Improving container loading efficiency through greedy algorithm in excel vba a case of constantia flexibles vietnam

Trang 1 MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION GRADUATION PROJECTMAJOR: INDUSTRIAL MANAGEMENTIMPROVING CONTAINER LOADING EFFICIENCY TH

MINISTRY OF EDUCATION AND TRAINING

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION

GRADUATION PROJECT MAJOR: INDUSTRIAL MANAGEMENT

IMPROVING CONTAINER LOADING EFFICIENCY THROUGH

GREEDY ALGORITHM IN EXCEL VBA: A CASE OF

CONSTANTIA FLEXIBLES VIETNAM

INSTRUCTOR: TO TRAN LAM GIANG STUDENT: NGUYEN THIEN TIN

Ho Chi Minh City, August 2023

S K L 0 1 1 6 3 4

Student : Nguyen Thien Tin Student ID : 19124330

Major : Industrial Management

Instructor : To Tran Lam Giang

Ho Chi Minh City, August, 2023

MINISTRY OF EDUCATION AND TRAINING

HO CHI MINH UNIVERSITY OF TECHNOLOGY AND EDUCATION

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ACKNOWLEDGEMENT

First and foremost, I would like to express my deepest gratitude to my advisor, To

Tran Lam Giang, for her invaluable guidance, feedback, and support throughout this

thesis project Her insights and direction were instrumental in helping me bring this

work to fruition I sincerely appreciate the time she invested in overseeing my research

and writing

I would also sincerely thank my colleagues at Constantia Vietnam who provided the

opportunity for me to deeply understand the intricacies of the manufacturing and

loading processes In particular, I am grateful to Production Manager Nguyen Anh

Huy for taking the time to explain the details of current packing operations and

constraints This field knowledge gave me the foundation to assess limitations and

propose solutions Their unwavering belief inspired me during challenging times I

could not have completed this milestone without such an uplifting community I look

forward to receiving feedback and comments from readers and teachers so that I can

complete my research and serve as a basis for further research in the near future

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LIST OF ABBREVIATION

CSCMP Council of Supply Chain Management Professionals

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LIST OF FIGURES

Figure 1.1 Headquarters of Constantia Flexibles group in Austria 4

Figure 1.2 Constantia Flexibles branches around the world 8

Figure 1.3 Organization structure of Constantia Flexibles Vietnam 12

Figure 2.1 Key elements of supply chain management 14

Figure 2.2 A visual example of the greedy algorithm 23

Figure 2.3 Wall Building method illustration in container by layer 25

Figure 2.4 A simple Genetic algorithm workflow 26

Figure 3.1 Information flow of a common new order 31

Figure 3.2 Column chart comparing export and domestic sales by year 32

Figure 3.3 Product proportion by quantity 34

Figure 3.4 Blister products from PVC seal with Blister Foil 34

Figure 3.5 Blister products from Coldform seal with Blister Foil 35

Figure 3.6 Heatmap of product distribution by width and OD 36

Figure 3.7 Column chart of sales ratio by product width 37

Figure 3.8 Column chart of sales ratio by product OD 37

Figure 3.9 Packing process for layer packing method 39

Figure 3.10 Pyramid packing for product rolls on pallets 39

Figure 3.11 Packing process for carton packing method 40

Figure 3.12 Standard container size model 41

Figure 3.13 Figure 3.12 Standard HT pallet size model at CVN 42

Figure 3.14 Model of how pallets stack boxes on top of each other 42

Figure 3.15 Interface of bioforce erp system 45

Figure 3.16 Line chart of delivered orders with insufficient tolerance since 2022 46

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Figure 4.1 Describe maximum pallet number in a container 52

Figure 4.2 Comparison between horizontal and vertical pallet stacking of pyramid packing method 54

Figure 4.3 Model of packing layer packing and carton packing 55

Figure 4.4 ETL process in Power Query 56

Figure 4.5 Rawdata export from BFO ERP system 57

Figure 4.6 Perform data transformation in Power Query 57

Figure 4.9 Flowchart steps of the calculation tool implementation 59

Figure 4.7 Interface of input sheet in Excel 60

Figure 4.8 Interface of container sheet in Excel 61

Figure 4.10 Optimal pallet estimation calculation results through new VBA tool 64

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LIST OF TABLES

Table 2.1 Algorithm assessment 29

Table 3.1 Summary of packaging standards by product group 38

Table 3.2 Description of information to be calculated from the order 48

Table 3.3 Assessment of the current order calculation method 50

Table 4.1 Implementation plan of new Excel VBA calculation tool 63

Table 4.2 Information about an order in January 2023 63

Table 4.3 Data information has been manually calculated by the inventory department for the order 64

Table 4.4 Line chart of delivered orders with insufficient tolerance after improvement plan 66

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LIST OF METRIC CONVERTIONS

1 feet (ft) = 0.3048 meter (m)

