INSTRUCTOR: Dr. Shailesh S. Kulkarni
OFFICE: 312-E
PHONE: 940-565-4769 FAX: 940-565-4935
EMAIL: Shailesh.Kulkarni@unt.edu
( preferred mode of contact outside of office hours )
CLASS HOURS and LOCATION: R 6:30-9:20 p.m. BLB 010
OFFICE HOURS: W 12:30 – 1:45 p.m. & R 5:00 – 6:15 p.m and by appointment.
PREREQUISITE: DSCI 5010 or equivalent (Also check UNT Graduate Catalog, 2011-2012)
COURSE WEB SITE : http://www.coba.unt.edu/itds/faculty/kulkarni/Teaching.htm
and http://learn.unt.edu
(especially for any online quizzes)
COURSE DESCRIPTION:
The primary aim of this course is to develop skills for solving complex business problems with the aid of management science techniques and with the use of decision technology. The management science techniques emphasized can be broadly categorized under Optimization,
Simulation and Decision & Risk Analysis. Course topics include the use of mathematical and conceptual models that are embedded in a business environment for dealing with both structured and semi-structured decision problems. The course will help you identify opportunities and problems for which the use of modeling enhances a decision maker's chance of success. Further, the course will introduce state-of-the-art decision support technology required to solve real problems, (which generally involve data management and analysis) primarily using spreadsheets and special software such as Xpress-MP and Mathematica.
Overall, through a combination of case studies, lectures, in-class exercises, simulation games and videos, the course introduces powerful tools and techniques, reviews current trends, and highlights key managerial issues.
BROAD COURSE OBJECTIVES :
1. Understand the modeling process and be able to apply it to problems in key functional areas of business such as marketing, finance and operations as well as to public sector and at the strategic to operational level of decision making hierarchies.
2. Implement model-based decision support systems using spreadsheets and other standalone state-of-the-art software. I will be using MS Excel 2010. It is available in the COBA labs.
If you wish to use an earlier version of MS Excel, you can do so, assuming that a comparable analysis of the problem is possible. The COBA labs have installed the new “Risk Solver
Platform”, which we may use from time to time, however all analysis in the “free” version of solver is acceptable and we will use it more often.
REQUIRED MATERIAL:
Text
Optimization Modeling with Spreadsheets, 2nd Edition, published 2011, (includes CD-ROM)
Author: Kenneth Baker
ISBN-13: 9780470928639 (It is OK if you get the 1st Edition – just make sure your assigned problems, readings and cases are congruent with the new edition)
1

Cases from Harvard Business Publishing:
I have created a course area on the Harvard Business Publishing website where you can order some of the required materials for this course. Please visit the link below to order the course materials. http://cb.hbsp.harvard.edu/cb/access/11740819
As expected, if you are not already registered with Harvard Business Publishing, you will first have to register and then you will be able to login.
After you register, you can get to the coursepack at any time by doing the following:
1. Visit hbsp.harvard.edu
and log in.
2. Click My Coursepacks , and then click Model-based Decision Making - Spring 2012
The downloaded course materials may be encrypted using SealedMedia. If so, use this link to download the plug-in; http://download.sealedmedia.com/unsealer/index.asp
.
Simulation Game Code:
You will be playing online simulation games for which you will need an access code. You can buy the code directly at http://mgr.responsive.net/Manager/ShowClient
.
This option may work out to be marginally cheaper than buying this code at the UNT bookstore. You will have to create an account to login. Note that the institution name in the drop down menu appears as
“North Texas” .
If you decide to buy it at the bookstore it should be available under the course listing i.e. DSCI
5210. The details of the games are outlined later in this syllabus.
COURSE POLICIES:
Class Attendance:
Regular class attendance is encouraged and expected. Excessive absences could cause you to be automatically dropped from the course with a grade of WF.
Code of Conduct and Ethics:
The policies stated here were derived from the University of North Texas Student Guidebook.
You are responsible for information published by the university in its official publication. What appears below is primarily to give you an idea about the code of conduct and ethics.
Scholastic integrity must be exhibited in your academic work, conduct, and methods.
Academic work for which you receive an individual grade must be your original, individual effort unless it is a group case/project. If, in my opinion, any evidence exists that all or part of the work you submit for grading is that of another person, you (and the other person) will be given a zero for the assignment. This is one form of scholastic dishonesty. A second incident of academic misconduct will result in a grade of F in this course. You (and anyone involved with you) will be given an F in this course, if you are found to have cheated on an exam, or collaborated on an assignment with another student. Further action on incidents of scholastic misconduct will be referred to the Dean of Students.
2

