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ORIE 3120 Industrial Data and Systems Analysis

Spring 2015

Instructors Prof. Peter L. Jackson School of Operations Research and Information Engineering 218 Rhodes Hall 607-255-9122 pj16@cornell.edu Office Hours T,Th 1:15-2:15 pm. Prof. Peter Frazier

School of Operations Research and Information Engineering 232 Rhodes Hall 607- 254-5243 pf98@cornell.edu http://people.orie.cornell.edu/pfrazier/

Class Schedule Lectures in Hollister B14

T, Th. 11:40 am. 12:55 pm. Recitations in Rhodes 471

W 2:30pm-4:25 pm F 12:20pm-2:15pm F 2:30pm-4:25 pm

Teaching Assistants Patrick Steele, prs233@cornell.edu Jian Wu, jw926@cornell.edu Office Hours Office hours will typically be held on Tuesday afternoons, but check Blackboard for specific dates & times, as they may occasionally change. Office hours for T.A.s will be held in Rhodes 431. Course Description Database and statistical techniques for data mining, graphical display, and predictive analysis in the context of industrial systems (manufacturing and distribution). Database techniques include structured query language (SQL), procedural event-based programming (Visual Basic), and geographical information systems (GIS). Statistical techniques include multiple linear regression, classification, logistic regression, clustering, and time series forecasting. Industrial systems analysis includes factory scheduling and simulation, materials planning, inventory planning, and lean manufacturing. Course Credits: 4

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Prerequisites: ENGRD 2700 If you have not already taken ENGRD 2700, it is okay to take this course concurrently with ORIE 3120, as long as you are willing to catch up on material from 2700 that we will use in 3120 if it appears before you see it in that class. Typically this is not a problem, because the material from 2700 used in 3120 does not appear until later in the semester. Resources A reading packet is available in the Campus Store. Blackboard: The course website can be found at http://blackboard.cornell.edu/. Look for ORIE 3120 Industrial Data & Sys. Analy. Jackson,P (11073_2015SP) Administrative announcements, lecture notes, homework assignments and grades will be posted on the course website. Please try to check the web page every other day. Piazza is a way to post questions. Anyone can respond. A faculty member or T.A. will attempt to respond within 24 hours. If you were registered for the class at the start of the semester, you should already be enrolled, but if not, you can enroll at https://piazza.com/cornell/spring2014/orie3120/home Course Requirements

Weekly Homework and Lab Assignments 20%, Attendance and participation 5%, Exam-I 25%, Exam-II 25%, Final Examination 25%.

Letter grades will be calculated from your weighted average as follows: If your weighted average x falls in the range

Then your letter grade will be

97.50 x A+ 92.50 x < 97.50 A 90.00 x < 92.50 A- 87.50 x < 90.00 B+ 82.50 x < 87.50 B 80.00 x < 82.50 B- 77.50 x < 80.00 C+ 72.50 x < 77.50 C 70.00 x < 72.50 C- 67.50 x < 70.00 D+ 62.50 x < 67.50 D 60.00 x < 62.50 D- x < 60.00 F

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We will use Blackboard to round to the nearest 100th of a percentage point before calculating the letter grade according to the table above. So for example, if your weighted average was a 92.48 on Blackboard, this falls in the range 90.00 x < 92.50, and so your grade will be an A-. End-of-Semester Grading Requests After the final exam is over, we sometimes receive emailed requests from students asking to do extra credit, or asking whether their weighted total can be rounded in a way that is different from the grading scheme described above, or simply asking for a higher grade without offering any justification. In response to such requests, we write that no extra credit is offered, and that the grading scheme described in the syllabus will be applied to all students. These requests are generally counterproductive: they do not result in a higher grade; they put the requestor in an unflattering light; and they consume time that the requestor and instructors could better spend doing something else. While we are glad to explain any aspect of the grading process that is unclear, we appreciate if students double-check the syllabus and other freely available course materials to see if their grading questions are already answered there before emailing. Homework Submission Homeworks are due on Wednesdays at 2:30pm in the course drop-box opposite Rhodes 206. Homeworks not received using this mechanism will be penalized at least 20%, unless you have made special arrangements with your teaching assistant. (Late homeworks severely disrupt the grading process.) Homeworks not received within one week of the due date will not be graded. Some homeworks require electronic submissions through the Blackboard course management system. These also are due by 2:30pm on Wednesdays. Homeworks must be labeled with your net id and section number to ensure prompt recording of your grade and efficient return to you in recitation. Medical reasons are accepted as excuses for late homework. Job interviews are not. We will discard your lowest homework score from the semester to make allowance for non-medical reasons for missing or incomplete homeworks. Although some homeworks are more difficult than others, we will weight all homeworks equally when dropping your lowest homework grade, and when computing your overall homework score. Credit for attendance and participation