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TABLE OF CONTENTS

COMMENTS OF ADVISOR i

COMMENTS OF THE COMMITTEE MEMBER ii

ACKNOWLEDGEMENT iii

LIST OF ABBREVIATION iv

LIST OF FIGURES v

List Of Tables vii

TABLE OF CONTENTS viii

INTRODUCTION 1

1 Overview of the study 1

2 Purpose of the study 2

3 Objectives of the study 2

4 Research methods 2

5 Structure of the study 3

CHAPTER 1: CONSTANTIA FLEXIBLES VIETNAM MANUFACTURING LIMITED LIABILITY COMPANY BACKGROUND 4

1.1 Overview of Constantia Flexibles group 4

Introduction to the Group 4

Introduction to Constantia Flexibles Vietnam 5

1.2 Constantia Flexibles' overall history 6

Constantia Flexibles group history 6

Constantia Flexibles Vietnam history 6

1.3 Constantia Flexibles business areas and main product 7

Constantia Flexibles group markets and products 7

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Constantia Flexibles Vietnam markets and products 9

1.4 Mission, Vision and core values of Constantia Flexibles group 11

1.5 Company Organizational Structure OF Constantia Flexibles Vietnam 12

CHAPTER 2: LITERATURE REVIEW 14

2.1 Key factors of Supply Chain Management 14

2.1.1 Overview of Supply Chain key elements: 14

2.1.2 Importance of logistics in supply chain management 16

2.1.3 Impacts of Container Optimization on Supply Chains: 17

2.2 Current challenges of container optimization 18

2.2.1 SKU challenges for container loading optimization 18

2.2.2 Packing Strategy Selection to Optimize Container Capacity Usage 19

2.2.3 The effect of product fragility on Loading Optimization 20

2.3 Review of Algorithms for Container Loading Optimization 21

2.3.1 Knapsack method 21

2.3.2 Greedy algorithms 22

2.3.3 Wall building 24

2.3.4 Genetic Algorithm 25

2.4 Container Space Optimization using Excel VBA 27

2.5 Assessing Loading method Suitability for VBA Implementation 28

CHAPTER 3: ANALYSIS AND EVALUATION OF THE CURRENT PACKING PROCESS 30

3.1 Overview of general information flow of an order 30

3.2 Describe main products and packing method in CVN 34

3.2.1 Choose the main products 34

3.2.2 Describe the variation in product dimension 36

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3.2.3 Describe current product packing standard 38

3.2.4 Current container and pallet type in CVN 41

3.2.5 Describe major customer specification 43

3.2.6 Current ERP system 44

3.3 Problem Statement 46

3.4 Describe current container estimate process 47

3.5 Assess the current packing process at CVN 49

CHAPTER 4: IMPROVING ORDER CALCULATING METHOD THROUGH EXCEL VBA 51

4.1 Introduction to improvement method 51

4.1.1 Defining the Scope: Product quantity optimization per pallet 52

4.1.2 Implementing Greedy Algorithm 53

4.1.3 Importing Product Data via Power Query 56

4.2 Constructing order calculation tool in VBA 59

4.2.1 The overall workflow of calculation tool 59

4.2.2 Excel sheet interface 60

4.3 Build an implementation plan for the vba order calculation tool 61

4.4 Achievement of result 63

4.4.1 Initial Order Analysis using the new Excel VBA tool 63

4.4.2 Improvements in accuracy and time in order calculation 66

CONCLUSION 68

REFERENCE 69

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INTRODUCTION

1 Overview of the study

Data and automation are becoming increasingly critical in manufacturing and supply chain management With more complex product portfolios and customer requirements, relying solely on manual processes is no longer feasible The aluminum product field faces this challenge as the number of SKUs proliferates Technology advancements provide solutions, but companies must take advantage of them

Automating previously manual calculations through Excel VBA can significantly improve accuracy and efficiency By minimizing human error in order calculations, automation enhances supply chain coordination and customer service Integrating optimization algorithms takes this a step further by dynamically adapting to evolving data

The prevalence of Excel makes VBA readily accessible without major IT investments Its familiar interface allows business users to leverage existing skills For SMBs without advanced systems, VBA delivers a rapid, low-cost automation solution Quick implementation provides near-term returns

Automation eliminates uncertainty inherent in manual projections Optimized calculations give supply chain partners shared data confidence Inventory, logistics and production can align more precisely around orders On-time delivery and customer satisfaction improve through consistent accuracy

But VBA’s impact goes beyond number-crunching Quality data unlocks broader gains like tighter integration, enhanced forecasting and happier customers While starting small, automation opportunities abound VBA lays a foundation for transforming performance across operational functions Data and automation may start in ordering, but the benefits cascade through the business

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2 Purpose of the study

General objectives: Create a calculation tool that can improve accuracy and speed compared to manual methods If there is any deviation on the order or against the standard, it will be visualized for the staff at Constantia Flexibles Vietnam to observe and correct immediately

Detailed objectives:

- Understand the current order calculation process

- Reduce manual order calculation time

- Reduce errors when calculating orders

- Demonstrate the feasibility of VBA and Power Query for automation

3 Objectives of the study

- Research subject: The current order calculation process and develop Excel-VBA prototype with Power Query integration

