Business Diagnostics

Syllabus

Thematic Outline of Lectures: 1. Approaches to Business Diagnostics – Theoretical Foundations, Process, and Methodology Business diagnostics constitutes a systematic process of evaluating an enterprise. The objective is to determine its strengths and weaknesses, analyze the factors influencing its performance, and propose solutions for optimization and development. This process aids managers in effectively allocating resources and making strategic decisions. Initially, diagnostics relied on financial indicators; however, modern approaches integrate system modeling, advanced statistics, and artificial intelligence. A key concept is the enterprise's normality – a state of balanced functioning. Establishing criteria for normality facilitates the detection of deviations in performance and the growth of risk factors. The diagnostic process typically comprises several phases: an initial analysis that identifies primary issues and objectives; data collection and evaluation regarding financial health, organizational processes, strategy, and the external environment; forecasting future developments using regression models, time series analysis, and simulations; evaluation of measures and alternatives via methods such as AHP or TOPSIS; and finally, assessment of the effectiveness of the solutions along with the formulation of recommendations. Business diagnostics, therefore, combines both quantitative and qualitative methods to ensure sustainable growth and competitiveness. 2. Diagnosis of the Relationship Between the Enterprise and Its Environment An enterprise does not exist in isolation. This theme focuses on diagnosing the relationship between the enterprise and its environment. It identifies key factors that affect the enterprise and employs scientific methods to assess the business environment. Students will learn to segment the business environment into components. The macro-environment includes macroeconomic, political-legal, socio-cultural, and technological factors. The micro-environment encompasses competition, suppliers, customers, regulatory authorities, and the enterprise’s partners. Special attention is given to the influence of global economic processes and the role of the state in shaping the business environment. This theme also covers the evaluation of environments at the country level using indicators such as Doing Business, the Global Competitiveness Index, and ratings from Moody’s, S&P, Fitch, and PAS. Both qualitative and quantitative methods are employed to assess external influences, including PESTEL analysis (evaluating political, economic, social, technological, environmental, and legislative factors), Porter’s Five Forces model (analyzing competitive environment, supplier and buyer bargaining power, and threats from new entrants and substitute products), and macro-level SWOT analysis for identifying opportunities and threats. Additionally, scenario analysis models potential external developments and their impacts, while the Delphi method utilizes expert opinions iteratively. Analysis of trends and time series is used to assess the evolution of macroeconomic indicators, and benchmarking compares business conditions across different countries, regions, or industries. Risk assessment methods, such as Monte Carlo simulation, predict the variability of external factors. Techniques such as media and social network sentiment analysis using NLP, as well as agent-based modeling, simulate dynamic interactions between the enterprise and its environment using artificial intelligence. 3. Classification of Enterprises into Groups – Classification Methods and Cluster Analysis This section focuses on comparing enterprises based on their performance, risk profile, or market position. Grouping enterprises allows for the identification of common characteristics, the uncovering of hidden patterns, and the support of decision-making processes. The lecture emphasizes scientific research methods in classification and cluster analysis, which enable the effective segmentation of enterprises into homogeneous groups. Statistical methods include dividing enterprises by quartiles, percentiles, deciles, and Z-score normalization. Classification methods such as decision trees, discriminant analysis, or the Naïve Bayes Classifier are utilized to predict the enterprise category based on historical data. Cluster analysis facilitates the automatic creation of enterprise groups based on similarity. Methods such as K-means clustering, Ward’s method, and DBSCAN identify natural segments, while the Silhouette Score assesses the quality of the clustering. Visualization techniques like PCA and T-SNE are used to effectively display the classification and reveal hidden patterns within the data. 4. Analysis of Relationships Among Business Variables Business processes are interrelated, and effective management requires understanding the relationships among key indicators. This theme focuses on the research methods used to explore these relationships with the aim of identifying the strength, direction, and potential causality among business variables. Students will be introduced to correlation methods (Pearson, Spearman, and Kendall correlations), regression models (linear and multiple regression, panel regression), and causal methods (Granger causality, structural equation modeling – SEM) that help differentiate true causal relationships from mere correlations. Modern methods for analyzing dependencies in business processes, such as Data Envelopment Analysis (DEA), Bayesian networks, and association rule analysis, are also presented to investigate complex relationships among business indicators. 