An improvement proposal for the distribution system of a … · 2018. 4. 24. · IVECO TORONTO...

An improvement proposal for the distribution system of a wholesale trading company

AUTHORS: JESÚS MADRID Y CHRISTIAN MOLLER

TUTOR: RAFAEL DÍAZ

The company

847 418Customers SKU

327 310Customers SKU

81 34Customers SKU

Problem statement Uncertainty of Delivery Times

Collapse of the distribution system when in high-peak demands

Indirect costs due to customer dissatisfaction

Need to outsource part of its distribution operations

Lack of performance indicators of the distribution system

Main Objective of the researchTo design an improvement proposal for the

distribution system of a wholesale trading company with

operations in the Venezuelan territory using

simulation as an experimental design tool

Research stages

Statistical analysisof relevant variablesand historical results

Building of the simulation

model

Evaluationof simulated experimental

scenarios

Design of improvements to

the system

The Distribution System

Client

Invoice Warehouse

Order picking Sorting of orders by zone of

delivery

DeliveryVehicle selection for delivery and cargo loading

Delivery zones

Frequency of delivery Zones Delivery time

travelDaily 2 Less than a day

Inter-daily 6 Less than a day

Weekly 1, 3, 4, y 5 Less than a day

Weekly 7, 8 y 9 More than a day

Statistical analysis of the model variables (Jan. ‘16 – Mar. ‘17)ABC analysis for Customers and SKU’s

• Segmentation by:

• Billing

• Registered sales

• Units sold

Statistical analysis of the model variables (Jan. ‘16 – Mar. ‘17)

Business Unit A B TotalCooper Welding Electric 64 354 418

Cooper Mascotas 78 232 310Cooper Diseños y Revestimientos 7 27 34

Grand total 149 613 762

AB analysis results

Business Unit A B TotalCooper Welding Electric 372 475 847

Cooper Mascotas 129 198 327Cooper Diseños y Revestimientos 35 46 81

Grand total 536 719 1255

Customers

SKU’s

Juguete con sonido steak 12 cm

Doctor Cooper 4 kg

Collar antipulgas natural gatos 33 cm

Cepillo para mascotas S 12.7 x 9 cm

Tienda de Animales Don Perro, C.A.

Av. Santa Teresa de Jesús, La Castellana, Chacao

Caracas

3345

12 03 2016

4

10

12

7

480,00

13.772,00

3124,00

1872,00

1920,00

137.720,00

37.488,00

13.104,00

367 7 días de crédito 1782

1. General ordering rate of each business unitOrders / Week

2. Ordering client Probability for client “i” of generating an order (fi)

3. Number of items orderes Based on a Frequency Histogram

4. Items (SKU) in the order Probability for item “i” of being ordered (fi)

Simulating an order in the model

Determining the driving speed of delivery vehicles in the model

Vehicle type Average speed(km/h)IVECO TORONTO 260E25 59,49

IVECO BLANCO 5012 55,99

IVECO FURGÓN 5012 62,68

IVECO TECTOR 170E22 55,38

MITSUBISHI L300 1 60,65

MITSUBISHI L300 2 60,00

Global average 58,74

Global standard deviation 2,88

• Real mouvement data for the company’s fleet of vehicles, obtained from

their installed GPS service

Why choosing Anylogic as the simulation software?• Comprehends a GIS environment (OpenMap) for realistic

routing and vehicle movement

• The software company provided us with a temporary

professional license for the research

• Support was provided while building the model

Installing model’s agents

Distribution center Customers SKU’s

Parameters: • Name• Location• Number of units per

vehicle type

Parameters: • Customer code• Corresponding business

unit• Probability of generating

an order (fi)• Servicing Distribution

center• Delivery zone

Parameters: • SKU code• Corresponding business unit• Unit volume• Unit weight• Unit price• Probability of being ordered (fi)• Average ordered quantity• Standard deviation of the ordered

quantity

Ordering rate per business unit• Order generation frequency

Cooper MascotasCooper Welding Electric

Cooper Diseños y Revestimientos

Simulating the ordering events• Followed Programming logic:

Ordering customerFunction randomtrue(fi)

1

Number of itemsIn the order

Empiricaldistribution

2

3

4

Final attributesof the order

ValueTotal valueTotal weight

5

Order processing at the DC

Order processing at the DC• Selecting the vehicle unit for delivery (Programming algorithm)

The available vehicles are ordered in the DC

by its volumetric capacity(lowest to highest)

1

When multiple units pervehicle type:

Random selectionUsing a Uniform probability distribution

3

Selection constraintsDelivery batch volume ≤ Vehicle cap. (m3)Delivery batch weight ≤ Vehicle cap. (kg)

Iteration

2

Simulating the fleet’s movement

Nearest customer function

Unserved ordering customers equal to 1?