- Research scope: Data is taken from the the related major product in CVN in 6 year (from 2017 to 2023) Besides, the research also studies the order calculation process of

the inventory department

- Research area: Inventory department of Constantia Flexibles Vietnam

4 Research methods

This study employs a mixed methods approach using both quantitative and qualitative techniques A quantitative methodology is utilized to collect and analyze data on the current manual order calculation process Qualitative methods guide the design and validation of the VBA and Power Query automation prototype

The research process comprises three key stages:

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Literature Review: A review of scholarly articles and industry publications related to container loading algorithms, Excel tools, and packaging optimization This secondary data informs the selection of appropriate technical approaches

Current Process Analysis: Primary data collection through observations, interviews, and data sampling to quantify the inputs, outputs, and limitations of the existing manual order calculation methods

Prototype Design: Qualitative design methods drive the development of the VBA and Power Query prototype, drawing on literature findings and process analysis Iterative testing and feedback inform improvements

5 Structure of the study

- Chapter 1: Constantia Flexibles kVietnam manufacturing limited liability company background

- Chapter 2: Literature Review

- Chapter 3: Analysis and evaluation of the current packing process

- Chapter 4: Improving order calculating method through Excel VBA

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CHAPTER 1: CONSTANTIA FLEXIBLES VIETNAM MANUFACTURING LIMITED LIABILITY COMPANY

BACKGROUND

1.1 Overview of Constantia Flexibles group

Introduction to the Group

Constantia Flexibles is a flexible packaging manufacturer headquartered in Vienna, Austria In recent years, the group has grown from a supplier focused heavily on the European region into a globally operating group in the most attractive and fastest-growing markets worldwide for flexible packaging Currently, Constantia Flexibles has approximately 8,500 employees at around 42 production sites in 18 countries, primarily

in Europe, North America, Africa, and Asia The group supplies its products to many multinational corporations and leading local market companies in the food, pet food, pharmaceutical, and beverage industries

Figure 1.1 Headquarters of Constantia Flexibles group in Austria

(Source: https://www.cflex.com/locations/constantia-sales-office-teich-austria/)

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The company manufactures flexible plastic and foil packaging transparent films, aluminum foils, and paper These are used individually or combined as mono- or multi-layer structures for primary and secondary packaging of food, non-food, pharmaceutical, and other products Overall, Constantia Flexibles' business activities are divided into three divisions: Food, Pharma, and Labels

Introduction to Constantia Flexibles Vietnam

- Business Type: Foreign Invested Limited Liability Company

- International Name: Constantia Vietnam Manufacturing Limited Liability Company

- Address: Lot III-6, Road No 11, Tan Binh Industrial Zone, Tan Phu District, Ho Chi Minh City

to GMP, Drug Master File (DMF), Food Safety System Certification (FSSC) 22000, ISO 9001 and ISO 15378 standards Currently, CVN is the first branch of Constantia Flexibles in Asia

The CVN factory located in Tan Binh Industrial Zone, covering nearly 17,000 m2, provides tons of pharmaceutical packaging products, accounting for around 60% market share in Vietnam Joining a joint venture in August 2016, but only until June

2019 did the Constantia Flexibles group gain full control of CVN Additionally, the company has undergone many organizational and business development strategy changes, shifts in corporate culture, etc with the goal of not only becoming the leading

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manufacturer in the industry but also adapting to the common standards and objectives

of the Constantia Flexibles Group

1.2 Constantia Flexibles' overall history

Constantia Flexibles group history

In its early stages, Constantia Flexibles was a group of companies originating from Constantia Teich, founded in 1912 in Weinburg, Austria, and Haendler & Natermann, founded in 1825 in Hann, Münden, Germany In 1952, Flexo printing technology was included in the company's production program, which was the first step in the successful refinement process that led to high-quality printed products In 1962, the company continued to innovate by starting to apply copper tube printing technology for their products At this time, the company was already one of the pioneers in film processing

Until 1984, European companies like Corona Capsules (DK), Ebert GmbH & CoKG (DE), and Jeanne d'Arc (FR) were acquired and merged into the group by Constantia Flexibles, from which point the company's scale grew even more rapidly Since 1993, Constantia Flexibles has continued to establish itself as the leading supplier in Europe through a consistent acquisition and growth strategy New advanced printing techniques like the new aluminum foil extrusion and UV Flexo printing have been grasped and applied by the company Most recently, the company has relocated its headquarters to the city of Vienna in Austria

Constantia's mission is anticipating trends with innovative, sustainable packaging that solves customer needs Leveraging its global reach and local agility has made Constantia a trusted flexible packaging partner for leading brands Expansion continues

to boost capacity across strategic product segments

Constantia Flexibles Vietnam history

The Constantia Flexibles group continued its global growth story by acquiring Vietnamese pharmaceutical packaging manufacturer Oai Hung Co Ltd in 2016 With

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headquarters in Ho Chi Minh City, Oai Hung was a family company established in

1988 The company achieved 25 million EUR in revenue in 2015 and currently has 240 employees The main products are aluminum blister foils and films for the growing local pharmaceutical market Additionally, the company had a significant presence in the milk cap sector, having only entered this market in the past two years The modern production facility is equipped with international standard cleanrooms

