edu, and should include the following: a curriculum vitae, up to three representative publications, brief research and teaching statements, an official graduate transcript (for the junior Assistant Professor level), information about teaching experience and performance evaluations, and three letters of recommendation. All applications completed by December 1, 2014 will receive full consideration, but candidates are urged to submit all required material as soon as possible. Applications will be accepted until the position is filled.

The University of Miami offers a comprehensive benefits package including medical and dental benefits, tuition remission, vacation, paid holidays, and much more. The University of Miami is an Equal Opportunity/Affirmative Action Employer.

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The First Sentence of the Great Analytics Novel

Thedarktower7 I’ve written many times before about the importance of promoting O.R. to the general public. One of the ideas that’s been suggested by several people is the possibility of writing a work of fiction whose main character (our hero) is an O.R./Analytics person. I still believe this is a great idea, if executed properly.

Today, my wife brought to my attention The Bulwer-Lytton Fiction Contest, which, according to their web page, consists of the following:

Since 1982 the English Department at San Jose State University has sponsored the Bulwer-Lytton Fiction Contest, a whimsical literary competition that challenges entrants to compose the opening sentence to the worst of all possible novels. The contest (hereafter referred to as the BLFC) was the brainchild (or Rosemary’s baby) of Professor Scott Rice, whose graduate school excavations unearthed the source of the line “It was a dark and stormy night.” Sentenced to write a seminar paper on a minor Victorian novelist, he chose the man with the funny hyphenated name, Edward George Bulwer-Lytton, who was best known for perpetrating The Last Days of PompeiiEugene AramRienziThe CaxtonsThe Coming Race, and – not least – Paul Clifford, whose famous opener has been plagiarized repeatedly by the cartoon beagle Snoopy. No less impressively, Lytton coined phrases that have become common parlance in our language: “the pen is mightier than the sword,” “the great unwashed,” and “the almighty dollar” (the latter from The Coming Race, now available from Broadview Press).

Just like an awful first sentence can be a good indicator of a terrible book, the converse can also be true. Take, for example, the first sentence of Stephen King’s The Dark Tower series, which I happen to be reading (and loving) as we speak:

The man in black fled across the desert, and the gunslinger followed.

It’s such a strong, mysterious, and captivating sentence…

…which brings me to the point of this post. If it’s going to be difficult to write The Great Analytics Novel, what if we start by thinking about what would be the perfect, most compelling sentence to start such a novel? Yes, I propose a contest. Let’s use our artistic abilities and suggest starting sentences. Feel free to add them as comments to this post. Who knows? Maybe someone will get inspired and start writing the novel.

Here’s mine:

Upon using the word “mathematical” he knew he had lost the battle for, despite the dramatic cost savings, their logical reasoning was instantly halted, like a snowshoe hare frozen in fear of its chief predator: the Canada lynx.

I can’t wait to read your submissions!

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Filed under Analytics, Books, Challenge, INFORMS Public Information Committee, Motivation, Promoting OR

Should You Hire Security When Tenting Your House?

Last week I had my house tented because of termites. For those of you who don’t know what “tenting” is (I didn’t until about a year ago), it amounts to wrapping an entire house inside a huge tent and filling the tent with a poisonous gas that kills everything inside (and by everything I really do mean everything). Those who have been through this experience know what a hassle it is. We received a to-do list of pre-tenting tasks, which included:

  • Remove or discard all food that isn’t canned or packaged in tightly-sealed, never-opened containers
  • Turn off all A/C units and open one window in each room of the house
  • Open all closet and cabinet doors
  • Turn off all internal and external lights (including those operating on a timer)
  • Prune/move all outdoor plants away from the house to have a clearance of at least 18 inches
  • Soak the soil around the house (up to a foot away from the structure) on the day of the tenting
  • Warn your neighbors about the tenting (so that they can keep their pets away from the house)
  • etc.

We had to sleep two nights in a hotel, with two dogs, one of which had just had knee surgery. What an adventure!

The main point of concern was that the house would stay vulnerable (open windows) and unattended during the process. On top of that, one of our neighbors told us that he knew of a house that had been robbed during tenting a couple of months ago. So we started to consider hiring a security guard to sit outside the house for 48 hours. Would that be a good idea? Let’s think about this.

