outbound logistics optimization may 2009
DESCRIPTION
Outbound Logistics Optimization May 2009. Miguel Juraidini Francis Wong. Agenda. Project Team. Sponsor: Mr. R. Sakaran Mentor: Mr. Veerabaskar Rohit Sarma. Project Overview. Outbound Logistics Optimization Understanding the distribution network Issues with outbound logistics - PowerPoint PPT PresentationTRANSCRIPT
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Outbound Logistics OptimizationMay 2009
Miguel JuraidiniFrancis Wong
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Agenda
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Project Team Sponsor:
Mr. R. Sakaran
Mentor: Mr. Veerabaskar
Rohit Sarma
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Project Overview Outbound Logistics Optimization
Understanding the distribution network
Issues with outbound logistics
Modeling and simulation of processes
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DistributionNetwork
2
1
3
45
6
7
89
10
1112
1314
15
16
17
18
19
20
6 Area Warehouse
Plant
3 PlantsHosur Mysore HP
4 Zones20 Distribution Centers600+ Dealers
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Many SKU’s available 3 Product families
Mopeds Motorcycles
Apache, Flame, Star Scooters
Scooty
Over 70 different SKU’s
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ORDER
ALLOCATION
1s
t 20th
25th
28th
1s
t
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ALLOCATION BILLED
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ALLOCATION
BILLED
1s
t 20th
25th
28th
1s
t
83% Service Level (SKU)
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Project Focus Understand existing distribution process Create numerical models for:
SKU level allocation forecast Simulation of vehicle distribution process
Help answer the questions: Will there be enough vehicles (at SKU level) to
meet allocation goals? Will there be enough shipping capacity to deliver
vehicles to dealers?
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Allocation Simulation
Decision Model
Parameters
Decision
Performance
Visibility
Spreadsheet
-Historical allocation
-Fast Vs. Slow
moving SKU’s
-Seasonality
Effect
-Percentage of dealers ordering
-Production schedule
and variability
-Expected SKU
allocation
-Expected shortages
-Expected ending
inventory
-Sensitivity Analysis
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Inputs
Scooty SKU ID Inventory Available Production Plan Available
B41900100D 500 956 #NAME?B41900104B 500 574 #NAME?B41900106G 500 290 #NAME?K3190030 500 351 #NAME?
K31900300D 500 4574 #NAME?K31900303H 500 2049 #NAME?K31900304B 500 1867 #NAME?K31900304H 500 74 #NAME?K31900306G 500 2415 #NAME?
Production VariabilityIncrease 8%Decrease 10%
Seasonality Effect
Seasonality Effect South 0
Seasonality Effect North 0
Seasonality Effect East 0
Seasonality Effect West 0
B41900100D B41900104B B41900106G K3190030 K31900300D K31900303H K31900304BSouth North East West
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Expected Allocation
1.4 1.8 2.2 2.6 3
5% 90% 5% 1.807 2.466
Mean=2135.263
Distribution for a K31901000D/S7
Va
lue
s in
10
^ -3
Values in Thousands
0.000
0.200
0.400
0.600
0.800
1.000
1.200
1.400
1.600
1.800
Mean=2135.263
1.4 1.8 2.2 2.6 3
@RISK Student VersionFor Academic Use Only
Summary Statistics
Statistic Value %tile Value
Minimum 1531 5% 1807
Maximum 2858 10% 1876
Mean 2135.263 15% 1917
Std Dev 203.5804972 20% 1955
Variance 41445.01885 25% 1992
Skewness 0.032223973 30% 2023
Kurtosis 2.835967072 35% 2053
Median 2133 40% 2075
Mode 2063 45% 2109
Left X 1807 50% 2133
Left P 5% 55% 2164
Right X 2466 60% 2192
Right P 95% 65% 2216
Diff X 659 70% 2246
Diff P 90% 75% 2281
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Shortages
Correlations for a K31901000D/S7
Correlation Coefficients
B41900100D / Available/D3-.018 K3190030AL / Available/D15-.025 K31900306H / Available/D12-.027 K31901003H / Available/D21-.028
K31900303H / Available/D8 .029 K3190030 / Available/D6 .029 K3190030BL / Available/D17 .031
K31900304B / Available/D9-.031 B41900104B / Available/D4-.032
