System for graph-based analysis of institutional succession coverage

Abstract

A computer-implemented system for graph-based analysis of institutional succession coverage, the system comprising the following: a storage unit that stores structured data records, including • a role matrix consisting of role identifier values, organizational unit identifiers, hierarchy level parameters, strategic impact parameters, operational impact parameters, replacement difficulty parameters, disruption risk parameters, and role dependency identifiers; • a person attribute matrix comprising person identifiers linked to role identifiers and generative attribute parameters, including parameters for contribution to the legacy system, parameters for mentoring contribution, parameters for innovation contribution, parameters for contribution to the community, and parameters for institutional alignment; • an interaction event matrix comprising relational interaction data records, including originating person identifier, targeting person identifier, interaction type identifier, interaction frequency parameters, interaction intensity parameters, interaction start timestamp, interaction end timestamp, and knowledge criticality parameters; and • a risk parameter matrix that includes person role risk parameters including exit probability parameters, proximity to retirement parameters, contractual risk parameters and external market risk parameters; a matrix preprocessing unit connected to the storage unit and configured to convert heterogeneous parameter scales stored in the person attribute matrix into normalized scalar values ​​within a predefined reference interval; a vector generation unit coupled to the matrix preprocessing unit and configured to create a multidimensional generative vector for each person identifier by arranging the normalized scalar values ​​of the generative attribute parameters into an ordered numeric vector; a graph construction unit coupled to the interaction event matrix and configured to generate an institutional interaction graph represented by an adjacency structure in which the nodes correspond to person identifiers and the edges to relational interaction datasets weighted by the interaction frequency parameter, the interaction intensity parameter, and the knowledge criticality parameter; a unit coupled to and configured with the role matrix for calculating role criticality, which calculates a role criticality index for each role identifier using a weighted aggregation of the strategic impact parameter, the operational impact parameter, the difficulty of replacement parameter, and the disruption risk parameter stored in the role matrix; a network metric calculation unit coupled with the graph construction unit and the vector generation unit, configured to calculate node relevance indicators, including graph degree values, mediation values, and proximity values ​​derived from the adjacency structure, and subsequently combines the node relevance indicators with the multidimensional generative vectors to generate generative centrality values ​​associated with the nodes; and A successor selection control unit coupled with the role criticality calculation unit, the network metric calculation unit, and the risk parameter matrix is ​​configured to identify successor candidate sets for roles with role criticality index values ​​exceeding a threshold by applying coverage constraints defined in a coverage parameter matrix, including minimum successor number parameters and readiness level constraints.

Description

Technical field of the invention

[0001] The present invention relates generally to the technical field of computer-aided decision support systems and organizational analysis tools. In particular, the invention relates to a structured, machine-implemented system for analyzing institutional succession planning by means of graph-based representation of relationship interactions, generative attribute vectors of individuals, and risk parameters related to role continuity. Background of the invention

[0002] Organizations heavily reliant on specialist knowledge, technical leadership, and institutional experience face ongoing challenges regarding succession planning and the continuity of critical functions. In complex organizations such as universities, research institutions, engineering firms, and technology companies, the departure of key personnel often leads to the loss of knowledge networks, disruption of collaborative structures, and operational delays. Traditional succession planning mechanisms typically rely on qualitative assessment methods such as management recommendations, performance appraisal matrices, or simple potential-performance matrices. While these methods provide an initial overview of talent distribution, they do not adequately capture the relational dynamics through which knowledge flows between individuals and teams.

[0003] Existing human resource information systems typically focus on storing personnel data and performance indicators, but fail to reflect the structural relationships that enable effective knowledge transfer. Organizational charts typically mirror formal reporting hierarchies, but do not depict informal mentoring structures, collaborative networks, or regular expert consultations, which often represent the actual channels through which institutional knowledge circulates. Therefore, identifying potential successors solely based on hierarchical or performance-related indicators can lead to overlooking individuals who play a crucial role in the network of relationships that safeguards the organization's expertise.

