Exercise N:1 (Simulation modeling of inventory systems) Ahmed home furnishing is currently having its year-end appliance clearance sale. The store has.

Slides:



Advertisements
Similar presentations
Statistical Inventory control models I
Advertisements

Channels of Distribution
5 P’s.
Dell Leverages the Internet Group D MGS 3040 Section 3 Professor Melworm.
Normal Distributions: Finding Probabilities
Product Manager calls Cotton Fruit to confirm order of 504 towels Finance department receives confirmation from product department and mails check to.
EXAMPLE 3 Construct a binomial distribution Sports Surveys
Properties of the Binomial Probability Distributions 1- The experiment consists of a sequence of n identical trials 2- Two outcomes (SUCCESS and FAILURE.
Spreadsheet Demonstration
Simkin Hardware. Among the many products stocked by the Simkin Hardware Store is the ACE Model 89 Electric Drill. Sales of the drill have been rather.
Simulation An Inventory Simulation. Example Daily demand for refrigerators at Hotpoint City has a probability distribution Lead time is not fixed but.
Discrete Probability Distributions Random variables Discrete probability distributions Expected value and variance Binomial probability distribution.
SIMULATION EXAMPLES. SELECTED SIMULATION EXAMPLES 4 Queuing systems (Dynamic System) 4 Inventory systems (Dynamic and Static) 4 Monte-Carlo simulation.
1 1 Slide © 2006 Thomson/South-Western Chapter 5 Discrete Probability Distributions n Random Variables n Discrete Probability Distributions.
Module C10 Simulation of Inventory/Queuing Models.
Simulation Examples Continued
Exercise Write the percent formula. percent x whole = part.
13-7 Modeling Randomness.
1. State the null and alternative hypotheses. 2. Select a random sample and record observed frequency f i for the i th category ( k categories) Compute.
Chapter 12 – Independent Demand Inventory Management
Chapter 12: Inventory Control Models
Marketing Vocabulary. Market Advertise or promote an item or service.
Chapter 5 Several Discrete Distributions General Objectives: Discrete random variables are used in many practical applications. These random variables.
© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
Chapter Six Discrete Probability Distributions 6.1 Probability Distributions.
5-1 Business Statistics: A Decision-Making Approach 8 th Edition Chapter 5 Discrete Probability Distributions.
Warm Up 1. P(red | red) 2. P(red | yellow) 3. P(yellow | yellow)
Business Statistics (BUSA 3101). Dr.Lari H. Arjomand Probability is area under curve! Normal Probability Distribution.
Discrete Probability Distributions n Random Variables n Discrete Probability Distributions n Expected Value and Variance n Binomial Probability Distribution.
Purchasing Lesson 2. Objectives Explain how purchasing impacts sales and profits List qulities of a good buyer Describe the lifecycle of inventory through.
Personal selling. They show certain variety of goods to you, try to explain the features of the products, if required demonstrate the functioning of the.
Example simulation execution The Able Bakers Carhops Problem There are situation where there are more than one service channel. Consider a drive-in restaurant.
BIA 2610 – Statistical Methods Chapter 5 – Discrete Probability Distributions.
4 - 1 © 1998 Prentice-Hall, Inc. Statistics for Business & Economics Discrete Random Variables Chapter 4.
1 1 Slide © 2004 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS St. Edward’s University Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
IT College Introduction to Computer Statistical Packages Eng. Heba Hamad 2010.
Distribution Notes Channels of Distribution. Channel of Distribution The pathway from a producer/manufacturer to the final user Manufacturer Middlemen.
7 Functions of Marketing By Steven Jessee and Mitchel Mallach.
Your 3rd quiz will cover sections a){HHH,HTT,THT,TTH,THH,HTH,HHT,TTT} {0,1,2,3} b) {1/8,3/8,3/8,1/8} d) P(x=2 or x=3)= P(x=2)+P(x=3)=3/8+1/8=1/2.
Using Properties of Addition Tell whether the commutative or associative property of addition allows you to rewrite the problem as shown. EXAMPLE 4 Explain.
Marketing Information Systems Chapter 28 Section 1.
 Marketing Functions: All activities that allow companies to bring products to the market for exchange ◦ Pricing ◦ Selling ◦ Distributing ◦ Promoting.
Simulation Chapter 16 of Quantitative Methods for Business, by Anderson, Sweeney and Williams Read sections 16.1, 16.2, 16.3, 16.4, and Appendix 16.1.
PRODUCT PLACE PRICE PROMOTION EACH MARKETING PLAN OF ACTION INCLUDES THESE 4 PS Marketing is the 4 Ps.
Marketing In Today’s World Freshman Seminar - Introduction to Business Dr. Hays Freshman Seminar - Introduction to Business Dr. Hays.
For a Merchandising Business. What is merchandise? A ‘good’ (anything really) Bought for a certain price Sold for a higher price Goods are bought and.
Module 14-3 Objectives Construct and interpret two-way frequency
Entrepreneurship. Lecture - 6 Market Research for Entrepreneurs.
1. State the null and alternative hypotheses. 2. Select a random sample and record observed frequency f i for the i th category ( k categories) Compute.
Making Predictions with Experimental Probability.
NS IT SOLUTIONS SALES AND INVENTORY. NS IT SOLUTIONS Home Page.
Simulation Discrete Variables. What is it? A mathematical model Probabilistic Uses the entire range of possible values of a variable in the model.
Marketing Mix Alternatives Alex Kroll Jasmine Valenta Alexis Magray Marketing - 3 rd hr.
Inter Arrival Times. Instead of giving a chance that someone, or something arrives in a particular time interval or not, we use the inter arrival times.
Simulation of Inventory Systems EXAMPLE 2: The Newspaper Seller's Problem A classical inventory problem concerns the purchase and sale of newspapers. The.
Random number generation
Exercise – On October 26th, Sell 324 Units for $30 each
Simulation.
Cost Accounting-I Examples.
Solutions Markov Chains 1
A manufacturing firm orders computer chips from three different companies: 10% from Company A; 20% from Company B; and 70% from Company.
Each marketing plan of action includes these 4 ps
IKEA Home furnishings.
Simulation Discrete Variables.
ABC Inventory Exercise
MATH 2311 Section 6.3.
MGT601 SME MANAGEMENT.
Example: An automobile manufacturer provides vehicles equipped with selected options. Each vehicle is ordered; - with or without an automatic transmission,
PROBABILITY AND STATISTICS
Addition Rule Objectives
Presentation transcript:

