Детальная информация

Целью исследования явилось разработка и валидация модели управления клиентским потоком, основанной на аналитике данных, для повышения эффективности обслуживания в предприятиях сферы услуг. Исследование выполнялось на базе операционных данных отделения розничного банка и анализа кейсов из здравоохранения и ритейла. Предмет ВКР моделирование, прогнозирование и оптимизация клиентских потоков с использованием аналитики данных и систем поддержки принятия решений. Методы исследования: применялись общенаучные методы исследования: контентный и сравнительный анализ, метод аналогий, а также специфические методы исследования применялись общенаучные методы: контентный и сравнительный анализ, метод аналогий. Основные результаты исследования: - обоснована необходимость предиктивных систем управления клиентским потоком; - разработана модульная архитектура модели управления; - проведено моделирование на основе реальных данных; - выполнена валидация эффективности модели по KPI; - предложена система визуальных дашбордов и алертов; - сформулированы стратегические рекомендации для предприятий; - доказана эффективность модели по снижению времени ожидания; - введена в оборот междисциплинарная методология; - уточнено влияние поведенческих факторов на восприятие ожидания. Научной новизной исследования является интеграция поведенческих аспектов сервиса с предиктивной аналитикой и визуализацией в единую прикладную модель управления клиентским потоком. Выводы. Задачи ВКР решены, цель достигнута, определена область применения результатов. Обоснованы перспективы и направления дальнейшего развития исследования. Полученные результаты обладают признаками научной новизны.

The purpose of the research is to develop and validate a data-driven model that enhances customer flow management efficiency in service industry enterprises. The study was carried out on the basis of operational data from a retail banking branch and additional case analysis in healthcare and retail environments. The subject of the thesis is the modeling, forecasting, and optimization of customer flow using data analytics and decision support systems. Research methods: We used general scientific research methods: content and comparative analysis, analogy method, as well as specific research methods including queuing theory, discrete-event simulation, machine learning, and business intelligence tools. The main results of the study: - justified the need for real-time, predictive customer flow control systems; - developed a modular architecture for a data-driven customer flow management model; - carried out simulations based on real-world data using SimPy; - completed performance validation through KPIs such as average wait time and abandonment rate; - offered a system of real-time visual dashboards and alerts; - formulated strategic recommendations for service organizations; - proven the models effectiveness in reducing queue times and improving staff allocation; - put into circulation an interdisciplinary methodological framework; - specified the influence of behavioral factors on queue perception. The scientific novelty of research is the integration of behavioral service principles with real-time analytics and predictive modeling into a unified, actionable framework for managing customer flow. Conclusions. The tasks of the thesis have been solved, the goal has been achieved, the scope of the results has been determined. Prospects and directions for further development of the study are grounded. The results obtained have signs of scientific novelty.

• INTRODUCTION
  • Background and Motivation
    • Problem Statement and Objectives
    • Research Questions and Hypotheses
      • Primary Research Question
      • Secondary Research Questions
      • Hypotheses
    • Research Methodology
      • Research Design
      • Data Collection Methods
      • Analytical Tools and Techniques
      • Validation and Evaluation
      • - Ethical Considerations
    • Structure of the Thesis
      • This thesis is structured into seven comprehensive chapters, each contributing to a systematic exploration of how data-driven methodologies can enhance customer flow management in service enterprises.
      • Introduction
      • Literature Review
      • Review of Existing Approaches and Case Studies
      • Methodological Framework
      • Development of the Data-Driven Customer Flow Management Model
      • Empirical Validation and Results
      • Conclusions and Recommendations
• 1.LITERATURE REVIEW
  • 1.1 Definitions and Classification of Service Industries
  • 1.2 Concepts of Customer Flow and Queueing Theory
    • Key Components of Customer Flow
    • Types of Queues and Service Disciplines
    • Application in Service Environments