Students with Disabilities:
The College of Business Administration complies with the Americans with Disabilities Act in making reasonable accommodations. Please see me as soon as possible to discuss this matter.
Miscellaneous Policies:
IMPORTANT DATES: Dates of drop deadlines, exams, final exams, etc., are published in the university catalog and schedule of classes. It is your responsibility to be informed with regard to these dates.
Suggested Readings & Problems Etc.:
From time to time I will hand out or suggest readings from the text or other sources and suggest problems for practice, which will not be graded. It is your responsibility to go through the handouts and suggested material and contact me with questions if any.
Videos Shown in Class:
I anticipate showing a few videos in class. These videos demonstrate real-world applications of the techniques learnt in this course. The announcement of a particular screening will be made at least one class session prior to the actual screening unless otherwise mentioned in the syllabus.
In-class “Tile” Game:
If time permits, we will play this game in class. It has been my experience that this reinforces some of the material learnt by means of a simple hands-on exercise. I will form ad-hoc teams on the spot. These teams may not correlate with the simulation game teams that will be formed eventually.
In-class Quizzes:
I will hold (some in-class, some online at learn.unt.edu) six quizzes that cover important concepts from various topics that we discuss in class. The time and topic of each quiz will be announced least one class session prior to the actual quiz. However, you can expect the topic to be “current” in that we will likely have a quiz on what has been covered in the past few sessions. The quizzes will be multiple choice and will likely be short in terms of number of questions (at most 10) and time (at most 20 minutes). These quizzes are also expected to serve as a primer for the types of questions on and format of, the in-class final exam.
Homework:
There will be three homework assignments primarily based on problems from the textbook.
You will be given ample time to complete these assignments and you are expected to work independently. Assignments will be due at the beginning of the class session on the respective dates outlined later in this syllabus.
Case Discussions:
The richness of the cases that we will discuss and analyze, both from Harvard and from the textbook, serves as a good metaphor for the complications that arise in real business. Some of the cases underscore one more emphasis of this course, which is the effective use of quantitative tools in meeting tangible goals related to and driven by a firm's overall strategic vision.
3

Mathematica® Modules
I have developed several interactive demonstrations and two online solvers for educational purposes using Mathematica® , which is arguably the world’s most sophisticated technical computing system. I will post these demonstrations and other relevant links on the course web page ( http://www.coba.unt.edu/itds/faculty/kulkarni/Teaching.htm
) from time to time. I expect you to experiment with these state-of-the-art applications.
Attendance and Participation :
Your class participation points will be decided in part based upon the cases that we discuss in class and partly on regular attendance. I will look for insights and creative input into the material being discussed, not simply an agreement/disagreement with my opinions. You are expected to have read and analyzed the cases ( I strongly encourage you to work with your peers ). You should also be ready for independent class participation i.e. I may simply turn over the discussion to you, for you to lead.
Simulation Game (Description and Deliverables)
1
:
The class includes "two" week-long exercises (denoted as Game 1 and Game 2 henceforth) using a web-based simulation of Littlefield Technologies. This is a team exercise. I will form teams of 3-4 students each. Details of the games will be covered in weeks preceding the specific game inception dates. We will simulate the operations of a very small firm that competes based on lead times. In Game 1 your team will have control over capacity and job sequencing decisions, whereas in Game 2, your teams will have control over capacity, investment, lot sizing, inventory management and job sequencing decisions as they oversee a simple production environment. The games are competitive in the sense that the grade will be based on the outcome (in terms of profit) relative to other teams in the class. For Game 1, the only deliverable is that you "play" the game and display some semblance of structure in your decisions which should likely translate into a very good performance relative to other teams. In short, you don't have turn in a write-up for Game 1. However, for Game 2, half of your grade will be based on a short (3-page maximum) write-up of the justifications for the decisions made by your group with particular attention paid to how topics covered in the course are applied in this virtual environment. This write-up is due at the beginning of the Week 15 session. A single write-up per group is expected.
Registration and Game Start-End Schedules:
I will go over the registration of teams for the simulation in class . Each team is required to register on-line at least one week before the first exercise begins if not sooner.
Registration Steps:
1. Go to the registration page at http://lab.responsive.net/lt/unt/start.html
2. Enter the code " dollars ".
3. Create your team ID and Password
4. Later in the form you will be asked for individual access codes. Enter the code for each of your team members (at most 4 members per team). (Ignore the "Section number" and "Adjust
1 See the last several pages of this syllabus for an overview of the simulation environment (Littlefield Technologies: Overview) and your specific assignments
(Game 1 - Capacity Management at Littlefield Technologies and Game 2 - Managing
Customer Responsiveness at Littlefield Technologies)
4

