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
Outbound Logistics OptimizationMay 2009
Miguel JuraidiniFrancis 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
Modeling and simulation of processes
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
Many SKU’s available 3 Product families
Mopeds Motorcycles
Apache, Flame, Star Scooters
Scooty
Over 70 different SKU’s
ORDER
ALLOCATION
1s
t 20th
25th
28th
1s
t
ALLOCATION BILLED
ALLOCATION
BILLED
1s
t 20th
25th
28th
1s
t
83% Service Level (SKU)
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?
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
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
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
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
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
Value
Increased Visibility Coordination between allocation and
production Flexibility and agility Improve SKU Service Level
1s
t Simulatio
n
Orders
Allocation
Billing
Modeling Distribution Process
Basic Structure
Shipment Decision Bases On Availability of vehicles Availability of trucks for delivery Availability of payment from dealer
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.
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
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
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
Screenshot of UTT Model
Screenshot of CHT Model
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Q & A