Tag: Dr. Steven Harrod

Guest Post: Teaching OM in the European Classroom

Dr. Steven Harrod is Associate Professor in the Department of Management Engineering at Technical University of Denmark.

Will this be on the exam?” In my decade of teaching OM in the U.S., this was by far the most frequent question from students. The typical American course is assessed on a running sum of credit for attendance, participation, written homework, midterm and progress exams, and a final exam, much like the bill after a stay at a Hilton resort.

Speaking from my experience in Denmark, the European evaluation process and value system is much different. One of the clearest ways I have experienced this is with Heizer/Render/Munson’s popular  MyOMLab system. In the U.S., MyOMLab was well received and many students actively worked homework assignments to accumulate grade points. Here in Denmark, the response was, “Just give us the questions and the answers, and we’ll figure it out ourselves.”

Students are definitely more independent here than in the U.S. Written exams, numerical or multiple choice, are limited to courses with more than 40 students. Since all of my courses are smaller, I have exclusively held oral exams. An oral exam typically offers the student the opportunity to set the topic of the exam. Very often the examiners are external, not participants in the course delivery, and brought in at exam time. The student has a very real chance to emphasize topics of strength and avoid topics of weakness, and is very much “in the driver’s seat.”

And the examiners often represent potential employers, which makes for a different student to job market relationship than in the U.S. Although students here desire recognition and good grades, there is a relaxed relationship between students and the job market that supports a more exploratory, investigative education. The typical course I teach here is a form of “mass customization.” The lesson plan often contains multiple streams of related concepts.

The learning environment in Europe offers some interesting opportunities for exploration and growth, but it is also dependent on the many structural and cultural elements of Europe.

Guest Post: A Teaching Tip for Visualizing Waiting Line Flows and Interarrival Times

steve harrodWhile Jay and Barry attend the INFORMS meeting in San Antonio, Dr. Steven Harrod, Assistant Professor of Operations Management at the University of Dayton provides this, his 5th Guest Post for our blog.

I find in teaching Waiting Line Models in Module D of the Heizer/Render text that the definitions and relationship of rates of flow and inter-arrival times are frustratingly difficult for some students to grasp. Here is a classroom exercise that may help:

Introduce the following video (http://youtu.be/O84FlZnP0qs ), explaining that it shows traffic starting after a red light turns to green. Start the video at 0:17 (prepare the video in advance so you don’t have to watch the commercial during lecture). Draw the students’ attention to the second lane of traffic, headed by the Range Rover.

Explain that this flow of traffic may be described either by how many cars cross the white stop line in a period of time, or by how long each car takes to cross the line. Either way, the traffic flow described is identical. Play the video, and count ten cars across the line (ignore the car that changes lanes). Car number ten is a yellow cab, so stop the video with the eleventh car at the white line. You should find the video counter at 0:42, or 25 elapsed seconds. This is a flow of 10 cars in 25 seconds, or 10/25 cars per second, or 1440 cars per hour.

Restart the video at 0:17. Now, count the seconds between each successive car at the white line. They are, approximately, 5, 3, 2, 1, 3, 2, 2, 3, 2, and 2. The average time between cars is then 2.5 seconds. Now, since in both cases we have witnessed the same traffic flow, make the argument that these two measurements are equivalent. Indeed they are, after you explain that the inter-arrival time of a flow and the rate of flow are inverses of each other. In this case in particular, (1/2.5) = (10/25) and [1/(10/25)] = 2.5.

For some students, this is a difficult concept, and I  repeat this approach multiple times in the course.

Guest Post: A Great Classroom Forecasting Exercise

steve harrodDr. Steven Harrod is Assistant Professor of Operations Management at the University of Dayton and can be reached at steven.harrod@udayton.edu. This is his 3rd guest post for our OM blog.

This large data set, Excel based, forecasting exercise is suitable for an hour lecture, after students have learned basic time series forecast methods in Chapter 4 of the Heizer/Render text. It gives a “real world” experience, and provides an excellent opportunity to visual the significance of error statistics in more detail. Here are instructions:

  1. Distribute the data (here is the link), but do not reveal its source.
  2. Ask the students to experiment (in Excel or Excel OM) by implementing a variety of time series forecast methods (moving average, exponential, etc.). Provide guidance, as you prefer. Give the students time to work independently (or at least in groups without you lecturing).
  3. Intermittently reveal your own progress in completing the steps on a projection screen. In a typical lecture period, you should be able to progress through two or three forecast models, and then pick one of those for error statistics.
  4. Discuss picking a “best” model according to error statistics (MAD, MSE, etc.).
  5. Pick one model, and demonstrate the calculation of the tracking signal. Chart this signal.

For discussion, ask: What are the data? Why is the tracking signal spiking? Reveal that the data are the recorded miles per gallon of a minivan at each fuel tank filling for a period of about two years. The tracking signal is spiking because the family takes vacations, and the mileage shifts from city driving to highway driving. When the tracking signal spikes, it is an indication that some fundamental change has occurred in the underlying process. The tracking signal measures whether the underlying process of the series data is stable. Since a forecast is simply the generation of a trend from a series of data points, the methodology is dependent on the underlying process being stable. If the underlying process is not stable, or experiencing a fundamental change in behavior, the forecast can not accurately predict the trend.

Guest Post: Teaching Inter-Arrival Times in Queuing Models

steve harrodDr. Steven Harrod is Assistant Professor of Operations Management at the University of Dayton and can be reached at steven.harrod@udayton.edu. Here is a link to his syllabus.

The single greatest challenge in teaching queuing in Module D of the Heizer/Render text is getting students to comprehend the difference between inter arrival and rate of flow. There is something deeply psychological and subconscious about this. Students frequently skim over the text and do not distinguish between the two expressions. Here is an in class exercise to practice this concept:
For each scenario in the table below, ask students to calculate the inter-arrival time and the flow rate (with answers shown in the right columns). Clearly state the unit of time measure for each answer. Each description refers to a random flow.

Inter-Arrival=time/(arrival count)       Flow, λ=(arrival count)/time=1/inter-arrivalSrteve Harrod mod. D graphic

Description

Inter-Arrival

λ
The ticket taker at the roller coaster   collected 45 tickets in 3 hours.

4 m

15/h
Cars arrive at the car wash once every 3   minutes.

3 m

20/h
Customers arrive at McDonald’s at the rate of   100 per hour.

0.6 m

100/h
Customers were recorded entering the store at   12:05, 12:07, 12:15, 12:22; 12:30: 12:31, 12:33, 12:40, and 12:50.

5 m

12/h
Fifteen passengers arrived for the 1 pm bus,   then twenty-five passengers arrived for the 2 pm bus, and then ten passengers   arrived for the 3 pm bus.

3.6 m

16.66    /h