opim 204: lecture #1 introduction to om opim 204 operations management instructor: jose m. cruz...
TRANSCRIPT
OPIM 204: Lecture #1
Introduction to OM
OPIM 204 Operations Management
• Instructor: Jose M. Cruz• Office: Room 332• Phone: (203) 236-9945 • E-mail: [email protected]• Web: www.sba.uconn.edu/Users/Mnunez/OPIM204_F2003.htm
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
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).
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
Subjects/Schedule
Subject/Activity Reading DatesIntroduction to O.M. & Decision Analysis Ch 1 , 2 & Supp 2 Sept 2Products & Services Ch 3 Sep 9Processes & Technologies Ch 4 Sep 16Forecasting Ch 8 Sep 23Statistical Control Ch 15 Sep 30Quality Ch 14 Oct 7First Examination Oct 14Supply Chain & Transportation Problem Ch 7 Oct 21Facilities & Facility Location Ch 5 & Supp 5 Oct 28Inventory Management Ch 10 Nov 4Second Examination Nov 11Waiting Line Models Ch 16 Nov 18Project Planning Ch 6 Dec 2Capacity & Aggregate Planning Ch 9 Dec 9Final Examination Dec
Evaluation and Course Policy
• Class Participation: 5%• Take-Home Assignments: 20%• First Partial Examination: 25%• Second Partial Examination: 25%• Final Examination: 25%
Ch 1 - 2© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
The Operations Function
• Operations as a transformation process
• Operations as a basic function
• Operations as the technical core
Ch 1 - 3© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Operations As A Transformation Process
OUTPUT
MaterialMachinesLaborManagementCapital
Goods or Services
INPUT Transformationprocess
Feedback
Ch 1 - 4© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Transformation Processes
• Physical (manufacturing)• Locational (transport/storage)• Exchange (retail)• Physiological (healthcare)• Psychological (entertainment)• Informational (communications)
Ch 1 - 5© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Operations As A Basic Function
MARKETING FINANCE
OPERATIONS
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.
Alternative Explanations
• Operator inexperience• Production shifts• Assembly room temperature• Welder maintenance: tuning up tools
Solution Through Histograms
• Histogram: frequency chart that can be used to understand data ranges and points of concentration
Ch 1 - 29© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Issues & Trends In Operations
1. Intense competition2. Global markets, global sourcing,
and global financing3. Importance of strategy4. Product variety and mass customization5. More services
Ch 1 - 30© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Issues & Trends In Operations
6. Emphasis on quality7. Flexibility8. Advances in technology9. Worker involvement10. Environmental and ethical concerns
Ch 1 - 34©2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 2/e
Strategy Of Productive Systems
–1. Introduction to Operations & competitiveness–2. Operations strategy–3. Quality management–4. Statistical quality control
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7
Ch 1 - 35© 2000 by Prentice-Hall IncRussell/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
Ch 1 - 36©2000 by Prentice-Hall IncRussell/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
Ch 2 - 3© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Competing On Cost
• Eliminate all waste• Invest in
–updated facilities & equipment–streamlining operations–training & development
Ch 2 - 4© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Competing On Quality
Please the customer–Understand customer attitudes toward and expectations of quality
Ch 2 - 5© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Competing On Flexibility
• Produce wide variety of products• Introduce new products• Modify existing products quickly• Respond to customer needs
Ch 2 - 6© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Competing On Speed
• Fast moves
• Fast adaptations
• Tight linkages
C2 Supp - 2© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Decision Analysis
• A set of quantitative decision-making techniques for decision situations where uncertainty exists