[0004] Another limitation of conventional approaches lies in the lack of quantitative metrics for assessing the structural importance of individuals within collaborative networks. Traditional systems rarely consider network-based metrics such as node centrality, relationship proximity, or mediation indicators, which can reveal which individuals act as key players in knowledge transfer. Without such metrics, succession planning decisions remain heavily dependent on subjective interpretation and may overlook weaknesses where knowledge is concentrated in the hands of a few individuals.

[0005] Furthermore, many organizations lack tools that can simultaneously integrate individual characteristics, relationship structures, and risk parameters associated with employee departures or retirements. Existing decision-making systems often analyze these factors independently, rather than combining them into a unified calculation model that can holistically assess institutional vulnerabilities and succession planning.

[0006] Another drawback of existing solutions lies in their inability to simulate the structural impact of potential key personnel departures. Conventional planning approaches typically assume static organizational structures and lack dynamic simulations that could assess how the departure of specific individuals affects knowledge transfer networks or succession planning. Consequently, organizations often only discover structural gaps after key personnel have left.

[0007] Furthermore, existing systems rarely offer formalized procedures for determining the minimum number of replacements required to ensure the continuity of critical functions. Decisions regarding replacement selection are often discretionary, and explicit coverage parameters defining the required number of potential replacements across different readiness horizons are lacking.

[0008] The lack of a unified technical system for modeling institutional interaction networks, calculating generative centrality indicators, incorporating risk variables, and determining restricted successor groups represents a significant limitation of existing succession planning technologies. Accordingly, there is a need for an improved computational system capable of creating relational knowledge graphs, integrating generative attributes of individuals, calculating role criticality indices, and automatically determining succession coverage for critical institutional roles.

[0009] Organizations that rely on specialist knowledge, professional expertise, and collaborative problem-solving face the ongoing challenge of maintaining the continuity of critical functions when experienced employees leave key positions. In fields such as engineering institutions, universities, research facilities, and large technology companies, operational capability is often based not only on formal job descriptions but also on implicit knowledge accumulated over years through collaboration, mentoring, and project participation. When individuals in such positions leave due to retirement, resignation, or restructuring, the loss frequently extends beyond the vacancy of a single position and can disrupt networks through which knowledge and experience are passed on.Therefore, institutions are increasingly looking for technical mechanisms that can identify potential successors and assess the structural vulnerability associated with the departure of key personnel.

[0010] Traditional management practices have long attempted to shape succession planning through administrative processes based on management judgments, employee evaluations, and hierarchical position analyses. Conventional approaches typically rely on qualitative assessments by leadership teams, identifying potential successors based on management recommendations, subjective evaluations of leadership potential, and historical performance indicators. While such processes provide an initial basis for identifying potential candidates for future leadership positions, they generally lack systematic computer-aided support and are frequently affected by cognitive biases, incomplete information, or inconsistent evaluation criteria.The lack of structured analysis mechanisms reduces the reliability of succession planning and limits the ability of organizations to predict disruptions in institutional knowledge transfer.

[0011] Many organizations use human resource management systems (HRMIS) to manage employee data, performance reviews, and competency profiles. These systems typically store structured personnel files containing information such as job titles, department affiliations, work experience, and training records. Some systems incorporate competency frameworks where employees are evaluated against predefined competency categories such as technical expertise, leadership potential, communication skills, or project management abilities. While these systems provide useful information repositories for employees, they primarily function as data storage platforms rather than analytical tools capable of mapping the dynamic relationships of knowledge flow within an organization.Therefore, these systems rarely provide information about how individuals contribute to institutional knowledge networks or how the departure of certain individuals might affect the continuity of the organization.

[0012] Performance management systems are a common approach to identifying potential successors. These systems often use performance-potential matrices that classify employees according to their current performance level and their perceived future leadership potential. A well-known example is the nine-quadrant talent matrix, which positions employees along two axes representing performance and potential. While such matrices offer a simplified method for categorizing employees, they fail to capture the structural relationships between individuals or the collaborative interactions that facilitate effective knowledge transfer. Employees holding key positions in collaborative networks may therefore be undervalued in such frameworks, even though their departure could significantly impact institutional operations.

