1. Steven Owen Nicole Robinson Nina Trujillo

2. 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

3. Background/Problem

4. 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

5. 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

6. 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

7. 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

8. 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.

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

10. 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

11. #Central Insurance, Color Web, Dallas Tile, Dominos 2, Dynamex #dist_ccddd.lp minimize 17.9x01 + 17.9X10 + 9.0x02 + 9.0x20 + 20.7x03 + 20.7x30 +21.1x04 +21.1x40 + 18.9x05 + 18.9x50 + 6.0x21 + 6.0x12 + 19.8x31+ 19.8x13 + 6.5x41 + 6.5x14 + 13.2x51 + 13.2x15 + 25.5x32 + 25.5x23 + 12.1x42 + 12.1x24 + 10.9x52 +10.9x25 +13.7x43 + 13.7x34 + 21.9x53 + 21.9x35 + 13.9x54 + 13.9x45 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

12. Methodology - Calculations Travel Cost = Distance of Route * $0.15 10 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)

13. Analysis-Comparison Constant Demand

16. Analysis-Comparison Variable Demand

19. 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.