Using a Spreadsheet for Active Learning Projects in Operations ...

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Vol. 8, No. 2, January 2008, pp. 75–88 issn 1532-0545
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I N F O R M S Transactions on Education

Using a Spreadsheet for Active Learning Projects in Operations Management Leslie Gardner

University of Indianapolis, 1400 East Hanna Avenue, Indianapolis, Indiana 46227, [email protected]

T

his paper describes a set of active learning strategies using or supplemented by spreadsheets that enables students to learn the logic behind software packages, to gain experience using a spreadsheet to solve real problems, to have a more clear view of the dynamics of business systems, and to learn to organize and manage small scale projects. Key words: spreadsheet; simulation; mrp; aggregate planning; forecasting; quality control; beer game History: Received: May 20, 2007; accepted: December 21, 2007. This paper was with the author 3 months for 3 revisions.

1.

Introduction

computing infrastructure and for institutions with small or tight budgets, having students build their own software in a spreadsheet can be a much more cost-effective alternative to using expensive, specialpurpose software, such as an Enterprise Resource Planning (ERP) system. The process that I follow for having students build their own software in a spreadsheet is: 1. Teach the underlying logic using one or more small examples in a lecture. 2. Have each student construct basic parts of the project in a computer lab under the supervision of the instructor who may do a step-by-step demonstration. This may be supplemented with a set of hints or detailed instructions, depending upon learning goals, the complexity of the project, and the students’ experience with other projects. Over the course of the semester, as students gain experience with spreadsheet projects, the detail level of instructions diminishes. This forces students to rely more and more on what they have learned from previous spreadsheet projects. 3. Have each student adapt the project to a unique data set provided to them. This may include constructing additional parts of the project that are similar to parts constructed in class. Step 3 needs be done with little or no help from the instructor because the goal is to teach students to work independently. For this reason, I usually provide each student with a different data set so that any collaboration exposes students to different problems and strategies rather than just doing another student’s work.

Active learning, where students are involved in hands-on activities, discussion, teamwork, and problem solving, is widely accepted as a far more effective mode of instruction than lecture alone (Chickering and Gamson 1987, Prince 2004). Using software in an operations management course for projects gives students hands-on activities that are more engaging than paper and pencil homework problems. Software can add relevance by enabling students to work on problems and data sets that are larger than textbook examples and similar in size to those they encounter in the real world. Having students build their own operations management software in a spreadsheet takes students beyond the learning experience of using special purpose software by enabling them: • to learn the logic behind software packages, • to gain experience using a spreadsheet to solve real problems, • to have a more transparent view of the dynamics of business systems, and • to learn to organize and manage small scale projects. Since most students are familiar with spreadsheets, the instructor can focus on the logic of the process, organizing the spreadsheet according to the logic while demonstrating the dynamics of business systems and processes, rather than teach the mechanics of the spreadsheet. Spreadsheet projects are particularly good tools to use with hands-on learners and the charting capabilities make them effective with visual learners. For small institutions with limited 75

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Spreadsheet simulations can also be used to teach business dynamics by supplementing hands-on business simulations such as the beer game. The beer game developed by the Systems Dynamics Group at the Massachusetts Institute of Technology’s Sloan School of Management, is a popular classroom exercise for demonstrating material and information flows in a supply chain. The beer game is played in class to introduce the students to the concepts taught by the game and the concept of simulation. Next, the spreadsheet simulation is introduced to allow students to experiment with and explore the concepts taught in the game. The spreadsheet simulation allows the students to test a strategy almost instantaneously. Testing the strategy by replaying the game would take at least an hour and a half. Again, students are familiar with spreadsheets so it is unnecessary to spend time teaching a specialized simulation package. This strategy introduces the concept of simulation in a concrete way that facilitates student learning.

2.

Learning the Logic Behind the Software Packages

My experience in the world of manufacturing has taught me that many people who work with ERP systems and other software packages really do not understand the logic behind them. Prior to taking an academic position, I worked as a consultant and software developer interfacing, manufacturing, and scheduling systems with ERP systems. I continue to interact with people and ERP systems in the industry through consulting, sabbatical projects, and professional meetings. The users that have the most difficulties with ERP systems, forecasting packages, and other software packages are the users to whom these software packages appear to be black boxes that take data inputs and spit out answers without giving them any insight into the solution process. Users who do not understand how the process behind the software works usually do not understand its limitations and can fail to recognize when data are missing or inappropriate for the application. I want my students to understand how software works so they can use software effectively to give their companies the competitive advantage that the software should give them. A spreadsheet is very effective at letting students see how a process works because all of the formulas and links to cells are visible in the spreadsheet. Many logic errors result in some sort of error message. Logic causing circular references is particularly easy to see in a spreadsheet such as Excel because Excel spreadsheets have arrows and messages that will trace problems to their sources. Data errors can also produce error messages and missing data can appear in the form of empty

cells. Tracking down process, logic, and data errors that are not so visible is an exercise meant to help students better understanding the process and data needs. People who understand the process behind the software can learn new software packages faster and better than those who do not. This is even the case when user interfaces change because the people who understand the process know what to look for. The shorter learning curve for employees who understand the process behind software packages gives companies a significant competitive advantage because new software can be implemented more quickly and be running effectively much sooner than would be the case for employees who do not understand the process. 2.1.

MRP in a Spreadsheet—An Example of Learning the Logic I have implemented a project for students to set up material requirements planning (MRP) logic in a spreadsheet to ensure that my students understand the MRP process in an of ERP system. I do not want my students to become part of the difficulties that companies have with implementing ERP systems because many of their managers do not really understand how MRP works. The project is based on a spreadsheet template from Jayavel Sounderpandian. I have gleaned ideas from Sounderpandian (1989, 1994), Shafer (1991), Frazer and Nakhai (1992), Stevenson (2007) and previous editions, Heizer and Render (2006) and previous editions, and from the The Association for Operations Management (APICS) Certified in Production and Inventory Management (CPIM) course materials. Students begin by setting up an MRP table as shown in Table 1 on a worksheet with 20 periods instead of eight. They enter the gross requirements from the master production schedule for the end item. A generic forecast that can be used for the master production schedule is shown in Table 2. They should also create a table similar to Table 4. Next, students enter formulas into the cells of the spreadsheet in Table 1 to compute net requirements, planned order receipts, and planned order releases. Depending on the proficiency of students with spreadsheets and with the logic, these steps can be facilitated by the instructor by demonstrating the step-by-step process in a computer lab, supplemented with the instructions in Table 3. Finally, the students copy the table several times on the worksheet and set up the MRP planning sheets for the components product having a bill of materials such as the one in Figure 1, the lead time and inventory information such as shown in Table 5 (that they type into the part of the spread sheet illustrated in Table 2), and the forecast for the end item such as shown in Table 2.