[0013] Furthermore, many existing succession planning models lack mechanisms for the structured and reproducible assessment of potential successors' suitability. Candidate suitability is often determined by subjective assessments from management bodies, based on categories such as "immediately suitable," "suitable in the future," or "development needed." While these classifications provide useful guidance for leadership development programs, they are often based on informal conversations rather than explicit analytical procedures that combine quantitative and qualitative information. The lack of formalized suitability criteria reduces the transparency of succession decisions and makes their review and justification within institutional governance processes more difficult.

[0014] Another drawback of traditional succession planning tools is the lack of mechanisms for determining the minimum number of successors required to ensure the continuity of critical positions. While organizations often identify multiple potential candidates for leadership roles, they frequently lack formal procedures to verify whether these candidates collectively meet the requirements for filling various positions across different time horizons. For example, a position may require at least one successor who can assume the role immediately, as well as additional successors who can take over within a defined development period. Without computerized mechanisms to assess such requirements, organizations may unknowingly face succession gaps despite identifying multiple potential candidates.

[0015] Furthermore, many existing systems lack the ability to simulate the structural impact of personnel changes within organizational networks. When a key individual leaves the company, the resulting disruptions can extend beyond the immediate vacancy, affecting multiple collaborative relationships that previously relied on that person's expertise or mentorship. Traditional succession planning tools typically lack simulation capabilities capable of modeling such scenarios. This limits decision-makers' ability to anticipate and mitigate potential disruptions before they occur.

[0016] In modern knowledge-based institutions, the complexity of collaborative networks, the diversity of employee skills, and the uncertainty of personnel dynamics combine to create a need for more advanced analytical tools capable of integrating diverse information sources into a unified computational model. Existing solutions typically address only one aspect of succession planning, such as performance appraisal, competency analysis, or network analysis, without providing a comprehensive mechanism for combining these elements into a single decision support system.

[0017] Therefore, there remains a significant need for a technical system capable of integrating structured role information, multidimensional generative attributes of individuals, relationship networks, and risk parameters related to staff turnover. Such a system should be able to create computer-based representations of institutional knowledge networks, calculate metrics that quantify the generative importance of individuals within these networks, and identify candidate groups for succession positions that meet predefined requirements for covering critical organizational roles. By addressing these limitations, an improved system could transform succession planning from a largely subjective administrative activity into a reproducible, analytical process based on structured data and graph-based computational methods. Summary of the invention

[0018] The present invention provides a system for graph-based analysis of institutional succession coverage, configured to process structured organizational data and relational interaction datasets to determine succession coverage for critical institutional roles.

[0019] In one embodiment, the system implements a generative system for talent assessment and institutional succession planning (GETSI). This system creates a dynamic institutional talent map that maps the generative attributes of individuals and the existing relational collaboration structures within an organization. The system integrates generative assessment parameters of individuals, including knowledge generation capacity, mentoring contributions, innovation participation, social engagement, and alignment with institutional values, with data on relational interactions. This data represents mentoring relationships, participation in joint projects, consultations, representation events, and other collaborative structures through which knowledge circulates within the institution.Based on these integrated datasets, the system calculates generative centrality indicators that represent the relative contribution of individuals to institutional knowledge circulation, determines role criticality indices that reflect the importance of organizational roles for institutional continuity, and assesses succession coverage indices that indicate whether sufficient successor candidates exist for critical roles across defined readiness horizons. The system also enables the simulation of departure scenarios for selected individuals to estimate the risks of knowledge gaps and potential disruptions to knowledge transfer networks. This generates prioritized succession pathways and development recommendations to strengthen institutional continuity.

[0020] The system includes a storage unit for structured data matrices, including a role matrix with role identifiers, strategic and operational impact parameters, succession difficulty parameters, and disruption risk parameters. The storage unit also stores a person attribute matrix with generative attribute parameters assigned to individuals, such as mentoring contributions, contribution to creating a sustainable legacy, innovation contributions, community engagement, and indicators of institutional alignment. Additionally, the system stores interaction event matrices representing interactions between individuals, as well as risk parameter matrices with indicators of potential exit probabilities and proximity to retirement.

