THE IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN LOGISTICS COMPANY IN RUSSIA (THE CASE OF TRAMIS COMPANY)
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INTRODUCTION 4
CHAPTER 1. COMPANY ANALYSIS AND DESCRIPTION OF THE EXTERNAL
ENVIRONMENT 7
1.1. Company Description 7
1.2. Current operational challenges at Tramis company 10
1.3. Review of the external environment of logistics business in Russia 12
1.4. Current situation of AI research in company activities 18
1.5. The examples of AI implementation in international logistics companies 24
1.6. PESTEL Analysis 27
Conclusion for Chapter 1 36
CHAPTER 2. THE DEVELOPMENT OF RECOMMENDATIONS FOR TRAMIS 37
2.1. Competitors' Analysis 38
2.2. SWOT analysis for Tramis 42
2.3. Matching SWOT analysis 44
2.4. Recommendations on AI application at Tramis 49
2.5. The contribution of AI implementation to solving Tramis’ problems 51
2.6. AI Implementation Plan 56
2.7. Financial Projections 58
CONCLUSION 62
LIST OF REFERENCES 65
CHAPTER 1. COMPANY ANALYSIS AND DESCRIPTION OF THE EXTERNAL
ENVIRONMENT 7
1.1. Company Description 7
1.2. Current operational challenges at Tramis company 10
1.3. Review of the external environment of logistics business in Russia 12
1.4. Current situation of AI research in company activities 18
1.5. The examples of AI implementation in international logistics companies 24
1.6. PESTEL Analysis 27
Conclusion for Chapter 1 36
CHAPTER 2. THE DEVELOPMENT OF RECOMMENDATIONS FOR TRAMIS 37
2.1. Competitors' Analysis 38
2.2. SWOT analysis for Tramis 42
2.3. Matching SWOT analysis 44
2.4. Recommendations on AI application at Tramis 49
2.5. The contribution of AI implementation to solving Tramis’ problems 51
2.6. AI Implementation Plan 56
2.7. Financial Projections 58
CONCLUSION 62
LIST OF REFERENCES 65
This final qualification work is conducted in collaboration with Tramis, a prominent logistics company, aiming to explore the potential integration of artificial intelligence (AI) into its business operations. The study encompasses an in-depth analysis of the external environment, focusing on the dynamics of the logistics sector within the Russian Federation. Notably, Tramis operates internationally, with a predominant focus on interactions with Asian countries.
Extensive research was conducted to evaluate the development of AI trends within the logistics domain, drawing insights from scientific articles, interviews, and relevant literature. Competitor analysis formed a crucial aspect of the study, revealing varying degrees of AI adoption among rival firms. While some competitors have already embraced AI, others are in the nascent stages of technological development, leveraging this advancement to gain a competitive edge. Major competitors included international logistics companies with established headquarters in Russia. Tramis stood out as a particularly intriguing subject for analysis due to direct access to its founders and employees, coupled with its seventeen-year track record of rapid growth and innovation. Interviews with key stakeholders underscored the company's ambitious goal of achieving market leadership within the Russian logistics landscape, with aspirations for global expansion in the future.
The chosen topic holds significance as the recommendations derived from this project are poised to solving the managerial problem in Tramis, which is the unsatisfactory level of the current logistic processes ’ organization that relate to the reduced competitiveness because of the dependency on manual processes, coordination issues, high employee turnover, and technological limitations. Hence, the recommendation developed as a result of the project contribute by propelling Tramis towards substantial advancements and elevated market positioning. Moreover, the insights garnered from this thesis serve as a valuable resource for identifying operational gaps, potentials, and threats associated with such transformative developments. Furthermore, this research extends its utility beyond Tramis, offering valuable insights for other logistics enterprises aspiring to enhance their competitiveness in the market. By delving into the intricacies of logistics processes and forecasting future trends in the Russian context, this thesis equips industry stakeholders with a deeper understanding of the evolving landscape. As the era of AI dawns upon the logistics industry, swift developments are anticipated across various operational domains. Both industry pioneers and established firms must vigilantly monitor market trends and seize opportunities to stay ahead in this rapidly evolving landscape.