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Table 1

Students Set Up the Spreadsheet for the MRP Planning Sheet in This Format

Period

1

2

3

4

5

6

7

Figure 1

Students Use This Generic Bill of Materials to Learn to Set Up MRP in a Spreadsheet

8

A

Gross requirements Scheduled receipts B(2)

Projected on hand Net requirements Planned order receipts Planned order releases

Table 2

Period Quantity

E(4)

Students Use This Generic Forecast to Set Up MRP in a Spreadsheet

11

12

17

13

14

12

15

15

16

17 10

18 24

19 18

C(3)

C

E(2)

E(2)

D

D

D

F(2)

F(2)

20 17 F(2)

Table 3

Students Use These Hints to Supplement the Lecture on MRP to Set Up Formulas in Their Spreadsheets

1. Type in the Master Production Plan for all 20 periods. 2. Type in the labels for the first MRP table with the extra columns to the left as shown in Table 4. 3. Type in the time periods at the top of the table. 4. Populate Gross Requirements row by referencing Master Production Plan, that is, type = and click on cell for that period in Master Production plan row. 5. Scheduled receipts are input by hand. 6. Projected on Hand for first period: initial on hand + scheduled receipts for first period. 7. Projected on Hand for other periods: Planned order releases from period before – (gross requirements from period Before – on hand from period before) + scheduled receipts for this period. 8. Net requirements: If gross requirements – projected on hand is positive, then this is gross requirements – projected on hand, otherwise 0. 9. Planned order receipts: =CEILING(net req. divided by $lot size, 1) times $lot size. 10. Planned order releases: planned order receipts offset by appropriate lead time or use index function =INDEX (planned order receipts row, $lead time) 11. Copy the table for the first item for each other item in the Bill of Materials and make appropriate adjustments to gross requirements and other parameters.

Table 4

Students Place This Information to the Left of the MRP Planning Sheet in the Spreadsheet

Item

A

Lot size

10

Period Gross requirements

Lead time

1

Scheduled receipts

On hand

0

Projected on hand Net requirements Planned order receipts Planned order releases

The next part of the project is for students to adapt the generic MRP template that they have created for a new data set. I have created a database of bills of

Table 5

Item

Students Use This Generic Information to Set Up MRP in a Spreadsheet

Lead time

On hand

A B C D E

2 weeks 1 week 1 week 2 weeks 1 week

25 30 50 0 60

F

2 weeks

70

Scheduled receipts Lot size

25 in week 1

lot for lot 40 20 lot for lot 50 100

material in tree form and I randomly generate master production schedules, lead times, lot sizes and scheduled receipts so that each student has a different data set. Unique data sets ensure that any collaboration is productive in the sense that it exposes students to a wider variety of problems and that each student learns. The grade on this project is based on an in-class check of this example and an electronically submitted spreadsheet with the student’s individual bill of materials, lead time and inventory information, and forecast for the end item. Depending on the level of the students, a variety of embellishments may be added to this project. Examples of such embellishments are adding demand for spare parts as an input, or creating a variety of output reports including planned orders, order releases, performance-control reports, planning reports, and exception reports. After implementing this project in my classes, I realized that this was not only a good vehicle for teaching students how MRP works but also for teaching them about the increasing “lumpiness” of demand as it is propagated to higher levels in the bill of materials due to lot sizing issues, shortages, and delays due to lead time changes. This makes some students curious about the business dynamics of MRP. The spreadsheet allows them to experiment with lot sizes and lead

Gardner: Using a Spreadsheet for Active Learning Projects in Operations Management

times on their own without significant computational overhead and to use their critical thinking skills to learn about how MRP responds to changes in input parameters.

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Figure 2

Linear Regression Forecast Showing 95% Confidence Intervals

General Motors stock price 90 80

2.2.

Forecasting in a Spreadsheet—Another Example of Learning the Logic Forecasting is another topic where students need to understand the underlying technique but where textbook examples that are small enough for hand computation are too small to be realistic. The spreadsheet allows students • to grasp mathematical concepts and techniques by enabling them to see the computation without having to do the computation and get lost in the mechanics, • to see the effects of changing parameters for various forecasting models, • to learn to adapt forecasting models to special situations, • to see the effects of data issues on forecasts, • to use critical thinking skills to evaluate forecasting models, and • to learn to organize their work. Charting capabilities enhance the effectiveness of spreadsheets as a learning tool by providing visual images that enable students to observe the properties and limitations of the various forecasting techniques. This works particularly well for students who are visual learners. Recursion is a mathematical concept that is difficult for many students. Copying formulas down a column in a spreadsheet using data generated in the previous row is a concrete example of recursion that lends itself to moving averages and exponential smoothing. Students who do not understand the concept of recursion from a lecture can grasp it from the spreadsheet example because they can see intermediate results used in recursive techniques. Moreover, they can easily see where data are missing. The mathematical concept of confidence intervals can also be difficult for students but can make more sense when they see charts of forecasts. A forecast as a range of values instead of a single value is fairly intuitive for most students. Confidence intervals for the data on which a regression line is based and for the forecast itself can be implemented in a spreadsheet and shown graphically on a chart as in Figure 2. The chart shows the bulk of the data inside the confidence interval lines which allows the students to connect the formula they have typed in the spreadsheet to the concept of a 95% confidence interval having 95% of the data inside the interval as shown on the chart. The lines projected into the future reinforce the concept of the forecast as a range. Such a chart can also show the widening of confidence intervals as the length of the