[0021] A matrix preprocessing unit converts heterogeneous measurement scales linked to generative attributes into normalized scalar values ​​within a predefined numerical interval. A vector generation unit creates multidimensional generative vectors for each person by arranging normalized attribute values ​​into ordered vector structures.

[0022] A graph construction unit processes interaction datasets to generate an institutional interaction graph represented by an adjacency structure in which nodes correspond to individuals and edges represent relational interactions weighted by frequency, intensity, and knowledge criticality.

[0023] A unit for calculating role criticality determines a role criticality index for each organizational role. This index is based on a weighted aggregation of parameters such as strategic impact, operational impact, difficulty of replacement, and failure risk. Using these indices, roles can be categorized as critical, associated, or non-critical.

[0024] A network metrics calculation unit determines structural indicators such as node degree, betweenness centrality, and proximity centrality for nodes within the institutional graph. These indicators are combined with generative vectors to generate generative centrality values ​​that represent the contribution of individuals to the dissemination and preservation of institutional knowledge.

[0025] A succession selection control unit processes role criticality indices, generative centrality values, readiness values, and risk parameters to identify candidate sets for successors that meet the coverage conditions defined in a coverage parameter matrix. The system thus determines the minimum number of successors required to ensure continuity for each critical role.

[0026] By integrating structured matrices, graph-based analysis, generative attribute modeling, and restricted successor selection procedures, the system transforms succession planning into a reproducible computational process based on structural proofs rather than subjective evaluation.

[0027] The present invention aims to provide a technically implemented system for graph-based analysis of institutional succession planning. This system enables organizations to assess the continuity of critical roles through structured, computer-aided processing of relationship data, generative attributes of individuals, and role-specific parameters. The invention aims to establish a machine-implemented analysis framework that can map institutional collaboration structures in the form of relationship graphs, thereby enabling a more precise assessment of the circulation of knowledge and expertise between individuals in different organizational roles.

[0028] A further objective of the invention is to provide a system for creating and processing structured data matrices that depict organizational roles, the individuals holding these roles, interaction events between individuals, and risk parameters related to personnel continuity. By organizing institutional information into structured matrices, which are stored in a memory unit and processed by specialized computing units, the invention enables the systematic analysis of succession planning while simultaneously ensuring traceability between the analysis results and the underlying organizational data.

[0029] A further aim of the invention is to provide a mechanism for generating multidimensional generative vectors linked to individuals. Attributes such as mentoring contributions, knowledge transfer, innovation participation, social engagement, and institutional orientation are transformed into normalized numerical values ​​that can be computationally processed. Using this vector representation, the system enables the comparative evaluation of individuals not only based on their formal roles but also on their ability to acquire, disseminate, and expand institutional knowledge.

[0030] A further objective of the invention is to provide a mechanism for calculating role criticality indices based on a weighted aggregation of parameters representing the strategic and operational impact, the difficulty of succession planning, and the disruption risk of organizational roles. Through this calculation, the system enables the classification of roles according to their relevance for institutional continuity and facilitates the prioritization of roles requiring succession analysis.

[0031] A further aim of the invention is to provide a computational method for determining indicators of generative centrality by combining structural metrics derived from the institutional interaction graph with multidimensional generative vectors assigned to individuals. The resulting indicators quantify the importance of individuals within the institutional knowledge network by considering both their relational position and their generative contribution to the organization.

[0032] A further objective of the invention is to provide a mechanism for calculating suitability assessments of potential successor candidates by aggregating normalized generative attributes, historical performance parameters, and role-related experience indicators. These suitability assessments enable the classification of candidates into standardized suitability levels corresponding to different succession periods, thus facilitating a transparent and reproducible evaluation of successor suitability.

[0033] A further objective of the invention is to provide a mechanism for selecting successors that identifies potential successors for critical positions by applying coverage restrictions and operational limitations defined in a structured coverage parameter matrix. This mechanism ensures that succession planning meets predefined organizational requirements, including a minimum number of successors, an even distribution of availability across different time horizons, and the limitation of role assignments to individual candidates.