Exercise N:1 (Simulation modeling of inventory systems) Ahmed home furnishing is currently having its year-end appliance clearance sale. The store has 12 refrigerators on sale; 5 are white, 4 are almond, and 3 are harvest gold. Each day, the company expects between 0 and 4 customers interested in buying refrigerator to arrive at the store according to the following probability distribution: P(0 arrivals) =.15; P(1 arrival) =.25; P(2 arrivals) =.30; P(3 arrivals) =.20; P(4 arrivals) =.10 For each of these customers, there is a 60% chance that the person will want to purchases one of the sale-priced refrigerators. Ahmed knows that 40% of customers desire a white refrigerator, 25% desire an almond refrigerator, and 35% desire a harvest gold refrigerator. If the store is sold out of a particular color choice, the customer will leave without making a purchase. Use random numbers from column 1 to determine the number of customer arrivals, column 2 to determine whether an arriving customer will wish to purchase a refrigerator, and column 3 to determine the choice of color. How many days will it take for Ahmed to sell all 12 refrigerators?

Solution [This exercise is based on Simulation modeling of inventory systems ieA survey of simulation techniques used in the modeling of in ventory systems.Ahamed took year end sales clearence,he came to know 12 refrigerators on sales of them 5 white color, 4 almond and 3 harvest gold color. Each day expect of buying between 0 to 4 probability distribution are P(0 arrivals) =.15; P(1 arrival) =.25; P(2 arrivals) =.30; P(3 arrivals) =.20; P(4 arrivals) =.10]

Find the cumulative probability P(x<=3)=p(x=0)+p(x=1)+p(x=2)+p(x=3) We can setup cumulative probability based on customer arrival. Cum prob# arrival

Cumprob #Arrival We can setup cum prob based on color of refrigerator Cumprobtype 0white 0.4almond 0.655gold We can setup chance for people to buy the refrigerator Cum probDecision 0yes 0.6No

Now we are going to find how many days needed to sale all the 12 refrigrerator