lOMoARcPSD|39211872 HOCHIMINH CITY INTERNATIONAL UNIVERSITY SCHOOL OF INDUSTRIAL ENGINEERING & MANAGEMENT TOPIC: SIMULATION OF KINH DO’S FRESH BREAD SUPPLY CHAIN IN NINH THUAN PROVINCE GROUP: Group 37 Advisor: Trần Đức Vĩ List of Full name - ID: Trần Tấn Phát - IELSIU22224 Huỳnh Khánh Nguyên - IELSIU22074 Phạm Bảo Minh - IELSIU22188 Vũ Hoàng Duy - IELSIU22179 Ho Chi Minh city, Vietnam August/2023 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 TABLE OF CONTENTS ACKNOWLEDGEMENT. LIST OF TABLES. LIST OF FIGURES. Introduction to Anylogistix.
Scope and limitation. CHAPTER 2: SYSTEM REQUIREMENT. Data collection method. DC and factory.
2 Downloaded by mon hon (monmon1@gmail. Process and output. CHAP 3: EXPERIMENTAL DESIGN. Scenario 1: Add one more DC.
Process and output. Result of Scenario 1. Scenario 2: Add 2 more DCs. Final result and conclusion.
Advantages and Disadvantages. Appendix 1: Customers’ locations. Appendix 2: Customers’ demand. 3 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 ACKNOWLEDGEMENT To complete the project with the topic: “Simulation of Kinh Đô bread supply chain in Ninh Thuan Province”, it is impossible not to mention the enthusiastic help of teachers and teaching assistants.
We extend our sincerest thanks to: - Dr. Trần Đức Vĩ, advisor, who provided our group with foundational knowledge and enthusiastic courage in the course of accomplishing the report. - Miss Đoàn Thúy Nhã, the teaching assistant, who helped us with technological problems and assessment during the experimentation in the simulation app - Anylogistix. We also want to extend our appreciation to lecturers and teaching assistants from Industrial Engineering Management for providing us with an opportunity to assess this tool and give us a clearer insight into our major.
4 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 ABSTRACT Mondelez Kinh Do company (or Kinh Do) is one of the most reputable companies famous for its traditional Vietnamese cake products. With a nationally - widespread supply chain network, Kinh Do has been the first choice of Vietnamese customers for nearly 30 years. This report shows the simulation of Kinh Do supply chain in Ninh Thuan province in the whole year 2022 with the support of the Anylogistix application. The report is based on data derived from reliable sources as well as sound assumptions.
First of all, the existing supply chain is simulated with actual data as a base case. Then, several hypotheses that are supposed to be able to optimize the profit are simulated as separate scenarios. The comparison between the results of three cases of simulation provides insights into the existing supply chain with its complex components, as well as the potential of optimizing the profit for the business. 5 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 LIST OF TABLES No.of Figures Content Page Table 1 DCs and factory location 19 Table 2 Products 20 Table 3 Unit conversion 20 Table 4 An example for Demand 21 Table 5 Facility Expenses 22 Table 6 Inventory 22 Table 7 Vehicles 23 Table 8 Paths 24 Table 9 Processing cost 25 Table 10 Processing time 25 Table 11 Production 26 Table 12 Shipping 26 Table 13 Sourcing 27 Table 14 Juxtaposition of results of the base case, 51 6 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 scenario 1 and scenario 2 LIST OF FIGURES No.of Figures Content Page Figure 1 General flowchart 16 Figure 2 Detailed inbound flowchart 17 Figure 3 Detailed outbound flowchart 18 Figure 4 Customers, DC, factory locations 19 Figure 5 Simulation experiment setting 28 Figure 6 Base case - simulation result (1) 28 Figure 7 Base case - simulation result (2) 29 Figure 8 Base case - simulation result (3) 29 Figure 9 Base case - simulation result - profit and lost 30 Figure 10 Base case - Revenue and Profit graph 31 Figure 11 Pre-existing supply chain 32 7 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 Figure 12 Scenario 1 - GFA setting - period 33 Figure 13 Scenario 1 - GFA setting - number of sites 33 Figure 14 Scenario 1 - GFA setting - default constraints 34 Figure 15 Scenario 1 - GFA setting - unit setting 34 Figure 16 Scenario 1 -GFA result - New DC location 35 (1) Figure 17 Scenario 1 - GFA result - New DC location 35 (2) Figure 18 Scenario 1 - GFA result - New DC location 35 (3) Figure 19 Scenario 1 - Simulation experiment - 36 structure Figure 20 Scenario 1 - Simulation experiment - group 36 Figure 21 Scenario 1 - Simulation experiment - DCs 36 and Factory Figure 22 Scenario 1 - Simulation experiment - Facility 37 8 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 expenses Figure 23 Scenario 1 - Simulation experiment - 37 Inventory Figure 24 Scenario 1 - Simulation experiment - Paths 38 Figure 25 Scenario 1 - Simulation experiment - 38 Processing cost Figure 26 Scenario 1 - Simulation experiment - 39 Processing time Figure 27 Scenario 1 - Simulation experiment - 39 Shipping Figure 28 Scenario 1 - Simulation experiment - 39 Sourcing Figure 29 Scenario 1 - Simulation result - Dashboard 40 Figure 30 Scenario 1 - Simulation result - Profit and 41 lost Figure 31 Scenario 1 - Simulation result - Revenue and 41 Profit graph 9 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 Figure 32 Scenario 2 - GFA setting - experiment 42 duration Figure 33 Scenario 2 - GFA setting - number of sites 42 Figure 34 Scenario 2 - GFA setting - default constraints 43 Figure 35 Scenario 2 - GFA setting - unit setting 43 Figure 36 Scenario 2 - GFA result - New 2 DCs 43 locations (1) Figure 37 Scenario 2 - GFA result - New 2 DCs 44 locations (2) Figure 38 Scenario 2 - GFA result - GFA DC locations 44 Figure 39 Scenario 2 - GFA result - GFA DC 2 45 location Figure 40 Scenario 2 - Simulation experiment - Flow 45 graph Figure 41 Scenario 2 - Simulation experiment - Group 46 10 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 Figure 42 Scenario 2 - Simulation experiment - DCs 46 and Factories Figure 43 Scenario 2 - Simulation experiment - Facility Expenses 46 Figure 44 Scenario 2 - Simulation experiment - Inventory 47 Figure 45 Scenario 2 - Simulation experiment - Paths 48 Figure 46 Scenario 2 - Simulation experiment - Processing Cost 48 Figure 47 Scenario 2 - Simulation experiment - Processing Time 48 Figure 48 Scenario 2 - Simulation experiment - Shipping 48 Figure 49 Scenario 2 - Simulation experiment - Sourcing 49 Figure 50 Scenario 2 - Simulation results - Dashboard 49 Figure 51 Scenario 2 - Simulation results - Profit and 50 loss Figure 52 Scenario 2 - Revenue and Profit graph 50 11 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 CHAPTER 1: INTRODUCTION 1.
Rationale In the context of logistics, simulation refers to the process of creating a computer- based model that imitates the real-world operations and processes of a logistic system. It involves using mathematical and statistical techniques to replicate the behaviour and dynamics of various components within the logistics network, such as transportation, inventory management, order processing, and facility operations. The purpose of the logistic simulation is to evaluate and optimize the efficiency, effectiveness, and cost-effectiveness of different logistical strategies, policies, and operational scenarios. By running simulations, logistics professionals can test different "what-if" scenarios, assess the impact of potential changes, and make informed decisions to improve the performance of their logistics operations.
Problem statement With a population of 99,959,381 people in 2023[1], Vietnam ranks 15th in the worldwide population ranking and has become a promising market for the food industry. The fresh bread market witnessed the same trend with an increasing number of competitors in its market share, namely Kinh Đô, Staff, Otto, Orion, etc. Although gaining advantages from the blooming market, these companies simultaneously face several challenges. The average expiry day for instant food like fresh bread is generally 3 to 7 days [2].
