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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