Module B – The OM Blog by Heizer, Render, & Munson

Guest Post: Using Solver’s Nonlinear Programming Procedure for Operations Models

Prof. Howard Weiss shares his insights on the power of Excel’s Solver.

I previously have posted for The OM Blog that in the operations course it is important to help students develop their Excel skills. Today I will introduce students to nonlinear programming in Excel’s Solver for Trend Analysis models, a topic in Chapter 4 of your Heizer/Render/Munson text. It highlights to the students exactly what is being optimized – sum of squared errors.

Trend Analysis
Example 8 from Chapter 4 illustrates the Solver process. The initial intercept of 10 and slope of 10 yield the forecasts in column D. Errors and squared errors follow from the forecasts and demands with the sum of squared errors shown in cell F12. This is Solver’s objective. The changing cells are the intercept and slope, there are no constraints, and the method in Solver is GRG Nonlinear. In addition, for least squares the “Make Unconstrained Variables Non-Negative” needs to be unchecked since slopes/trends can be negative in forecasting– although not in this example.

After solving, the solution, not displayed here, appears as Intercept 56.71, slope=10.54 (as shown in the text) and the minimum sum of squares, not shown in the text or figure above, is 773.

OM Podcast #29: The Art & Science of Developing Supply Chain Strategies

Happy New Year! After a brief end of semester break to enjoy the holidays with family, we’re back with the first podcast of 2025. In this podcast, Barry Render interviews Alex Klein, Senior Manager of Supply Chain Solutions for APL Logistics, a third-party logistics provider. Alex and Barry discuss the art and science of developing supply chain strategies for shippers with three fascinating, wide-ranging real world examples from Alex’s career.

Heizer Render OM Podcast · OM Podcast #29: The Art & Science of Developing Supply Chain Strategies

Transcript

A Word document of this podcast will download by clicking the word Transcript above.

Have you subscribed to this podcast on Apple podcasts? Just go to your Apple podcasts app, search “Heizer Render OM Podcast,” and subscribe to get all our podcasts on your mobile device as soon as they come out!

Instructors, assignable auto-graded exercises using this podcast are available in MyLab OM. See our earlier blog post with a recording of author and user Chuck Munson to learn how to find these, or contact your Pearson rep to learn more! https://www.pearson.com/en-us/help-and-support/contact-us/find-a-rep.html

Video Tip: Using Our Five Alaska Airlines Video Case Studies

Barry and Jay filming in an Alaska Airlines cockpit — Barry and Jay filming the videos in an Alaska Airlines cockpit

The Wall Street Journal‘s annual scorecard of U.S. airline performance (Jan. 12, 2017), which ranks major carriers on 7 different measures important to travelers, has just been released. We note that the company we prominently feature in our latest edition, Alaska Airlines, topped the scorecard as the best overall performer for the 4th-straight year, edging out Delta. Alaska also scored 1st in: on-time arrivals, least extreme delays, least 2-hour tarmac delays, and in least number of complaints. It was 3rd in cancelled flights and involuntary bumping, and 4th in mishandled bags.

The Seattle-based airline says its poor baggage showing in the 2016 scorecard drove a deep study of which flights were causing the most mishandled bags. Alaska began bar-code scanning of every bag going on and off planes. It also figured out which cities, which shifts and which flights had the most problems and found delays with bags transferring from other airlines. So instead of waiting for bags to come through an airport sorting system, Alaska now takes carts to other airlines in Seattle and waits for connecting bags at the tails of arriving airplanes.

Here are the 5 short videos we provide free to adopters:

Quality Counts at Alaska Airlines (Ch.6): “If it is not measured, it is not managed,” says one Alaska exec in this case that provides explicit performance metrics.

Alaska Airlines: 20-Minute Baggage Process–Guaranteed! (Ch.7): Students can flowchart the process a bag follows from kiosk to destination carousel after watching this video.

The People Focus: Human Resources at Alaska Airlines (Ch.10): The employee “Empowerment Toolkit” reminds us of Ritz Carlton’s famous customer service philosophy.