[0034] A further objective of the invention is to provide a system that integrates risk parameters related to employee attrition—including probability of departure, proximity to retirement, contractual risks, and external market risks—into succession planning. By incorporating these risk variables into the calculation model, the system enables the early detection of situations in which critical positions are exposed to a high risk of vacancy.

[0035] Another objective of the invention is to provide a simulation function that allows nodes representing selected individuals to be temporarily removed from the institutional interaction graph in order to assess the potential structural impact of their departure. This function enables organizations to estimate disruptions in knowledge transfer networks and to identify areas where succession planning or knowledge encoding measures should be prioritized.

[0036] A further objective of the invention is to provide a reproducible and traceable succession planning system in which parameters, calculation methods, and analysis results are recorded and stored in the system memory. This traceability enables institutional decision-makers to review and validate the results of succession planning using explicit data structures and calculation methods instead of relying on discretionary decisions.

[0037] A further objective of the invention is to provide a scalable device architecture capable of regularly updating organizational data sets and recalculating succession indicators as soon as new information on roles, employee interactions, and risk conditions becomes available. This ensures that the system provides an up-to-date representation of institutional knowledge networks and guarantees that succession planning decisions reflect the current organizational context.

[0038] Taken together, these goals are achieved through the integration of structured data storage, graph construction mechanisms, generative attribute modeling, risk parameter processing, and restricted successor selection procedures into a unified device architecture that supports institutional succession analysis and continuity planning. BRIEF DESCRIPTION OF THE IMAGE

[0039] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a computer-implemented system for graph-based analysis of institutional succession coverage.

[0040] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0041] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0042] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0043] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0044] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0046] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0047] Fig.Figure 1 shows a block diagram of a computer-implemented system for graph-based analysis of institutional succession planning. The system 100 comprises: a storage unit (102) for storing structured data sets, including a role matrix with role identifiers, organizational unit identifiers, hierarchy level parameters, strategic and operational impact parameters, succession difficulty parameters, disruption risk parameters, and role dependency identifiers; a person attribute matrix with person identifiers mapped to role identifiers and generative attribute parameters, including parameters for contribution to the existing system, mentoring, innovation, community, and institutional alignment;An interaction event matrix with relational interaction data records, including identifiers of the source and target person, identifiers of the interaction type, parameters of interaction frequency and intensity, start and end time of the interaction, and parameters of knowledge criticality; and a risk parameter matrix with person-role risk parameters, including parameters for probability of leaving, parameters for proximity to retirement, parameters for contractual risk, and parameters for external market risk. A matrix preprocessing unit (104) connected to the storage unit and configured to convert heterogeneous parameter scales stored in the person attribute matrix into normalized scalar values ​​within a predefined reference interval;a vector generation unit (106) connected to the matrix preprocessing unit and configured to create a multidimensional generative vector for each person identifier by ordering the normalized scalar values ​​of the generative attribute parameters into an ordered numerical vector; a graph construction unit (108) connected to the interaction event matrix and configured to generate an institutional interaction graph represented by an adjacency structure in which nodes correspond to person identifiers and edges to relational interaction datasets weighted by the interaction frequency parameter, the interaction intensity parameter, and the knowledge criticality parameter;a role criticality calculation unit (110) connected to the role matrix and configured to calculate a role criticality index for each role identifier using the weighted aggregation of the strategic impact parameter, operational impact parameter, replacement difficulty parameter, and disruption risk parameter stored in the role matrix; a network metric calculation unit (112) coupled to the graph construction unit and vector generation unit and configured to calculate node relevance indicators, including graph degree values, mediation values, and proximity values ​​derived from the adjacency structure, and to combine the node relevance indicators with the multidimensional generative vectors to generate generative centrality values ​​assigned to the nodes;and a successor selection control unit (114) coupled to the role criticality calculation unit, the network metric calculation unit, and the risk parameter matrix, the successor selection control unit being configured to determine sets of successor candidates for roles whose role criticality index values ​​exceed a threshold by applying coverage constraints defined in a coverage parameter matrix, including minimum number parameters for successors and readiness level constraints.