According to Pierre-Henri Bruchon - Executive Vice President and Head of the Pharma Division of Constantia Flexibles, "With the acquisition of Oai Hung, we have obtained our first pharmaceutical production site in Asia and entered the high growth Southeast Asian region We also see Oai Hung as a platform for further expansion in the region in the future." Until now, CVN's customers have fully trusted it as a reliable partner, consistently providing quality standard products like long-established factories in Europe

1.3 Constantia Flexibles business areas and main product

Constantia Flexibles group markets and products

Constantia Flexibles serves a diverse global customer base of leading consumer products and pharmaceutical companies that require innovative flexible packaging solutions The company's recent customer survey spanned 146 accounts across these industries, underscoring Constantia's broad reach Pharmaceutical customers gave outstanding ratings of 8.60 out of 10, praising Constantia's quality, service, cooperation, and responsiveness Consumer goods customers similarly highlighted strong collaboration, problem-solving skills, and on-time delivery This positive feedback affirms Constantia's trusted partnerships with multinational CPG and pharma corporations needing specialized flexible packaging

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With over 8,400 employees across 43 production facilities in 18 countries, Constantia Flexibles has achieved substantial scale as a top player in flexible packaging production globally The company's valuation exceeds €1 billion, based on majority shareholder Wendel's €540 million controlling equity stake and additional investments As a mature industry leader, Constantia serves customers in over 115 countries, leveraging its geographic footprint and technical expertise in aluminum, paper, and film packaging materials Constantia's growth has attracted reputable investors like Wendel and Maxburg Capital Partners focused on long-term, socially responsible returns

Constantia specializes in manufacturing cold foil, blister foil, laminates, and other innovative flexible products that provide barrier protection, extended shelf life, and appealing aesthetics The company has expanded internationally to serve major multinationals across food and beverage, confectionery, personal care, pet food,

Figure 1.2 Constantia Flexibles branches around the world

(Source: Internal document)

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pharmaceuticals, and additional consumer segments Constantia's scale, diversification, and customer loyalty position it as a preeminent global provider of flexible packaging solutions for leading CPG and pharma brands

- Main Product Groups:

Constantia Flexibles produces a wide range of flexible packaging solutions across three core product segments - Consumer Goods, Pharma, and Labels The Consumer Goods division focuses on food applications like dairy, confections, meat, and snacks Key products are aluminum foils for trays, lids, and food packaging Customers include major brands like Nestle, Kraft, Unilever, and Mars Constantia leverages R&D and efficiency to meet customer demands

The Pharma division manufactures specialized packaging for pharmaceuticals and cosmetics, including push-through blisters, peelable blisters, laminates, cold-forming foils, and bubbles Stringent quality standards like cleanroom production allow serving healthcare clients

1 Consumer Goods: focuses on food applications like dairy, confections, meat,

and snacks Key products are aluminum foils for trays, lids, and food packaging Customers include major brands like Nestle, Kraft, Unilever, and Mars Constantia leverages R&D and efficiency to meet customer demands

2 Pharma: Specialize in packaging for pharmaceuticals and cosmetics, including

push-through blisters, peelable blisters, laminates, cold-forming foils, and bubbles Stringent quality standards like cleanroom production allow serving healthcare clients

3 Labels: Produces bottle and in-mold labels for beverage, food, and other CPG

companies Production sites worldwide and customer proximity enable a personalized approach

Constantia Flexibles Vietnam markets and products

Currently, the packaging market is a very strong growing industry in Vietnam with a growth rate of 10%/year, for plastic packaging specifically 25%/year (according to

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Nguyen Duc Trung, Deputy Director of the Enterprise Development Department) Additionally, the packaging market has many different product types like paper, plastic, metal foils, PET bottles, etc but revenue from plastic, paper packaging, and cardboard boxes account for over 80% Overall, the Vietnamese packaging market requires very diverse packaging types, sizes, order quantities, film types, etc Therefore, CVN enters the market with comprehensive processing procedures - including coating, lamination, printing, and slitting operations - so the company does not depend on downstream suppliers as well as enables sustainable development The product group CVN targets laminated aluminum foils, taking aluminum as the core to compete with other smaller competitors since the company has mastered all the core processes to modify film properties according to customer needs

CVN Main products:

CVN's core capability lies in manufacturing specialized aluminum foils from raw materials rather than basic lamination This allows customizing alloy, thickness, coatings, and parameters for each application Key aluminum-based products include:

Blister Foil: Specially coated aluminum film used to seal medications in cavities

molded from cold-forming foil The heat seal lacquer bonds to plastics when heated, while the primer glue adheres to printed card stock Provides moisture and oxygen barriers

PVC Film: Clear polyvinyl chloride plastic sheet laminated to blister foil Serves as a

transparent window to view pills in sealed cavities Adds puncture and UV light resistance Provides durable blister package

Cold Forming: Soft tempered aluminum allowing presses to mold capsules and trays

without heat Maintains shape for filling Coated with lubricants enabling blister cavity release after sealing with PVC and blister foil