This project underscores the importance of applying AI in international logistics and demonstrates how technological innovations can help companies like Tramis overcome operational and strategic challenges. We will explore how the integration of AI can lead to profound changes in logistics process management, from optimizing routes to automating warehouse operations, which is particularly relevant in the context of market globalization and international trade.
This study contributes to solving of the company’s managerial problem and understanding of how companies can use AI to enhance their competitive advantages, as well as improve operations in the context of international logistics. Implementing AI not only enhances current efficiency but also opens new horizons for development and innovation in logistics, making the process more sustainable and adaptable to changes in the global market.
Goal
The goal of this work is to provide recommendations for the implementation of AI in the logistics company Tramis.
Objectives
To accomplish the goal of the project, the following objectives were formulated:
1. To analyze the current operations of Tramis and identify the key areas where artificial intelligence (AI) can bring improvements.
2. To explore the external environment and internal structure of Tramis to understand the potential benefits and challenges of AI implementation.
3. To provide recommendations for AI implementation to enhance operational processes, decision-making, customer satisfaction, and market competitiveness.
Object and subject
The object is Tramis company, which is a logistics company that implements AI and offers its program to the market.
The subject is the complex of logistic processes of Tramis, wherein the AI technologies are to be implemented.
Characteristics of the data
The data used in the work is both primary and secondary, which allowed to conduct a more comprehensive analysis and development of recommendations. The primary data was gathered from the 3 semi-structured interviews conducted with the founder of Tramis company and its employees: an IT specialist, who manages the implementation of various technological solutions into the company’s operations, and with the senior leader of the Sales management team. These interviews provided insights into the internal workings, challenges, and strategic orientation of the company, which helped to structure the developed recommendations accordingly. Additionally, the primary data was derived from conducting the competitor analysis, studying market trends, and performance metrics of Tramis, used to identify benchmarks and measure the potential impact of AI.
...
Extensive research was conducted to evaluate the development of AI trends within the logistics domain, drawing insights from scientific articles, interviews, and relevant literature. Competitor analysis formed a crucial aspect of the study, revealing varying degrees of AI adoption among rival firms. While some competitors have already embraced AI, others are in the nascent stages of technological development, leveraging this advancement to gain a competitive edge. Major competitors included international logistics companies with established headquarters in Russia. Tramis stood out as a particularly intriguing subject for analysis due to direct access to its founders and employees, coupled with its seventeen-year track record of rapid growth and innovation. Interviews with key stakeholders underscored the company's ambitious goal of achieving market leadership within the Russian logistics landscape, with aspirations for global expansion in the future.
The chosen topic holds significance as the recommendations derived from this project are poised to solving the managerial problem in Tramis, which is the unsatisfactory level of the current logistic processes ’ organization that relate to the reduced competitiveness because of the dependency on manual processes, coordination issues, high employee turnover, and technological limitations. Hence, the recommendation developed as a result of the project contribute by propelling Tramis towards substantial advancements and elevated market positioning. Moreover, the insights garnered from this thesis serve as a valuable resource for identifying operational gaps, potentials, and threats associated with such transformative developments. Furthermore, this research extends its utility beyond Tramis, offering valuable insights for other logistics enterprises aspiring to enhance their competitiveness in the market. By delving into the intricacies of logistics processes and forecasting future trends in the Russian context, this thesis equips industry stakeholders with a deeper understanding of the evolving landscape. As the era of AI dawns upon the logistics industry, swift developments are anticipated across various operational domains. Both industry pioneers and established firms must vigilantly monitor market trends and seize opportunities to stay ahead in this rapidly evolving landscape.
This project underscores the importance of applying AI in international logistics and demonstrates how technological innovations can help companies like Tramis overcome operational and strategic challenges. We will explore how the integration of AI can lead to profound changes in logistics process management, from optimizing routes to automating warehouse operations, which is particularly relevant in the context of market globalization and international trade.
This study contributes to solving of the company’s managerial problem and understanding of how companies can use AI to enhance their competitive advantages, as well as improve operations in the context of international logistics. Implementing AI not only enhances current efficiency but also opens new horizons for development and innovation in logistics, making the process more sustainable and adaptable to changes in the global market.
Goal
The goal of this work is to provide recommendations for the implementation of AI in the logistics company Tramis.