Our insurance’s deductible is $2500. I assume that if thieves are willing to risk their lives (wearing gas masks; oh yeah! they do that!) to enter a tented house, they’d steal more than $2500 worth of stuff. Therefore, being robbed would cost us $2500. This doesn’t take into account that one might have irreplaceable items in the house. However, most of the time those can be taken with you (unless they are too big or inconvenient to carry). In my case, I took the external hard drive to which I back up my data, and the mechanical pencil I’ve owned and used since 1991 (yes, you guessed right, the eraser at the end doesn’t exist any more). The security company we called would charge $15 per hour for an unarmed guard to be outside our house. Multiplying that by 48 hours brings the cost of hiring security to $720.

Let’s say that the likelihood (a.k.a. probability) of being robbed while your house is tented without a security guard is p_1 (in percentage terms; for example, p_1 for the White House is pretty close to 0%), and when a security guard is on duty that likelihood is p_2. Unless p_1 > p_2, there’s no point in having this entire discussion, so I’ll assume that is true. Here’s a pretty neat rule of thumb that you can use: divide the cost of hiring security by your deductible to get a number n between zero and one (of course, if hiring a guard costs more than your deductible, don’t do it!). Unless the presence of the guard reduces your chance of being robbed (p_1) by more than n, you should not hire security! (Later on, I’ll explain where this rule comes from.) For example, in my case 720/2500 is approximately equal to 29%. If the chance of being robbed without security is 30%, unless hiring a guard brings that chance down to 1% or less, it’s better not to do it. If the value of p_1 is less than or equal to 29% to begin with (I live in a reasonably safe neighborhood), the answer is also not to hire security (probabilities cannot be negative). This rule works regardless of the value of p_1; what matters is how great the improvement to p_1 is.

In addition to looking at the numbers, we also took into account the following clause from the security company’s contract:

…the Agency makes no warranty or guarantee, including any implied warranty of merchantability or fitness, that the service supplied will avert or prevent occurrences or the losses there from which the service is designed to detect or avert.

In other words, if you hire us (the security company) and still get robbed, we have nothing to lose!

So what did we do? We chose not to hire security and, fortunately, our house was not robbed. However, even though the tenting instructions  say that you don’t have to wash your glasses and plates after returning home, we decided to do so anyway (as they say in Brazil: “seguro morreu de velho”).

Disclaimer: The advice contained herein does not guarantee that your house will not be robbed. Use it at your own risk!

Details of the Analysis

So where does that rule of thumb come from? We can look at this problem from the point of view of a decision tree, as pictured below.

In node 0, we make one of two decisions: hire a security guard (payoff = -$720, i.e. a cost), or not (payoff = -$0). For each of those decisions (branches), we create event nodes (1 and 2) to take into account the possibility of being robbed. At the top branch of the tree (node 2), the house will be robbed with probability p_2, in which case we incur an additional cost of $2500, and the house will be safe with probability (1-p_2), in which case we incur no additional expense. Therefore, the expected monetary value of hiring security, which we call EMV_2, is to spend $720+$2500 with probability p_2, and to spend $720 with probability (1-p_2). Hence

EMV_2 = - 3220p_2 - 720(1-p_2) = - 2500p_2 - 720

Through a similar analysis of the bottom branch (node 1), we conclude that the expected monetary value of not hiring security, which we call EMV_1, is to spend $2500 with probability p_1 and to spend $0 with probability (1-p_1). Therefore

EMV_1 = -2500p_1 - 0(1-p_1) = - 2500p_1

Hiring security will be the best choice when it has greater expected monetary value than not hiring security, that is when EMV_2 > EMV_1, which yields

-2500p_2 - 720 > -2500p_1

\Downarrow

2500(p_1 - p_2) > 720

\Downarrow

p_1 - p_2 > \frac{720}{2500}

which is the result we talked about earlier (recall that p_1 > p_2).

How Does Analytics Fit In?

The Analytics process is composed of three main phases: descriptive (what does the data tell you about what has happened?), predictive (what does the data tell you about what’s likely to happen?), and prescriptive (what should you do given what you learned from the data?). In this problem we can identify a descriptive phase in which we try to obtain probabilities p_1 and p_2. This could be accomplished by looking at police or insurance company records of robberies in your area. It’s not always possible to get a hold of those records, of course, so one might need to get a little creative in estimating those numbers. Having knowledge of the probabilities, the calculation described above could be classified as a prescriptive phase: what’s the course of action? Hire security if (cost of security)/(insurance deductible) < p_1 - p_2. There is no predictive phase here because our analysis does not require the knowledge of any future event (only how likely it is to occur). Operations Research can be used in some or all of these phases. Most of what I do in my research and consulting projects lies in the prescriptive phase (optimization). Recently, however, I’ve decided to broaden my horizons and learn more about the other two phases as well, starting with some self-teaching of data mining.