Paper Foil: Combined aluminum foil and paper providing barrier properties with

sustainability Used for packaging requiring moisture, gas, and light protection with paper appearance

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Lidding Foil: Ultrathin aluminum film for heat sealing containers post-filling to

maintain product integrity Available plain or pre-laminated to paper or plastic films Applicable across food, pet food, nutraceuticals, etc

Membrane Foil: Aluminum foil laminated with paper and plastics to seal dairy and

produce packaging Provides gas and light protection while allowing easy peel opening Available in multiple layer configurations

Effervescent Foil: Specially coated packaging with a very low moisture vapor

transmission rate to protect effervescent tablets Constructed from laminated aluminum, plastics, and paper Prevents pre-mature tablet reaction

Laminated Pouches: Made from laminations of aluminum foil, plastics, and paper

Combines barrier properties, durability, and flexibility Used for shelf-stable foods, powders, liquids, and other products Reclosable zipper or tear notch options

1.4 Mission, Vision and core values of Constantia Flexibles group

Vision: Constantia Flexibles envisions a world in which packaging brings people the highest benefits with the lowest possible impact on the environment

Mission: The company rethinks packaging every day to make a positive, sustainable, and meaningful contribution to customers and the environment People are driven by a passion and desire for the secrets to make life healthier, better, and safer for everyone Core Values:

For customers: The company focuses on innovation and quality to help customers become more successful Continuous innovation and prompt support in case of production issues help ensure customer productivity

For society and the environment: The company balances economic success with environmental and social responsibility, aligning with each local context where it operates

For the company: The result of all company strategies is profitable and sustainable growth that creates value for all stakeholders

The company is not simply a pharmaceutical packaging manufacturer, just simply transforming aluminum into packaging solutions At Constantia Flexibles Vietnam, they ensure products reach end-users most cleanly and safely Therefore, the company's mission can be summed up in its brand slogan: "Let's save lives together"

FLEXIBLES VIETNAM

Figure 1.3 Organization structure of Constantia Flexibles Vietnam

(Source: Author research)

Currently, CVN is a medium scale with around 300 employees, organized in a functional structure At the top is the board of directors, followed by the general director with a supervisory board position to oversee management and operations The general director then manages the operations of all departments through the department

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CHAPTER 2: LITERATURE REVIEW

2.1 KEY FACTORS OF SUPPLY CHAIN MANAGEMENT

2.1.1 Overview of Supply Chain key elements:

Effective management of supply chains is critical for companies to remain competitive

in today's global business landscape According to supply chain expert Martin Christopher, coordinating and integrating a series of key elements is essential for effective supply chain management (SCM) (Christopher, 2022) Firms must understand these fundamental components and optimize their interconnections to enhance overall supply chain performance

Christopher identifies six primary elements that together constitute a robust, efficient supply chain in the manufacturing sector “Planning”, “Sourcing (Procurement)”,

“Demand and Inventory (Warehousing)”, “Manufacturing”, “Delivery and Logistics”, and “Returning”

Figure 2.1 Key elements of supply chain management

(Source: Author research)

Planning – This starts with accurate demand forecasting to estimate expected customer needs and market trends Sales and operations planning then aligns production scheduling and capabilities with projected demand Finally, overarching supply chain

SCM Key Elements

Planning

Sourcing

Warehousing

Manufacturi ng

Deliver and Logistics Returning

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strategies are defined that map out objectives, resource allocation, and optimization initiatives across all elements Extensive planning provides a strategic roadmap for the entire supply chain

Sourcing (Procurement) – This encompasses selecting optimal suppliers and vendors to source essential materials and parts for manufacturing Procurement must balance factors like cost, quality, reliability, and sustainability when evaluating suppliers Ongoing supplier relationship management ensures collaboration on pricing, lead times, and transparent data exchange

Demand and Inventory (Warehousing) – This element again involves demand forecasting as a critical input to inform inventory planning and warehouse operations Inventory levels must be optimized to balance availability with holding costs Warehouse management handles receiving, storage, picking, and dispatch of raw materials and finished goods Efficient warehousing and inventory management support both production and order fulfillment

Manufacturing – This function transforms purchased materials and components into final products produced for customers Manufacturing focuses on production scheduling, capacity planning, workforce management, and lean initiatives to maximize output while minimizing waste Agile manufacturing capabilities allow responding to shifts in demand

Delivery and Logistics – This encompasses the transportation, distribution, and order fulfillment activities required to deliver finished products to customers Key considerations include shipment routing, fleet management, cargo space optimization, warehouse selection, and omni-channel order processing Fast, flexible, and reliable delivery is imperative for customer satisfaction

Returning – Also known as reverse logistics, this element manages the return, recycling, reuse, resale, and disposal of any materials, packaging, or products flowing

in the opposite direction along the chain Efficient reverse logistics contributes to sustainability, cost savings, and customer service

Another definition by the Council of Supply Chain Management Professionals (CSCMP), logistics management "plans, implements, and controls the efficient, effective forward and reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption" Key activities include transportation, warehousing, inventory management, materials handling, and order fulfillment