70 60 50 40

CLOSE Forecast Lower mu Upper mu Lower forecast Upper forecast

30 20 10 0 7/26/1993 7/26/1994 7/26/1995 7/25/1996

7/25/1997 7/25/1998

7/25/1999

forecast period is increased relative to the amount of data used in the forecast. Seasonality is not a difficult concept for students but averaging cycle-to-cycle to compute seasonal indices is a generally difficult concept for students to grasp. The spreadsheet allows cycles to be overlaid in a chart as in Figure 3, so that this concept can be clarified. Moreover, the chart can be a good source of discussion. Spreadsheets have the flexibility to allow students to adapt forecasting models to special situations. My industrial experience has shown me that this is very useful in real-world settings. Over the years, I have used a variety of data sources for student projects. The most interesting seasonal data that I have obtained is attendance data from churches. The special situation associated with this data is that the most seasonal data point in an annual cycle for churches is Easter but Easter does not coincide with the same calendar date from year to year. Different Easter dates can be seen in Figure 3 between 10 and 20 weeks. In this case, a special seasonal index is created for Easter that is used for seasonalizing the date for Easter in the forecast period. Another example of a special situation that one of my students encountered was in forecasting audience size for a local civic theater. He found that audience size was related to Figure 3

Chart of Seasonal Data Overlaid to Show Seasonal Patterns

Overlay of year-to-year total attendance 600 500

Attendance

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400 300 200 1995 1996 1997

100 0 0

10

20

30

Week

40

1998 1999 50

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Figure 4

Comparison of Exponential Smoothing Parameters

Exponential smoothing 80 70 60 50 CLOSE 0.1 forecast 0.3 forecast 0.6 forecast 0.9 forecast

40 30 20 10 0 Jul-91

Jun-92

Jun-93

Jun-94

Jun-95

Jun-96

Jun-97

Jun-98

Date

both the time of year and the type of show (musical, comedy, drama, family-friendly, etc.). He was able to adapt his forecasting project to his real-world job using this special index concept. The effect of changing parameters can be taught very effectively with a spreadsheet. In the case of exponential smoothing, smoothing constants can be maintained in cells that are referenced from the formulas so the constants can be easily changed and the formulas and charts updated automatically. By constructing the spreadsheet to accommodate multiple forecasts, the lagging and smoothing effects of different smoothing constants can be illustrated in a chart as in Figure 4. This again allows the student to mentally connect the concepts of the parameter, the formula, and the visual image of the chart. The effects of data issues on forecasts and use of critical thinking skills can be illustrated by the start date for the data on which a regression forecast is based. This date can be changed and the chart updated with slightly more effort than changing an exponential smoothing constant. Figure 5 illustrates such an updated chart based on the same data as Figure 3 with a later start date. This reinforces the point I make in lectures, which is that changes can take place that make the forces acting on the data in the past different from those acting in the present and future. For example, a problem with the safety of a drug resulting Figure 5

Regression Forecast with Changed Start Date

General Motors stock price 90 80 70 60 50 40

CLOSE Forecast Lower mu Upper mu Lower forecast Upper forecast

30 20 10 0 12/5/1994

12/5/1995

12/4/1996

12/4/1997

12/4/1998

79

in a lawsuit can drive down the stock value of a pharmaceutical company. Similarly, a pastoral change in a church could suddenly change the attendance trend. Students must then use their critical thinking skills to assess the relevance of data for use in forecasting and make decisions about where to start the regression for their projects. Critical thinking skills may also be used to assess whether forecasting models are appropriate for a given data set. Typically, I have students do some exploratory data analysis and then set up some or all of the following models for their data: • moving average, • exponential smoothing, • time-series regression, • seasonalized time-series regression, and • regression on some economic indicator. Because each student has an individual data set which may come from a variety of sources ranging from the stock market to family businesses to churches and other nonprofits, many students in a class may find that some forecasting models simply do not work for their data. For example, seasonalized time-series regression works very well for church attendance data and very badly for the stock market. I can have students walk around the computer lab looking at each other’s projects so they can see a variety of situations where models are and are not appropriate. Finally, the forecasting project teaches students to organize their work into workbooks by model and worksheets by data set and name the workbooks and worksheets so they can find the data and models quickly. Having students organize their work in this way is also helpful to the instructor when it comes time for grading. I usually have students work on two related data sets, such as stock price and volume, time bucketed both monthly and weekly. I also suggest an organization in my instructions. This project teaches document control, which can have a major impact on the efficiency and corporate security of a business enterprise.

3.

Gaining Experience Using a Spreadsheet to Solve Real Problems

The feedback that I get from my students is that the analytic and systematic approach to solving the problems and creating the spreadsheets that I teach prepares them to attack real world problems very well. The main reasons for this are: • The spreadsheet supports the development of critical thinking because it allows students to quickly construct and compare several scenarios for a problem. Students can focus on the evaluation of scenarios rather than the drudgery of computation while still benefitting from the understanding of how the solution process works.

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• If they have developed the spreadsheet themselves, they have a better understanding of how the decision variables impact the dynamics of the underlying problem because they have at least looked at the relationships among decision variables and the criteria for evaluating scenarios. This helps them to construct input values for decision variables that produce better results. • The spreadsheet allows them to see uses of spreadsheet functions in a more realistic setting. More importantly, they learn when to use relative cell addresses and absolute cell addresses. The following sections present an aggregate planning project that is very effective in teaching students how to solve real world problems with a spreadsheet while sharpening students’ critical thinking skills. 3.1.

