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Statistics For A Distribution Center Of Cola

5030 words - 21 pages

Group Project: Coca Cola CocaProProposal
Group Project: Coca Cola CocaProProposal

Megan Bond (Team Lead)
Taryn Keenan
Allysa Kiedpool
Krista Samples
Nicole Smith

July 12, 2014

BA 615

Dr. Mohammad Oskoorouchi
Megan Bond (Team Lead)
Taryn Keenan
Allysa Kiedpool
Krista Samples
Nicole Smith

July 12, 2014

BA 615

Dr. Mohammad Oskoorouchi

Contents

Executive Summary………………………………………………………………………………………………3

Analysis & Approach…………………………………………………………………………………………….4

Pie Charts……………………………………………………………………………………………………4

Line Charts……………………………………………………....……………………………………..…5

Descriptive Statistics and Variation…………………………………….………………………8

...view middle of the document...

Mispicks occur when an order builder in the warehouse picks the wrong product for an order, or does not put enough cases on the pallet. For example, if an order calls for 8 cases of 12pack Coca Cola, but the order builder accidently pulls 8 cases of 12pack Sprite or only picks 7 cases of 12pack Coca Cola, this would be considered a mispick or pick error. Out of stocks (OOS) occur when the local warehouse runs out of product for a customer order. Delivery errors occur if the driver is not able to deliver the load within the customer time window, or if the customer decides they want to refuse or cut cases off the order. Planning errors arise from the sales team if they try to order an unauthorized product for a customer or do not give correct pricing for a product.
For our project, we analyzed the OTIF metric for the Coca Cola distribution center located in Orange, CA for the first half of 2014. We analyzed this data in several different ways. First, we created pie charts in order to observe how each component contributed to the OTIF score for the first half, or first 2 quarters of 2013 and 2014. We then created line charts to see if there were trends over time with OTIF and trends in the components of OTIF in 2013 vs. 2014.
Next, we looked at the descriptive statistics for the OTIF score from the first half of 2013 and 2014, as well as each component that makes up the OTIF score each year: OOS, mispicks, delivery, and planning errors. We looked at the mean and variability of each score to determine how they relate to one another, as well as their relationship year over year. We were able to look closely at the variability by finding the coefficient of variation for each metric, as well and comparing them to one another. After this, we looked at the descriptive statistics on OTIF for the second half of 2013 and the overall 2013 year to see if it improved the second half of the year and affected the overall OTIF score in a negative or positive way. Overall it looks like OTIF has been trending down and getting worse since the beginning of 2013.
We then analyzed the distribution for each measurement by developing histograms. From the descriptive statistics and distribution we were able to see that the data for the components making up OTIF was right skewed, while the overall OTIF data was left skewed. This confirms what we saw with the median and mean from the descriptive statistics. Based on this analysis, 2014 looks like it has more variability than 2013 for most the measurements.
After we analyzed portions of the data, we wanted to see what Coca Cola can expect for the second half, or quarters 3 and 4 of 2014. We created 95% confidence intervals for the OTIF score, as well as each of the components. This will gave us an idea of what range to expect each measurement to be in with 95% confidence.
Each market unit within Coca Cola creates goals based on the performance of last year so the company can always strive for...

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