Transcript Validation Using Simulation of a New Cross Docking

Validation Using
Simulation of a New
Cross Docking Facility
Design
Presenters: José Antonio Mesa R.
Diego Mesa R.
Javier Orjuela C.
Universidad Distrital “Francisco José de Caldas”
Bogotá, Colombia, South America.
Validation Using Simulation
of a New Cross Docking
Facility Design
Sponsors:
Two Bogotá´s City Hall agencies:
Fondo Local de Desarrollo de
Ciudad Bolívar.
Unidad Ejecutiva de Servicios
Públicos. UESP.
Team:
Javier Orjuela (Director)
José Antonio Mesa R.(Developer)
Diego Mesa R. (Analyst)
Guillermo Urrutia (Designer)
Francisco Soler (Designer)
Universidad Distrital “Francisco José de Caldas”
GICIC Research Team
Bogotá, Colombia, South America.
Abstract
 Simulation with ProModel is being used
succesfully in the validation process of a
new logistic cross docking facility in
Bogotá, Colombia, as part of a Master
Plan intended to optimize the logistic
process of food suplying and make
possible, thanks to cost lowering, an
efficient and economic solution for poor
communities, and stablishing a proven
technology for furthers developments.
Presenters
 Javier Orjuela C. MSc Operations Research.
Production Engineering Specialist. Industrial
Engineer. Former experience in food plants.
 José A. Mesa R. Production Engineering
Specialist. Computer Science Engineer. Former
experience managing production plants and
Simulation projects.
 Diego B. Mesa. Business Manager. Operation
Management Specialist. Logistics Specialist.
Application Summary
 The Universidad Distrital was contracted to
develop a Market Study in order to build a
whole new Food Distribution System intended
to reduce the cost of food (It has been
estimated that 1´000.000 people in Bogotá
have only a food at day)
 Important inefficiencies had been found, and
they generate a lot of wastes, increasing cost
for the final consumer
 The second phase of the project lead to
design a network of logistics facilities, using
the market studies results.
Application Summary
 A kind of facility must satisfy the market
demand for a specific geographic zone, and
its physical design and it procedures must be
tested before making any final choice.
 The Simulation Model is being developed as
part of the works of GICIC research group
Agenda
 The problem
 The City Hall Food Suplying Master Plan
 The Model



Goals
Model Inputs
Recomendations
The Problem
 Validate the Design of a Crossdocking Facility where
goods will be received via heavy trucks, organized
temporaly in pallets and then disposed according
summarized purchase orders sent to five logistic
zones using smaller vehicles
 The facility must operate up to 230 tons of food daily,
classified in 33 different items, this agregated demand
satisfy the requirements of 272.060 habitants and
delivered through a network of 807 small stores
located on an average distance of 1.6 kms.
 The model should allow formulate policies and deploy
strategies to balance resources allocation to
processes inside the facility
Bogotá, Colombia, South
America
Briefing of Bogotá food
supplying problem
 Bogotá is a city of 7´000.000 people.
 Extremely Poor overpopulated communities,
about 30%, could have only a meal at day
 They have a dietary deficience: only receive
46% of required calories and 20% of required
proteins
 With extremely low salaries and laboral
inestability, families uses 31% of their incomes
in “nutritional” requirements
 Between 65 and 70% of the food market is
buyed daily at the “corner´s store”
But Bogotá could be “Well
Supplied”
 Bogota requires about
10.000 tons of food daily
 33% comes from the
Ring one, Bogotá and 19
other near cities
 44% comes from the
Ring two, 4 near
provinces
 23% comes from the
country and the world
But Food supplying has been
diagnosed “Chaotic and
inefficient”
 Inadecuate handling can produce at
least 17% of wastes
 Packaging wastes are 9% of the total
mass transported
 Big dealers and retailers operations
adds 21% to cost
 Food marketing infraestructure
underutilized
 Truck fleet is used only at 48%
Bogotá´s Food Supplying
Master Plan
 A public policy to ensure that population will have
acces to the consumption of meals and
nourisments in conditions of:




Quantity
Variety
Quality and
Inocuity
Plan Principles
Citizens
Promote
Food
Access
Enviromental
Sustainability
SAAB (Food Supplying System)
 SAAB will allow that producers and small store owners can deal directly
makin easier for they his operation in a supply chain knowing offer,
demmand and prices.
 Improves costs because minimices the quantity of intermediaries and
transport
 Food are well handled, processed and transported
 Improves life quality for the whole population
Facilities
(Equipments, Infrastructure)
SGL
infraestructura
Logística
Operador de
Demanda
AGRORED
Operador de
Oferta
SGI
NUTRIRED
SGC
 Logistics Management System
 Quality System (ISO9000, ISO14000, HACCP)
 Information System
Facility before the project
The Model
 Main Goal: Validate one Design of the
future Cross Docking Facility
 Design:



Physical
Functional
Procedural
 Modeling the system’s components and
operations the team wished to obtain a
simulation model to test, plan and
manage the facility, identifying the
possible bottlenecks and causes,
capacity restrictions, possible response
to different load levels, optimal resource
quantities, trucks arriving schedule,
results of contrasting technologies and
required room to fit needs.
Main Features
 Complexity
 Deterministic
 Reutilizability
 Visualization
 Flexibility
Constraints
 Platform works from:


04:00 to 08:00 Receiving (loading)
08:00 to 17:00 Delivering (Unloading)
 Only One direction at time, it means,
You´re loading or you´re Unloading, and
you must finish loading to start
unloading.
 Area: 3000 sq mts (~27000 sq ft)
Complexity
 83 locations
 35 entities (Trucks, baskets, pallets)
 3 lines of items:



From milk to cheese (dairy) and poultry to
meat. 6 types of entities.
From tomatoes to Bananas. FRUVER.
Fruits and Vegetables. 11 types of entities.
From Sugar to Noodles. Groceries. 13
types of entities.
 Two Path Networks, 90 nodes
Complexity
 8 resource types (Delivering Trucks,
forktrucks, pallet trucks, operators,
lifters)
 Entity Attributes making easier
programming. Entity basket uses an
attribute that allows to determine the
type of item, making easier the
programming process
Processing
 // Abarrotes
 // El peso en toneladas del camion es: At_carga_en_camion *








Array_kilos_canastilla[At_Tipo_producto]
// Si viene con mas de 8 toneladas se descarga con 4 operarios
a 4 ton/ min,
// sino a N(7.5 , 1) ton por min con dos operarios
IF At_carga_en_camion *
Array_kilos_canastilla[At_Tipo_producto] < 8000
THEN
USE 2 Op_descarga FOR (At_carga_en_camion *
Array_kilos_canastilla[At_Tipo_producto] * N(7.5 , 1) / 1000)
ELSE
USE 4 Op_descarga FOR (At_carga_en_camion *
Array_kilos_canastilla[At_Tipo_producto] * 4 / 1000)
ORDER At_carga_en_camion ENT(At_prod_en_camion) TO
Cola_ZI1
How the three lines were
chosen
Demanda (en Kg) por tipo de alimento
Acelga, 140
Espinaca, 380
Visceras, 610
Azucar, 1190
Frijol, 1610
Pasta, 1930
Queso, 1930
Sal, 1980
Chocolate, 2190
Yuca, 2310
Guayaba, 2400
Leguminosas, 2730
Arveja verde, 2890
Cebolla, 3440
Panela, 4340
Productos Descripción
Aceites, 4500
Menudencias, 5180
Tomate, 5200
Pollo, 5470
Huevo, 6000
Banano, 6430
Papaya, 6430
Zanahoria, 6430
Harina de tigo, 6650
Carne de res, 7400
0
5000
10000
Milk, potatoes, oranges,
plantain and rice are
the top five, 55% of
consumption!!
Arroz, 13120
Platano, 16080
Citricos, 18010
Papa, 19290
15000
20000
Leche, 43730
25000
30000
35000
40000
45000
Kilogramos
 Trying to balance the quantity of baskets loaded per line to the
facility to ensure a permanent flow
 A previous market study was developed by GICIC research team
 These quantities were used to design the ideal fleet size and
scheduling for loading platform
Arrays
Producto
Numero
Nombre
Descripción
Kilos
Kilos/Unidad
Demanda
1
2
3
Leche
Queso
Carne de res
43730
1930
7400
29
30
Banano
Guayaba
6430
2400
25
15
Numero de Entidades
Camiones
Canas/camion
Canas/camion
movida
representa requeridos
teóricas
Asignadas
24
25
25
1822 Canastilla 3.6441667
77 Canastilla
0.386
296 Canastilla
1.48
208
200
200
216
210
210
24
35
35
200
333
210
360
35
60
 Five arrays are loaded from an
Excel File