Aggregate Planning—Gaining Experience in Solving Real Problems I have implemented a spreadsheet-based aggregate planning project because my students do not seem to comprehend the spectrum of alternatives available to solve real world problems without a means of experimenting with decision alternatives. I want my students to go beyond learning how aggregate planning works to learn about the power of the spreadsheet as a tool for “what if” experimentation. They do aggregate planning problems by hand and see how time consuming it is to redo a plan when only a few numbers change. The spreadsheet allows them to experiment “on the fly” with placement of overtime, hiring, layoffs, subcontracting, and working hours and to watch changes in the bottom line as they do it. Experimentation and evaluation of different plans helps them to develop critical thinking skills. I require each student to turn in a report with five different plans that build an argument to justify the best solution for the set of criteria that he or she wants to optimize. Most students want to maximize profitability. However, some get concerned about the ethics of laying off employees and the impact on morale or on customer service. Students may also become concerned about quality issues that could arise with new hires and temporary workers, training, running too much overtime, and subcontracting. I maintain a database of forecasts, beginning inventories and backlogs that present a variety of challenges to students so that I can give each student a different data set. This allows me to assign a wide variety of situations to any given class so that I can facilitate in-class discussions over a range of situations, issues, and solutions. The assignment itself begins with a scenario such as this: “Your plant currently has thirty employees and operates one 8-hour shift for twenty days per month. Each employee produces four units per day, which is eighty units per month. This means that regular time

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pay for thirty workers at $10 per hour is $48,000. You must pay your employees whether they are producing or not so regular time wages are separate from the cost of regular time production. You may run up to two hours of overtime per day. Overtime wages are included in the overtime cost (time and a half). You may subcontract as much as you wish. You may hire up to five more employees and layoff as many as you believe necessary. The maximum inventory level for which storage is available is 5,000 units. Customer service requires that backlog never exceed 2,400 units.” Students set up a template in the spreadsheet according to the format in Table 6. The format is colorcoded so that students understand that white cells are for data, such as the forecast and other initial values or for decisions such as hours of overtime, amount of subcontracting, and so forth. The light gray cells contain formulas to automatically calculate information. The students receive a set of hints shown in Table 7 for setting up these formulas. Depending on the proficiency of students with spreadsheets and with the logic, these steps can be facilitated by the instructor demonstrating the step-by-step process in a computer lab. Students may do quality assurance on their template for correct setup by comparing it to an example provided by the instructor. Finally, they copy the template to develop the set of aggregate plans for their data set. The original aggregate planning student project was based on the aggregate planning chapter of Stevenson (2007) and ideas from Shafer (1991). Over the years, I have incorporated ideas from various colleagues, textbooks, students, and practitioners.

4.

Learning the Dynamics of How Business Systems Behave

The MRP project certainly teaches students about the dynamics of how manufacturing planning systems responds to decisions with regard to lot sizing and lead times. For example, it allows them to experiment with lot sizes and lead times and see the increasing “lumpiness” of demand as it is propagated to higher levels in the bill of materials as lot sizes increase. The aggregate planning project also allows students to see how profitability responds to a variety of decision variables regarding increasing or decreasing capacity and the timing of those changes. Another strategy that I use to teach business dynamics in a hands-on way is by supplementing learning games with spreadsheet simulations. Supplementing a hands-on game or physical demonstration with spreadsheet simulation makes the learning experience more efficient and effective by allowing participants to see and experiment with

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Table 6

Students Set Up an Aggregate Planning Worksheet with This Format

Period

Initial

1

2

3

4

5

6

7

8

9

10

11

12

Total

Forecast Output Regular Overtime Subcontract Output – Forecast Inventory Beginning Ending Average Backlog Costs:

/unit

Output Materials

10

Reg.hrs/emp/day

2

Regular wages

10

Ovr.hrs/emp./day Overtime Subcontract Number employed

2 15 42 30

Hire (number) Hire (cost)

8,000

Layoff (number) Layoff (cost)

15,000

Inventory

3

Backorders

5

Total cost Revenue

40

Profit

many scenarios in a very short time. This, in turn, enables an instructor to demonstrate a broad spectrum of behaviors of business systems so that students can see the impact of a variety of decision alternatives and to critically evaluate these alternatives. Following with a game or demonstration with a simulation also teaches the difficult concept of simulation in a very concrete way. The next three sections present spreadsheet simulations to follow a demonstration and two games. 4.1.

Tampering—Teaching the Business Dynamics of Quality Control The goal of this demonstration is for students to graphically see the results of tampering, that is, adjusting a process when it is not out of control. I begin with a demonstration where I drop some objects that leave marks onto a paper target on the floor where the objects are held at arm’s length and shoulder height.

Water droplets, bits of chalk, or a sturdy felt tipped pen can work for dropped objects. The random process is the slight movement of my hand. I demonstrate how adjusting my hand position can actually move the drops farther and farther from the center of the target. Next, I do a spreadsheet demonstration in class or provide students with their own workbook to work with. They can experiment with control limits in a spreadsheet and see the process swinging wildly out of control when the control limits are set too low. The spreadsheet demonstration has the number of standard deviations
 for the control limits, the starting process mean, and standard deviation as inputs (Table 8). It generates a chart of 1,000 observations from a normal distribution with the current updated process mean and given standard deviation. The updated means are generated by testing whether the last observation was in or out of control, and if

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Table 7

Formula Hints for Aggregate Planning Project

Figure 6

Output: Sum of regular, overtime, and subcontract output. Regular: Number of items made this month on regular time. Multiply number of workers times regular hours per day times number of days in month (20) divided by time to make one item (2 hours). Overtime: Number of items made this month on overtime. Similar to regular time but use overtime hours instead. Subcontract: Type number to be made by subcontractor. Output-forecast: Output-forecast. Beginning inventory: Ending inventory from last period. Ending inventory: If beginning inventory plus (output-forecast) less backlog is positive, then this is beginning inventory plus (output-forecast) less backlog. Otherwise it is zero. (Use an if statement.) Average inventory: (Beginning plus ending) divided by 2. Backlog: If beginning inventory plus (output-forecast) less backlog is negative, then this is the opposite (negative) of beginning inventory plus (output-forecast) less backlog. Otherwise it is zero. (Use an if statement.) Material cost: Per unit cost times (amount made on regular and overtime). Regular wages: Rate times regular hours per employee per day times number of employees times number of days worked in the month. Overtime wages: Similar to regular wages except use overtime. Subcontract: Number made by subcontractor times cost. Number employed: Number from last period plus hires minus layoffs. Hire cost: Number hired times hiring cost. Layoff cost: Number laid off times layoff cost. Inventory cost: Average inventory times per unit cost. Backorder cost: Backorders times backorder cost. Total cost: Add up costs. Revenue: Selling price times the minimum of supply and demand. Supply is output plus beginning inventory. Demand is forecast plus backlog.