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Filed under Analytics, Applications, Decision Trees, INFORMS Monthly Blog Challenge, Security

INFORMS 2010 Wrap-Up

I had a very productive and fun time at the INFORMS Annual Meeting in Austin, Texas. So I thought I’d share with you some of my observations about the meeting:

  • Analytics initiative by INFORMS: great idea! I believe that we (INFORMS members) have to jump onto the Analytics bandwagon and let everyone know that we can do Analytics too! It’s all about the power of words these days (see the country’s political arena for a perfect example). Starting today, I’ll tell everyone that I can do “Advanced Prescriptive Analytics” (optimization).
  • Panel on Social Networks: it was nice to hear from some of the most popular OR bloggers and learn about their motivations, fun stories, and blogging strategies. Great job Mike and Laura!
  • John Birge‘s plenary (Omega Rho Distinguished Lecture): very informative and entertaining. The simple and powerful take-home message was: align incentives. What’s good for the employees has to be good for the company as well.
  • RAS Problem Solving Competition: Michael Trick and I (a.k.a. Team MATHY) received an honorable mention at the Railway Applications Section (RAS) 2010 Challenge (certificate + photo :-). After watching the three finalists’ presentations, we were happy to see that our solution value of $11,399,670.88 was equal to the best solution found by the winning team, and better than the solution found by the other two finalists. Nobody managed to prove optimality, though. Interestingly, none of the finalists thought about scaling the problem (thinking in terms of tanks of gasoline instead of gallons of gasoline), which made a huge difference in the performance of our model. Overall, it was a lot of fun to participate in this competition and I want to thank the organizers for putting it together. Here’s a picture of team MATHY with Juan Morales from BNSF Railway.

    And here’s a picture of the railroad network we had to deal with:

  • Technical sessions: I watched many interesting and inspiring talks (as a matter of fact, I had some great ideas for my own research while watching a number of presentations). It’s nice to see that *a lot* of people are using Latex Beamer these days. Let’s aim for a Powerpoint-free INFORMS by 2020!
  • Idea for an iPhone App? I applaud the going-green initiative of reducing the number of conference program booklets that have to be printed out. However, for this to work it requires a lot of organization from each of us: we have to go over the program in advance, select the talks we want, and print the appropriate pages. I don’t know about you, but I never manage to get this done. So I propose we create an iPhone (mobile) app to allow participants to browse the program on-the-go. It’s not convenient to browse the program PDF on a phone. We need an app. We need to be able to filter by author, by chair, by topic/keyword, etc. We want a time-sensitive app that tells you what’s next. We want an app that sends notifications to your phone reminding you that a talk/event is coming up so that you ask for the check in time. If we had something like that, I think that a lot fewer people would ask for a printed program (myself included).
  • Thumbs up for all the vegetarian food: being a vegetarian myself, I was impressed with the generous availability of vegetarian food (i.e. not only salad) at both the Sunday and Tuesday receptions. Well done!
  • Austin’s Convention Center: in addition to being huge, the convention center’s numerous under-construction areas made it very hard to navigate from session to session. I always felt like I was taking the longest path from point A to point B.
  • Meeting old and new friends: it was great to make many new friends and to meet old friends from my PhD days at Carnegie Mellon at the Tepper Alumni reception. I also had some very productive research meetings with several colleagues.

Last but not least, I’d like to thank John Hooker, Christopher Beck, and Willem-Jan van Hoeve for agreeing to give a talk in my session, and Willem for inviting me to present in his session.

Time to say bye-bye to weird Austin and fly back to Miami! Hence, I had to put on my “U” shirt:

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Filed under Analytics, Challenge, INFORMS, iPhone, Travel

Cited by Competing on Analytics, by mistake…

I just found out that one of my papers (Building Efficient Product Portfolios at John Deere and Company, co-authored with D. Napolitano, A. Scheller-Wolf and Sridhar Tayur), has been cited in chapter 2 of the book “Competing on Analytics: The New Science of Winning“, by Thomas Davenport and Jeanne Harris. The chapter is entitled “What Makes an Analytical Competitor?” (link to PDF). Another interesting coincidence was that today I listened to the Science of Better Podcast featuring Thomas Davenport! I was excited to see the context of the citation, but it all turned out to be a mistake :-( The authors refer to the work on “direct derivative estimation of non-stationary inventory” (which was also done by SmartOps, saving Deere 1.2 billion dollars over 5 years) and cite our paper as a reference for it. Could it have been because our paper has the words “John” and “Deere” in the title? On a positive note, however, this may drive some readers to our paper, which is, IMHO, a great read anyway.

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