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This includes participating in lecture, attending recitation (this will be recorded by your T.A.), answering others questions on Piazza, and seeking help in office hours if you have difficulty in the course. If you need to occasionally go to a recitation that is different from the one you are enrolled in, this is ok, but make sure that the T.A. marks you as having attended. 1 point of attendance/participation credit will also be given for filling out the course evaluation form at the end of the semester. Academic Integrity We have experienced problems of academic integrity in this course in previous years. We have and will continue to prosecute offenses. Unless clearly indicated to the contrary, assignments must be individual work in this class. You may certainly discuss ideas together but the graders are instructed to be on the lookout for identical or near-identical submissions. In such cases where identical assignments are turned in, the grade will be split between the students sharing the work. If one of the students involved is determined to be a victim of the copying, academic integrity charges will be brought against the offending student. Similarly, rules for exam-taking will be strictly observed. One disturbing trend must be stopped: students have left the exam room without first requesting permission and then they have re-entered to resume the exam. They have assumed, incorrectly, that requests to use the restrooms are unnecessary. Our policy will be that, with the exception of medical emergencies, students will not be permitted to resume the exam if they have not first been excused from the room. The exam proctor is authorized to deny requests to visit the restrooms until the students absence can be monitored adequately. The objective is to make the conditions under which students take exams to be as uniform as possible. Fairness, in this case, is achieved by uniformity. Exam Re-grade Policy Requests for re-grades must be submitted within one week of a solution being posted. Include a cover sheet explaining your request in detail. Unless it is an error of addition, we have the option of re-grading the entire exam. A re-grade request, other than for addition errors, therefore has the risk of reducing your grade as well as increasing it. Exam Conflicts

Please do not schedule May travel until after the final exam schedule has been posted. Do not ask to take the final exam early because of travel or summer job plans. In the case of exam conflicts, please notify the instructor at least one week prior to the exam.

Recitations Students can log into the lab computers in Rhodes 471 using their NET ID credentials (theres no need for a separate ORIE account). If students experience an issue logging onto these machines, they will need to reset their NET ID password, which can be done here: https://netid.cornell.edu/NetIDManagement/

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Bibliography (Weeks 1-11)

1. Hoffman, James. 2001. Introduction to Structured Query Language. Version 4.66. http://www.highcroft.com/highcroft/sql_intro.pdf

2. Buede, Dennis M. 2000. The Engineering Design of Systems: Models and

Methods, New York, NY: John Wiley and Sons, pp. 66-74 (16 pages).

3. Hopp, W.J., and M.L. Spearman. 2001. Factory Physics. Second Edition. McGraw-Hill. Pp. 49-53, 215-219, 252, 265-271.

4. Jackson, P.L., 2011.Fill-Rate-Driven Safety Stock. Class Notes. 5. Muckstadt, J.A., and A. Sapra. 2010. Principles of Inventory Management:

When You Are Down to Four, Order More. New York, NY: Springer. Pp. 262-266.

6. Black, JT , (2007) 'Design rules for implementing the Toyota Production

System', International Journal of Production Research, 45:16, 3639 - 3664 URL: http://dx.doi.org/10.1080/00207540701223469

7. McClain, J.O., L.J. Thomas, and J.B. Mazzola. 1992. Operations

Management: Production of Goods and Services. 3rd Edition. Prentice Hall: Englewood Cliffs, NJ) Pp. 252-260, 291-299.

8. Venables, W.N., D. M. Smith, and the R Core Team, An Introduction to R,

Chapters 1-7, Version 3.0.2, 2013. http://cran.r-project.org/doc/manuals/R-intro.html

9. Ruppert, D., Statistical Computing Using R, ORIE 3120 Class Notes, 2011.

10. Frazier, P. Linear Regression Theory, ORIE 3120 Class Notes, 2013.

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Lecture & Recitation Schedule Week Week of Lecture Series (3 credits) Recitation Series ( 1 credit)