Students May See a Pattern Like This When They Set the Control Limits to Zero Standard Deviations Tampering

200 150 100 50 0 0

200

400

600

800

1,000

Observation Figure 7

Students May See a Pattern Like This When They Set the Control Limits to One Standard Deviation

Tampering 200 150 100 50 0 0

200

400

600

800

1,000

Observation Figure 8

Students May See a Pattern Like This When They Set the Control Limits to Two Standard Deviations

Tampering 200

Table 8

1

Students Use a Spreadsheet for the Tampering Demonstration with the Data Set Up in This Format

A Control limit

B

C

100 50

1 0

2

0

3

Starting process mean

4

Standard deviation Obs. no.

Observation

7

100

1

101.019032

8

98.980968

2

100.0329624

9

98.980968

3

98.74136392

10

101.258636

4

102.2669542

11

97.7330458

5

98.39055818

400

600

800

1,000

Observation Figure 9

Updated mean

200

100

5 6

150

Students May See a Pattern Like This When They Set the Control Limits to Three Standard Deviations

Tampering 200 150 100 50

out of control, the process mean is updated by how far it is from the original. Students adjust the control limits and observe what happens with the chart. For example, a student might try out the sequence of control limits 0, 1, 2, and 3 and see the sequence of charts in Figures 6–9.

0 0

200

400

600

800

1,000

Observation

The ideas for the tampering demonstration have come from a variety of speakers at meetings and seminars put on by the Indianapolis Section of the

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American Society for Quality over the last fifteen years. 4.2.

Just-In-Time—Teaching the Dynamics of Lot Size and Variance Reduction The JIT simulation is a based on a hands-on classroom activity using poker chips and dice that simulates the material flow in a manufacturing system. Students learn about both JIT and stochastic simulation by first playing the hands-on JIT game and then experimenting with the spreadsheet simulation. In the hands-on game, students are seated at a long table, each having a place mat with squares representing incoming and outgoing docks, with four poker chips on their incoming dock, and a die. Each student represents a worker in an assembly line, the poker chips are the product he/she is working on, and the die is used to introduce randomness into the operation of the system. The first student in line is the receiving department and the last student is the shipping department. The students run the assembly line for a simulated month under each of four different operating strategies. A simulated month consists of 20 dice rolls with students moving poker chips after each roll of the die to simulate material flows. The students roll their dice 20 times to represent 20 working days in a month. The first operating strategy uses a fixed transfer batch size of four and allows the amount processed to be any number rolled on the die subject to availability of raw material. That is, students roll the die and move the number of chips from the incoming to outgoing dock indicated by the die. For example, if the student rolls a three, he or she moves three chips from the incoming to the outgoing dock. If the student has fewer chips on the incoming dock than the number rolled on the die, the student moves all of the chips to the outgoing dock. This means that if the student rolls a six but only has four chips on the incoming dock, he or she moves all four chips to the outgoing dock. After the students move the chips to their outgoing docks, they may pass on transfer batches of four chips to the next student’s incoming dock. The second operating strategy is identical to the first except that the transfer batch size is changed to one. That is, the students may move all of the chips on their outgoing dock to the next student’s incoming dock. The third operating strategy involves variance reduction in the number of chips that can be processed. If a student rolls a one, two, or three on the die, he or she moves three chips from the incoming to the outgoing dock. If a student rolls a four, five, or six on the die, he or she moves four chips from the incoming to the outgoing dock. The transfer batch remains the same.

83

The final operating strategy has all players synchronized by moving three or four chips as called by the roll of the die by the player who is at shipping. Under each operating strategy, each student processes 3.5 chips on average because that is the average of the numbers that appear on a die. The theoretical output of the system is a total of 70 chips, that is, 3.5 per simulated day over a period of 20 days. Under the first strategy, students rarely achieve an output of more than 50 and it is often much lower. Under the second strategy, they usually produce slightly more than under the first strategy, but almost never more than the upper 50s. Under the third and fourth strategies, the students usually come very close to achieving the goal of 70. The most dramatic results are achieved if at least ten students are in an assembly line. Students often do not believe the results of the hands-on game because they believe the improvement in throughput is due to the randomness of the dice, not the operating strategy. Limitations of class time prevent replication, but using a computer model for the replications overcomes the time problem. I give students a spreadsheet workbook with a worksheet for each operating strategy. Replications of the spreadsheet simulation can be generated with a keystroke as opposed to 20 minutes for the hands-on version. Students can look at the throughput values for several replications of each operating strategy in a matter of seconds and decide whether their classroom experience is typical of the behavior of the system. The first four days of the spreadsheet simulation for one of the operating strategies are shown in Table 9. Workstations are represented by rows. Material flows from the in cell of workstation n to its out cell and then on to the in cell of workstation n + 1. The number of chips on the incoming dock is maintained in the in column and the number of chips on the outgoing dock is maintained in the out column. Four more chips are added each day to the in cell of wkstn1. The rnd column gives number of chips that can be moved from in to out subject to their availability and uses the RANDBETWEEN function. The ideas for the Just-In-Time (JIT) Simulation have come from a variety of speakers at meetings and seminars put on by the Central Indiana Chapter of the APICS over the last fifteen years. 4.3.