[0048] In one embodiment, the graph construction unit (108) generates the adjacency structure using interaction data sets whose interaction frequency parameter exceeds a predefined interaction threshold and whose knowledge criticality parameter exceeds a predefined knowledge transfer threshold.

[0049] In one embodiment, the matrix preprocessing unit (104) transforms heterogeneous measurement scales of the generative attribute parameters into normalized values ​​by means of percentile transformation or min-max scaling operations.

[0050] In one embodiment, the vector generation unit (106) stores each multidimensional generative vector as a fixed-length numeric vector that is assigned to the corresponding person identifier in the storage unit.

[0051] In one embodiment, the graph construction unit (108) assigns direction attributes to edges of the institutional interaction graph that represent mentoring relationships, consulting relationships, substitution relationships and cooperation relationships.

[0052] In one embodiment, the role criticality calculation unit (110) calculates the role criticality index using a weighted sum expression that includes weighting parameters associated with the strategic impact parameter, the operational impact parameter, the replacement difficulty parameter, and the disruption risk parameter.

[0053] In one embodiment, the network metric computation unit (112) calculates betweenness centrality values ​​by determining the proportion of shortest graph paths that pass through each node of the institutional interaction graph.

[0054] In one embodiment, the network metric calculation unit (112) calculates proximity indicators for nodes of the institutional interaction graph using graph distance values ​​derived from the adjacency structure.

[0055] In an embodiment further comprising a readiness assessment unit configured to generate readiness values ​​for person-role pairs using a weighted aggregation of normalized generative vector values ​​and historical performance parameters stored in the memory unit.

[0056] In one embodiment, the unit for calculating the readiness value classifies the readiness values ​​into readiness categories such as immediate readiness, short-term readiness, medium-term readiness and delayed readiness based on predefined threshold parameters.

[0057] In one embodiment, the successor selection control unit (114) determines a minimum number of successors for each role by selecting combinations of persons that meet the coverage constraints stored in the coverage parameter matrix, including: parameters for the minimum number of successors, parameters for the minimum immediate availability, and parameters for the maximum role assignment per person.

[0058] In an embodiment further comprising a dependency determination computation unit configured to calculate dependency indicators representing the concentration of generative centrality values ​​among nodes associated with a critical role.

[0059] In one embodiment, the risk parameter matrix stores exit probability parameters for several time horizons, including a twelve-month and a thirty-six-month time horizon.

[0060] In an embodiment further comprising a network simulation unit configured to temporarily remove nodes associated with selected person identifiers from the institutional interaction graph and recalculate the generative centrality values ​​to estimate the disruption of structural knowledge transfer.

[0061] In an embodiment further comprising a successor assignment storage unit configured to store successor assignment records, including role identifier, successor person identifier, readiness category indicator, and generative centrality value assigned to the successor.

[0062] In certain implementations, the system implements a generative system for talent assessment and institutional succession planning (GETSI). This system creates a dynamic institutional talent map that integrates the generative attributes of individuals with relational interaction structures within an organization. The system operationalizes generative assessment tools that evaluate dimensions such as knowledge generation capacity, mentoring activities, innovation participation, social engagement, alignment with institutional values, and other indicators of individuals' generative capacity to maintain and expand institutional knowledge. The results of these assessment tools are transformed into structured numerical parameters and integrated into the person attribute matrix stored in memory.This allows qualitative talent management concepts, as described in organizational development frameworks, to be transformed into computer-analyzable data structures.

[0063] In further embodiments, the invention reconstructs the effective architecture of collaboration and knowledge sharing within the institution by modeling relational interaction events between individuals. These interaction events can include mentoring relationships, joint project participation, technical consultations, temporary substitutions in operational functions, and other forms of collaboration that contribute to the transfer of institutional knowledge. The system processes these interaction events to generate a relational interaction graph in which individuals are represented as nodes and knowledge transfer relationships as edges. The resulting graph reflects the real-world collaboration structures that support knowledge dissemination within the organization and enables the system to identify influential individuals and structural dependencies within institutional knowledge networks.

