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MGTSC 352: Operations Management Lecture 1

This course focuses on various aspects of operations management, including forecasting, simulation, aggregate planning, distribution planning, inventory management, and congestion management.

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MGTSC 352: Operations Management Lecture 1

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  1. MGTSC 352: Operations ManagementLecture 1

  2. My name is ... Kenneth Schultz Office 340G Business Telephone 492-3068 Email klschult

  3. This course is … … a continuation of MGTSC 312 Not ... Mgtsc != Stats

  4. “Traditional” University Course • Class • Come to class (sometimes) • Listen to The Prof (maybe not) • Take notes (perhaps) • Get bored • Study • Read the text (maybe not) • Memorize stuff (wondering why—maybe not) • Write exams • Sometimes multiple choice • Sometimes regurgitation

  5. This course • Class • Come to class, try to follow the lecture, participate • Come to lab/work on your own and try to repeat what was done in lecture • Study • Read the notes/text • Read/post to discussion forums • Do the HWs • Do exams (on-line)

  6. We want you to… • … think with us (lectures, labs) • … interact with us • … take initiative/responsibility • … experiment aggressively • … learn by DOING • This ain’t no sit-back-and-relax, you-pays-your-fees-and-you-gets-your-credits course.

  7. Evaluation

  8. Grade Distribution • Similar to other 3rd / 4th year courses • Your relative mark is what matters

  9. Active Learning • Form groups of two • Whose birthday is earlier in the year? • You’re the recorder • Question: What have you heard about this course? • Time: 1 minute

  10. Why Active Learning?

  11. What is this course about? Production and delivery of … … goods and services • Forecasting • Simulation • Aggregate Planning • Distribution Planning • Inventory Management • Congestion Management

  12. Show me a chart

  13. Process Management Information structure Network of Activities and Buffers Inputs Outputs Goods Services Flow units (customers, data, material, cash, etc.) Labor & Capital Resources Another Chart: the “Process View”

  14. Inputs: Customer orders Books, CDs Packing material Outputs Shipped orders Flow units Customer orders Cash Books Resources: Capital: contact centres, warehouses Labor: agents, order-pickers, web programmers Inventory Activities: Order taking, order filling, shipping Process management: Warehouses, inventory, distribution, capacity. Information structure: Transaction data for each order Example: Amazon.Com

  15. Active Learning • In your groups again • Task: fill in as much of the next slide as you can • Time: 2 minutes

  16. Inputs: Outputs: Flow units: Resources: Capital: Labor: Activities: Process management: Information structure: Example: Business School

  17. Do I have to take this course? • Majors that need 352 ASAP • Operations Management • Decision and Information Systems • Distribution Management • Majors that require 352 • Accounting • Business Studies • Finance • International Business • Management Info. Systems • Marketing • Retailing • Majors that do not require 352 • Business Economics and Law • Entrepreneurship and Small Business • Human Resource Management • ______ Studies (language programs) • Organizational Studies

  18. Who are we? • Instructor: Kenneth Schultz • Lab Masters: • Morgan Skowronski • Jen Tyrkalo • Grading: Jared Coulson • Tech Master: Angela Kercher • Lab Accelerators

  19. Kenneth Schultz • Wharton Undergraduate • 12 Years United States Army • Ph.D. 1997, Cornell • Research: Including human behavior in Operations Management models.

  20. My course priorities are:I’m fairYou learn

  21. Morgan Skowronski

  22. Jen Tyrkalo

  23. Things To Do Before Next Class • Course web • Read the “things to do” pageWINTER 2007 MGTSC 352 LEC B1 > COURSE DOCUMENTS > RESOURCES > GENERAL RESOURCES • Read FAQWINTER 2007 MGTSC 352 LEC B1 > COURSE DOCUMENTS > RESOURCES > GENERAL RESOURCES > FREQUENTLY ASKED QUESTIONS • Get familiar with course web and discussion forums • Read Introduction chapter (Course pack) • Read syllabus Musical Break ... do not leave

  24. Excel Basics • Jan 20, 11 – 1, B24/B28 • Free • Basic Excel skills

  25. Course Packs • $20 • Today, 3-5 in B20 • Wed, 10-12 in B20 • Friday in labs

  26. Model • Selective abstraction of reality • Model airplane • Floor plan of a house • Map of Alberta • Spreadsheet (algebraic) models • Define decision cells (variables) • Express relations between cells (formulas)

  27. Output Inputs Inputs Outputs MODEL Revenue = Quantity x Price

  28. Why model? • Provides a precise and concise problem statement • Establishes what data are necessary for decision • Clarifies relationships between variables • Enables the use of known solution methods • Enables us to generalize knowledge to solve problems we haven’t encountered before, to go beyond experiential learning. Example

  29. Fisheries Management • Lake currently has 1,000 trout • Carrying capacity = 100,000 trout • Fish population expands in May and June • Fishing allowed in September • Trout population at end of August: PAug = PApr + (a – (b  PApr))  PApr), a = 0.45, b = a / capacity. • Each fish can be sold for $11 in any year • Discount rate is 6%. • Which policy maximizes the NPV?