cash" boxes on this page if you see them)
5. To login and access the game, go to: http://lab.responsive.net/lt/unt/entry.html
For Game 1, teams can make changes to the simulation no earlier than 6:30 PM on April 12,
2012. The simulation will advance one virtual-day per real hour for the following 168 real hours
(1 week). Teams can make changes to the simulation up until 6:30 PM on April 19, 2012
For Game 2, teams can make changes to the simulation no earlier than 6:30 PM on April 26,
2012 The simulation will advance one virtual-day per real hour for the following 168 real hours
(1 week). Teams can make changes to the simulation up until 6:30 PM on May 3, 2012.
Although the games serve many purposes, primary among them the demonstration of systems modeling, simulation and queuing and the use of quantitative tools for decision-making, they are not intended to involve a very large time commitment. I highly recommend that each team meet to discuss the first 50 days of data, and use this data to aid in planning a strategy. This data should be available as soon as you register. After Game 1 is over, the results will be reset and you will have a new set of 50 days of data available to review before the beginning of
Game 2. You will be able to track your progress and make changes along the way. I suspect that you will need to meet at least once after each game begins to go over what is happening and to make adjustments.
GRADING POLICY:
3 - Homework Assignments (100 points each) ……………………….…….. 300 points
6 - In-Class Quizzes (50 points each)…………………………………… ….. 300 points
Simulation Game 1…………………………………………………………….… 100 points
Simulation Game 2…………………………………………………………….… 100 points
Attendance and Class Participation…………………………………………… 100 points
Comprehensive In-class Final Examination…………………………………… 100 points
Total………………………………………………………………………………… 1000 points
Letter Grade Allocation
900 & above - A; 800 & above (<900) – B; 700 & above (<800) – C; 600 & above (<700) –
D; Below 600 - F .
SETE (Student Evaluation of Teaching Effectiveness):
The Student Evaluation of Teaching Effectiveness (SETE) is a requirement for all organized classes at UNT. This short survey will be made available to you at the end of the semester, providing you a chance to comment on how this class is taught. I am very interested in the feedback I get from students, as I work to continually improve my teaching. I consider the
SETE to be an important part of your participation in this class.
The spring administration of the SETE, will remain open through the week of finals.
5

(The final exam will be open book, open notes. The outline below is TENTATIVE and subject to change at my discretion. We will not rush a topic simply because the syllabus indicates the last day of its exposition. “TCase” indicates a case that is available in the textbook and corresponding to the chapter that is being covered)
DATE
Week 1
TOPICS
Introduction and Course Policies
Introduction to Spreadsheet Models for
Optimization
Week 2
Introduction to Spreadsheet Models for
Optimization
Allocation Covering and Blending Models
Week 3
Allocation Covering and Blending Models
Week 4
HW1 Assigned
TCase: Flora Farmer’s
Gladiolus Bulbs
Week 5
Network Models
Network Models
Week 6
Sensitivity Analysis
Graphical Methods in Linear Programming
T Case: Hollingsworth Paper
Company
HW1 Due
Play in-class “Tile” game
Use solver.
Week 7
Sensitivity Analysis
Graphical Methods in Linear Programming
HW2 Assigned
TCase: Cox Cable and Wire
Company
Week
Integer Programming – Binary Choice Models Simulation groups created
Week 9
Integer Programming – Binary Choice Models
Integer Programming – Logical Constraints Discuss Doctor and Ramble
(Case appears later in this syllabus)
6

