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OPIM 204: Lecture #1 Introduction to OM OPIM 204 Operations Management Instructor: Jose M. Cruz Office: Room 332 Phone: (203) 236-9945

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Presentation on theme: "OPIM 204: Lecture #1 Introduction to OM OPIM 204 Operations Management Instructor: Jose M. Cruz Office: Room 332 Phone: (203) 236-9945"— Presentation transcript:

1

2 OPIM 204: Lecture #1 Introduction to OM

3 OPIM 204 Operations Management Instructor: Jose M. Cruz Office: Room 332 Phone: (203) 236-9945 E-mail: Jose.Cruz@business.uconn.eduJose.Cruz@business.uconn.edu Web: www.sba.uconn.edu/Users/Mnunez/OPIM204_F2003.htm

4 How to get help? Read syllabus Go to course web page Attend office hours: M, Th 4-6 pm, Send e-mail Phone call during office hours

5 Textbook & Software Requirements Russell & Taylor – Operations management –Prentice-Hall, fourth edition, 2002. Make sure that it includes free student CD-ROM with Excel OM, we will use it a lot in class! MS Excel Solver Add-in (middle of the semester).

6 Objectives Learn about OM: –How OM activities are performed –How goods and services are produced –What operations managers do –How OM affects costs in any organization Develop qualitative and quantitative decision- making skills in operations Learn basic OM Excel tools

7 Subjects/Schedule

8 Evaluation and Course Policy Class Participation: 5% Take-Home Assignments: 20% First Partial Examination: 25% Second Partial Examination: 25% Final Examination: 25%

9 Ch 1 - 2 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e The Operations Function Operations as a transformation process Operations as a basic function Operations as the technical core

10 Ch 1 - 3 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Operations As A Transformation Process OUTPUT Material Machines Labor Management Capital Goods or Services INPUT Transformation process Feedback

11 Ch 1 - 4 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Transformation Processes Physical (manufacturing) Locational (transport/storage) Exchange (retail) Physiological(healthcare) Psychological (entertainment) Informational (communications)

12 Ch 1 - 5 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Operations As A Basic Function MARKETING FINANCE OPERATIONS

13 Computer Exercise: Histograms South Laser Inc.: Manufacturer of custom laser transmitters Lasers: low-volume, high-end product, usually hand made Problem: A very sensitive module can easily break. Number of broken modules has increased recently.

14 Alternative Explanations Operator inexperience Production shifts Assembly room temperature Welder maintenance: tuning up tools

15 Solution Through Histograms Histogram: frequency chart that can be used to understand data ranges and points of concentration

16 Ch 1 - 29 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Issues & Trends In Operations 1. Intense competition 2. Global markets, global sourcing, and global financing 3. Importance of strategy 4. Product variety and mass customization 5. More services

17 Ch 1 - 30 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Issues & Trends In Operations 6. Emphasis on quality 7. Flexibility 8. Advances in technology 9. Worker involvement 10. Environmental and ethical concerns

18 Ch 1 - 34 ©2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 2/e Strategy Of Productive Systems –1. Introduction to Operations & competitiveness –2. Operations strategy –3. Quality management –4. Statistical quality control

19 Ch 1 - 35 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Designing Productive Systems –5. Product & service design –6. Process planning, analysis and reengineering –7. Facility layout –8. Human resources in operations management –9. Supply chain management

20 Ch 1 - 36 ©2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Operating Productive Systems –10. Forecasting –11. Capacity planning & aggregate production planning –12. Inventory management –13. Materials requirements planning –14. Scheduling –15. Just-in-time systems –16. Waiting line models for service improvement –17. Project management

21 Ch 2 - 3 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Competing On Cost Eliminate all waste Invest in –updated facilities & equipment –streamlining operations –training & development

22 Ch 2 - 4 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Competing On Quality Please the customer –Understand customer attitudes toward and expectations of quality

23 Ch 2 - 5 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Competing On Flexibility Produce wide variety of products Introduce new products Modify existing products quickly Respond to customer needs

24 Ch 2 - 6 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Competing On Speed Fast moves Fast adaptations Tight linkages

25 C2 Supp - 2 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Decision Analysis A set of quantitative decision-making techniques for decision situations where uncertainty exists

26 C2 Supp - 3 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Decision Making States of nature –events that may occur in the future –decision maker is uncertain which state of nature will occur –decision maker has no control over the states of nature

27 C2 Supp - 4 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Payoff Table A method of organizing & illustrating the payoffs from different decisions given various states of nature A payoff is the outcome of the decision

28 C2 Supp - 5 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Payoff Table States Of Nature Decisionab 1Payoff 1aPayoff 1b 2Payoff 2aPayoff 2b

29 C2 Supp - 6 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Decision-making Criteria Under Uncertainty Maximax criterion –choose decision with the maximum of the maximum payoffs Maximin criterion –choose decision with the maximum of the minimum payoffs Minimax regret criterion –choose decision with the minimum of the maximum regrets for each alternative

30 C2 Supp - 7 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Hurwicz criterion –choose decision in which decision payoffs are weighted by a coefficient of optimism,  –coefficient of optimism (  ) is a measure of a decision maker’s optimism, from 0 (completely pessimistic) to 1 (completely optimistic) Equal likelihood (La Place) criterion –choose decision in which each state of nature is weighted equally