C2 Supp - 3© 2000 by Prentice-Hall IncRussell/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
C2 Supp - 4© 2000 by Prentice-Hall IncRussell/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
C2 Supp - 5© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Payoff Table
States Of NatureDecision a b
1 Payoff 1a Payoff 1b2 Payoff 2a Payoff 2b
C2 Supp - 6© 2000 by Prentice-Hall IncRussell/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
C2 Supp - 7© 2000 by Prentice-Hall IncRussell/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
C2 Supp - 8© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Decision-making Under Uncertainty Example
Expand$ 800,000 $ 500,000Maintain status quo 1,300,000 -150,000Sell now320,000 320,000
States Of Nature
Good Foreign Poor Foreign
Decision Competitive Conditions Competitive Conditions
C2 Supp - 9© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Maximax Solution
Expand: $800,000Status quo: 1,300,000
-- MaximumSell: 320,000
Decision: Maintain status quo
C2 Supp - 10© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Maximin Solution
Expand: $500,000 -- Maximum
Status quo: -150,000Sell: 320,000
Decision: Expand
C2 Supp - 11© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Minimax Regret Solution
$ 1,300,000 - 800,000 = 500,000 $ 500,000 - $500,000 = 01,300,000 - 1,300,000 = 0 500,000 - (-150,000) = 650,0001,300,000 - 320,000 = 980,000 500,000 - 320,000 = 180,000
Good Foreign Poor Foreign
Competitive Conditions Competitive Conditions
Expand: $500,000 -- MaximumStatus quo: 650,000Sell: 980,000Decision: Expand
Regret Value
C2 Supp - 12© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Hurwicz Solution
= 0.3, 1- = 0.7
Expand: $ 800,000 (0.3) + 500,000 (0.7) = $590,000 -- MaximumStatus quo: 1,300,000 (0.3) -150,000 (0.7) = 285,000Sell: 320,000 (0.3) + 320,000 (0.7) = 320,000
Decision: Expand
C2 Supp - 13© 2000 by Prentice-Hall IncRussell/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 -- MaximumStatus quo: 1,300,000 (0.50) -150,000 (0.50) = 575,000Sell: 320,000 (0.50) + 320,000 (0.50) = 320,000
Decision: Expand
C2 Supp - 14© 2000by Prentice-Hall IncRussell/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
EV x p ix ixi
n
where
ix outcome i
p ix probability of outco
( )
1
me i
C2 Supp - 15© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Expected Value
C2 Supp - 16© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Expected Value Example
70% probability of good foreign competition30% probability of poor foreign competition
EV(expand) = $ 800,000 (0.70) + 500,000 (0.30) = $710,000EV(status quo) = 1,300,000 (0.70) -150,000 (0.30) = 865,000 -- MaximumEV(sell) = 320,000 (0.70) + 320,000 (0.30) = 320,000
Decision: Maintain status quo
C2 Supp - 17© 2000 by Prentice-Hall IncRussell/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)
C2 Supp - 18© 2000 by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
EVPI ExampleGood 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
C2 Supp - 19© 2000 by Prentice-Hall IncRussell/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
C2 Supp - 20© 2000 by Prentice-Hall IncRussell/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
0.40No market
growth$225,000
Market growth$2,000,000
$3,000,000
$700,000
$2,300,000
$1,000,000
$210,000
Marketgrowth
Marketgrowth
No marketgrowth
No marketgrowthSell land
Sell land
0.80
0.40
0.70
0.30
No marketgrowth (3 years,
$0 payoff)
Marketgrowth (3 years,
$0 payoff)
0.20
0.60
Decision Tree Example
C2 Supp - 21© 2000by Prentice-Hall IncRussell/Taylor Oper Mgt 3/e
Evaluations At Nodes
Compute EV at nodes 6 & 7EV(node 6) = 0.80($3,000,000) + 0.20($700,000) = $2,540,000EV(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
C2 Supp - 22© 2000 by Prentice-Hall IncRussell/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
$2,540,000Expand
(-$800,000)
Warehouse(-$600,000)
0.60
0.40No market
growth$225,000
Market growth$2,000,000
$3,000,000
$700,000
$2,300,000
$1,000,000
$210,000
Marketgrowth
Marketgrowth
No marketgrowth
No marketgrowthSell land
Sell land
0.80
0.40
0.70
0.30
No marketgrowth (3 years,
$0 payoff)
Marketgrowth (3 years,
$0 payoff)
$1,290,000
0.20
0.60
Decision Tree Solution