Objectives
To accomplish the goal of the project, the following objectives were formulated:
1. To analyze the current operations of Tramis and identify the key areas where artificial intelligence (AI) can bring improvements.
2. To explore the external environment and internal structure of Tramis to understand the potential benefits and challenges of AI implementation.
3. To provide recommendations for AI implementation to enhance operational processes, decision-making, customer satisfaction, and market competitiveness.
Object and subject
The object is Tramis company, which is a logistics company that implements AI and offers its program to the market.
The subject is the complex of logistic processes of Tramis, wherein the AI technologies are to be implemented.
Characteristics of the data
The data used in the work is both primary and secondary, which allowed to conduct a more comprehensive analysis and development of recommendations. The primary data was gathered from the 3 semi-structured interviews conducted with the founder of Tramis company and its employees: an IT specialist, who manages the implementation of various technological solutions into the company’s operations, and with the senior leader of the Sales management team. These interviews provided insights into the internal workings, challenges, and strategic orientation of the company, which helped to structure the developed recommendations accordingly. Additionally, the primary data was derived from conducting the competitor analysis, studying market trends, and performance metrics of Tramis, used to identify benchmarks and measure the potential impact of AI.
...
The goal of this consulting project was to provide recommendations for the implementation of AI in the logistics company Tramis for the managerial problem, which was the unsatisfactory level of the current logistic processes’ organization. The goal was ultimately achieved through analyzing the current operations of Tramis and identifying key areas where the problems are concentrated. Through the analysis of leading companies and general industry trends, it was established that artificial intelligence (AI) has great potential for bringing improvements into the operations of logistics companies. Thus, via exploring the external environment and internal structure of Tramis to understand the potential benefits and challenges of AI implementation, a customized and practically oriented recommendations were developed, that would leverage inner logistics companies of Tramis.
The integration of Artificial Intelligence (AI) in Tramis's logistics operations represents a transformative opportunity that directly aligns with enhancing operational processes, decisionmaking, customer satisfaction, and market competitiveness.. This final qualification work systematically explored the external environment, evaluated the company's internal structure, and provided recommendations for AI implementation to meet these objectives.
An in-depth analysis of the external environment revealed key market trends, including the rapid adoption of AI globally, increasing competition, and evolving customer expectations. The assessment of geopolitical and economic factors highlighted the impacts of geopolitical tensions, economic instability, and regulatory changes on logistics operations in Russia. Additionally, the significance of AI, digitalization, and automation in the logistics industry was underscored, emphasizing the need for Tramis to embrace these technological advancements.
The comprehensive internal analysis of Tramis’s operations revealed several critical insights. The existing logistics processes were evaluated to identify key pain points such as manual dependencies, coordination issues, and technological limitations. The current technological framework, particularly the MyTramis software, was examined for its role in operational efficiency. This internal review also highlighted the strengths of Tramis, including its advanced digital solutions, strategic locations, and robust operational infrastructure.
Detailed SWOT and PESTEL analyses were conducted to identify the strengths, weaknesses, opportunities, and threats, providing a strategic overview of Tramis's position in the market. The SWOT analysis established that Tramis has several strengths, such as advanced digital solutions like MyTramis, strategic locations, and a high operational infrastructure. However, it also identified weaknesses, including overdependence on technology, coordination issues, and challenges in managing human resources. The analysis further recognized opportunities for
expansion into European and Asian markets, utilizing AI and automation, and adopting green logistics practices. Simultaneously, it highlighted threats from high competition, regulatory changes, and economic instability.
Based on these insights, specific recommendations for AI deployment in various logistics processes were provided. These recommendations include automating order processing, shipment tracking, route optimization, and resource management. The AI integration strategy emphasizes leveraging Tramis's strengths to exploit opportunities, such as expanding market reach and improving operational efficiency through advanced technologies. The proposed AI model for Tramis includes automating booking and coordination, providing real-time market analysis, utilizing predictive analytics, and integrating communication channels for seamless customer interaction.
The implementation plan was meticulously outlined, focusing on a phased approach with detailed steps: preliminary assessment and planning, infrastructure development, AI system development, integration, training and change management, full-scale usage. Costs for each phase were estimated to ensure an achievable roadmap for AI integration.
