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 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. Sponsors: Two Bogotá´s City Hall agencies: Fondo Local de Desarrollo de Ciudad Bolívar. Unidad Ejecutiva de Servicios Públicos. UESP.

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´ 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 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´ 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 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 Promote Food Access Citizens 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) Logistics Management System Quality System (ISO9000, ISO14000, HACCP) Information System AGROREDNUTRIRED Operador de Oferta Operador de Demanda SGL SGC SGI infraestructura Logística

Facility before the project

The Model Main Goal: Validate one Design of the future Cross Docking Facility Design: Physical Functional Procedural

Modeling the systems 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 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 Milk, potatoes, oranges, plantain and rice are the top five, 55% of consumption!!

Arrays 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

Shifts Facility Load resources and locations (0400 to 0800 hrs) Facility Unload resources and locations (0800 to 1700 hrs) Mixed Load operators, movers, Unload operators, people preparing orders, deliery trucks Fork trucks, pallet trucks

Shifts Loading facility Unloading facility

Atributes

Model inputs (arrivals file) 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

Dynamic Plots

Benefits Number of Parking lots for Trucks in system were determined (14) Trucks permanence in system (average 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. Diego Mesa R. Javier Orjuela