    • Psychological Dimensions of Queuing
    • Advanced Queueing Models and Simulation
    • Integration with Data-Driven Decision Making
    • Case Example: Smart Queueing at Disney
  • 1.3 Behavioral Aspects of Customer Waiting Experience
    • Perceived vs. Actual Waiting Time
    • Emotional and Cognitive Reactions to Waiting
    • Influence of Wait Design on Satisfaction
    • Cultural and Demographic Factors
    • Psychological Cost of Waiting in Different Industries
    • Behavioral Economics and Waiting
    • Emerging Strategies to Improve Waiting Experiences
  • 1.4 Fundamentals of Data-Driven Management
    • 1.4.1 Evolution and Conceptual Foundations
    • 1.4.2 Core Principles of Data-Driven Management
    • 1.4.3 Organizational Enablers and Cultural Transformation
    • 1.4.4 DDM in the Context of Customer Flow Management
  • 1.5 Data Analytics Lifecycle and Frameworks (CRISP-DM, SEMMA, KDD)
    • 1.5.1 The Importance of Structured Analytics Processes
    • 1.5.2 CRISP-DM: Cross-Industry Standard Process for Data Mining
    • 1.5.3 SEMMA: SAS Institute's Methodology
    • 1.5.4 KDD Process: Knowledge Discovery in Databases
    • 1.5.5 Comparative Overview and Practical Implications
    • 1.5.6 Integration into Service Operations
  • 1.6 Technologies Enabling Data-Driven Service Systems (BI, IoT, AI)
    • 1.6.1 Business Intelligence (BI): Visualizing Service Performance
    • 1.6.2 Internet of Things (IoT): Bridging the Physical and Digital
    • 1.6.3 Artificial Intelligence (AI): Learning and Optimization
    • 1.6.4 Synergistic Use and Platform Integration
    • 1.6.5 Challenges and Considerations
  • 1.7 Summary and Key Theoretical Gaps
    • 1.7.1 Summary of Theoretical Foundations
    • 1.7.2 Identification of Key Theoretical Gaps
• 2.REVIEW OF EXISTING APPROACHES AND CASE STUDIES
  • 2.1 Traditional Methods of Managing Customer Flow
    • Queue Design and Physical Layout
    • FIFO and Priority Systems
    • Appointment and Reservation Systems
    • Customer Flow Mapping and Time-and-Motion Studies
    • Staff-Based Control and Intuition
  • 2.2 Benchmarking Data-Driven Solutions in Global Companies
    • Amazon: Predictive Analytics and Operational Efficiency
    • Disney: Smart Flow Control with Wearable Technology
    • Starbucks: Integrating Mobile Technology and Predictive Modeling
    • Uber: Real-Time Decision Support and Adaptive Pricing
    • Delta Air Lines: Operational Analytics and Proactive Service Recovery
    • McDonald’s: Demand Forecasting and Kitchen Optimization
    • Zara (Inditex): Data-Centric Supply Chain and Customer Responsiveness
    • Royal Bank of Canada (RBC): Customer Journey Analytics and AI Chatbots
    • Key Themes in Global Benchmarking
  • 2.3 Industry-Specific Applications (Banking, Healthcare, Retail, Public Sector)
    • 2.3.1 Banking Sector
    • 2.3.2 Healthcare Sector
    • 2.3.3 Retail Sector
    • 2.3.4 Public Sector
  • 2.4 Comparative Analysis of Success Factors and Failures
    • 2.4.1 Critical Success Factors
    • 2.4.2 Common Reasons for Failure
    • 2.4.3 Cross-Industry Comparison Table
  • 2.5 Lessons Learned and Directions for Innovation
    • Key Lessons Learned
    • Directions for Innovation
• 3.METHODOLOGICAL FRAMEWORK
  • 3.1 Research Design and Methodology
    • 3.1.1 Research Philosophy and Paradigm
    • 3.1.2 Research Approach
    • 3.1.3 Research Strategy
    • 3.1.4 Time Horizon
    • 3.1.5 Methodological Triangulation
    • 3.1.6 Unit of Analysis
    • 3.1.7 Ethical Considerations in Method Design
    • 3.1.8 Summary of Research Design
  • 3.2 Data Sources and Collection Methods
    • 3.2.1 Primary Data Sources
    • 3.2.2 Secondary Data Sources
    • 3.2.3 Data Collection Procedures
    • 3.2.4 Data Quality Considerations
    • 3.2.5 Ethical Considerations in Data Collection
    • 3.2.6 Summary of Key Datasets
  • 3.3 Data Preprocessing and Feature Engineering
    • 3.3.1 Overview of Preprocessing Workflow
    • 3.3.2 Handling Missing, Duplicate, and Inconsistent Data
    • 3.3.3 Outlier Detection and Treatment
    • 3.3.4 Data Transformation and Normalization
    • 3.3.5 Feature Engineering: Creation of Analytical Variables
    • 3.3.6 Feature Selection Techniques
    • 3.3.7 Visualization of Engineered Variables
    • 3.3.8 Summary
  • 3.4 Selection of Analytical Tools (Power BI, Python, SQL, ML algorithms)
    • 3.4.1 Criteria for Tool Selection
    • 3.4.2 Power BI for Dashboarding and Real-Time Monitoring
    • Figure 8 – XGBoost Feature Importance
    • The chart presents the relative importance of selected input variables used in the predictive model. 'Wait time' and 'service type' emerged as the most influential features, informing operational decisions and real-time queue management.
