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Lecture Operations management: Creating value along the supply chain (Canadian edition) - Chapter 13S

Chapter 13 supplement - Operational decision-making tools: Simulation. This chapter includes contents: Monte carlo simulation, computer simulation with excel, areas of simulation application. | Supplement 13-1 Robert S. Russell, Bernard W. Taylor III, Ignacio Castillo, Navneet Vidyarthi CHAPTER 13 SUPPLEMENT Operational Decision-Making Tools: Simulation OPERATIONS MANAGEMENT: Creating Value Along the Supply Chain, Canadian Edition 1 Lecture Outline Monte Carlo Simulation Computer Simulation with Excel Areas of Simulation Application Supplement 13-2 2 Simulation Mathematical and computer modeling technique for replicating real-world problem situations Modeling approach primarily used to analyze probabilistic problems It does not normally provide a solution; instead it provides information that is used to make a decision Physical simulation Space flights, wind tunnels, treadmills for tires Mathematical-computerized simulation Computer-based replicated models Supplement 13-3 3 Monte Carlo Simulation Select numbers randomly from a probability distribution Use these values to observe how a model performs over time Random numbers each have an equal likelihood of being selected at | Supplement 13-1 Robert S. Russell, Bernard W. Taylor III, Ignacio Castillo, Navneet Vidyarthi CHAPTER 13 SUPPLEMENT Operational Decision-Making Tools: Simulation OPERATIONS MANAGEMENT: Creating Value Along the Supply Chain, Canadian Edition 1 Lecture Outline Monte Carlo Simulation Computer Simulation with Excel Areas of Simulation Application Supplement 13-2 2 Simulation Mathematical and computer modeling technique for replicating real-world problem situations Modeling approach primarily used to analyze probabilistic problems It does not normally provide a solution; instead it provides information that is used to make a decision Physical simulation Space flights, wind tunnels, treadmills for tires Mathematical-computerized simulation Computer-based replicated models Supplement 13-3 3 Monte Carlo Simulation Select numbers randomly from a probability distribution Use these values to observe how a model performs over time Random numbers each have an equal likelihood of being selected at random Supplement 13-4 4 Probability Distribution of Demand Supplement 13-5 LAPTOPS DEMANDED FREQUENCY OF PROBABILITY OF PER WEEK, x DEMAND DEMAND, P(x) 0 20 0.20 1 40 0.40 2 20 0.20 3 10 0.10 4 10 0.10 100 1.00 5 Roulette Wheel of Demand Supplement 13-6 90 80 60 20 0 x = 2 x = 0 x = 4 x = 3 x = 1 6 Generating Demand from Random Numbers Supplement 13-7 DEMAND, RANGES OF RANDOM NUMBERS, x r 0 0-19 1 20-59 r = 39 2 60-79 3 80-89 4 90-99 7 Random Number Table Supplement 13-8 8 15 Weeks of Demand Supplement 13-9 WEEK r DEMAND (x) REVENUE (S) 1 39 1 4,300 2 73 2 8,600 3 72 2 8,600 4 75 2 8,600 5 37 1 4,300 6 02 0 0 7 87 3 12,900 8 98 4 17,200 9 10 0 0 10 47 1 4,300 11 93 4 17,200 12 21 1 4,300 13 95 4 17,200 14 97 4 17,200 15 69 2 8,600 = 31 $133,300 9 Computing Expected Demand Supplement 13-10 E(x) = (0.20)(0) + (0.40)(1) + (0.20)(2) + (0.10)(3) + (0.10)(4) = 1.5 laptops per week Difference between 1.5 and 2.07 is due to small number of periods analyzed (only 15 weeks) Steady-state .