System performance indicators

System performance indicators

Experimental research design

• New DC’s incorporation: for western & Eastern Venezuela

• Variation of number of vehicle units in each DC

• Order frequency factor(rate) variation

*Simulation runs for one year

81/230

Scenario 0: Current situation, DC: Charallave – 7 vehicles

Global resultsDelays percentage

(delays / registered sales) 6.60%

Average Delivery time 6.12 days

Delivery time standard deviation 4.38 days

Delay penalties (Bs) 95 MM

2

5012 63.5%

6012 26.8%1170E22 33.5%

260E25 58.9%

L300 37.5%

2

1

19

Scenario 1: DC Charallave – Additional 5012 vehicle type

Global resultsDelays percentage

(delays / registered sales) 7%

Delivery average time 5.72 days

Delivery time standard deviation 4.38 days

Delay penalties (Bs) 138 MM

2

5012 40.5%

6012 34.6%1170E22 31.4%

260E25 67.5%

L300 32.5%

3

1

1

114/247

9

Scenario 2: DC Charallave – Additional 260E25 vehicle type

Global resultsDelays percentage

(delays / registered sales) 3.0%

Delivery average time 5.54 days

Delivery time standard deviation 3.58 days

Delay penalties (Bs) 34 MM

2

5012 59.9%

6012 34.8%1170E22 24.5%

260E25 30.4%

L300 37.4%

2

2

1

27/104

*Scenario 3 DC Charallave – Additional 5012 & 260E25 vehicle types.Similar results

6

Scenario 4: DC’s Charallave & Barcelona – 5012 vehicle type transfer to new DC

Global resultsDelays percentage

(delays / registered sales) 40.80%

Delivery average time 18.90 days

Delivery time standard deviation 22.36 days

Delay penalties (Bs) 731 MM

2

5012 98.0%

6012 21.0%1170E22 03.5%

260E25 14.6%

L300 37.1%

1

1

1

362/1386

*Scenario 5 CD Charallave & Barcelona – Additional 5012 vehicle type for new DC (Barcelona)No improvements5012 99.1%1

2

Scenario 6: DC’s Charallave & Barcelona – Two new vehicles for new DC (Barcelona)

Global resultsDelays percentage

(delays / registered sales) 1.10%

Delivery average time 3.78 days

Delivery time standard deviation 2.67 days

Delay penalties (Bs) 14 MM

2

5012 56.5%

6012 20.7%1170E22 11.9%

260E25 14.6%

L300 38.8%

2

1

116/33

1

5012 72.7%

L300 33.0%

1

7

Scenario 8: DC’s Charallave & Cabudare – Two vehicles for DC on Cabudare

Global resultsDelays percentage

(delays / registered sales) 0.50%

Delivery average time 4.18 days

Delivery time standard deviation 3.34 days

Delay penalties (Bs) 9 MM

2

5012 29.5%

6012 22.7%1170E22 20.1%

260E25 49.0%

L300 27.6%

2

1

1

14/14

1

5012 39.6%

L300 66.9%

1

*Scenario 7: DC’s on Charallave & Cabudare – One 5012 vehicle type on CabudareNo improvements

9

Scenario 9: DC’s on Charallave, Barcelona & Cabudare – Two vehicles for each DC

Global resultsDelays percentage

(delays / registered sales) 0.30%

Delivery average time 2.79 days

Delivery time standard deviation 1.92 days

Delay penalties (Bs) 1.7 MM

2

5012 29.3%

6012 07.2%1170E22 02.7%

260E25 04.0%

L300 25.1%

2

1

1

2/3

1

5012 46.0%

L300 55.9%

1

1

5012 69.0%

L300 37.7%

1

9

Scenario 10: DC’s Charallave, Barcelona & Cabudare – Without two vehicles from Charallave

Global resultsDelays percentage

(delays / registered sales) 0.00%

Delivery average time 2.72 days

Delivery time standard deviation 1.75 days

Delay penalties (Bs) 0 MM

2

5012 26.5%

6012 13.8%0170E22 00.0%

260E25 00.0%

L300 27.5%

2

0

1

1

5012 41.8%

L300 64.2%

1

1

5012 65.1%

L300 37.5%

1

Scenario 11: Progressive increase in order frequency for the current situation

• System response to Company’s growth

Scenario 12: Progressive increase in order frequency for setting parameters of Scenario 10 (Two additional DC’s)

Conclusions• Remarkable improvement by simply adding one 260E25 vehicle type (highest capacity) in the

current situation.

• The transfer of vehicles from the existing DC to a new one installed generates a collapse in the system.

• When installing a new DC, it would be a best option to place it in Cabudare (Western Venezuela)

• If a new DC is installed, it must have a fleet of at least two vehicles

• If two DC’s are installed (east and west side), the highest capacity vehicles (170E22 & 260E25) from the original DC in Charallave (Central Venezuela) are no longer needed.

• Results from scenario 10 hold strong up to an increase of 40% of the order frequency rate, when compared with the current situation.

Improvement proposals for the distribution system

1. Purchase an additional unit of 260E25

vehicle type

• Investment level:Relatively low

• Delay percentage: 3%(Reduction of 41%)

• Delivery average time: 5,54 days (Reduction of 0.6 days)

• Delay penalties:34 MM Bs.(Reduction of 64%)

• Order frequency:Can hold up to 25% increase

2. Install a new DC at Cabudare (Eastern

Venezeual) and purchase two vehicles

(L300 & 5012 type)

• Investment level:Medium

• Delay percentage : 0.5%(Reduction of 92.42%)

• Delay average time: 4.18 days(Reduction of 2 days)

• Delay penalties:9 MM Bs.(Reduction of 90.52%)

• Order frequency:Can hold up to 30% increase

3. Install new DC’s at Cabudare & Barcelona (east & west) with two vehicles each (L300 & 5012

type). Sell highest capacity vehicles (170E22 & 260E25 type)

• Investment level:HIgh(Capital recovery by selling

two vehicles)

• Delay percentage: 0%(Reduction of 100%)

• Delivery average time: 2.72 days(Reduction of 4 days)

• Delay penalties:0 MM Bs.(Reduction of 100%)

• Order frequency:Can hold up to 40% increase