an improvement proposal for the distribution system of a … · 2018. 4. 24. · iveco toronto...
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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
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The company
847 418Customers SKU
327 310Customers SKU
81 34Customers SKU
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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
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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
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Research stages
Statistical analysisof relevant variablesand historical results
Building of the simulation
model
Evaluationof simulated experimental
scenarios
Design of improvements to
the system
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The Distribution System
Client
Invoice Warehouse
Order picking Sorting of orders by zone of
delivery
DeliveryVehicle selection for delivery and cargo loading
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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
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Statistical analysis of the model variables (Jan. ‘16 – Mar. ‘17)ABC analysis for Customers and SKU’s
• Segmentation by:
• Billing
• Registered sales
• Units sold
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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
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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
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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
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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
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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
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Ordering rate per business unit• Order generation frequency
Cooper MascotasCooper Welding Electric
Cooper Diseños y Revestimientos
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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
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Order processing at the DC
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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
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Simulating the fleet’s movement
Nearest customer function
Unserved ordering customers equal to 1?
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System performance indicators
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System performance indicators
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Scenario 11: Progressive increase in order frequency for the current situation
• System response to Company’s growth
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Scenario 12: Progressive increase in order frequency for setting parameters of Scenario 10 (Two additional DC’s)
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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.
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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