outbound logistics optimization may 2009

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