Transcript Team Delirious Antony, Lakshmi, Lee and Ranjit 2) Systems

Team Delirium
Anthony, Lakshmi, Lee and Ranjit
Systems finished goods network optimization
• ISYE 6203 – Transportation and Supply Chain Team Project
• December 6th, 2011
Agenda
Introduction + Acknowledgements
Project Overview
Performance Metrics
Service Levels Analysis
Air to Ground Optimization
LTL Consolidation
Summary, Q&A
Introduction + Acknowledgement
Ranjit Menon
He is a Final Year Master's
student in Industrial
Engineering. He has a
bachelor's in Industrial
Engineering and Production
Engineering. He plays the
electric guitar and used to
play in a Rock Band
Sanghun Lee
He is a 1st year MS Student in
industrial engineering. He is
from South Korea. He has a
nice collection of classic shoes
and love his three Bichon Frise.
Lakshmi Peesapati
Anthony Bonifonte
He is a PhD student in
transportation systems
engineering (civil engineering) and
a masters student in Industrial and
Systems Engineering at Georgia
Tech. He is interested in an
academic position after his PhD. In
his free time he plays tennis or
badminton and listens to music.
He is a 1st year Ph.D. student in
operations research. He has a
BS in mathematics from Oberlin
College. He has a large
collection of exotic pets.
We would like to thank Elizabeth Koncaba for her supervision and constant assistance, Professor John
Vande Vate for his instruction and assistance, Allen Belletti from the ISYE staff for helping with IT challenges,
and Dell for allowing us to work on this project.
Data Considered
• 6 months of Dell finished goods shipments considered, with ship
dates Jan – June 2011. Maps of cycle time were averaged per
3 digit zip code.
• Parcel Shipments sent via FedEx
Major Origins considered: Nashville, Mt. Juliet, Wood Dale,
Torrance, El Paso, Juarez
• Parcel Shipments sent via UPS
Major Origins considered: Nashville, Mt. Juliet,
Wood Dale, El Paso
• Non Parcel Shipments via EUSA, KLSA, ODFL, SAIA
Major Origins considered: Nashville, Austin, El Paso
Carrier
# of records
# of records distinct ProBill
FedEx
11,450k
8,340k
UPS
2,177k
1,978k
NonParcel
788k
367k
Project Overview
Systems finished goods network optimization
Carrier Performance Metrics
Service Levels analysis
Air to ground Optimization
LTL consolidation
•
• Zone, Weight, and Cycle Time Distributions
•
• Match service requested, required, and delivered
• Opportunities to move from air to ground
• Ex: Savings from sending KLSA instead of EUSA/HWA
• Estimating savings from LTL consolidation for
• major Non-Parcel Shipments
Performance Metric: Cycle Time for FedEx
El Paso
Juarez
Avg. CT
Avg. CT
Juarez
Volume
Volume
El Paso
Total
Shipments
Considered (% Of
total records from
Origin)
Average
Cycle Time
80th
Percentile CT
2,191k (77%)
2.54 days
3 days
164k (76%)
2.44days
4 days
6
Performance Metric: Cycle Time for FedEx
Torrance
Avg. CT
Volume
Wood Dale
Avg. CT
Total
Shipments
Considered (% Of
total records from
Origin)
Average
Cycle Time
80th
Percentile
CT
Torrance
240k (44%)
1.85 days
3 days
Wood Dale
314k (39%)
2.02 days
2 days
Volume
7
Performance Metric: Cycle Time for FedEx
Mt Juliet
Nashville
Avg. CT
Avg. CT
Nashville
Volume
Volume
Mt Juliet
Total
Shipments
Considered (%
Of total records
from Origin)
Average
Cycle Time
80th
Percentile CT
912k (24%)
1.92 days
3 days
548k (63%)
2.21 days
3 days
8
Performance Metric: Zone Averages for FedEx
Ju
Juarez
Average Shipping Zone
Nash and Mt Jul
4.8
Juarez and El Paso
6.1
Wood Dale and Torrance
4.0
9
FedEx Summary
• Shipping Zones and Cycle Times of the paired
facilities are very similar to each other
• Average Billed Weights of the paired facilities
are similar to each other (details omitted),
except for Juarez, which ships significantly
heavier average weights than El Paso.