This short life cycle can put substantial time pressure on the fresh bread supply chain. Insufficient facilities and distribution systems in Vietnam are also additional hindrances to meeting wholly customer demand. We are recognizant of unsatisfactory service levels and unoptimized costs, which come from unelaborated supply planning, inaccurate market prediction and lack of cost- effectiveness in transportation. Kinh Do Corporation is a well-known food-and-beverage company in Vietnam that produces a variety of food-and-beverage products.
In 2015, Kinh Do sold 80% of its shares to Mondelēz International, a multinational corporation based in the United States, which represents a significant example of cross-border investment and collaboration in the food industry. For the sake of simplification, we use the title “Kinh Do” to refer to Mondelez Kinh Do Company in this report. Kinh Do has a complex supply chain with an extensive distribution network across Vietnam and other Southeast Asian countries, as well as its sourcing of raw materials 12 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 from various suppliers. Bread is a staple food in Vietnam and an important part of Kinh Do's product portfolio, making it a key area of focus for the company's supply chain management.
Through our study of Kinh Do fresh bread in Ninh Thuan province specifically, we have observed that there is only one distribution centre in Ninh Thuan province serving over 100 customers. Being aware of some undiscovered shortcomings in the provincial supply chain, we conduct a study on Kinh Do's bread supply chain. By simulating different supply chain scenarios with varying numbers of distribution centres, we aim to provide insights into the potential benefits and drawbacks of such changes and offer informed recommendations for optimizing Kinh Do's supply chain in Ninh Thuan province. Introduction to Anylogistix AnyLogistix is a supply chain optimization and simulation software developed by AnyLogic.
It enables users to model and analyze complex supply chain networks, evaluate strategies, and make data-driven decisions. With AnyLogistix, users can create detailed models of their supply chain, simulate various scenarios, optimize key performance indicators, and visualize results. It offers advanced optimization capabilities and helps businesses improve efficiency, reduce costs, and meet customer demands effectively. Objective By employing the AnyLogistix software for the supply chain of Kinh Do bread, our objective is to simulate the Kinh Do bread supply chain in Ninh Thuan Province to study the real-life system without disrupting the existing operations.
Furthermore, our goal is to assess the performance of the current supply chain, identify any deficiencies, and propose suitable solutions to optimize profitability and service levels. Scope and limitation 1. Scope We will study the supply chain of Kinh Do fresh bread from 1/1/2022 to 31/12/2022. Our supply chain starts from a factory in Binh Duong Province to a distribution centre (with its warehouse) in Ninh Thuan Province and 100 retailers in Ninh Thuan 13 Downloaded by mon hon (monmon1@gmail.com) lOMoARcPSD|39211872 Province are the last destination for our products.
We focus on the demand for three signature products: “Sandwich chà bông”, “Burger bò” and “Pizza xúc xích” due to the limited information provided. Also, it is reported by salers that these three products earn significant revenue compared to others. The evidence can be seen in Validation, 1. Limitation Despite achieving satisfactory results aligned with our expectations, the model development and analysis encountered obstacles and limitations.
Time constraints due to team members' existing professional commitments posed a primary impediment, making organising meetings for in-depth discussions challenging. In addition, being novice users of AnyLogistix required significant time investment in familiarizing ourselves with the associated terminology and techniques. Another significant challenge was the accuracy of certain data. Despite our best efforts, crucial data points such as demand and transportation cost remained unavailable.
Moreover, the limited scope of the bread supply chain project, confined to a single province, represents an inherent weakness. This restricts the generalizability and broader applicability of our results, as they may not capture the complexities of larger-scale supply chains. Despite these constraints, our group made efforts to mitigate these issues and derived meaningful insights within our available resources and expertise. CHAPTER 2: SYSTEM REQUIREMENT 2.