DATE
Week 10
TOPICS Home-Work Assignments
Integer Programming – Logical Constraints HW 2 Due
Discuss Mars Online Proc.
(Suggested discussion questions appear later in this syllabus)
Week 11
Special Topic: Decision and Risk Analysis
HW3 Assigned
Week 12
Special Topic: Decision and Risk Analysis
Class will convene as usual and the first class hour will be devoted to group meeting for
Game 1 strategy. You can either work on personal laptops with an internet connection
or go to the Graduate computer lab.
Discuss Merck and Company
Game 1 initialized (6:30 pm)
(April 12)
(Suggested discussion questions
appear later in this syllabus)
Week 13
Special Topics: Monte Carlo Simulation
and Queuing (simple models)
Game 1 De-brief
Week 14
Nonlinear Programming
HW 3 due
Class will convene as usual and the first class
hour will be devoted to group meeting for
Game 2 strategy. You can either work on
personal laptops with an internet connection
Game 2 initialized (6:30 pm)
(April 26)
or go to the Graduate computer lab.
Week 15
Nonlinear Programming
Game 2 De-Brief
Review for Final Exam
TCase: Delhi Foods
Game 2 write-up Due
Week 16
***** COMPREHENSIVE FINAL EXAM * *****
(Same room, May 10 th
, same time)
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Mars Online Procurement
Case Related Questions:
1. What are the basic types of auctions? Do some research!
2. What features of the proposed Mars’ auction present implementation challenges?
3. What features of the Mars’ auction suggest the use of optimization?
4. How can the requirement that first bid wins in the case of ties, be met within the optimization framework?
5. Does the need for the exact optimum solution to be determined, result in any difficulties in implementing an optimization algorithm?
6. Could a Mars’-type combinatorial auction be operated without the use of optimization?
7. Construct a model that Mars might use to operate its online procurement auction.
Merck and Company: Evaluating a Drug Licensing Opportunity
Case Related Questions:
1.
Build a decision tree that shows the cash flows and probabilities at all stages of the FDA approval process.
2.
Should Merck bid to license Davanrik? How much should they pay?
3.
What is the expected value of the licensing arrangement to LAB? Assume a 5% royalty fee on any cash flows that Merck receives from Davanrik after a successful launch.
4.
How would your analysis change if the costs of launching Davanrik for weight loss were $
225 million instead of $100 million as given in the case?
5.
Finally, how has Merck been able to achieve substantial returns to capital given the large costs and lengthy time to develop drugs?
Doctor and Ramble Inc..
(Author: Dr. Shailesh Kulkarni)
You are part of the elite Core Strategic Planning Group for Doctor and Ramble Inc. The company has recently embarked on an aggressive marketing campaign in order to sell its new brand of detergent in Ohio. In order to generate sales at the grass-roots level, the company relies on sales representatives. These sales representatives make house calls in various cities.
You have decided that each sales rep. will be assigned to a primary county and to a secondary county. The sales rep. then travels within these two counties. That way, once a sales rep. is assigned to a particular primary and secondary county, the populations of those counties are covered. As an example refer to the map of Ohio given in the textbook. Let us say that you were considering assigning 2 sales reps to the following 6 counties: Williams, Defiance, Paulding,
Henry, Fulton and Lucas. The populations of these counties are: 36369, 39987, 21302, 28383,
37751 and 474741 respectively.
You would probably assign Lucas as a primary county with Fulton as its secondary. Further, you would assign Defiance as primary and Williams as its secondary. This would maximize
8

