LECTURE 32 LSM 733 PRODUCTION OPERATIONS MANAGEMENT By

LECTURE 32 LSM 733 -PRODUCTION OPERATIONS MANAGEMENT By: OSMAN BIN SAIF 1

CHAPTER : MATERIAL REQUIREMENT PLANNING (Contd. ) 2

Lot-Sizing Techniques þ Lot-for-lot techniques order just what is required for production based on net requirements þ þ May not always be feasible If setup costs are high, lot-for-lot can be expensive þ Economic order quantity (EOQ) þ EOQ expects a known constant demand MRP systems often deal with unknown and variable demand 3

Lot-Sizing Techniques þ Part Period Balancing (PPB) looks at future orders to determine most economic lot size þ The Wagner-Whitin algorithm is a complex dynamic programming technique þ þ Assumes a finite time horizon Effective, but computationally burdensome 4

Lot-Sizing Summary For these three examples Lot-for-lot EOQ PPB $700 $730 $490 h it w n a l p a d e ld e i y e v a h d l u wo n i t i h W r e n 5 5 4 Wag $ f o t s o c a total 5

ERP and MRP Figure 14. 11 6

CHAPTER : JIT, Lean Operations

Just-In-Time, TPS, and Lean Operations þ JIT is a philosophy of continuous and forced problem solving via a focus on throughput and reduced inventory þ TPS emphasizes continuous improvement, respect for people, and standard work practices þ Lean production supplies the customer with their exact wants when the customer wants it without waste

Eliminate Waste þ Waste is anything that does not add value from the customer point of view þ Storage, inspection, delay, waiting in queues, and defective products do not add value and are 100% waste

Ohno’s Seven Wastes þ Overproduction þ Queues þ Transportation þ Inventory þ Motion þ Overprocessing þ Defective products

The 5 Ss þ Sort/segregate – when in doubt, throw it out þ Simplify/straighten – methods analysis tools þ Shine/sweep – clean daily þ Standardize – remove variations from processes þ Sustain/self-discipline – review work and recognize progress

The 5 Ss þ Sort/segregate – when in doubt, throw it out þ Simplify/straighten – methods analysis tools Two additional Ss þ Shine/sweep – clean daily þ Safety ––build in good practices þ Standardize remove variations from processes þ Support/maintenance – reduce þ Sustain/self-discipline – reviewdowntime work and variability and unplanned recognize progress

JIT and Competitive Advantage Figure 16. 1

JIT and Competitive Advantage Figure 16. 1

JIT Partnerships Figure 16. 2

Kanban þ þ Kanban is the Japanese word for card þ A sequence of kanbans pulls material through the process þ Many different sorts of signals are used, but the system is still called a kanban The card is an authorization for the next container of material to be produced 16

Kanban 1. User removes a standard sized container 2. Signal is seen by the producing department as authorization to replenish Signal marker on boxes Figure 16. 8 Part numbers mark location 17

Kanban Finished goods Kanban Customer order Work cell Ship Raw Material Supplier Kanban Final assembly Kanban Purchased Parts Supplier Kanban Subassembly Figure 16. 9 18

CHAPTER : MAINTENANCE AND RELIABILITY OPERATIONS 19

Strategic Importance of Maintenance and Reliability þ Failure has far reaching effects on a firm’s þ þ þ þ Operation Reputation Profitability Dissatisfied customers Idle employees Profits becoming losses Reduced value of investment in plant and equipment 20

Maintenance and Reliability þ The objective of maintenance and reliability is to maintain the capability of the system while controlling costs þ Maintenance is all activities involved in keeping a system’s equipment in working order þ Reliability is the probability that a machine will function properly for a specified time 21

Important Tactics þ Reliability 1. 2. Improving individual components Providing redundancy þ Maintenance 1. 2. Implementing or improving preventive maintenance Increasing repair capability or speed 22

Maintenance Strategy Employee Involvement Information sharing Skill training Reward system Employee empowerment Maintenance and Reliability Procedures Clean and lubricate Monitor and adjust Make minor repair Keep computerized records Results Reduced inventory Improved quality Improved capacity Reputation for quality Continuous improvement Reduced variability Figure 17. 1 23

Failure Rate Example 20 air conditioning units designed for use in NASA space shuttles operated for 1, 000 hours One failed after 200 hours and one after 600 hours FR(%) = FR(N) = 2 20, 000 - 1, 200 MTBF = (100%) = 10% =. 000106 failure/unit hr 1. 000106 = 9, 434 hrs 24

Failure Rate Example 20 air conditioning units designed for use in NASA space shuttles operated for 1, 000 hours One failed after 200 hours and one after 600 hours Failure rate FRper (%) trip = FR(N) = 2 20 FR = FR(N)(24 hrs)(6 days/trip) 2 FR = (. 000106)(24)(6) 20, 000 - 1, 200 FR =. 153 failures per trip MTBF = (100%) = 10% =. 000106 failure/unit hr 1. 000106 = 9, 434 hr 25