    • 3.4.4 SQL for Data Extraction and Transformation
    • 3.4.5 Machine Learning Algorithms
    • 3.4.6 Integration Strategy
    • 3.4.7 Summary
  • 3.5 Evaluation Metrics and Validation Techniques
    • 3.5.1 Evaluation in Classification Tasks
    • 3.5.2 Evaluation in Regression Tasks
    • 3.5.3 Evaluation in Time Series Forecasting
    • 3.5.4 Model Validation Techniques
    • 3.5.5 Business-Driven Validation
    • 3.5.6 Summary
  • 3.6 Ethical Considerations
    • 3.6.1 Data Privacy and Confidentiality
    • 3.6.2 Fairness and Algorithmic Bias
    • 3.6.3 Transparency and Explainability
    • 3.6.4 Human Oversight and Accountability
    • 3.6.5 Psychological Impact and Customer Perception
    • 3.6.6 Summary
• 4.DEVELOPMENT OF THE DATA-DRIVEN CUSTOMER FLOW MANAGEMENT MODEL
  • 4.1 Conceptual Architecture of the Proposed Model
    • 4.1.1 Design Objectives
    • 4.1.2 Key Components
    • 4.1.3 Data Ingestion Layer
    • 4.1.4 Data Processing and Storage Layer
    • 4.1.5 Analytics and Modeling Layer
    • 4.1.6 Decision Support and Optimization Layer
    • 4.1.7 Visualization and Interaction Layer
    • 4.1.8 Integration and Modularity
    • 4.1.9 Scalability and Deployment
    • 4.1.10 Summary
  • 4.2 Components: Data Ingestion, Processing, Decision Support
    • Data Ingestion
    • Data Processing
    • Decision Support
    • Integration and Interoperability
  • 4.3 Predictive Analytics for Customer Demand
    • 4.3.1 The Role of Predictive Analytics in Customer Flow
    • 4.3.2 Analytical Techniques and Algorithms
    • 4.3.3 Data Sources and Feature Engineering
    • 4.3.4 Model Training, Validation, and Performance Monitoring
    • 4.3.5 Practical Applications Across Sectors
    • 4.3.6 Explainability and Business Value
  • 4.4 Real-Time Visualization and Alert Systems
    • 4.4.1 The Need for Real-Time Situational Awareness
    • 4.4.2 Data Stream Integration and System Architecture
    • 4.4.3 Dashboards: Principles and Best Practices
    • 4.4.4 Alerts: Design, Delivery, and Escalation
    • 4.4.5 Feedback Loops and Learning
    • 4.4.6 Cross-Industry Examples
  • 4.5 Integration with Existing IT Infrastructure
    • 4.5.1 The Challenge of Legacy Systems
    • 4.5.2 Integration Architecture: Middleware and APIs
    • Figure 12 - Logical Architecture of Customer Flow Analytics System
    • The architecture illustrates how customer interaction data is transmitted from operational systems through middleware layers into a centralized analytics engine, where it is processed and visualized in real time.
    • 4.5.3 Data Governance and Security Considerations
    • 4.5.4 Workflow and Process Integration
    • 4.5.5 Performance Optimization and Infrastructure Load
    • 4.5.6 Case Illustration: Integration in a Retail Chain
  • 4.6 User Interface and Dashboard Prototypes
• 5. EMPIRICAL VALIDATION AND RESULTS
  • 5.1 Simulation Environment and Pilot Implementation
  • 5.2 Application of the Model on Real-World Data
  • 5.3 Quantitative Results: Wait Time, Throughput, Resource Utilization
  • 5.4 Stakeholder Feedback
  • 5.5 Hypothesis Testing Results
  • 5.6 Discussion of Findings
• CONCLUSIONS AND RECOMMENDATIONS
  • Summary of Key Findings
    • 1. Data-Driven Approaches Significantly Enhance Operational Efficiency
    • 2. Real-Time Monitoring and Decision Support Improve Service Consistency
    • 3. Positive Customer and Employee Perception Reinforces Adoption
    • 4. Strategic Value is Created Through Analytical Capability
    • 5. Implementation Requires Careful Consideration of Legacy Systems and Culture
    • 6. The Proposed Model is Scalable and Generalizable
  • Theoretical Contributions
    • 1. Bridging Service Management and Predictive Analytics
    • 2. Advancement of Data-Driven Decision-Making Frameworks
    • 3. Proposing a Scalable, Modular Architecture for Service Analytics
    • 4. Contributions to Behavioral Queueing and Fairness in Digital Systems
    • 5. Validation of Interdisciplinary Research in Service Innovation
  • Managerial Implications
    • 1. Data-Driven Resource Allocation
    • 2. Enhancing Customer Experience with Analytics
    • 3. Supporting Agile and Informed Decision-Making
    • 4. Enabling Strategic Differentiation through Personalization
    • 5. Promoting Cross-Functional Collaboration
    • 6. Balancing Automation with Ethical Responsibility
  • Limitations of the Study
    • 1. Limited Scope of Industry Representation
    • 2. Constraints in Real-Time Data Acquisition
    • 3. Simulation-Based Validation
    • 4. Generalization of Behavioral Parameters
    • 5. Limited Consideration of Human Factors in Adaptation
    • 6. Ethical Trade-offs Not Fully Quantified
  • Directions for Future Research
    • 1. Longitudinal and Multi-Site Validation
    • 2. Customization for Sector-Specific Flows
    • 3. Integration with Employee Flow and Workforce Optimization
    • 4. Inclusion of Behavioral AI Components
    • 5. Ethical and Regulatory Frameworks for Intelligent Flow Management
    • 6. Economic Impact Modeling
    • 7. Real-Time Feedback Loops and Self-Learning Systems
• APPLICATIONS
• Appendix A. Dataset Description and Data Dictionary
• Appendix B. SQL Queries
• Appendix C. Python Code: Data Preprocessing
  • Justification:
  • Justification: (1)
  • Justification: (2)
  • Justification: (3)
  • Justification: (4)
• Appendix D. Python Code: Simulation and Forecasting Models
• Appendix E. Power BI Dashboards