population coverage with 2 sales reps. Note that (unlike the set covering problem shown in class) although Henry is adjacent to both Lucas and Defiance, it is not covered, since the rep. can only travel to his/her primary-secondary counties. Also, note that there exist alternative solutions with the same total population covered; for example just switch Fulton as primary with Lucas as its secondary and so on. You would like to thoroughly analyze this problem. Here are the specifics.
1.
Assuming that initially you have 5 sales representatives, which counties would you choose to assign them to (primary and secondary) in order to maximize population coverage. Analyze this problem using three different approaches: a.
Develop a Greedy Heuristic for solving the problem b.
Develop the most concise Integer Programming Model to solve the problem optimally. Show the algebraic formulation and implement and solve it in Excel.
2.
What is the minimum number of sales reps required so that the entire state of Ohio is covered, i.e. the entire population is covered? Is the answer to this question trivial? Do a sensitivity analysis and provide a graph showing how the population covered increases with the number of sales reps. increasing from 5 to 20 in steps of 1.
3.
a. Solve the problem in question 1 (parts a and b only), when a sales rep is assigned two secondary counties along with the primary county. Show the new algebraic formulation and implement it in Excel. By how much does your solution improve over part 1b?
4.
For question 1b and question 3a, can you think of an efficient procedure to find alternative optima especially new solutions, not just switching of primary and secondary?
How would you modify the IP formulation for the same? How many alternative optima can you find and what is their practical justification? Please try and find as many alternate optima as possible.
Please use the Sun.xls file from the textbook CD-ROM.
Some tips which may prove helpful:
The most elegant IP model’s algebraic formulation is compact, but it is likely that it will work out to be much larger than the Sun Bank model when explicitly written out, especially in the number of variables. Nevertheless, solve a small instance of the problem (a mickey-mouse version!) first. Verify if it is providing you with the correct optimal solution. That way you minimize the chances of debugging the full-scale model. Please visit http://www.solver.com/ in order to download a limited time full-blown version of solver.
9

Stanford University Graduate School of Business November 1998
Littlefield Technologies is a job shop which assembles Digital Satellite System receivers.
These receivers are assembled from kits of electronic components procured from a single supplier. The assembly process consists of four steps carried out at 3 stations called board stuffing , testing and tuning . The first step consists of mounting the components onto PC
Boards and soldering them. This is done at the board stuffing station. The digital components are then briefly tested at the testing station in step 2. In the third step, key components are tuned at the tuning station. Finally, the boards are exhaustively “final tested” in step 4 at the testing station before delivery to the customer. Every receiver passes final test.
All the stations consist of automated machines which perform the operations. You may purchase additional machines during the assignment. Board Stuffing machines cost
$90,000, testers cost $80,000, and tuning machines cost $100,000. You can also sell any machine at a retirement price of $10,000, provided there is at least one other machine left at that station. The operators are paid a fixed salary, and increasing the number of machines at a station does not require any increase in the number of operators.
Written by Sunil Kumar and Samuel C. Wood, both Assistant Professors at the Stanford University Graduate
School of Business. Copyright 1998. No part of this document may be reproduced without permission from
Responsive Learning Technologies, Inc., at info@responsive.net.