K31900306G / Available/D11-.037 K3190030BB / Available/D16 .039
K31900307H / Available/D13-.039 K31900304H / Available/D10-.047
K31901000D / Available/D19 .048 B41900106G / Available/D5-.059
K31900300D / Available/D7 .062
@RISK Student VersionFor Academic Use Only
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
-60 -30 0 30 60 90 120
5% 90% 5% -23 68
Mean=19.977
Distribution for Expected ShortageK71900100D/X8
0.000
0.002
0.004
0.007
0.009
0.011
0.013
0.016
0.018
0.020
Mean=19.977
-60 -30 0 30 60 90 120
@RISK Student VersionFor Academic Use Only
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Ending Inventory
2.2 2.7 3.2 3.7 4.2
5% 90% 5% 2.917 3.811
Mean=3363.237
Distribution for Expected Ending inventoryK31901000D/S9
Va
lue
s in
10
^ -3
Values in Thousands
0.000
0.200
0.400
0.600
0.800
1.000
1.200
1.400
1.600
Mean=3363.237
2.2 2.7 3.2 3.7 4.2
@RISK Student VersionFor Academic Use Only
Correlations for Expected Endinginventory K31901000D/S9
Correlation Coefficients
K31901006M / Available/D23 .007 K31900303H / Available/D8-.007
K31901003H / Available/D21-.01 K31901004H / Available/D22-.011
K31900306H / Available/D12 .013 K31900304B / Available/D9 .016 K31900307H / Available/D13 .021 K31900306G / Available/D11 .021
K3190030 / Available/D6-.021 B41900104B / Available/D4 .021 B41900106G / Available/D5 .023
K31901002H / Available/D20-.04 K31900300D / Available/D7-.044
K31900304H / Available/D10 .055 K3190030BB / Available/D16-.076
K31901000D / Available/D19 .647
@RISK Student VersionFor Academic Use Only
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
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Value
Increased Visibility Coordination between allocation and
production Flexibility and agility Improve SKU Service Level
1s
t Simulatio
n
Orders
Allocation
Billing
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Modeling Distribution Process
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Basic Structure
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Shipment Decision Bases On Availability of vehicles Availability of trucks for delivery Availability of payment from dealer
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Uncertainties Payment Availability
Which dealer will pay and when will they pay? Truck Availability
Will a truck be available for delivery? Transit time variability
Distance from Plant to Dealer/Warehouse varies. Distance from Warehouse to Dealer varies. Road and traffic condition varies.
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Model Monte Carlo Simulation Model in Excel Random shuffle of dealers to simulate the
order of dealer payment Use queuing model as the basis
Time between payment receive = interarrival time Number of trucks available = no of process station
available Transit time = process time
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Creating the model Entire system with 3 factories, 200+ dealers
in the South Zone, 20 Area Warehouses and 400 dealers in East, North and West Zones too large.
Goal – a frame work of modeling the system Start with modeling a small area warehouse Continue with a larger area with multiple trucks
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Result 2 models were built to demonstrate how to
simulate the distribution process First model – Uttarachal (North Zone)
One of the smallest area 1 truck (21 vehicle capacity) 5 dealers
Second model – Chattisgarh (West Zone) 3 trucks (25 vehicles capacity) 11 dealers
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Screenshot of UTT Model
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Screenshot of CHT Model
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Learnings from India Hospitality Culture Diverse but still one Amazing driving skills Must stop at Kamat on the way to Mysore IPL Cricket
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Learnings from TVS CSR
Serving emerging market
World class manufacturing operation
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Q & A