Lean Operations at Alaska Airlines (Ch.16): The company’s aggressive implementation of Lean includes its 6-sigma Green Belt training, Kaizen events, Gemba Walks, and 5S applications.

Scheduling Challenges at Alaska Airlines (Module B and Ch.15): Good scheduling of crews and planes means optimization–the perfect fit for our coverage of LP and scheduling.

OM is indeed a centerpiece of Alaska’s success and we think your students will enjoy these videos.

Good OM Reading: The Hard Work Behind Analytics Success

The hype around business analytics, our topics in Modules A-F, has reached a fever pitch. From baseball to biomedical advances, stories abound about data scientists applying their wizard like talents to find untapped markets, make millions, or save lives. Data has been described as the new oil, the new soil, the next big thing, and the force behind a new management revolution. Despite the hype, the reality is that many companies still struggle to figure out how to use analytics to take advantage of their data. The experience of managers grappling, sometimes unsuccessfully, with ever-increasing amounts of data and sophisticated analytics is often more the rule than the exception, concludes a new MIT Sloan Management Review study (March, 2016).

Five key findings came from the research:

Competitive advantage with analytics is waning. The percentage of companies that report obtaining a competitive advantage with analytics has declined significantly as increased market adoption of analytics levels the playing field and makes it more difficult for companies to keep their edge.
Optimism about the potential of analytics remains strong, despite the decline in competitive advantage. Most managers are still quite positive about its potential. They’ve seen increased interest in analytics over the past few years, and they expect its use to continue to grow.
Achieving competitive advantage with analytics requires a sustained commitment to changing the role of data in decision making. This commitment touches many organizational aspects, from revamping information management to adapting cultural norms.
Companies that are successful with analytics are much more likely to have a strategic plan for analytics, and this plan is usually aligned with the organization’s overall corporate strategy.
Most companies are not prepared for the investment and cultural change that are required to achieve sustained success with analytics, including expanding the skill set of managers who use data and broadening the types of decisions influenced by data.

Guest Post: Building Furniture in a Linear Programming Class

Today’s Guest Post is by Dr. Robert Donnelly, Professor of Management at Goldey-Beacom College, in Delaware, who describes one exercise he uses to teach LP (Module B).

One of the challenges of teaching linear programming is the intangible nature of the topic which causes many students to have anxiety. One method that I use in my LP class is to pass out 6 large and 8 small Duplo-sized Legos to small groups of students and ask them to build tables and chairs. This is an example of a product mix problem with the objective of maximizing profit.

As you can see, each table has 2 large and 2 small blocks while each chair has 1 large and 2 small blocks. Each table contributes $16 in profit while each chair contributes $10 in profit.

	Tables	Chairs	Availability
Large Blocks	2	1	6
Small Blocks	2	2	8
Profit	$16	$10

I start a class discussion on the feasible solutions that were discovered along the way to the optimal solution, which is to produce 2 tables and 2 chairs earning a profit of $52.

Tables	Chairs	Profit
0	4	$40
1	3	$46
2	2	$52
3	0	$48

I solve this problem graphically showing the 4 corner points and the optimal solution.

I introduce shadow prices by offering the students an additional large block and ask how much they are willing to pay for it (answer = $6). I show a second large block and ask how much the students are willing to pay for it (answer = $6). Finally, I show the students a third large block and ask how much they will pay for it (answer = $0). I lead a discussion on the concept of shadow prices and finally wrap up with solving the Legos problem on Excel.

I find that playing with Legos in class lightens the mood and makes LP more understandable.

OM in the News: Employee Scheduling with Commercial Software

Dayforce software gives users an efficiency score for their scheduling

Say you own a thriving bakery that’s open 12 hours a day, seven days a week, and you have 9 full-time employees and 11 part-timers. The shop is busiest between 7 a.m. and 9 a.m. and then again from 11:30 a.m. to 3 p.m. How many people do you need on the noon to 6 p.m. shift? This common problem is one we discuss in both Chapter 15 (Short-Term Scheduling) and Module B (Linear Programming). Behind the scenes are scores of software programs available to help businesses manage their scheduling conundrums, reports The Wall Street Journal (Sept. 16, 2013).