5. Diagnosis of the Enterprise Life Cycle and the Selection of Location as a Strategic Decision The analysis of an enterprise’s life cycle employs statistical analyses, enterprise development modeling, and benchmarking, with particular attention paid to the business plan as a foundational document for establishing the enterprise. The selection of a location necessitates diagnosing the factors that influence an enterprise’s placement. Topics include factors related to acquisition (availability of land, its price, natural resources, the quality and structure of the workforce, and infrastructural and logistical conditions), production factors (technical conditions of the location, energy costs, and the availability of raw materials), market-oriented factors (purchasing power of the population, competition, and the feasibility of implementing market policies), and state-influenced factors (taxes, tariffs, subsidies, and regional support). Utility value analysis allows for the comparison of multiple locations based on quantified criteria. Scoring methods use weighted evaluation of individual factors for objective decision-making. The Steiner-Weber model is applied for optimizing enterprise location with the aim of minimizing transportation costs. Break-even analysis is used to evaluate the economic efficiency of an enterprise’s operations in different locations. The Lorenz curve is employed to assess the distribution of economic factors within a region and analyze disparities in income and market conditions. Geographic Information Systems (GIS) enable visualization and spatial analysis of location factors. Multidimensional Scaling (MDS) facilitates the evaluation of the similarity of locations based on multiple criteria. Fuzzy logic and fuzzy decision models are used to assess location factors in situations characterized by uncertainty and imprecise data. Spatial correlation analysis identifies geographical clusters of favorable or unfavorable business conditions. Agent-based modeling is utilized to simulate the interaction between enterprises and their environments and to assess the long-term impacts of location decisions. 6. Estimation and Planning of Enterprise Growth Using Time Series Analysis This topic focuses on analytical tools employed in forecasting growth and planning strategic decisions in business practice. Students will be introduced to time series analysis, which serves to identify trends, seasonal patterns, and cyclical fluctuations in business data. Methods such as ARIMA (AutoRegressive Integrated Moving Average), Holt-Winters exponential smoothing, and VAR (Vector AutoRegression) are applied to predict business variables based on their historical trajectories. 7. Enterprise Growth Models and Growth Crises This theme is dedicated to growth models that analyze mechanisms of expansion, assess factors influencing growth, and identify risks associated with excessive or unsustainable expansion. A distinction is made between organic (internal) growth and inorganic (external) growth – the latter including mergers, acquisitions, strategic alliances, and joint ventures. Students will be introduced to key growth models, such as Marris’s model of managerial enterprise growth, which examines the balance between maximizing profit growth and enhancing shareholder value. Graphical representations of the model and its relation to the Penrose effect—which explains growth limitations stemming from organizational and managerial capacities—will be analyzed. The Gordon model for dividend growth is also discussed, linking dividend growth to reinvestment rates and the required return on capital. Additionally, the Harrod-Domar model emphasizes the relationship between savings, investments, and economic growth, while the Solow growth model investigates the role of technological progress in increasing productivity. Attention is also given to deterministic and stochastic growth models. The topic further covers growth crises that may occur due to inadequate management of expansion, lack of resources, poor investment decisions, or unexpected changes in the external environment. Greiner’s model of organizational growth describes the various developmental phases of an enterprise, and the crisis points that necessitate a change in managerial approach. 