Maintenance þ Two types of maintenance þ Preventive maintenance – routine inspection and servicing to keep facilities in good repair þ Breakdown maintenance – emergency or priority repairs on failed equipment 26

CHAPTER : Decision Modeling 27

The Decision-Making Process Quantitative Analysis Problem Logic Historical Data Marketing Research Scientific Analysis Modeling Decision Qualitative Analysis Emotions Intuition Personal Experience and Motivation Rumors 28

Models and Scientific Management • Can Help Managers to: to – Gain deeper insights into the business. – Make better decisions! • Better assess alternative plans and actions. – Quantify, reduce and understand the uncertainty surrounding business plans and actions. 29

Decision Theory Terms: Alternative: Course of action or choice. Decision-maker chooses among alternatives. State of nature: An occurrence over which the decision maker has no control. 30

Decision Table States of Nature State 1 State 2 Alternative 1 Outcome 2 Alternative 2 Outcome 3 Outcome 4 A-31

Decision Making Under Uncertainty Criteria • Maximax - Choose alternative that maximizes the maximum outcome for every alternative (Optimistic criterion). • Maximin - Choose alternative that maximizes the minimum outcome for every alternative (Pessimistic criterion). • Expected Value - Choose alternative with the highest expected value. 32

Example - Maximax States of Nature Alternatives Favorable Unfavorable Construct large plant Construct small plant Do nothing Market $200, 000 Market -$180, 000 $100, 000 -$20, 000 $0 $0 Maximax decision is to construct large plant. 33

Example - Maximin States of Nature Market $200, 000 Market -$180, 000 Minimum in Row -$180, 000 $100, 000 -$20, 000 $0 $0 $0 Alternatives Favorable Unfavorable Construct large plant Construct small plant Do nothing Maximin decision is to do nothing. (Maximum of minimums for each alternative) 34

Expected Value Equation Number of states of nature EV N Value of Payoff ( A i ) = V i *P (V i ) Probability of payoff i =1 = V 1 *P (V 1 ) + V 2 * P (V 2 ) +. . . +V N * P (V N ) Alternative i 35

Example - Expected Value Suppose: Probability of favorable market = 0. 5 Probability of unfavorable market = 0. 5 States of Nature Market $200, 000 Market -$180, 000 Expecte d Value $10, 000 $100, 000 -$20, 000 $40, 000 $0 $0 $0 Alternatives Favorable Unfavorable Construct large plant Construct small plant Do nothing Decision is to “Construct small plant”. 36

Decision Theory Terms: • Alternative: Course of action or choice. • State of nature: An occurrence over which the decision maker has no control. Symbols used in decision tree: o A decision node from which one of several alternatives may be selected. ¡ A state of nature node out of which one state of nature will occur. 37

Decision Tree State 1 1 e v ati rn e t Al Alt ern ativ Decision Node e 2 1 State 2 State 1 2 State 2 Outcome 1 Outcome 2 Outcome 3 Outcome 4 State of Nature Node

Larger Decision Tree - Solution $10. 4 2 $10. 4 0. 2 $10. 28 0. 6 0. 3 1 $9. 6 $8. 3 $8 $12 $9 0. 5 3 0. 4 $11 0. 4 0. 6 $6 $12 0. 4 $8 0. 3 $9 0. 3 $8

CHAPTER : Transportation Modeling 40

Transportation Problem Cleveland (200 units required) Des. Moines (100 unit capacity) Albuquerque (300 units required) Boston (200 units required) Evansville (300 units capacity) Fort Lauderdale (300 units capacity) 41

Initial Solution Using the Northwest Corner Rule To Albuquerque From (A) Des Moines 5 100 (D) Evansville 8 200 (E) 9 Fort Lauderdale (F) Warehouse 300 Requirements Boston (B) 100 200 Cleveland Factory (C) Capacity 4 3 7 5 200 100 300 700

Stepping-Stone Method: Tracing a Closed Path - Des Moines to Cleveland To Albuquerque Boston From (A) (B) 4 5 Des Moines 100 (D) 4 8 Evansville 200 100 (E) + 9 7 Fort Lauderdale 100 (F) + Warehouse 300 200 Requirements - Cleveland Factory (C) Capacity Start + - -200 3 3 5 100 300 700

Initial Solution Using the Intuitive Lowest-Cost Method Second, cross out column C From To. Albuquerque Boston Cleveland Factory (A) (B) (C) Capacity Des Moines (D) Evansville (E) 5 4 8 4 Fort Lauderdale (F) 9 Warehouse Requirements 200 100 7 200 3 5 300 3 200 100 300 700 First, cross out top row Third, cross out row E