The research goal of providing actionable recommendations for AI implementation at Tramis has been comprehensively addressed as a way to solve the posed managerial problem of the company. Each objective, from analyzing current operations to exploring external environments and formulating AI-based solutions, has been meticulously fulfilled. The recommendations aim not only to solve existing managerial problems but also to propel Tramis towards substantial advancements and elevated market positioning.
The insights garnered from this thesis extend beyond Tramis, offering valuable knowledge for other logistics enterprises aspiring to enhance their competitiveness. The proposed AI integration model emphasizes the importance of embracing technological innovations to streamline operations, reduce errors, and meet customer demands more effectively. As the logistics industry evolves, swift developments are anticipated across various operational domains. The strategic implementation of AI will enable Tramis and other logistics companies to stay ahead in this rapidly changing landscape. Continuous monitoring, regular updates, and a commitment to sustainability will be crucial for long-term success.
...
The integration of Artificial Intelligence (AI) in Tramis's logistics operations represents a transformative opportunity that directly aligns with enhancing operational processes, decisionmaking, customer satisfaction, and market competitiveness.. This final qualification work systematically explored the external environment, evaluated the company's internal structure, and provided recommendations for AI implementation to meet these objectives.
An in-depth analysis of the external environment revealed key market trends, including the rapid adoption of AI globally, increasing competition, and evolving customer expectations. The assessment of geopolitical and economic factors highlighted the impacts of geopolitical tensions, economic instability, and regulatory changes on logistics operations in Russia. Additionally, the significance of AI, digitalization, and automation in the logistics industry was underscored, emphasizing the need for Tramis to embrace these technological advancements.
The comprehensive internal analysis of Tramis’s operations revealed several critical insights. The existing logistics processes were evaluated to identify key pain points such as manual dependencies, coordination issues, and technological limitations. The current technological framework, particularly the MyTramis software, was examined for its role in operational efficiency. This internal review also highlighted the strengths of Tramis, including its advanced digital solutions, strategic locations, and robust operational infrastructure.
Detailed SWOT and PESTEL analyses were conducted to identify the strengths, weaknesses, opportunities, and threats, providing a strategic overview of Tramis's position in the market. The SWOT analysis established that Tramis has several strengths, such as advanced digital solutions like MyTramis, strategic locations, and a high operational infrastructure. However, it also identified weaknesses, including overdependence on technology, coordination issues, and challenges in managing human resources. The analysis further recognized opportunities for
expansion into European and Asian markets, utilizing AI and automation, and adopting green logistics practices. Simultaneously, it highlighted threats from high competition, regulatory changes, and economic instability.
Based on these insights, specific recommendations for AI deployment in various logistics processes were provided. These recommendations include automating order processing, shipment tracking, route optimization, and resource management. The AI integration strategy emphasizes leveraging Tramis's strengths to exploit opportunities, such as expanding market reach and improving operational efficiency through advanced technologies. The proposed AI model for Tramis includes automating booking and coordination, providing real-time market analysis, utilizing predictive analytics, and integrating communication channels for seamless customer interaction.
The implementation plan was meticulously outlined, focusing on a phased approach with detailed steps: preliminary assessment and planning, infrastructure development, AI system development, integration, training and change management, full-scale usage. Costs for each phase were estimated to ensure an achievable roadmap for AI integration.
The research goal of providing actionable recommendations for AI implementation at Tramis has been comprehensively addressed as a way to solve the posed managerial problem of the company. Each objective, from analyzing current operations to exploring external environments and formulating AI-based solutions, has been meticulously fulfilled. The recommendations aim not only to solve existing managerial problems but also to propel Tramis towards substantial advancements and elevated market positioning.
The insights garnered from this thesis extend beyond Tramis, offering valuable knowledge for other logistics enterprises aspiring to enhance their competitiveness. The proposed AI integration model emphasizes the importance of embracing technological innovations to streamline operations, reduce errors, and meet customer demands more effectively. As the logistics industry evolves, swift developments are anticipated across various operational domains. The strategic implementation of AI will enable Tramis and other logistics companies to stay ahead in this rapidly changing landscape. Continuous monitoring, regular updates, and a commitment to sustainability will be crucial for long-term success.
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