Steven Owen Nicole Robinson Nina Trujillo. Background/Problem Coca-Cola Bottling Company of North Texas Inefficient Forecasting Model  Simple linear.

Slides:

Advertisements

Similar presentations

Cost estimation - Cost behavior What we really want to understand is how spending will vary in a variety of decision settings. Cause-effect relations and.

Advertisements

The optimum shipment size. Empirical observations The observed cost per ton of a given vehicle or vessel is generally linear with distance. The observed.

Welcome to Econ 420 Applied Regression Analysis

1 Overview of Simulation When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions set for analytic.

11 Simulation. 22 Overview of Simulation – When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions.

Quantitative Review I. Capacity and Constraint Management (Break-Even Analysis)

Decision Making and Relevant Information

Marketing Applications: Media selection

Chapter 11, Part A Inventory Models: Deterministic Demand

Strategic Capacity Planning Prepared by Bhakti Joshi June 20, 2014.

Using Reciprocals.

5.1 Accumulated Changes Example 1: An objects travels with a velocity of 15 mph. What is the distance traveled after 4 hours t v Distance = area.

Inventory Control Models

Operations Management

Linear Programming Applications

Facilities and Aggregate Planning. Overview As you know there are 24 hours a day, 7 days a week. What can you get done in a week period? Firms need to.

Inventory Management for Independent Demand

Learning to Drive A Marketing Machine Ted Mitchell.

Fall, 2012 EMBA 512 Demand Forecasting Boise State University 1 Demand Forecasting.

McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 1.

Correlation and Linear Regression

Correlation and Linear Regression

McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 13 Linear Regression and Correlation.

Correlation and Linear Regression Chapter 13 Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.

EXAMPLE 3 Use a quadratic model Fuel Efficiency

Introduction Data surrounds us in the real world. Every day, people are presented with numbers and are expected to make predictions about future events.

Slope Is a Rate of Change

Inference for regression - Simple linear regression

Linear Regression and Correlation

Forecasting for Operations

Efficient Model Selection for Support Vector Machines

Chapter 8 – Further Applications of Integration 8.5 Probability 1Erickson.

Product Mix & Master Production Scheduling

1 What Is Forecasting? Sales will be $200 Million!

The Normal Distribution. Bell-shaped Density The normal random variable has the famous bell-shaped distribution. The most commonly used continuous distribution.

Inventory Inventory is the stock of goods, commodities or other economic resources that are stored for the smooth and efficient running of a business.

1 Minimum Cost Flows Goal: Minimize costs to meet all demands in a network subject to capacities (combines elements of both shortest path and max flow.

The Common Shock Model for Correlations Between Lines of Insurance

What’s your number? By Don Ross, Manatee District School February 8 th 2013.

Math 1050 Project Transportation Costs

McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 13 Linear Regression and Correlation.

1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Finding a Linear Model Section 2.4. Lehmann, Intermediate Algebra, 4ed Section 2.4 A company’s profit was $10 million in 2005 and has increased by $3.

Inference for regression - More details about simple linear regression IPS chapter 10.2 © 2006 W.H. Freeman and Company.

Simple Linear Regression ANOVA for regression (10.2)

Arben Asllani University of Tennessee at Chattanooga Chapter 5 Business Analytics with Goal Programming Business Analytics with Management Science Models.

RATES LESSON 46POWER UP JPAGE 329. RATES  Miles per hour (mph)  Miles per gallon (mpg)  Dollars per hour  Per: to divide.

 You can use weighted averages to solve uniform motion problems when the objects you are considering are moving at constant rates or speeds.

Writing Inequalities from Graphs. Definition An inequality is a mathematical statement formed by placing an inequality symbol between numerical or variable.

1 A Logistics Problem The Dispatch Manager for ABC Logistics needs to send a fleet of 8 small trucks and 4 large trucks from a depot to pick up items at.

Inference for regression - More details about simple linear regression IPS chapter 10.2 © 2006 W.H. Freeman and Company.

Hayden Mobile 3000 By Mr. Hayden. Cost of the Car Cost: $58, Cost with Sales Tax  Sales Tax = $58, x.07 = $4,  Total Cost = $58,

13 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting 13 For Operations Management, 9e by Krajewski/Ritzman/Malhotra.

Chapter 5 Lecture 8 Capacity Planning FOR Products and Services.

Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Linear Programming Applications

Prepared By: Maxine Cunningham John Schmidt Jonathan Braams 4/4/2009 Production Facility Optimization.

Correlation and Linear Regression

Supplementary Chapter B Optimization Models with Uncertainty

Correlation and Linear Regression

operational planning & control

Idling M.P.G. Mid Year Directors meeting

Moore Mobile 3000 Mrs. Moore.

Quantifying the Impact of Deployment Practices on Interplant Freight Volatility Kurn Ma Manish Kumar.

Techniques for Data Analysis Event Study

The velocity is constant and the distance is:

Statistical Thinking and Applications

The velocity is constant and the distance is:

Presentation transcript:

Steven Owen Nicole Robinson Nina Trujillo

Background/Problem Coca-Cola Bottling Company of North Texas Inefficient Forecasting Model  Simple linear regression  Distributor guesswork Relatively High Refill Rate on Machines  Ave. of 55.13% Capacity refill ( 37 machines) On average Machines are over ½ full upon refill

Background/Problem

Objective Estimate cost saving effectiveness of implementing real time inventory technology Develop a Linear Program to minimize costs in regard to stocking soft drink vending machines Use simulation models to demonstrate effectiveness of new methods

Methodology Pick location of numerous vending machines Gather Data Machine capacity Distance between machines Actual days between service Average cans per day Costs  Fuel  Labor

Methodology Simulations Over a period of 90 days Five Simulations  Constant Demand/Daily Refill/Previous Refill Rate  Constant Demand/Daily Refill/New Refill Rate  Constant Demand/Weekly Refill/New Refill Rate  Variable Demand/Daily Refill/Previous Refill Rate  Variable Demand/Daily Refill/New Refill Rate Staggered Starting Inventory Uniform Distribution between.15 and 1

Methodology-Simulation Constant Demand Average Cans/Day = (Machine Capacity*Actual% Capacity Fill) Actual Days Between Service Variable Demand Normal Distribution  μ = Average Cans/Day  σ = 20% of Average Cans/Day

Methodology – Refill Period Refill Rate The percentage of inventory remaining that determines the need for refilling on a specific machine. (Previous 55.13%, New 10%) Daily Refill Machine would be refilled the day that it was estimated to drop below the respective refill rate. Weekly Refill Machine would be refilled at the beginning of the week in which it was estimated to drop below the estimated refill rate.

Methodology Linear Programming Models Developed to optimize route distances on specific days with more than two service locations

Methodology -Linear Programming Model Objective function: Minimize X 01 + X 02 +…+X ij ij = traveling from i ending at j. (Respective distances as coefficients). Subject to: X 01 + X 02 +…+X 0j = 2 only two segments connecting to distributor. X 10 + X 12 +…+X 1j = 2 only two segments connecting to location #1. X 20 + X 21 +…+X 2j = 2 only two segments connecting to location #2.. = 2.. X 60 + X 61 +…+X 6j = 2 only two segments connecting to location #6

#Central Insurance, Color Web, Dallas Tile, Dominos 2, Dynamex #dist_ccddd.lp minimize 17.9x X x x x x x x x x x x x x x x x x x x x x x x x x x x x x45 subject to x01 + x02 + x03 + x04 + x05 = 2 x01 + x12 + x13 + x14 + x15 = 2 x02 + x12 + x23 + x24 + x25 = 2 x03 + x13 + x23 + x34 + x35 = 2 x04 + x14 + x24 + x34 + x45 = 2 x05 + x15 + x25 + x35 + x45 = 2 x01 + x13 + x35 + x05 <= 3 binary x01 x02 x03 x04 x05 x12 x13 x14 x15 x23 x24 x25 x34 x35 x45 end Linear Programming Example

Methodology - Calculations Travel Cost = Distance of Route * $ mpg * 1.50/gallon of fuel = $0.15/mile Travel Time = Distance of Route / 30mph Refill Time =.333 hours to refill each machine * number of machines on route Labor Cost = $10/hour for labor * (Travel Time + Refill Time)

Analysis-Comparison Constant Demand

Analysis-Comparison Variable Demand

Conclusion/Recommendation As our preliminary hypothesis suggested, there is considerable room for improvement in the efficiency in soft drink distribution. We recommend implementing a real time inventory system to capitalize on this cost saving opportunity.