2.1.2 Importance of logistics in supply chain management

In the other hand, Logistics is a smaller part of the supply chain management framework While logistics focuses on product flows, supply chain management (SCM) coordinates integrated business processes across purchasing, manufacturing, logistics, sales, and marketing to maximize customer value As noted by Mentzer et al (2001), SCM aligns and utilizes logistics capabilities together with other functions to build customer-driven value chains Effective SCM strategies integrate logistics flows with business operations through cross-functional coordination, data sharing, and optimized processes

Logistics provides the core infrastructure enabling the seamless product flows needed for overall supply chain integration and customer satisfaction Optimized logistics

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capabilities confer a strategic advantage by allowing firms to deliver the right items in the right quantities to the right places at the right time and the optimal cost

2.1.3 Impacts of Container Optimization on Supply Chains:

Improving container loading and packing efficiency within the logistics framework holds significant advantages for supply chain management (SCM) This optimization encompasses several key logistic aspects

Firstly, by maximizing the quantity of goods packed into each container, it will enhance utilization rates This leads to more precise shipment forecasting and production planning, creating a closer alignment between output and transportation capacity Additionally, higher container density yields valuable data for refining demand forecasts, which subsequently reduces the need for excessive inventory buffers Ultimately, these improvements in container packing reliability contribute to dependable delivery schedules and proactive supply chain planning

Moreover, optimized container packing has a direct impact on supplier coordination It enhances visibility into procurement sizes and timing, allowing suppliers to align their production batches with loading requirements This flexibility in sourcing is further strengthened by the capacity to accommodate fluctuations in supplier deliveries within the context of density-optimized container loads

In terms of inventory management, optimized container loading will help reduce cyclical inventory requirements This reduction comes from the ability to add more volume of product to each shipment and reduce the need for safety stock to prevent out-of-stock situations Furthermore, the optimized process enhances warehouse throughput by accelerating the picking and assembly of each container load As a result, storage space requirements are lowered, alleviating the problem of overstocking Load optimization is a critical lever for efficiency in both inventory management and warehousing

Finally, in manufacturing, the increased container density resulting from optimized packing enables better synchronization of production batch sizes with cargo volumes

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This alignment promotes smoother manufacturing workflows, as production output can

be scaled to match container load dimensions and quantities Additionally, standardized packing procedures simplify cross-training for workers, enabling greater labor flexibility on the production floor during periods of fluctuating demand

In conclusion, the optimization of container loading and packing processes has a substantial impact on various sides of supply chain management, including transportation, supplier coordination, inventory management, warehousing, and manufacturing By paying close attention to these logistics, organizations can unlock efficiencies and enhance their overall supply chain performance

2.2 CURRENT CHALLENGES OF CONTAINER OPTIMIZATION

2.2.1 SKU challenges for container loading optimization

The continuous increase in distinct product variants and SKUs presents major challenges for optimizing loading patterns and space utilization in containers When companies offer more product sizes, shapes, and types to meet customer needs, it greatly complicates the container fit puzzle In a research paper developing a tree search algorithm to handle increased product varieties, (Fanslau and Bortfeldt ,2010) stated that tkhe more article types have to be loaded, the more difficult the problem gets More distinct items reduce loading flexibility For example, a company may ship 100 different product sizes rather than 10, with varying diameters, widths, and lengths This diversity of shapes and dimensions severely limits the ability to establish standard loading patterns that maximize density and volume usage What may fit efficiently for one SKU size may leave lots of space for another Accommodating all these irregular product forms restricts and constrains possible loading arrangements Additionally, more SKUs means more customer-specific packing rules based on SKU types ordered, limiting positions and orientations With exponential growth in SKUs, manually calculating and visualizing optimal arrangements becomes infeasible Automated optimization algorithms are needed to assess huge combinations and find ideal loading plans, but must balance effectiveness with computational resources Overall, the

As a result, inventory visibility suffers, leading to higher safety stock needs, more

stockouts, and reduced loading flexibility due to inventory availability constraints

The proliferation of product variants has dramatically increased the required skillset for packing and loading personnel to achieve optimized container utilization across varying SKUs (Dyckhoff et al., 2004) As the diversity of product sizes, shapes, weights, and fragility characteristics expands, employees must master an ever-wider range of specialized cargo arrangement, stacking, techniques and knowledge (Bortfeldt & Homberger, 2001) This makes it harder to develop expert packers capable of applying nuanced loading methods tailored to each product's unique optimization needs

2.2.2 Packing Strategy Selection to Optimize Container Capacity Usage

Studies emphasize the significant impact packing methodology can have on container space optimization for irregular items Simply aligning items on a grid rather than random placement can increase utilization by over 15%, by enabling a more structured fit (Bortfeldt et al., 2003) Standardizing packing rules has both advantages and disadvantages Integrating some tailored patterns for asymmetry while maximizing consistency enables optimizing both cube use and product protection according to characteristics (Bortfeldt & Gehring, 2001)

In summary, research provides tangible evidence that methodical, structured packing tied to product qualities can significantly increase container optimization versus ad-hoc placement (Lim et al., 2005) But balancing with fragility, variability, and customization needs is critical