8. Enterprise Stabilization – Diagnosis of Operational and Market Stability The diagnostic focus in this phase is on evaluating operational stability and analyzing the enterprise’s market position, combining both quantitative and qualitative diagnostic methods. The Balanced Scorecard (BSC) method is utilized to integrate financial and non-financial performance indicators. Data Envelopment Analysis (DEA) measures the efficiency of resource utilization within the enterprise, identifying areas of waste or suboptimal productivity. The BCG (Boston Consulting Group) matrix classifies the enterprise’s products based on market share and market growth, and the ADL (Arthur D. Little) matrix evaluates the competitive position of the enterprise based on its technological stance and market cycle. A SWOT analysis of the enterprise’s stability is conducted to identify key strengths and weaknesses relative to competitors, thereby determining strategic factors that may affect the sustainability of the stabilization phase. The subsequent step involves establishing the enterprise’s market position. 9. Enterprise Crisis and Its Diagnosis – Causes and Analytical Methods The diagnosis of an enterprise crisis serves as a management tool for its early identification, evaluation of its causes and magnitude, and implementation of effective measures to stabilize the enterprise. A crisis may be viewed as a static condition or as a dynamic process. This theme addresses the comprehensive diagnosis of a business crisis. Methods include Quick Test Analysis for immediate assessment of the enterprise’s state; Rough Analysis for a more detailed overview of the economic situation; and Deep Analysis, which entails an in-depth diagnostic of business processes, performance, and strategic positioning. The concept of economic normality is used to identify deviations from typical economic parameters. A crisis barometer monitors the evolution of key financial and non-financial indicators. The DuPont analysis decomposes return on equity (ROE) into its constituent factors. Altman’s Z-score is a robust model for predicting bankruptcy, while the Tamari risk index extends this evaluation to include credit risk and managerial stability. The Springate model provides predictions of financial instability, and Taffler’s model is tailored to the European business environment by incorporating specific market factors. Beaver’s univariate model assesses bankruptcy probability based on cash flow developments and indebtedness, and the IN index is employed for the early diagnosis of insolvency risk. Multicriteria decision-making methods (such as AHP, TOPSIS, and PROMETHEE) enable the evaluation of various crisis factors and the selection of optimal remedial measures. 10. Analysis of Market Risks and Macroeconomic Impacts on the Enterprise This topic focuses on scientific methods for identifying, measuring, and modeling market risks and macroeconomic factors with the objective of forecasting their impacts on business processes and performance. Students will be introduced to regression models for market risks, including the beta coefficient—which measures the systematic risk of the enterprise relative to the overall market—and GARCH models (Generalized Autoregressive Conditional Heteroskedasticity) which evaluate the volatility of market returns and its influence on corporate financial decision-making. Additionally, cointegration analysis (using the Engle-Granger or Johansen tests) is employed to examine long-term relationships between macroeconomic variables and business indicators. Students will learn how to apply Vector Autoregression (VAR) and Vector Error Correction Models (VECM) to model the interrelations among multiple macroeconomic factors and the evolution of the enterprise. 11. Solutions for Business Crises – Strategies for Stabilization, Recovery, and Transformation Addressing a business crisis requires a systematic approach. Enterprises may opt for formal solutions such as court-supervised restructurings, legally binding agreements with creditors, or bankruptcy proceedings, which become necessary in cases of insolvency and severe financial distress. Alternatively, informal solutions include operational consolidation, negotiations with creditors, restructuring through cost optimization, or sourcing new capital. One of the most effective approaches is restructuring, which may be financial, operational, organizational, or strategic in nature. Financial restructuring involves optimizing the capital structure and securing new financing sources, while operational restructuring focuses on streamlining production processes and reducing fixed costs. Organizational restructuring introduces changes in management and internal processes, and strategic restructuring may entail a shift away from the original business model and entry into new market segments. The selection of an optimal strategy is facilitated by multicriteria evaluation methods, which objectively compare different solutions based on various factors. Methods such as the Analytic Hierarchy Process (AHP), TOPSIS, ELECTRE, and PROMETHEE provide tools for evaluating restructuring alternatives based on financial, operational, and strategic criteria. Successfully managing a business crisis requires a combination of analytical tools and decision-making methods with an emphasis on flexibility and the swift implementation of measures. 