• Wood Dale and Torrance, which are designed
to serve customers in their geographic
regions, have well optimized shipping patterns
Performance Metric: Zone Averages for UPSN
Average
Shipping
Zone
Shipments
from Origin
Considered
Nashville
4.7
157k
Mt Juliet
4.8
228k
El Paso
6.1
22k
Wood Dale
3.9
9k
11
Performance Metric: Cycle Time for UPSN
El Paso_UPSN
Total Shipments: ~22k (50% of
total records from El Paso)
Average Cycle Time: 4.5days
80th Percentile: 6 days
Avg. CT
El Paso_FedEx
Total Shipments: ~164k
Average Cycle Time: 2.4 days
80th Percentile: 4 days
Avg. CT
12
12
Performance Metric: Cycle Time for Non-Parcel
Cycle time data was unreliable- looking at both the
aggregate and broken down by carriers for each origin,
we were not confident in the results.
Average
Cycle Time
Shipments
Considered
Austin
3.2 days
20k
Nashville
3 days
34k
El Paso
3.8 days
38k
Performance Metric: Cycle Time Maps for Non-Parcel
Austin
Avg. CT
El Paso
Avg. CT
Nashville
Non Parcel Summary
Avg. CT
With the data we used, we
are unable to draw any
significant conclusions or
form recommendations.
Service Levels Analysis
Fedex: Is Dell paying for Service they aren’t getting?
Fedex: Is Dell Paying too much for unnecessary service?
Motivation
To Estimate Dell Paid for Service they did not receive:
For Ex: Dell Pays for Next day shipment, but it is delivered 1day
late,2days late etc. Similarly 2nd day, 3rd day
Fedex Ground records had no reliable target cycle times to determine
delays, therefore we discarded any possible savings
Also looked at where Dell Paid too much for service; Ex: 2nd day
shipments sent within 1 day, 3rd day sent within 2 days etc. We did not
find substantial savings.
Service Quality Savings: FedEx from Juarez
•
•
•
$ 439,000 cost of shipments delayed for
Overnight, 2day and 3 day shipping
9.0% of total spent on Overnight, 2day and 3 day
shipping out of Juarez ($4,840,000).
2.9% of total spent on shipping out of Juarez
($15,000,000)
Service Quality Savings : FedEx from Nashville
•
•
•
•
•
$ 409,000 cost of shipments delayed for
Overnight, 2day and 3 day shipping
7.0% of total spent ($5,856,000) for Overnight,
2day and 3 day shipping from Nashville
5% of total spent on ($8.3 Million) on Shipping in
Nashville
$ 32,000 - cost of “customer requested 3rd day”
shipments being sent as 2nd day shipments.
0.5Million – cost of “customer requested Fedex
Ground” shipments being sent as 3rd and 2nd day
shipments.
Service Quality Savings : FedEx from El Paso
•
•
•
$ 105,000 cost of shipments delayed for
Overnight, 2day and 3 day shipping
7.8% of total spent ($1,347,000) for Overnight,
2day and 3 day shipping from El Paso
$ 30,000 cost of “customer requested 3rd day”
shipments being sent as 2nd day shipments.
Service Quality Savings : FedEx from Mt Juliet
•
•
$ 101,000 cost of shipments delayed for
Overnight, 2day and 3 day shipping
6.3% of total spent ($1,600,000) for Overnight,
2day and 3 day shipping Mt. Juliet.
Non-Parcel Air to Ground Optimization
Carriers
• EUSA
• KLSA
Origins
• Nashville
• Austin
• El Paso
Can some air shipments be sent on ground? How much would it save?
Estimate the cost of ground shipment from Radical Tools® software.
Use ground carriers (ODFL and SAIA) data to estimate discount for
dell
Savings = Invoice Freight Paid – Discounted Ground Shipment Rate
Air to Ground Optimization
EUSA – $160,000 potential savings for Nashville (Next Day, 2nd day and 3rd day shipments), $ 330,000 potential savings
in 4th, 5th and 6th door-to-door shipments.