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2.2.3 The effect of product fragility on Loading Optimization

When optimizing container loading, accounting for product fragility is essential to avoid damaging vulnerable items through excessive compression or motion, according

to studies (Bortfeldt & Gehring, 2001) This requires careful handling techniques tailored to the unique vulnerabilities of each product type For highly malleable aluminum rolls prone to denting, proper stabilization and separation are necessary to prevent indentations from stacking pressure Davies & Bischoff (1999) recommend boundary walls and cushioning materials to protect delicate product edges from friction damage during packing Such customized fragile item placement should be integrated into optimization algorithms, to automate customized placements and compressive force limits that protect vulnerable materials

However, as Lim et al (2005) discuss, some manual monitoring is still required, as sensor technologies that monitor real-time impressions during packaging allow human judgment to intervene if any unexpected issue appear The balanced optimization method must take into account both improving space efficiency and meeting fragile cargo handling requirements Awareness of these material properties allows for optimization of density gain while minimizing product damage

Some common ways that the product fragility issue is handled when loading and optimizing container shipments of aluminum film rolls and sheets:

• Padding - Placing protective foam, cardboard, or other cushioning materials between rolls, on edges, and along container walls helps prevent denting and friction damage during loading and transport movement Padding absorbs force and dampens impressions

• Boundary Blocking - Loading rigid, durable products or blocks around the container perimeter creates protected boundaries that shelter more fragile aluminum rolls and sheets in the center cavity

• Interleaving - Alternating layers of aluminum rolls with other sturdier products

or cardboard separates the metal surfaces to prevent contact damage

• Handling Process Adjustments - Using lighter pressure, gentler motions, slower speeds, and no dropping or bouncing during loading and unloading protects delicate materials

• Optimizing Placement - Algorithms or manual inspection optimize the position

of fragile items based on container stress analysis and pressure data

OPTIMIZATION

2.3.1 Knapsack method

The knapsack algorithm presents a practical and intuitive approach to optimizing container loading which is an essential aspect of logistics and supply chain management This method is rooted in the classic knapsack problem, where the goal is

to pack items of known weights and values into a container with a fixed capacity in a way that maximizes the total value of the loaded items Essentially, it's about making the most of limited space and weight constraints by choosing the right combination of items and their arrangement

To illustrate the concept with a real-world example, consider a scenario where having a backpack with a weight capacity of 100 kilograms The goal is to load it with various items, each with its weight and value For instance, there are 10 kilograms of Item A valued at $500, 15 kilograms of Item B valued at $300, and so on The knapsack method operates iteratively, selecting the item with the highest value-to-weight ratio that can fit within the remaining capacity of the backpack This process continues until there's no more space left in the backpack

By prioritizing large objects first, greedy loading prevents smaller items from occupying space later needed for heavier cargo However, Lim et al (2005) noted greedy placement often clustered bulky items unevenly, creating suboptimal weight distribution unless rearrangement occurred Careful scoring of position suitability accounting for balance constraints improves results

The key weakness of greedy algorithms is getting stuck in local optima Given the large search space for your case, some local optimal risks may be unavoidable Though basic greedy loading has limitations, research demonstrates it provides respectable utilization

in a fraction of the runtime of sophisticated methods This efficiency makes greedy

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placement an attractive option for real-time loading decisions With heuristic adjustments to enhance solution quality, greedy algorithms offer a balanced tradeoff between speed and optimization

Figure 2.2 A visual example of the greedy algorithm

(Source:

https://vikram-bajaj.gitbook.io/cs-gy-6033-i-design-and-analysis-of-algorithms-1/chapter1)

The biggest drawback of greedy algorithms is that they tend to get stuck in local optimization rather than determining the best overall packing arrangement By always choosing the locally optimal item insertion step based on maximizing immediate density or usage, greedy methods cannot look ahead to the bigger picture This myopic focus on incrementally greedy decisions has prevented that profit, a larger short-term profit that can yield many long-term benefits For example Figure 2.2, in the 2nd step after calculating and comparing to go in the direction of the larger value, the algorithm chose to follow the right branch with a value of 12 large 3 on the left branch, The result finally found a value of 6 at the end of the solution However, globally, it is the left branch that is truly the globally optimal solution with a value of 99 Although simple and fast, greedy techniques cannot escape the local traps This means that quality lag approaches will thoroughly explore the entire decision space when looking for the most optimal combination

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2.3.3 Wall building

A common construction approach for container loading is the wall-building method, first introduced by George and Robinson (1980) This technique involves sequentially filling the available space with walls assembled from boxes within a maximum thickness limit Similarly, layer-building strategies construct layers of boxes with comparable height dimensions and stack these layers on top of each other from the container floor upwards (Pisinger 2002, Bischoff 2006, Iori et al 2020) Compared to wall-building, layer-building tends to provide greater load stability but functions best when boxes have aligned dimensions allowing stable vertical stacking Related methods first cluster items into stacks or blocks of uniform objects, and then load these aggregated larger structures (Gehring and Bortfeldt 1997, Bortfeldt et al 2003) Pre-assembling uniform items enables the building of stable composite units to fill the container space efficiently