1 Jan. 19 Basic Improvement Cycle; Relational Databases

No Recitation

2 Jan. 26 Database Joins, Functional Modeling using IDEF0

#1, Relational database queries

3 Feb. 2 Inventory Concepts: Throughput, Flow Time, Littles Law

#2, Advanced queries

4 Feb 9 Inventory Concepts: Decoupling, Safety Stock

#3, The Distribution Game

5 Feb. 16 FEB. BREAK / Simulation Programming

#4, Visual Basic Programming: The Rocket Game

6 Feb. 23 Simulation Programming #5, The PULL Game 7 Mar. 2 Geographical Information Systems

/Lean Manufacturing EXAM I: Tues. Mar. 3, 2015; 7:30-9:30 pm. Location: KMB B11 and UPS B17

#6, Geographical Information Systems

8 Mar. 9 Aggregate Production Planning /Lean Manufacturing

#7, Procedural Programming with Databases in R

9 Mar. 16 Material Requirements Planning (MRP) / Flow Shop Simulation

#8, UNION queries and MRP

10 Mar. 23 Cyclic Scheduling / Multiple Linear Regression

#9, Bill of Materials, and Plotting Using R

Mar. 30 SPRING BREAK 11 Apr. 6 Multiple Linear Regression

EXAM II: Tues. Apr. 7, 2015; 7:30-9:30 pm Location: KMB B11 and UPS B17

#10, Multiple Linear Regression

12 Apr. 13 Classification and Logistic Regression

#11, Classification and Logistic Regression

13 Apr. 20 Quality Improvement and Design of Experiments

#12, Quality Improvement and Design of Experiments

14 Apr. 27 Forecasting and Time Series #13, Forecasting Sales 15 May. 4 Clustering

No Recitation

EXAM III. Final Exam Period. The date & time will be announced by the registrar on Feb 14, and will be announced to the class then. The registrar will also post this information at: http://registrar.sas.cornell.edu/Sched/EXSP.html

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Recitation Modules for ORIE 3120 (1 credit hour)

1. Relational database queries. Basics of SQLite: tables and queries. Importing

data. Query builder. SQL for simple queries: SELECT, DELETE, INTO, WHERE, ORDER BY. Exporting data.

2. Advanced relational database queries. JOIN and GROUP BY queries. Statistical summaries of data. Pareto analysis. Problem focus: Pareto analysis of problems for throughput improvement.

3. Roles of Inventory: The Distribution Game. Illustrate three of the four roles of inventory discussed in lecture: pipeline stock, cycle stock, and safety stock. Problem focus: monitor system wide inventory levels and make replenishment and allocation decisions to maximize operating profit.

4. Visual Basic Programming. Using MS Excel and Visual Basic for Applications (VBA). Basics of VBA: modules, declarations, functions, return values, assignment statements, conditional statements. Transferring data between Excel and VBA. Problem focus: Create a rocket landing game.

(Lecture) Event graphs for discrete event simulation modeling. Events, conditional triggers, delays, states, state transitions, and randomized durations. Using MS Excel to draw and simulate event graphs. Plotting queue length processes. Statistics of queue length processes. Students have found this approach to be a fun way to get into procedural programming. It also equips them with a general-purpose simulation tool early in their career. Problem focus: Simulating queues in front of a multi-product oven system. 5. Lean Manufacturing. The MFD PULL Game. The Toyota Production

System. Problem focus: Experience scheduling a multi-product press with long changeover times. Experience scheduling the same press using a kanban production control system.

6. Geographical information systems. Layers. Joining external databases to geographical databases using zipcodes. Displaying and highlighting geographical data queries. Use free GIS package (Quantum-GIS). Problem focus: Understanding regional sales data.

7. Procedural database operations. Procedural programming with databases in R. Loops for record processing. Problem focus: Aggregate planning from sales forecast using procedural code.

8. Recursive computation. Bill of Material and Process Flow Computations. From-to relationships. UNION queries. Level by level decomposition. Problem focus: Compute demand rates of raw materials from aggregate production plan.

9. Plotting Using R. Problem focus: Bills of material, plots of cumulative demand and production.

10. Multiple linear regression and diagnostics applied to a variety of engineering relationships. Problem focus: optimizing a product design

11. Classification and logistic regression. Problem focus: discovering how design parameters affect the likelihood of a defective product

12. Quality Improvement and Design of Experiments including variance

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components, and design of experiments with a focus on their use in quality improvement. Variance components analysis is the analysis of variance with the focus on characterizing the sources of variance in a process. Problem focus: quality improvement of soup package filling operation to minimize variation

13. Time series and forecasting Problem focus: predicting sales data.