KLSA - $245,000 potential savings for Austin (Next Day, 2nd day and 3rd day shipments)
Total - $735,000 potential savings. (3.67 % of “total freight paid” for EUSA and KLSA)
Non-Parcel Consolidation
Carriers
•
•
•
•
EUSA
KLSA
ODFL
SAIA
Origins
• Nashville
• Austin
• El Paso
Can individual LTL shipments be consolidated into a larger shipment? How much would it
save?
Potential Consolidation - Shipments from same origin going to the same destination zip
code on the same day.
Check if the Master Probills were unique (means not consolidated)
Based on the consolidated weight and destination, compute the consolidated cost.
Savings = Sum of individual costs – consolidated cost
Consolidation – EUSA and KLSA
EUSA – maximum consolidations possible for 3rd day shipments
Potential savings - $196,000 for Nashville and $227,000 for Austin
KLSA – maximum consolidations possible for next day shipments
Potential savings - $363,000 for Austin
Total potential savings = $786,000 from 10,000 potential consolidations (4% of “total freight paid” on
LTL Consolidation – ODFL and SAIA
Both ODFL and SAIA – maximum consolidations possible for shipments requested by std. ground
Both ODFL and SAIA – maximum potential savings and consolidations for ElPaso
ODFL - $419,000 savings for Nashville , $712,000 savings for EL Paso
SAIA – $313,000 savings for Nashville , $696,000 savings for EL Paso
Total potential savings = $2.2 million from 14,500 potential consolidations (10 % of “total freight
paid” on ODFL and SAIA
• 6 months of data analyzed.
Summary
• 30 % of parcel data had “actual delivery date” and hence cycle times.
• Appx. $1 million spent by Dell on Fedex shipments for “service that they did not receive”.
• Appx. $0.6 million worth of shipments being sent on a more expensive shipment mode than what
the customer requested.
• Not significant savings for Dell in terms of shipments reached earlier than what they paid for.
• $735,000 potential savings. (3.67 % of “total freight paid” for EUSA and KLSA) from air to ground
optimization
• $786,000 potential savings from 10,000 potential consolidations (4% of “total freight paid” on EUSA
and KLSA.
• $2.2 million potential savings from 14,500 potential consolidations (10 % of “total freight paid” on
ODFL and SAIA
Recommendations
• Expend money and resources to manage data better.
• Establish a Relational Database Management System (RDMS) and normalization techniques to
reduce redundancy in the data.
• Explore further the financial implications of “paid for service not received” due to shipments being
delayed on carrier’s end.
• Explore why 70% of the shipments at El Paso, where customer requested “3rd Day” were being sent
on “Fedex Air Economy(2 day)”
• Explore the potential cost savings from Non Parcel “Air to Ground” optimization for EUSA and
KLSA by analyzing data from a larger time frame.
• In order to achieve the savings through consolidations, it is recommended that the shipping orders
be placed only at the end of the day, especially when the customer requested “3rd day” or “standard
ground” so that the orders can be consolidated.
Thank You
Questions?
Performance Metric: Billed Weight for FedEx
Average Billed Weight
Nashville
13.9 lbs
Mt Juliet
7.9 lbs
• Nashville handled heavier weight of
shipments on average than Mt Juliet.
• The Mt Juliet Order System Count data was
inconsistent.
28
Performance Metric: Billed Weight for FedEx
Average Billed Weight
San Jeronimo
46.6 lbs
El Paso
24.4 lbs
Performance Metric: Billed Weight for FedEx
Average Billed Weight
Torrance
11.3 lbs
Wood Dale
11.6 lbs
30
Performance Metric: Billed Weight for UPSN
Average Billed Weight
Nashville
16.4 lbs
Mt Juliet
3.6 lbs
El Paso
19.2 lbs
Wood Dale
6.6 lbs
31
Austin_Air_CT
Austin_Ground_CT
El Paso_Air_CT
El Paso_Ground_CT