The Beer Game—Teaching the Dynamics of Supply Chains As discussed in the introduction section of this paper, the beer game is a popular classroom exercise for demonstrating material and information flows in a supply chain. It was developed at Massachusetts Institute of Technology (Sterman 1989, 1992; Chen and Samroengraja 2000). The beer game dramatically

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84 Table 9

INFORMS Transactions on Education 8(2), pp. 75–88, © 2008 INFORMS

Students See First Four Days of the Just-In-Time Simulation in This Format

Day 1

Day 2

Day 3

Table 10

Control Panel for Input of Anchoring and Adjustment Parameters

Anchoring and Adjustment Parameters Retail Wholesale Distribute Manufacture

Day 4

in rnd out in rnd out in rnd out in rnd out wkstn1

4

1

1

7

1

2 10

6

8

wkstn2

4

5

4

0

4

0

0

1

wkstn3

4

4

4

4

2

2

2

4

wkstn4

4

5

4

4

3

3

1

6

4

4

5

4

wkstn5

4

2

2

6

3

5

3

5

4

4

wkstn6

4

1

1

3

1

2

6

5

4 7

4 5

1

4

8

2

2

0

8

4

0

1 1

0

1

wkstn7

4

4

4

0

2

0

0

5

0

4

4

4

wkstn8

4

2

2

6

2

4

4

1

1

3

5

4

wkstn9

4

6

4

0

1

0

4

2

2

2

3

4

wkstn10

4

5

4

4

4

4

0

3

0

0

4

0

wkstn11

4

1

1

7

6

7

5

5

8

0

6

0

wkstn12

4

1

1

3

6

4

4

1

1 11

5

6

wkstn13

4

6

4

0

2

0

4

0

2

0

4

3

3

5

5

8

4 0

5

wkstn14

5

0

4

2

2

wkstn15

4

1

1

3

6

4

8

6

6

2

3

4

wkstn16

4

1

1

3

5

4

4

3

3

5

6

8

wkstn17

4

5

4

0

3

0

4

5

4

0

5

0

wkstn18

4

4

4

4

2

2

2

3

4

4

4

4

wkstn19

4

1

1

7

1

2

6

6

8

4

4

4

wkstn20

4

6

4

0

5

0

0

3

0

8

6

6

impresses students with the bullwhip effect, which is the amplification of variance in demand up the supply chain resulting first in large backlogs and then in excessive inventories. The spreadsheet simulation allows students to experiment with different strategies or instructors to demonstrate the effectiveness of different strategies to reduce the bullwhip effect. The beer game spreadsheet simulation allows users to control the ordering strategies and supply line adjustment strategies for all four players using by entering anchoring and adjustment parameters based on Sterman’s model (1989) of ordering behavior into the user interface of the simulation. Sterman describes ordering behavior in the beer game in terms of an anchoring and adjustment heuristic using the expression: IOt = Lˆ t + ASt + ASLt where IOt is the order rate in time period t, the anchor, Lˆ t , is the expected demand in period t, and the adjustments are the discrepancies between the desired and actual stock, ASt , and the desired and actual supply line, ASLt in time period t. The spreadsheet demonstration is a deterministic discrete event simulation with one order per period so IOt is not a rate but instead is the order placed in time period t. In this simulation, the anchor, Lˆ t , is the expected demand in period t, based on an exponential smoothing forecast so the anchoring parameter is the smoothing constant.

Exponential smoothing parameter 0 ≤ θ ≤ 1 Stock adjustment parameter 0 ≤ αS ≤ 1 Supply line adjustment parameters αSL Orders 0 ≤ αSLO ≤ 1 Material 0 ≤ αSLM ≤ 1 Upstream backlog 0 ≤ αSLB ≤ 1 POS only (yes = 1, no = 0)

Table 11

1

1

1

1

0

0

0

0

0 0 0

0 0 0

0 0 0

0 0 0

0

0

0

0

Control Panel for Initial Setup Specifying Desired Inventory Levels

Initial setup start out inv in out inv in out inv in out inv in info 4 retail 4 4 whol 4 4 dist 4 4 mnfr 4 mat’l 4 12 4 4 12 4 4 12 4 4 12 4

The adjustment for stock AS specifies the fraction of the inventory level deducted from the order. The control panel for entering Sterman’s parameters for exponential smoothing forecasts is shown in Table 10. The simulation user can enter an anchoring parameter and a stock adjustment parameter for each player in the beer game. The supply line consists of the sum of the orders in information delays, the material in shipping delays, and the immediately upstream supplier’s backlog. The simulation user can also enter a supply line adjustment parameter into the user interface for each component of the supply line for each player specifying the fraction of each component of the supply line deducted from the order. The control panel for the initial inventory levels, orders in the supply line, and material in the supply line for a workbook with a lead time of two is given in Table 11. The initial inventory level is the desired inventory level on which the stock level adjustment heuristic is based. The control panel for customer demand is shown in Table 12. It allows the user to specify a baseline level of demand and whether or not a normally distributed random amount should be added. The user has the additional capability to add a constant demand adjustment for each week as shown in the first column of Table 13. To specify the demand Table 12

Control Panel for Customer Demand

Customer order parameters Baseline demand Normal demand (yes = 1, no = 0) Mean Std. dev.

4 0 0 1

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85

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Table 13

Spreadsheet for One Time Period (Simulated Week) of the Beer Game

Demand adjustment

4

Week

2

place/adv ord

out

inv

info

4

retail

4

4

mat’l

4

12

4

4

backlog

0

advmatl

out

inv

info

4

retail

4

4

mat’l

0

16

4

0

backlog

0

fillord

out

inv

info

0

retail

4

0

mat’l

4

12

4

4

backlog

0

in out

inv

in out

inv

in out

whol

4

4

dist

4

12

4

4

12

4

0

0

in out

inv

in

4

mnfr

4

4

12

4

inv

in

0

in out

inv

in out

whol

4

4

dist

4

4

mnfr

4

16

4

0

16

4

0

16

0

inv

in

0

0

in out

pattern usually seen in the classroom version of the beer game, the baseline demand is set to four and the normal demand turned off as in Table 12. The demand adjustment of Table 13 is set to zero for the first several periods, then set to four for the remaining periods of the game. The simulation itself uses spreadsheet formulas in workbook cells rather than Visual Basic macros to move material and orders through the supply chain. The layout of the cells for one period of the simulation is shown in Table 13. The anchoring and adjustment parameters are incorporated into the formula for the in values of the info line of the place/adv ord section of the spreadsheet for each player. Another formula in the out value of the info line of the place/adv ord section for the retailer allows a variety of demand functions to be incorporated as specified in the control panel. The advantage of using workbook cells rather than visual basic macros is that it gives the user the flexibility to examine in detail the state of material and information flows during any time period by looking at the workbook cells or to look at the game from a high level by examining the spreadsheet charts. A lesson based on the spreadsheet simulation can demonstrate the reduction of the bullwhip effect as more information about the supply line is incorporated. The series of cases on which the lesson is based is summarized in Table 14. The lesson can be done as a lecture wherein the instructor shows the series of charts in as a spreadsheet demonstration by changing parameters in a control panel. Charts for Cases 1, 2, 5, and 6 showing the reduction in the bullwhip effect that could be used in this lesson are shown