The wall-building technique provides an intuitive approach to container loading, sequentially packing items against a wall that incrementally shifts outward Subsequent adaptations increased flexibility by varying partition placement based on object dimensions

The wall-building technique provides an intuitive, incremental approach to container loading optimization

The basic method involves sequentially packing items against a wall that gradually shifts position within the container For example:

- Step 1: Start with the wall at the left side of a container that is 20ft wide

- Step 2: Place the largest eligible item that fits against the left wall without exceeding the width

- Step 3: Shift the wall 2ft to the right

- Step 4: Pack the next largest suitable item against the shifted wall

- Step 5: Repeat steps 3-4, moving the wall and adding items, until the container is fully packed

2.3.4 Genetic Algorithm

Genetic algorithms represent a powerful computational approach, often employed to solve intricate optimization challenges such as container loading These algorithms draw inspiration from the principles of natural evolution, striving to systematically improve solutions over time The process unfolds as follows:

Initialization: The algorithm commences by generating an initial population of potential solutions In the context of container loading, these solutions correspond to different ways of arranging items within the container

Evaluation: Each solution within the population undergoes a rigorous evaluation process This evaluation centers on specific criteria relevant to the problem at hand,

Figure 2.3 Wall Building method illustration in container by layer

(Source:

https://towardsai.net/p/machine-learning/genetic-algorithm-optimization-2)

Crossover: This step emulates the idea of combining favorable traits from parent solutions Pairs of selected solutions have their attributes merged, akin to mixing genetic material in biological reproduction This process generates new candidate solutions

Mutation: Genetic algorithms introduce an element of randomness by occasionally making small, random alterations to solutions This randomness mimics genetic mutations in nature and promotes diversity among solutions

Iteration: The entire process, from evaluation to mutation, iterates over multiple generations Each iteration refines the population, potentially leading to improved solutions This iterative refinement continues until a termination condition is met, often defined by a specific number of generations or achieving desired solution quality

Figure 2.4 A simple Genetic algorithm workflow

(Source: https://towardsai.net/p/machine-learning/genetic-algorithm-optimization-2 )

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Despite their effectiveness in solving complex optimization problems, genetic algorithms do have notable drawbacks One major challenge is the difficulty in Problem Representation Adapting real-world problems to a format understandable by genetic algorithms can be akin to translating a complex puzzle into a language it can comprehend, posing a significant hurdle

Additionally, genetic algorithms can be computationally intensive Imagine searching for a needle in a haystack, but the haystack spans the size of a football field The substantial computational demands can extend the time required to reach a solution

2.4 CONTAINER SPACE OPTIMIZATION USING EXCEL VBA

Excel Visual Basic for Applications (VBA) suggested for use by Paghrut et al (2020)

to optimize space usage when loading containers The method allows input of item dimensions, quantities, and priorities as well as container dimensions It then applies a heuristic algorithm to generate a loading plan to fit the maximum items into the minimum containers The key steps in their approach are:

- Model the problem by defining item dimensions, quantities, priorities, and container dimensions in Excel

- Implement a heuristic packing algorithm in VBA that sorts items, and packs using First-Fit Decreasing, find solutions, and optimizes

- Generate loading plans and visualizations to show the optimized packing arrangement

- Apply the tool to a real-world shipment planning case study

Eventually, the VBA platform provides an accessible way to implement the optimization without specialized software or training Reported benefits include increasing space utilization from 84-87% to over 90%, reducing order cycle times, and increasing sales opportunities

Limitations are the constraint on problem size due to Excel Solver capacity The interface is not automated to update solutions if manually edited The basic algorithm could also be improved by more advanced techniques

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Overall, the key takeaway is demonstrating the power of VBA for implementing complex optimization tasks The paper shows an innovative application to container loading problems in logistics It provides an easy-to-use and low-cost solution for small industries compared to dedicated software Further enhancements to the algorithm and interface would make it more robust and user-friendly

IMPLEMENTATION

To determine the optimal loading optimization method for implementation in VBA in the next chapter, it is valuable to assess the suitability and tradeoffs of the six key techniques from the literature review The following analysis will briefly evaluate the pros and cons of each algorithm based on computational demands, optimization quality, coding needs, and flexibility This comparison will establish the rationale for selecting a hybrid greedy knapsack approach as the primary method explored for improving CVN's packing process in the next chapter The goal is to choose an algorithm that balances optimization resource uses, speed, and simplicity within the capabilities of VBA

insertion logic that is easy

to understand

- Can be adapted to handle different constraints and objectives

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Greedy algorithm - Intuitive logic of packing

most fit item first

- Intuitive logic of packing the largest item first

- Does not guarantee finding the most optimal solution

- Greedy choices early limit later options

Wall Building algorithm - Intuitive approach of

adding items against a wall

- Incremental construction simplifies coding

Genetic Algorithms - Well-suited for highly

complex packing problems

- Computational overhead

of evolving generations of solutions

evaluations and mutations can be challenging

Table 2.1 Algorithm assessment

(Source: Author research)