The biggest name in the “workforce management” software business, Kronos Inc., offers Workforce Ready, which allows small employers to start out with a single application, such as basic scheduling, and then add extra features, from calculating accrued time off to administering payroll. Kronos’s Workforce Central lets larger employers oversee a complex multistate or global employee base.

Because they can monitor workers’ hours better than traditional methods, the programs also allow employers to keep closer track of employees—reducing overtime, for instance, and syncing up with electronic time clocks to monitor tardiness and break times. And scheduling programs offer the ability to integrate with payroll software to tally workers’ paychecks, which helps reduce the simple mathematical errors that can plague manual scheduling and payroll processes.

There are benefits for workers as well. Ceridian’s Dayforce allows employees to view their schedules online, swap shifts with co-workers and record their availability. Guitar Center Inc., a music-instrument retailer with around 240 locations, began using Dayforce in 2010 after years of managing schedules with Excel spreadsheets. “Now we load customer traffic and transactions in 15-minute intervals into Dayforce, and it generates labor-demand curves that let each store know how many people they should staff for every 15 minutes,” says a Guitar Center exec.

Classroom discussion questions:

1. How can linear programming be used to solve scheduling problems?

2. Why is commercial software so useful?

Guest Post: Making LP Relevant to Students

Dr. Steven Harrod is Assistant Professor of Operations Management at the University of Dayton. He shares a tip on teaching LP today.

It takes some creativity to make linear programming (see Module B in the Heizer/Render text) relevant to students. Here is an activity that offers a discussion of energy, transportation, and air pollution. The topic is coal-burning electric power plants, and it is an example of the blending problem.

Nearly half of all electricity in the U.S. is produced by burning coal, and nearly all of this coal moves by rail. Coal is an organic material that varies considerably in cost, power, and pollution content. Power plants frequently blend different coals to achieve their desired performance. Trains magazine published a detailed article on the movement of coal and its consumption by electric power plants in 2010. The readings and class materials may be downloaded here.

The documents package includes a quiz you may assign to motivate the reading assignment, a longer version of this Guest Post, and a sample spreadsheet model. Start the class discussion by drawing the class’s attention to the power plant at Monroe, Michigan. If you have an overhead projector with internet access, use Google maps to display a satellite photo of the plant. The lakeside plant has a prominent railroad loop and coal storage facility. You may also wish to explain how a power plant converts coal into electricity, and the environment concerns (sulfur causes acid rain and ash must be disposed of).

The challenge question for the students is: what coal should this plant purchase to satisfy energy and pollution limits at minimum cost? The formulated and solved LP leads to an optimal blend of three of the five coal sources. Ask the students, “is this intuitive?” Would you have been able to reach this conclusion without LP? Discuss at length and experiment with reducing or eliminating the pollution limits. This exercise may lead to a lengthy discussion of energy policy, environmental policy, and their joint effect on transportation demand.

Guest Post: Using Excel Teaching Videos at U. of Dayton

Dr. Steven Harrod is Assistant Professor of Operations Management at the University of Dayton. If you teach Excel modeling in your OM class, you will enjoy Steve’s detailed “how to” videos.

Videos are a great way to help students learn the complexities of modeling in spreadsheets. Students can be overwhelmed when this material is presented live in lecture. Too often, students experience computer technical problems, and become lost in lecture while they attempt to fix their technical issue. These videos allow students to follow the lesson at their own pace, as they resolve problems such as installing add-ins (or even charging their battery!).

The first video supports Chapter 4, Forecasting. Titled “Creating an Exponential Forecast in Excel, Including Error Statistics” (http://youtu.be/uHy5tG1Rdvg), it walks
students through the process of formatting a spreadsheet, generating an exponential forecast, and calculating error statistics for that forecast. This video, and the others, was purposely recorded at a low screen resolution and high magnification, so that the visual clarity is high. The runtime for this video is 23 minutes.

The second and third videos are fundamentally linear programming exercises. “How to Create a Linear Programming Transportation Model” (http://youtu.be/RZX2bmoCzLI) is self-explanatory (runtime 14 minutes). Sharp eyed readers will recognize this as Problem 16 from Module B.