inv

inv

0

in out

inv

in out

whol

4

0

dist

4

0

mnfr

4

12

4

4

12

4

4

12

0

0

0

Table 14

0

Anchoring and Adjustment Cases Simulated

Case

Lˆ t

ASt

1

θ=1

αS = 1

αSLO = 0, αSLM = 0, αSLB = 0, (none)

2

θ=1

αS = 1

αSLO = 1, αSLM = 0, αSLB = 0, (less orders)

3

θ=1

αS = 1

αSLO = 0, αSLM = 1, αSLB = 0, (less material)

4

θ=1

αS = 1

5

θ= 1

αS = 1

αSLO = 1, αSLM = 1, αSLB = 0, Less material and orders αSLO = 1, αSLM = 1, αSLB = 1, Less material, orders, and upstream supplier’s backlog

6

θ = 1 α S = 0 None αSLO = 0, αSLM = 0, αSLB = 0, None

7

POS Not applicable Not applicable

ASLt

in Figures 10–17. Alternatively, the lesson can take place in a computer lab if it is possible to accommodate all students with a computer and the spreadsheet model. Either way, the students can watch the stepwise changes to the charts as the instructor modifies parameters for individual players. The instructor can also generate cases that are not in Table 14 that will result in questions from students. The lesson is most effective if it follows playing the beer game in class.

5.

Learning to Organize and Manage Small Scale Projects

The final benefit of the spreadsheet projects such as the MRP project, the forecasting project, and the aggregate planning project is that students must learn to plan, organize, and assure quality in their work. To teach students to plan and organize their work, I • break the projects down into components,

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Figure 10

Figure 13

Case 1: Inventory Levels for Maximum Bullwhip Effect Without Supply Line Information Beer game inventory level for lead time 2

1,200

Case 2: Diminished Bullwhip Effect on Orders with Adjustment for Orders in Supply Line Beer game orders lead time 2

450

Retail

Retail

Wholesale

Wholesale

400

Distribute

1,000

Distribute

Manufacture

Manufacture

350

800

600

Order size

Inventory-backlog

300

400

250 200 150

200 100

0 0

50

100

150

200

250

300

350

400

450

500

550

600

650

50

700

_ 200

0 0

_

50

100

150

200

250

300

350

400

450

500

550

600

650

700

Week

400 Week

Figure 11

Case 1: Orders forMaximum Bullwhip Effect Without Supply Line Information

Figure 14

Case 5: Elimination of Bullwhip Effect on Inventory Level by Compensation for Material, Orders, and Upstream Supplier’s Backlog

Beer game orders lead time 2 450

Beer game inventory level for lead time 2 1,200

Retail Wholesale

400

Retail

Distribute

Wholesale

1,000

Manufacture

350

Distribute Manufacture

800

250

Inventory-backlog

Order size

300

200 150

600

400

200

100 0 0

50

50

100

150

200

250

300

350

400

450

500

550

600

650

700

_ 200

0 0

50

100

150

200

250

300

350

400

450

500

550

600

650

700 _ 400

Week

Week

Figure 12

Case 2: Diminished Bullwhip Effect on Inventory Level with Adjustment for Orders in Supply Line

Figure 15

Case 5: Elimination of Bullwhip Effect on Orders by Compensation for Material, Orders, and Upstream Supplier’s Backlog in the Supply Line

Beer game inventory level for lead time 2 1,200

Beer game orders lead time 2

Retail

450

Wholesale

1,000

Retail

Distribute

400

Manufacture

Wholesale Distribute

800

Manufacture

350

600

300 Order size

Inventory-backlog

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86

400

250 200

200 150

0 0

50

100

150

200

250

300

350

_ 200

400

450

500

550

600

650

700

100 50

_ 400

0 0

Week

50

100

150

200

250

300 350 Week

400

450

500

550

600

650

700

Gardner: Using a Spreadsheet for Active Learning Projects in Operations Management

Figure 16

Case 6: Elimination of Bullwhip Effect on Inventory Levels by Pull Strategy Beer game inventory level for lead time 2

30 Retail Wholesale

20

Distribute

87

independently. The way that I provide less detailed instructions and less guidance in constructing spreadsheets over the course of the semester forces students to take increasingly more responsibility for managing their spreadsheet projects.

Manufacture

Inventory-backlog

10

0 0

5

10

15

20

25

30

35

40

45

50

-10 - 20 -30 -40 Week

Figure 17

Case 6: Elimination of Bullwhip Effect on Orders by Pull Strategy Beer game orders lead time 2

20 Retail

18

Wholesale Distribute

16

Manufacture

14

Order size

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12 10 8 6 4 2 0 0

5

10

15

20

25 Week

30

35

40

45

50

• provide detailed instructions for each component for a generic data set, • walk through setting up a spreadsheet for a generic data set in class in a computer lab, and • have the students adapt the generic project to their specific data set and/or set up multiple alternative scenarios. This improves attendance and punctuality because I will not help students during office hours unless they attend class. To assure quality, I provide the students with checklists to check their own work and I require them to e-mail their spreadsheets to me so I can check correctness of formulas. I send a reply with any problems that I find but not corrections. If I have to check it a second time, they must come in during office hours. The individual attention to students actually improves my teaching ratings. Over the course of the semester, I expect students to learn how to organize and manage their work independently as well as learn to construct spreadsheets

6.