Demanded baskets for each
product
Kg of each product in a basket
Basket per pallet
Baskets per truck
Entidad
257 Canastilla
160 Canastilla
1.286
0.48
Carga de arreglos en Promodel
El arreglo
Array_kilos_canastilla
Array_canastillas_estiba
Array_demanda
Array_canast_camion
Array_cargado
Se carga con Columna
movida
canas/estiba
Numero de Entidades
Canas/camion
Canas/camion
canas/estiba
Shifts
 Facility Load
resources and
locations (0400 to
0800 hrs)
 Facility Unload
resources and
locations (0800 to
1700 hrs)
 Load operators,
movers,
 Unload operators,
people preparing
orders, deliery
trucks
 Fork trucks, pallet
 Mixed
trucks
Shifts
 Loading
facility
 Unloading
facility
Atributes
Model inputs (arrivals file)
Nombre
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Camion
Location
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Oferta_agregada
Qty Time Ocurrences Freq. At_Tipo_producto
At_linea_camion
At_carga_en_camion
1 240
4
45
1
3
456
1 380
1
45
2
27
77
1 240
1
45
3
8
296
1 380
1
45
4
31
24
1 280
1
45
5
16
207
1 280
1
45
6
14
218
1 320
1
45
7
17
375
1 380
1
45
8
28
321
1 380
1
45
9
30
47
1 380
1
45
10
25
99
1 380
1
45
11
24
87
1 240
1
45
12
7
1049
1 320
1
45
13
21
218
1 380
1
45
14
29
128
1 320
1
45
15
18
434
1 280
1
45
16
13
4347
1 240
1
45
17
9
133
1 240
2
45
18
4
482
1 240
2
45
19
6
322
1 320
1
45
20
23
115
1 280
1
45
21
15
260
1 320
1
45
22
19
286
1 280
1
45
23
10
428
1 380
1
45
24
32
76
1 380
1
45
25
26
14
1 320
1
45
26
20
144
1 240
1
45
27
5
720
1 280
1
45
28
11
357
1 280
1
45
29
12
257
1 320
1
45
30
22
160
 An Excel File containing registers of trucks arriving to
the platform, arrival time, type of product and baskets
quantity transported in each truck.
 This is the base of flexibility
After loading is complete…
 Start preparing purchase orders, send trucks (3 tons)
and deliver them to five different logistic zones!!!
Output processing
 Groups of orders are prepared inside
each delivering truck and sent to each
one of five logistics zones
 Final orders per store are prepared at
the store front
 This proces was not detailed deeply, the
main scope were operations inside the
platform
Final design after adjustments
Final design after adjustments
Final design after adjustments
Dynamic Plots
Benefits
 Number of Parking
lots for Trucks in
system were
determined (14)
 Trucks permanence in
system (average
84.21 minutes) was
determined
Future Aplications
 Test other 17 platforms (City Logistics)
 Define costs and Include ABC
methodology
 Create a Big model
 Once running, stochasticity will be
measured and implemented to the
models
Thanks!!
 José Antonio Mesa R.
[email protected]
 Diego Mesa R.
[email protected]
 Javier Orjuela
[email protected]