31 C2 Supp - 8 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Decision-making Under Uncertainty Example Expand$ 800,000$ 500,000 Maintain status quo1,300,000-150,000 Sell now320,000320,000 States Of Nature Good ForeignPoor Foreign DecisionCompetitive Conditions Competitive Conditions

32 C2 Supp - 9 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Maximax Solution Expand: $800,000 Status quo: 1,300,000 -- Maximum Sell: 320,000 Decision: Maintain status quo

33 C2 Supp - 10 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Maximin Solution Expand: $500,000 -- Maximum Status quo: -150,000 Sell: 320,000 Decision: Expand

34 C2 Supp - 11 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Minimax Regret Solution $ 1,300,000 - 800,000 = 500,000$ 500,000 - $500,000 = 0 1,300,000 - 1,300,000 = 0500,000 - (-150,000) = 650,000 1,300,000 - 320,000 = 980,000500,000 - 320,000 = 180,000 Good ForeignPoor Foreign Competitive Conditions Expand:$500,000-- Maximum Status quo:650,000 Sell:980,000 Decision: Expand Regret Value

35 C2 Supp - 12 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Hurwicz Solution  = 0.3, 1-  = 0.7 Expand:$ 800,000 (0.3) + 500,000 (0.7) = $590,000 -- Maximum Status quo:1,300,000 (0.3) -150,000 (0.7) = 285,000 Sell:320,000 (0.3) + 320,000 (0.7) = 320,000 Decision: Expand

36 C2 Supp - 13 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Equal Likelihood Solution Two decisions, weight = 0.50 for each state of nature Expand:$ 800,000 (0.50) + 500,000 (0.50) = $650,000 -- Maximum Status quo:1,300,000 (0.50) -150,000 (0.50) = 575,000 Sell:320,000 (0.50) + 320,000 (0.50) = 320,000 Decision: Expand

37 C2 Supp - 14 © 2000by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Decisionmaking With Probabilities Risk involves assigning probabilities to states of nature Expected value is a weighted average of decision outcomes in which each future state of nature is assigned a probability of occurrence

38 C2 Supp - 15 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Expected Value

39 C2 Supp - 16 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Expected Value Example 70% probability of good foreign competition 30% probability of poor foreign competition EV(expand)= $ 800,000 (0.70) + 500,000 (0.30) = $710,000 EV(status quo)= 1,300,000 (0.70) -150,000 (0.30) = 865,000 -- Maximum EV(sell)= 320,000 (0.70) + 320,000 (0.30) = 320,000 Decision: Maintain status quo

40 C2 Supp - 17 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Expected Value Of Perfect Information The maximum value of perfect information to the decision maker EVPI = (expected value given perfect information) - (expected value without perfect information)

41 C2 Supp - 18 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e EVPI Example Good conditions will exist 70% of the time, choose maintain status quo with payoff of $1,300,000 Poor conditions will exist 30% of the time, choose expand with payoff of $500,000 Expected value given perfect information = $1,300,000 (0.70) + 500,000 (0.30) = $1,060,000 EVPI = $1,060,000 - 865,000 = $195,000

42 C2 Supp - 19 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Sequential Decision Trees A graphical method for analyzing decision situations that require a sequence of decisions over time Decision tree consists of –Square nodes - indicating decision points –Circles nodes - indicating states of nature –Arcs - connecting nodes

43 C2 Supp - 20 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e 2 1 3 4 5 6 7 Expand (-$800,000) Purchase Land (-$200,000) Expand (-$800,000) Warehouse (-$600,000) 0.60 0.40 No market growth $225,000 Market growth $2,000,000 $3,000,000 $700,000 $2,300,000 $1,000,000 $210,000 Market growth Market growth No market growth No market growth Sell land 0.80 0.40 0.70 0.30 No market growth (3 years, $0 payoff) Market growth (3 years, $0 payoff) 0.20 0.60 Decision Tree Example

44 C2 Supp - 21 © 2000by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e Evaluations At Nodes Compute EV at nodes 6 & 7 EV(node 6) = 0.80($3,000,000) + 0.20($700,000) = $2,540,000 EV(node 6) = 0.30($2,300,000) + 0.70($1,000,000) = $1,390,000 Expected values written above nodes 6 & 7 Decision at node 4 is between $2,540,000 for Expand and $450,000 for Sell land Choose Expand Repeat expected value calculations and decisions at remaining nodes

45 C2 Supp - 22 © 2000 by Prentice-Hall Inc Russell/Taylor Oper Mgt 3/e 2 1 3 4 5 6 7 Expand (-$800,000) Purchase Land (-$200,000) $1,160,000 $1,360,000 $790,000 $1,390,000 $1,740,000 $2,540,000 Expand (-$800,000) Warehouse (-$600,000) 0.60 0.40 No market growth $225,000 Market growth $2,000,000 $3,000,000 $700,000 $2,300,000 $1,000,000 $210,000 Market growth Market growth No market growth No market growth Sell land 0.80 0.40 0.70 0.30 No market growth (3 years, $0 payoff) Market growth (3 years, $0 payoff) $1,290,000 0.20 0.60 Decision Tree Solution

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