  30. Come again? May population = 12,000 August population? PAug = PApr + (a – b  PApr)  PApr) = ? In your groups! Time: 1 min. b = a / Cap = .45 / 100,000

  31. Come again? May population = 12,000 August population? PAug = PApr + (a – b  PApr)  PApr) = 12,000 + (0.45 – (0.45 / 100,000  12,000))  12,000 = 12,000 + (0.396)*12,000 = 16,752 b = a / Cap = .45 / 100,000

  32. Recap • Data • Starting population • Capacity • Growth parameter (a) • Discount rate • Price • Variables: # of fish caught, for every year. • Output: NPV(and fish population every year)

  33. The Operations Management Club organizes industry mixers, seminars, technical workshops, and conferences for students with an interest in Operations Management and Management Science. If you are interested in joining the OM Club, or are considering a major in Operations Management and have any questions about the degree, we would like to hear from you. For more information on the club, membership, and events, visit http://studentweb.bus.ualberta.ca/om/ or email eshin@ualberta.ca Meeting: Tuesday, January 16 at 5:00 PM, Bus 4-10

  34. Announcements • HW 1 due Wednesday, 11:59 PM • OM Club Excel workshops • Jan 20, 11 AM – 1 PM • Free • Watch for a sign up link on the course page • Don’t have course pack yet? • Get one Friday in Lab

  35. MGTSC 352 Lecture 2: Forecasting Why forecast? Types of forecasts “Simple” time series forecasting methodsIncluding SES = Simple Exponential Smoothing Performance measures

  36. Plant Site Selection • Alberta Manufacturer • Has one old plant, in Calgary • Planning to build new plant, but where? • Edmonton or Calgary?

  37. Recent Demand Figures

  38. What Would you Do?

  39. Perspectives on Forecasting • Forecasting is difficult, especially if it's about the future! Niels Bohr • Rule #0: Every forecast is wrong! • Provide a range More sarcastic quotes about forecasting: http://www.met.rdg.ac.uk/cag/forecasting/quotes.html

  40. What is the Driver Doing?

  41. Forecasting • Technological forecasts • New product, product life cycle (Ipod, Blackberry) • Moore’s Law • Gates’ Law • Economic forecasts • Macro level (unemployment, inflation, markets, etc.) • Demand forecasts • Focus in MGTSC 352

  42. Moore's Law: Computing power doubles about every two years. Gates’ Law: “The speed of software halves every 18 months.” Data from ftp://download.intel.com/museum/Moores_Law/Printed_Materials/Moores_Law_Backgrounder.pdf

  43. Economic Forecasts An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today. Evan Esar Why do economists make forecasts? “We forecast because people with money ask us to.” Kenneth Galbraith

  44. Forecasting – Quantitative • Time series analysis: uses only past records of demand to forecast future demand • moving averages • exponential smoothing • ARIMA • Causal methods: uses explanatory variables (timing of advertising campaigns, price changes) • multiple regression • econometric models

  45. Active learning • Groups of two • Recorder: person that is born closest to Telus 150. • Task: think of three quantities that you’d like to forecast • 1 minute

  46. Choosing a Forecasting Method

  47. Simple models • Notation • Dt = Actual demand in time period t • Ft = Forecast for period t • Et = Dt - Ft = Forecast error for period t • Problem: Forecast the TSX index 4 simple models Excel

  48. (Simple) Exponential Smoothing • Generalization of the WMA method • Uses a single parameter for weights 0  LS  1 • Three steps • Initialization ... F2 = D1 • Calibration ... Ft+1 = LS Dt + (1 - LS) Ft • Prediction ... same formula Note the formula is a weighted average of Demand and Forecast from last period Excel

  49. SES weights • Decrease “exponentially” as data age • Most recent data gets a weight of LS • Ft+1 = [LS Dt ] + [(1 - LS) Ft ] Rearrange... • Ft+1 = Ft + LS (Dt - Ft) = Ft + LS Et • A learning model

  50. How do we choose LS • Active learning (1 min.): • High LS(≈ 1)results in .... • Low LS(≈ 0)results in .... • Suggested range for LS: (0.01,0.3) • Performance measures (formulas in course pack, pg. 21) • BIAS • MAD • SE • MSE • MAPE Excel

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