12. Diagnosis of the Shadow Economy and Business Therapeutics – Identification of Risks and Corrective Measures The diagnosis of the shadow economy is a critical tool for its identification, analysis, and the formulation of measures to mitigate its impact. This theme simultaneously addresses business therapeutics, which represents a methodical approach to diagnosing and remedying so-called “business diseases” – systemic issues within enterprises. Within the categorization of the shadow economy, various forms are analyzed, and the causes of their emergence are identified. The consequences of its existence have both macroeconomic and microeconomic implications. The diagnosis requires a combination of quantitative and qualitative methods. Statistical approaches such as the MIMIC model (Multiple Indicators Multiple Causes), which is based on the differences between official and unofficial economic flows, the discrepancy approach analyzing the divergence between consumption and declared incomes, and methods based on cash flow analysis, enable quantification of the shadow economy at both the enterprise and national levels. Forensic financial analysis methods are also employed to detect anomalies in accounting, discrepancies in VAT, and manipulations in supplier-customer relationships. Modern techniques include the use of artificial intelligence for detecting anomalies in transaction data, social network analysis, and econometric modeling to identify suspicious behavioral patterns among business entities. Business therapeutics relies on therapeutic methods and procedures that may be preventive, stabilizing, or curative. Preventive measures include the introduction of internal control mechanisms, transparency in accounting, and ethical codes. Stabilizing measures encompass internal audits, the reinforcement of process compliance, and the implementation of advanced monitoring systems to detect discrepancies. Curative approaches involve forensic audits, managerial restructuring, ethical training, and the reorganization of business processes aimed at increasing the integrity and transparency of the enterprise. Modern therapeutic approaches draw on behavioral economics, machine learning for fraud detection, and predictive risk modeling, thereby advancing business diagnostics toward proactive problem management. 13. Contemporary Trends and the Future of Business Diagnostics – Challenges, Technologies, and Innovative Approaches Modern approaches increasingly employ data analytics, artificial intelligence, machine learning, and advanced predictive modeling, thereby enhancing the accuracy and speed of risk and opportunity identification. Digitalization and automation facilitate the integration and real-time processing of large datasets. A key challenge lies in adapting to the constantly evolving business environment. The future of diagnostics will largely depend on the utilization of artificial neural networks, generative models, and predictive analytics, which will enable the simulation of various scenarios and the formulation of optimal strategies for enterprises under conditions of uncertainty. Modern methods include predictive modeling using AI techniques such as random forest, gradient boosting, and neural networks for assessing enterprise performance and forecasting potential crises. Advances in Natural Language Processing (NLP) allow for sentiment analysis from both internal and external data sources, providing enterprises with valuable insights into market trends and consumer behavior. Blockchain technologies are expected to enhance transparency and security in financial stability assessments and supply chain management. Moreover, integration with cognitive systems, which can autonomously identify patterns in business data and propose process optimizations, will become increasingly significant. Methods such as digital twins of enterprises allow for the simulation and testing of various strategies prior to their real-world implementation. Quantitative methods, such as Monte Carlo simulations and Bayesian networks, will be increasingly combined with qualitative approaches. The challenge remains in addressing ethical issues and regulating the use of data in business diagnostics. Thematic Outline of Seminars: 1. Approaches to Business Diagnostics – Theoretical Foundations, Process, and Methodology Students will work with a case study of a selected enterprise, where, using basic analytical methods, they will gather and evaluate data related to financial stability, organizational processes, and strategic orientation. The exercise is designed so that students independently identify the strengths and weaknesses of the enterprise based on simple statistical calculations and analyses. The objective is to provide tools and methods to verify the reference state of the enterprise’s normality while enabling an understanding of how basic analytical procedures are applied under real-world conditions. Students will be required to present their findings and discuss the methodological approaches employed during the case analysis. The discussion will focus on identifying possible causes for deviations from normality, how these deviations can be quantified, and the implications they may have on the overall stability of the enterprise. 