“Aggregate Planning on Microsoft Excel, Transportation Model” (http://youtu.be/m44gSMpb3Ic) is another transportation model, this time from Chapter 13, Aggregate Planning. This problem is “Planning Example 1” from the Heizer/Render text (Tables 13.2 and 13.3). This video is longer, at 36 minutes, because the data entry and model structure for the aggregate planning problem are more complex.

I hope you find these videos useful in your course presentation, and I welcome your comments at steven.harrod@udayton.edu.

OM in the News: Art and Science of Scheduling the N.F.L.

nfl scheduling — Howard Katz, NFL scheduling tzar

“We’re geniuses one day and absolute morons the next,” says Howard Katz, director of scheduling for the National Football League. That’s because Katz must consider a confounding array of factors, from the N.F.L.’s expanded Thursday night package, which gives each team a game in a short week, to potential baseball playoff situations that could affect the availability of stadiums and parking lots in October.

The New York Times (April 20,2012) reports that for the networks that pay billions of dollars to carry N.F.L. games, Katz’s staff has been mostly geniuses. N.F.L. games were watched by an average of 17.5 million viewers last season. N.F.L. games accounted for 23 of the 25 most-watched television shows among all programming, and the 16 most-watched shows on cable last fall.

Designing a schedule that generates those ratings, while also guaranteeing competitive fairness, is more complicated than ever, even though software spits out 400,000 complete or partial schedules (once done entirely by hand) from a possible 824 trillion game combinations. Katz starts with thousands of seed schedules, empty slates in which a handful of critical games with attractive story lines are placed in select spots. Then the computers generate possibilities around those games.

The N.F.L. also feeds the computer with penalties for situations it prefers to avoid — three-game trips, for example, or teams starting with two road games. There are requests not to play at home on certain holidays — the Jets and the Giants typically ask not to play home games during the Jewish High Holy Days. This year, the software generated 14,000 playable schedules, which were reduced to 150 with an eyeball test. Katz reviewed those 150 by hand, scoring them for each team and each network.

Linear programming may be at the heart of scheduling, but the process is definitely part art and part science.

Discussion questions:

1. Why is scheduling sports teams so complex?

2. Are all the teams happy with the final schedules?

Good OM Reading: Using LP to Schedule NCAA Basketball Tournament Games

What could be more timely than an article in the Journal of the Operations Research Society called “Team Assignments and Scheduling for the NCAA Basketball Tournament.” The paper, by U. of Alabama professors S.H. Melouk and B.B. Keskin, provides a wonderful example to use in class when you teach linear programming, in Module B.

The authors write: “The buzz of the tournament and the fanatical behavior of the followers of the participating teams serve as our motivation to examine and develop a team assignment model that maintains the integrity of the tournament while also attempting to place teams closer to their campus location, thus making it easier for both fans and teams to travel to the game sites. Observation of game venues shows a decrease in the actual attendance at early round tournament games. In 2010, actual attendance at the early round game sites was, on average, 83.5% of capacity. This statistic is surprisingly low. A likely contributing factor is the long distances that fans must travel to attend games.”

The growing NCAA concern is travel expenses of the participating teams, as the NCAA reimburses each team for their travel to tournament games. Given there are 68 tournament teams, it is a significant expense to transport the players, their equipment, and coaching staffs to game sites. In an effort to curb expenses, the NCAA requires a minimum distance of 350 miles from a game site before air travel is reimbursable.

The article describes the development of an integer LP program designed to optimize team assignments in the sense of minimizing the total distance travelled by teams to game sites. Results of testing the model against actual tournament assignments show consistent and significant cost savings and reductions in distance travelled. In fact, 28,202 travel miles were saved in 2010 with use of the LP model.

Good OM Reading: Analytics–The Widening Divide

Why don’t more managers embrace the business analytic tools we use in so many aspects of our OM courses? A new report by MIT Sloan Management Review (Nov.8, 2011) answers the question with a survey of 4,500 executives regarding the integration of analytics in their enterprises. The report, Analytics: The Widening Divide, concludes that cultural biases, such as the need for new management competencies and organizational resistance to new ideas –more than technological hurdles–are the primary barriers.