Orders arrive randomly at the factory. Each order is for 60 receivers. When an order arrives it is matched up with 60 raw kits and becomes a manufacturing lot. If an order arrives and there are less than 60 raw kits in the materials buffer, the order waits in the customer order queue pending arrival of raw kits. In addition, orders are not accepted if the total number of orders in the system (waiting for kits or in process) exceeds 100. If it is allowed in the assignment, the 60 kits may be sub-divided into several manufacturing lots just prior to entering the factory. Processing a lot on each machine entails performing a setup on the machine, processing each kit in the lot (one at a time), and then sending the completed lot on to the next station. Once all the receivers in an order are completed, the order is shipped immediately. A job is not shipped to its customer until all of the lots in the job are completed.
Raw kits are purchased from a single supplier and cost $10 per kit ($600 per order). There is also a fixed cost of $1000 per shipment of raw kits, independent of the shipment size.
The supplier requires four days to ship any quantity of raw kits. An order for new raw kits is placed with the supplier when the following three criteria are met: (1) the inventory of raw kits is less than the material reorder point, (2) there are no orders for raw kits currently outstanding, and (3) the factory has sufficient cash to purchase the specified order quantity. If it is allowed in the assignment, you may set the reorder point and order quantity independently to any multiple of 60 kits, as long as that multiple is greater than zero.
The current pricing contract is as follows. An order does not leave the factory until all 60 kits in the order are completed. A customer order filled within the quoted lead time of 24 hours earns $1000. If an order is still in the factory 24 hours after it arrived, then a lateness penalty is incurred. Specifically, the total revenue for an order linearly decreases from
$1000 for a 24-hour lead time to $0 for the maximum lead time of 72 hours. Orders that take longer than 72 hours to fill generate no revenue at all. If it is allowed in the assignment, you may select from a menu of other contracts for future orders. More lucrative contracts will have shorter quoted lead times and shorter maximum lead times.
You will have some cash on hand when the assignment begins. This amount is depleted by buying machines as well as by buying raw kits from the supplier. The revenue earned from filled orders increases the cash balance. The balance earns interest (compounded every simulated day) at a compounded rate of 10% per year. There are no taxes. All fixed overhead over which you have no control, such as salaries, rent, utilities, etc. are ignored. To reduce the chance of bankruptcy, you are not allowed to purchase a machine if the resulting cash balance would be too low to purchase an order of raw materials at the current order quantity.
The winning team is the team with the most cash at the end of the game.
You can compare the cash status of your team to other teams by clicking on the “Overall Standing” button on the bottom of the web page.
Before the first assignment begins, you will need to create and register your team. There should be four students per team. Come up with a team name consisting only of lowercase letters (no punctuation) and a team password. Your instructor will give you the address for the registration web page. The web page is shown at the top of the next page.
Littlefield Technologies: Overview 2

On the registration page, you will first have to enter the code given in class. Once you have entered the code, you will be asked for the team name and the password that you came up with. Finally, you will be asked for the names of each of the team members. After you submit this information from the web page, your team will be registered. Later, if you decide to change you team name, password, or members before the assignment begins, you can simply return to the registration page, enter the same team name and password you entered the first time, and then make your changes. To completely remove your team, delete all of the team members’ names and save the resulting team. You will not be able to make any changes to your team after the assignment begins.
When the assignment begins, you can access your factory from the entry web page using the team name and password that you previously registered. Your instructor will give you the address for the entry web page. The entry web page is shown below.
The following points about configuring your browser are crucial to successful access.
However, these will also be the defaults on most PC’s, so you will only need to worry about this list if things don’t appear to be working right. You can access the web page
Littlefield Technologies: Overview 3

from any Netscape Navigator 3.0 or later browser, or any Internet Explorer 3.0 or later browser. The following instructions apply to Netscape 3. If you use a different browser, you should take equivalent actions.
• You should enable Java and Javascript on your browser. To do this on Netscape 3, select Network Preferences under the Options menu of Netscape, choose Languages , and check Enable Java and Enable Javascript .
• To be able to download data from graphs later, you should select General Preferences under the Options menu of Netscape 3. Then choose the Helpers menu and choose Save to Disk as the action for text/plain. This will save data you download from the web page as a text file, which you can then open using Microsoft Excel.
The web-based simulator runs continuously. That is, if you view the site at 10 am on January 16 and then view it again at 11 am on the same day, you will see that some simulated time has elapsed. One hour of real time corresponds to 24 hours of simulated time. You have no control over the simulator’s clock. You may need to wait for a few simulated days to see the effects of your decisions, so constant monitoring is not necessary.
When you login, your factory’s status is automatically updated. Whenever you make a change (like increasing the number of machines), the factory is also updated. To update the factory status otherwise, you must click on the update button on the bottom of the web page. In light of the slow speed of the simulator, there will usually be no need for frequent updating.
More information on the assignments will be distributed shortly before each assignment begins. This information will specify the number of days that each simulation will run.
While the assignments are running, you can access the page as frequently as you wish. At the end of the assignments, the page is frozen (i.e. you cannot alter anything on it), and the simulator is immediately run for several additional simulated days. You can then access your factory’s final status for that assignment.
The web page seen after logging in will have a schematic diagram of the job shop as seen in the figure on page 1. Clicking on an icon on the schematic will reveal a menu and corresponding data. For example, clicking on a station icon will reveal a menu that gives data about the station, as well as buttons for additional menus that allow you to change the number of machines at the station or view the historical utilization the station. In similar fashion, you can get other information as described in the table on the following page.
You will also be able to download the data into text files which can be opened by
Microsoft Excel, for further analysis, by clicking on a button in these menus.
Littlefield Technologies: Overview 4