2. Diagnosis of the Relationship Between Enterprise and Its Environment Students will apply theoretical knowledge to a case study, utilizing international indicators such as Doing Business and the Global Competitiveness Index, alongside other relevant indicators that reflect the business environment in various regions. These tools will enable them to quantify the conditions under which the enterprise operates and to reveal qualitative aspects that may significantly affect its strategic decisions. The outcome will be a systematic analysis that integrates identified macroeconomic factors and the microenvironment with the internal characteristics of the enterprise. During the discussion, students will present their findings and critically analyze the impact of the identified factors on management’s strategic decisions. The discussion will address how the enterprise may optimize its strategies in view of the dynamic external environment and what steps management should undertake to ensure competitiveness under changing conditions. 3. Classification of Enterprises into Groups – Classification Methods and Cluster Analysis Students will be introduced to basic statistical methods such as segmenting enterprises according to quartiles, percentiles, deciles, or by applying Z-score normalization, which enable a fundamental segmentation based on economic indicators. Classification techniques, such as decision trees, discriminant analysis, or the Naïve Bayes Classifier, will be presented; these methods facilitate the prediction of an enterprise’s category based on historical data and contribute to a better understanding of the financial stability of subjects. The focus will be on the application of cluster analysis, which enables the automatic formation of groups of enterprises based on their degree of similarity. Specifically, students will examine methods such as K-means clustering, Ward’s method, and DBSCAN, with an emphasis on evaluating the quality of the generated clusters using the Silhouette Score. Visualization techniques, such as PCA (Principal Component Analysis) and T-SNE, will be utilized to effectively display the results of the classification and to reveal hidden patterns in the data, thereby allowing for a clear comparison of the different groups of enterprises. 4. Analysis of Relationships Among Business Variables Students will work with actual data from a selected enterprise, where, through simple analyses, they will identify correlation relationships among individual variables. They will learn to construct a basic linear regression model, interpret its coefficients, and discuss how changes in one variable may impact another, thereby deepening their understanding of the dynamic interrelations within business processes. In addition to quantitative methods, the seminar will also address causal analysis to distinguish genuine causal relationships from mere correlations. Students will be introduced to the method of Granger causality and to the concept of structural models (SEM), which provide a framework for comprehensive assessment of causality among business indicators. 5. Diagnosis of the Enterprise Life Cycle and the Selection of Location as a Strategic Decision Students will analyze available data, identify the current phase of the enterprise’s life cycle, and determine strategic priorities for further development. Based on this analysis, they will proceed to the decision-making process regarding the selection of an optimal location. In evaluating the location, they will employ a scoring method to assess factors such as land availability, energy costs, the quality and structure of the workforce, logistical infrastructure, and access to raw materials. Optimization models, for instance the Steiner-Weber model, will also be implemented to help minimize transportation costs and secure the optimal placement of the enterprise in a spatial context. Students will discuss how changing market conditions, technological innovations, and regulatory frameworks affect management’s strategic decisions. The discussion will provide an opportunity to critically evaluate the trade-offs that managers must consider when choosing between different locations. 6. Estimation and Planning of Enterprise Growth Using Time Series Analysis Students will work with historical data from a selected enterprise. Their task will be to develop forecasts that estimate the future trajectory of revenues or cash flows. Through practical exercises, students will set up ARIMA and Holt-Winters models, and the obtained predictions will be subjected to Monte Carlo simulations, which allow the simulation of various scenarios, and the identification of risk areas associated with uncertainty in future returns. The discussion will concentrate on the critical evaluation of the predictive results, the identification of potential discrepancies between modeled and actual developments, and the formulation of recommendations for managerial decision-making. 