Improving Teaching and Learning

7.

Conclusion

With the exception of the beer game simulation, the projects described in this paper were developed and implemented during the time period of 1994–1999. During that time I tracked and statistically analyzed the composite score obtained from the twelve standard questions on the student evaluations used for the operations management class during that time. After 1999, multiple changes in course evaluation systems and in curriculum made an analysis of the later data inconsistent with the prior data. The raw data is in Table 15. The column labels in the second row indicate the year and semester. For example, 922 is the second semester of the 1992–1993 academic year. I have plotted the above composite scores and the line of the regression equation in Figure 18. Visual inspection of Figure 18 indicates that the composite scores are increasing over time and the regression line confirms this. Tests of significance (analysis of variance on regression slope, t-test on regression slope) on whether the scores are actually improving yield P -values of 16%. This is not as significant as I would hope but it does give some indication of improvement. It is worth noting that none of the projects described in this paper had been implemented in the 1992–1993 or 1993–1994 school years. The standard student evaluations are not the only way to measure the quality of improvements to teaching and learning. Since the time period covered by the data analysis, I have monitored test scores, questioned students orally about their comprehension of material, and submitted student papers to competitions to benchmark my students’ work against the work of students from other schools. Starting in 1999, my students have consistently won or placed in the top three of the Region 13 (Indiana and Illinois) Donald W. Fogarty International Student Paper Competition sponsored by the APICS. I have taught one international winner.

Having students build their own operations management software in a spreadsheet provides students with active learning experiences that enable them: • to learn the logic behind software packages, • to gain experience using a spreadsheet to solve real problems, • to have a more transparent view of the dynamics of business systems, and

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Table 15

Evaluation Data

1 922

2 3 931 932

4 5 6 7 941 951 952 961

8 9 10 962 981 982

1 Good use of examples 2 Interesting

4.1 3.9

4.0 3.6

4.1 3.9

4.3 4.2

3.8 3.8

4.5 4.3

4.3 4.1

4.2 4.1

4.6 4.0

4.7 4.7

3 Thorough knowledge

4.7

4.8

4.4

4.5

4.4

4.8

4.8

4.9

4.8

4.9

4 Clear objectives, requirements, grading 5 Well-organized 6 Treats students with respect

4.4

4.2

4.0

4.7

4.0

4.9

4.6

4.2

4.6

4.6

4.3 4.7 4.7

4.4 4.4 4.4

4.3 4.4 4.3

4.8 4.5 4.6

3.9 3.8 3.9

4.8 4.8 4.8

4.7 4.4 4.5

4.4 4.2 4.2

4.8 4.8 4.6

4.7 4.9 4.6

4.1 4.6

3.8 4.1

3.9 4.3

4.1 4.4

3.7 4.0

4.6 4.6

4.6 4.6

4.0 3.8

4.4 4.8

4.7 4.7

Semester Evaluation question

7 Enthusiastic 8 Stimulates thinking 9 Helpful when students have problems 10 Well-prepared

4.4

4.2

4.2

4.7

4.1

4.9

5.0

4.6

4.6

4.7

11 Listens carefully to students

4.6

3.9

4.3

4.4

3.7

4.6

4.4

3.9

4.6

4.7

12 Grades and returns papers in a reasonable amount of time

4.6

4.6

4.5

4.5

3.7

4.9

4.7

4.3

5.0

4.9

Mean

Figure 18

4.43 4.20 4.22 4.48 3.90 4.71 4.56 4.23 4.63 4.73

• to the instructor’s teaching style and real-world experience. Being able to adapt, rather than adopt, teaching materials can provide a more satisfying teaching and learning experience for both instructors and students.

Regression Line on Student Evaluation Data

Production/operations management 5.0

Composite score

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88

Actual scores Regression line

4.5

4.0

References

3.5 1

2

3

4

5

6

7

8

9

10

Semester

• to learn to organize and manage small scale projects. Decreasing the detail level of instructions for creating the spreadsheets and diminishing the level of support and guidance that the instructor provides over the course of the semester causes the students to work more independently as they gain more knowledge about the subject matter and they learn the spreadsheet better. Most textbooks have spreadsheet models to accompany them, although that was not the case when some of the projects described in this paper were initially implemented. Some texts, such as Barlow (2005) are actually spreadsheet-based. Having students build their own operations management software in a spreadsheet enables improves and learning by allowing the instructor to tailor the software and the instructions for developing it: • to the students’ level of knowledge and ability, • to any textbook, and

Barlow, J. 2005. Excel Models for Business and Operations Management, 2nd ed. John Wiley & Sons, Inc., Hoboken, NJ. Chen, F., R. Samroengraja. 2000. The stationary beer game. Production Oper. Management 9(1) 19–30. Chickering, A., Z. Gamson. 1987. Seven principles for good practice. Amer. Association Higher Ed. Bull. 39 3–7. Frazer, J. D., B. Nakhai. 1992. The problems with MRP on microcomputer spreadsheets. Production Inventory Management J. 33(3) 1–5. Heizer, J., B. Render. 2006. Operations Management, 8th ed. Prentice Hall, Upper Saddle River, NJ. Prince, M. 2004. Does active learning work? A review of the research. J. Engrg. Ed. 93(3) 223–231. Shafer, S. M. 1991. A spreadsheet approach to aggregate scheduling. Production Inventory Management J. 32(4) 4–10. Sounderpandian, J. 1989. MRP on spreadsheets: A do-it-yourself alternative for small firms. Production Inventory Management J. 30(2) 6–11. Sounderpandian, J. 1994. MRP on spreadsheets: An update. Production Inventory Management J. 35(3) 60–64. Sterman, J. D. 1989. Modeling managerial behavior: Misperceptions of feedback in a dynamic decision–making experiment. Management Sci. 35(3) 321–339. Sterman, J. D. 1992. Teaching takes off—Flight simulators for management education. OR/MS Today 19(5) 40–44. Stevenson, W. J. 2007. Production/Operations Management, 9th ed. Irwin/McGraw-Hill, Chicago.