CHAPTER : Queuing Models 45

Common Queuing Situations Situation Supermarket Arrivals in Queue Grocery shoppers Highway toll booth Automobiles Doctor’s office Patients Computer system Programs to be run Telephone company Callers Service Process Checkout clerks at cash register Collection of tolls at booth Treatment by doctors and nurses Computer processes jobs Bank Customer Switching equipment to forward calls Transactions handled by teller Machine maintenance Harbor Broken machines Repair people fix machines Ships and barges Dock workers load and unload Table D. 1 46

Parts of a Waiting Line Population of dirty cars Arrivals from the general population … Queue (waiting line) Service facility Dave’s Car Wash Enter Arrivals to the system Arrival Characteristics þ Size of the population þ Behavior of arrivals þ Statistical distribution of arrivals Exit the system In the system Waiting Line Characteristics þ Limited vs. unlimited þ Queue discipline Exit the system Service Characteristics þ Service design þ Statistical distribution of service Figure D. 1 47

Queuing System Designs A family dentist’s office Queue Arrivals Service facility Departures after service Phase 2 service facility Departures after service Single-channel, single-phase system A Mc. Donald’s dual window drive-through Queue Arrivals Figure D. 3 Phase 1 service facility Single-channel, multiphase system 48

Queuing System Designs Most bank and post office service windows Service facility Channel 1 Queue Service facility Channel 2 Arrivals Departures after service Service facility Channel 3 Figure D. 3 Multi-channel, single-phase system 49

Queuing System Designs Some college registrations Queue Arrivals Figure D. 3 Phase 1 service facility Channel 1 Phase 2 service facility Channel 1 Phase 1 service facility Channel 2 Phase 2 service facility Channel 2 Departures after service Multi-channel, multiphase system 50

Queuing Models The four queuing models here all assume: þ Poisson distribution arrivals þ FIFO discipline þ A single-service phase 51

Poisson Distribution e - x for x = 0, 1, 2, 3, 4, … x! P ( x) = where P(x) x e = = probability of x arrivals number of arrivals per unit of time average arrival rate 2. 7183 (which is the base of the natural logarithms) Poisson is A statistical distribution showing the frequency probability of specific events when the average probability of a single occurrence is known. E. g What is the probability that more than 600 people will come for the dinner at a specific restaurant. 52

ADDITIONAL CHAPTER: CAPACITY AND CONSTRAINT MANAGEMENT 53

Planning Over a Time Horizon Options for Adjusting Capacity Long-range planning Add facilities Add long lead time equipment Intermediaterange planning Subcontract Add equipment Add shifts Short-range planning * Add personnel Build or use inventory * Modify capacity * Difficult to adjust capacity as limited options exist Schedule jobs Schedule personnel Allocate machinery Use capacity Figure S 7. 1 54

Tactics for Matching Capacity to Demand 1. Increasing/decreasing employees and shifts 2. Adjusting equipment u Purchasing additional machinery u Selling or leasing out existing equipment 3. Improving processes to increase throughput 4. Redesigning products to facilitate more throughput 5. Adding process flexibility to meet changing product preferences 6. Closing facilities 55

Bottleneck Analysis and Theory of Constraints u Capacity analysis determines the throughput capacity of workstations in a system u A bottleneck has the lowest effective capacity in a system u A bottleneck is a limiting factor or constraint 56

Break-Even Analysis u Technique for evaluating process and equipment alternatives u Objective is to find the break-even point in dollars and in units at which cost equals revenue u Requires estimation of fixed costs, variable costs, and revenue 57

Expected Monetary Value (EMV) and Capacity Decisions -$14, 000 Market favorable (. 4) Market unfavorable (. 6) t lan p e g Lar all -$90, 000 $18, 000 Market favorable (. 4) Medium plant Sm $100, 000 Market unfavorable (. 6) pla n t $60, 000 -$10, 000 $13, 000 Do n Market favorable (. 4) ot hin g Market unfavorable (. 6) $40, 000 -$5, 000 $0 58

Phases of Quality Assurance Inspection of lots before/after production Inspection and corrective action during production Acceptance sampling Process control The least progressive Quality built into the process Continuous improvement The most progressive 59

Statistical Process Control • Variations and Control – – Random variation: Natural variations in the output of a process, created by countless minor factors Assignable variation: A variation whose source can be identified 60

SPC Errors • Type I error – Concluding a process is not in control when it actually is. • Type II error – Concluding a process is in control when it is not. 61

Use of Control Charts • At what point in the process to use control charts – X chart – R chart – c chart – p chart • What size samples to take • What type of control chart to use – Variables – Attributes 62

Process Capability • Tolerances or specifications – Range of acceptable values established by engineering design or customer requirements • Process variability – Natural variability in a process • Process capability – Process variability relative to specification 63

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