First, a definition of business analytics: “the use of statistical, quantitative, predictive, and other models to drive fact-based planning, decisions, execution, management, measurement, and learning. Analytics may be descriptive, predictive. or prescriptive”.

The MIT Sloan report breaks companies down into 3 categories: Transformed (heavy users), Experienced (moderate users), and Aspirational (companies least experienced in the use of business analytics). The good news is that the number of firms in the 1st two categories, who use analytics for competitive advantage, has surged by 57% in the past year. The Aspirational group’s use of analytics actually declined by 5%. Transformed organizations have set the pace in expanding use of analytics and were found to be 2.2 times more likely to substantially outperform industry peers.

The Transformed group keenly appreciates the value of precise and real-time decisions, and is 3 times more likely to focus on speed of decision-making than Aspirational firms. This means managing operations and improving output levels based on real-time supply and demand management. Inventory replenishment processes, for example, are automated and production is optimized in these companies. As a case study, the report follows McKesson, which processes 2 million hospital orders per day. McKesson does so by embedding algorithms into customer orders to manage the inventory process without human intervention.

When students ask you why analytics are important in your OM course, this report provides a ready response.

Teaching Tip: Helping Your OM Students Find Jobs with Business Analytics

Just a day or two ago, I got a nice email from our colleague Barry Spraggins, who is Chair of the Managerial Sciences Department at the University of Nevada-Reno. Barry uses our text Operations Management, 10th ed., and noted that he teaches heavily from all the Quantitative Modules, including Decision Making Tools (Module A), LP (Mod. B), Transportation (Mod.C), Queuing (Mod.D), Learning Curves (Mod.E), and Simulation (Mod.F). He writes: “I still think these are relevant components”. Not by coincidence, we find that The Wall Street Journal (Aug. 4,2011), Jay and I, and IBM all agree.

The Journal reports that finding qualified graduates in business analytics has proven difficult–and colleges are finally stepping up to meet industry demand. IBM, which spent more than $14 billion since 2005 to buy a flock of analytics companies, has now teamed up with over 200 colleges to develop analytics courses. Fordham and Indiana U. are unveiling analytics curricula, as well as certificate and degree programs. Indiana, for example, is offering certificate programs in business analytics to both Deloitte and Booz Allen employees. Fordam has a required course called Marketing Analytics for MBAs. U. of Virginia, also working with IBM, is introducing an analytics track this fall.

“Analytics is certainly one of the top five things executives are worried about and investing in heavily”, says the president of Teradata. “Industry is going to demand it. Students are going to demand it”. As IBM and other big firms drive the software implementation process to the board room, we in academia may very well see a resurgence of demand for the very topics Prof. Spraggins has long espoused. In a tough job market, this is one way to help our students gain competitive advantage.

Video Tip: Scheduling Employees at Hard Rock Cafe

One of my favorite videos is a short (4.5 min.) study of how Hard Rock Cafe schedules its 160 servers at the giant 1,100 seat Hard Rock here in Orlando. I show it when I teach scheduling (Ch.15) and linear programming (Mod.B in the hard cover text). The topic is one many students relate to, especially if they have worked in retail or restaurants, where schedules are always a sensitive subject.

In Hard Rock’s case, the sales forecast is critical. Many factors are considered in deciding how many servers to call in, including historical sales, major conferences in town, season, etc. Each employee submits a weekly request form, and then an LP package takes over, with the objective of minimizing the number of employees per shift. It turns out that the system works quite well and employees are usually satisfied. Turnover, even during non-recession times, is 1/2 the industry average.

What we don’t mention in the video is that the managers never mastered the scheduling software, which is actually somewhat complex. But one, very enterprising, young server offered to handle the weekly task on his own. He collects all the forms, goes down into a basement office every Saturday, where it takes him about 6 hours to input the data and churn out the schedules. He does this for no additional pay! Why, you ask? Because constraints and schedules are set by seniority, and he is allowed to assign himself the highest priority, a 9. It turns out that a great schedule, at the right work stations, can make the difference of $100’s a week in tips.

This topic is one that students with jobs are more than happy to discuss..