ICON
Order Queue:
INFORMATION AVAILABLE
• Number of new customer orders by day
• Average number of orders waiting for kits by day
• Current pricing contract for arriving orders
• Lot size
Materials Buffer:
• Average number of kits (raw materials) in the buffer by day
Station Queues:
• Average number of receivers in process waiting while all the machines in the station are busy, by day
Stations: • Number of machines in each station
• Scheduling Policy used (for tester only)
• Historical utilization of the station by day (i.e., the average fraction of time a machine was busy at that station during that day)
Completed Jobs: • Numbers of orders completed by day (by pricing contract)
• Average order lead time by day (by pricing contract)
• Average revenue per order by day (by pricing contract)
Clicking on the above icons will also enable you to change certain features of the factory such as the number of machines in a station. Assignment handouts will explain which features you can change.
Sources and uses of cash can be obtained by clicking on the cash button on the web page.
Sources of cash are revenue, money raised from the sale of machines, and interest. Uses of cash are raw material (kit) purchases and the purchase of additional machines. Finally, you can check the overall standing of your team using the overall standing button.
Littlefield Technologies: Overview 5

Stanford University Graduate School of Business rev. Jan. 1999
In early January, Littlefield Technologies (LT) opened its first and only factory to produce its newly developed Digital Satellite System (DSS) receivers. LT mainly sells to retailers and small manufacturers using the DSS’s in more complex products. LT charges a premium and competes by promising to ship a receiver within 24 hours of receiving the order, or the customer will receive a rebate based on the delay.
The product lifetime of many high-tech electronic products is short, and the DSS receiver is no exception. After 268 days of operation, the plant with cease producing the DSS receiver, retool the factory, and sell any remaining inventories. In the initial months, demand is expected to grow at a roughly linear rate, stabilizing after about 5 months. After another month, demand should begin to decline at a roughly linear rate. Although orders arrive randomly to LT, management expects that, on average, demand will follow the trends outlined above.
Management’s main concern is managing the capacity of the factory in response to the complex demand pattern predicted. Delays resulting from insufficient capacity would undermine LT’s promised lead times and ultimately force LT to turn away orders.
It is now late February, and LT has started to notice that a few of their receivers have been delivered after their due dates. In response, management has installed a high-powered operations team (you) to manage the factory’s capacity. For the next 168 simulated days you must buy or sell machines to maximize the factory’s overall cash position. Currently there is one board stuffing machine, one tester, and one tuning machine.
You may also change the way testing is scheduled. Currently, jobs at the tester are scheduled First-In-First-Out (FIFO), but you can give priority status either to the short initial tests or the long final tests.
When the assignment begins, there will already be 50 days of history available for your review, representing the period from early January to late February. The simulator will run at a rate of 1 simulated day per 1 real hour for the next week. After the assignment window ends, an additional 50 days of simulation will be executed at once. Thus, there will be a total of 268 days of simulation corresponding to a product life time of about 9 months.
After this simulation is over, you can check the status of your "non-running" factory.
This note was written by Samuel C. Wood and Sunil Kumar, assistant professors at the Stanford University
Graduate School of Business