7. Enterprise Growth Models and Growth Crises Based on actual or simulated data, students will analyze growth indicators, model potential expansion scenarios, and identify growth limits arising from organizational and managerial capacities. The practical exercises will involve not only the computation of growth parameters but also the formulation of recommendations for managerial interventions that help prevent the emergence of growth crises. The discussion will focus on a critical evaluation of the applicability of the various models under specific business conditions, the identification of the strengths and weaknesses of the proposed growth strategies, and the formulation of recommendations for effective managerial decision-making. 8. Enterprise Stabilization – Diagnosis of Operational and Market Stability Students will address the issue of enterprise stabilization. Among the basic diagnostic approaches is the Balanced Scorecard. Furthermore, students will be introduced to the Data Envelopment Analysis method. In the context of market stability, the competitive positions of enterprises will be analyzed using tools such as the BCG matrix and the ADL matrix. Integrating these methods with a SWOT analysis of the enterprise’s stability will allow for the creation of a comprehensive overview of its strengths and weaknesses regarding external threats and opportunities—a prerequisite for formulating effective strategic recommendations. The session will conclude with a moderated group discussion during which students will present their findings and critically discuss the synergy between operational and market stability. They will analyze the advantages and limitations of each method, discuss their practical applicability, and formulate recommendations for managerial decision-making. 9. Enterprise Crisis and Its Diagnosis – Causes and Analytical Methods Students will work with a case study of an enterprise that has experienced or is experiencing a crisis period. Based on the data provided, they will identify key indicators of crisis, perform calculations using analytical models, and construct a so-called crisis barometer. An interactive group discussion is also an integral component, during which participants will present their analyses, discuss the identified causes of the crisis, and propose specific solutions. The discussion will focus on a critical evaluation of the strengths and weaknesses of the various analytical methods, the integration of theoretical knowledge with practical implications, and the formulation of recommendations for managerial decision-making during times of crisis. 10. Analysis of Market Risks and Macroeconomic Impacts on the Enterprise Students will apply theoretical insights to real macroeconomic data. Their task will be to analyze the impact of individual macroeconomic indicators on business variables, and to conduct scenario analysis and stress testing to simulate extreme market conditions, such as an economic recession or sharp changes in interest rates. They will master techniques that enable the identification of risk factors and the formulation of recommendations for managerial decision-making aimed at minimizing the negative impacts of macroeconomic shocks. Students will present and interpret the results obtained. The discussion will focus on linking theoretical methods with practical case studies and on formulating recommendations that support strategic planning and risk management within the enterprise. 11. Solutions for Business Crises – Strategies for Stabilization, Recovery, and Transformation The seminar will include a detailed analysis of datasets containing indicators of the enterprise’s financial and operational performance before, during, and after a crisis. Students will employ multicriteria decision-making methods such as the Analytic Hierarchy Process (AHP), TOPSIS, or PROMETHEE to objectively compare different remediation variants and to identify the most effective recovery strategies. This approach will allow for the quantification of the effectiveness of individual measures while also considering the synergistic effects among financial, operational, and organizational aspects of the enterprise. A moderated group discussion will follow, during which students will present their analytical results and debate the suitability of the proposed solutions under specific conditions. 12. Diagnosis of the Shadow Economy and Business Therapeutics – Identification of Risks and Corrective Measures This seminar focuses on applying theoretical knowledge to a case study of an enterprise, where indicators of the shadow economy are identified, and business “diseases” are diagnosed. The session concludes with a moderated group discussion during which students present their analyses, discuss the identified risks, and propose both preventive and remedial measures from the perspective of business therapeutics. 13. Contemporary Trends and the Future of Business Diagnostics – Challenges, Technologies, and Innovative Approaches Students will work with existing generative AI tools to gain practical experience in generating interpretations of business indicators and proposing optimization measures based on automated analyses. The objective is for them to acquire the ability to use these tools not only to streamline the diagnostic process but also to support managerial strategic decisions through the rapid processing and interpretation of complex data.