Stanford University Graduate School of Business rev. July 2003
Littlefield Technologies (LT) has developed another DSS product. The new product is manufactured using the same process as the product in the assignment “Capacity Management at Littlefield Technologies” — neither the process sequence nor the process time distributions at each tool have changed. The LT factory began production by investing most of its cash into capacity and inventory. Specifically, on day 0, the factory began operations with three stuffers, two testers, and one tuner, and a raw materials inventory of 9600 kits.
This left the factory with zero cash on hand. Customer demand continues to be random, but the long-run average demand will not change over the product’s 318-day lifetime. At the end of this lifetime, demand will end abruptly and factory operations will be terminated. At this point, all capacity and remaining inventory will be useless, and thus have no value.
Management is currently quoting 7-day lead times, but management would like to charge the higher prices that customers would pay for dramatically shorter lead times. In addition, because the factory is essentially bootstrapping itself financially, management is worried about the possibility of bankruptcy. To minimize this threat, management policy dictates that new equipment cannot be purchased if the remaining cash balance would be insufficient to purchase at least one order quantity’s worth of raw materials.
LT uses a Reorder Point / Order Quantity raw material purchase policy. That is, raw kits are purchased as soon as the following three criteria are all met: (1) the inventory of raw kits is less than the reorder point, (2) there are no orders for raw kits currently outstanding, and (3) the factory has sufficient cash to purchase the reorder quantity. No order is placed if any of these three criteria are not met. Kits are purchased in multiples of 60 because orders arrive in batches of 60. A reliable supplier delivers exactly the order quantity of batches, four days after the order is placed and paid for. Management considers physical cost of holding inventory negligible compared to the financial costs. Other details concerning the purchasing policy can be found in the “Littlefield Technologies — Overview” note. The current reorder point and reorder quantity can be changed by clicking on “Edit
Data” on the Materials Buffer icon.

Currently (as in the first assignment) the factory releases orders into the factory in processing lots of 60 receivers. However, in this assignment, you may split the incoming orders into smaller processing lots as they are released into the factory. Specifically, you may choose to release each order of 1 lot of 60 receivers, 2 lots of 30 receivers, 3 lots of 20 receivers, 5 lots of 12 receivers, or 10 lots of 6 receivers. In at least some of the stations, a fixed setup time (e.g., to load kits and components) is incurred for each lot.
Customers are willing to pay a premium for fast lead times, and you now have three pricing contracts to choose from:
• price = $750; quoted lead time = 7 days; maximum lead time = 14 days. (This is the contract that the factory starts with).
• price = $1000; quoted lead time = 1 day; maximum lead time = 2 days.
• price = $1250; quoted lead time = 0.5 days; maximum lead time = 1 day.
A contract is assigned to an order as soon as it arrives at the factory, and that contract cannot be changed subsequently for that order. Contracts for future orders can be selected by clicking on “Edit Data” on the Customer Order icon.
Once the market has matured on day 150, a bank is extending a line of credit to Littlefield
Technologies at 20% annual interest compounded daily. In addition, a processing fee of
5% is incurred right after the loan is received. You can borrow and repay loans beginning on day 150 by clicking on “Cash” on the menu bar below the factory schematic, and then clicking on the appropriate button on the bottom of the resulting window.
You are also allowed to buy and sell machines and change the scheduling rule at the tester.
The factory has been running for 50 simulated days, and management has recalled the high-powered operations team (you) to manage the capacity, scheduling, purchasing, lot sizing, and contract quotations to maximize the cash generated by the factory over its lifetime. Management is not providing any operating budget beyond the cash generated by the factory itself. You will have control of the factory from day 50 to day 218. At 1 hour per simulated day, this translates to 7 real days. At day 218, you lose control of the factory, and the simulation will quickly run another 100 days of simulation. When you lose control of the factory, management expects you to leave the factory parameters set to maximize the factory’s cash position when the factory shuts down on day 318. After the simulation ends on day 318, you can check the status of your factory, but the factory will no longer be running. Team scores and ranking are based “cash balance,” which Littlefield Technologies defines as cash on hand minus debt.
Your team should turn in one summary of what actions you took during the week you had access to the factory, why you took those actions, and in retrospect whether you think you did the right thing. Show analysis to justify your conclusions. The summary cannot exceed 3 pages in length, and no appendices are allowed.