[0064] In some implementations, the system calculates generative centrality indicators that represent the relative contribution of individuals to the creation, preservation, and dissemination of institutional knowledge. These indicators are derived by combining graph-based structural metrics, such as degree, betweenness, and proximity indicators, with multidimensional generative vectors assigned to individuals. The resulting generative centrality value quantifies both an individual's relational position within the institutional network and their generative contribution through mentoring, innovation, and the creation of a sustainable knowledge legacy. This composite metric enables the system to identify nodes that play a critical role in maintaining the flow of institutional knowledge between organizational units and collaborative structures.

[0065] In one embodiment, the system also identifies indicators of succession planning vulnerability, representing the risk of a knowledge gap due to the potential departure of individuals in key positions. This risk can be assessed by combining role criticality indices with indicators of generative centrality and risk parameters that consider proximity to retirement, the probability of departure, or exposure to external labor market factors. By analyzing the concentration of generative centrality values ​​associated with specific roles and individuals, the system identifies areas where the continuity of institutional knowledge depends disproportionately on a small number of people. This analysis enables the early detection of weaknesses in succession planning and supports proactive succession planning strategies.

[0066] In certain implementations, the succession selection unit generates structured succession plans that outline prioritized development paths for potential successors in critical positions. These plans can consider suitability assessments, indicators of generative centrality, and the proximity of relationships to existing job holders to identify realistic development paths for preparing successors over defined timeframes. The system can also generate development recommendations aimed at strengthening mentoring relationships, intensifying collaboration, or promoting the codification of critical knowledge related to high-risk positions. In doing so, the system supports evidence-based talent development and institutional continuity planning.

[0067] In certain embodiments, the system architecture described here implements the conceptual framework of a generative system for talent assessment and institutional succession planning (GETSI). Institutional succession planning is achieved through the integration of data from generative assessment, relationship networks, and an analysis of role criticality. Within this framework, the invention creates a structured representation of organizational talent by linking each individual with a multidimensional generative profile and positioning these individuals in a graph that depicts effective cooperation and knowledge transfer relationships. The resulting representation forms a dynamic talent map of the organization, enabling the identification of individuals whose generative attributes and relationship networks contribute significantly to the continuity of institutional knowledge and leadership competence.This representation also enables the system to identify areas of structural dependencies, assess the readiness for succession in critical roles, and determine development paths to strengthen mentoring relationships and knowledge transfer within the organization.

[0068] In certain implementations, the system additionally assesses indicators of knowledge gap risk, representing the potential loss of institutional knowledge associated with the departure of individuals from key positions. This risk can be determined by analyzing the concentration of generative centrality values ​​between nodes assigned to a specific role, in combination with risk parameters such as departure probability, proximity to retirement, and external market exposure. Roles with a high concentration of generative centrality coupled with an increased departure risk are identified as weaknesses in succession planning. This allows the system to prioritize the promotion of mentoring, the codification of knowledge, or the accelerated development of successors for these roles.

[0069] In certain implementations, the relational interaction graph and the generative attribute vectors jointly define a dynamic institutional talent map that represents the structural distribution of knowledge, mentoring influence, and innovation capacity within the organization. The talent map can be regularly updated as new interaction datasets, generative assessment results, or role information become available. This ensures a current representation of the institutional knowledge network and the availability of successor candidates for key positions.

[0070] The present invention relates to a system for graph-based analysis of institutional succession planning. It is configured as a machine-implemented device architecture and can process structured organizational data to determine succession readiness and institutional continuity for critical positions. The system comprises a storage unit, a matrix preprocessing unit, a vector generation unit, a graph construction unit, a unit for calculating role criticality, a unit for calculating network metrics, a unit for calculating readiness scores, a unit for controlling successor selection, and a storage unit for succession planning. These units are interconnected via a processing unit and a communication interface.The storage unit stores several structured matrices, including a role matrix, a person attribute matrix, an interaction event matrix, a risk parameter matrix, and a coverage parameter matrix. Each of these matrices contains structured parameters linked to roles, people, relational interactions, and succession planning constraints.

[0071] During operation, the system first performs initialization and data acquisition, loading structured organizational data into memory. The role matrix contains structured data records representing organizational roles. Each data record includes a role ID, an organizational unit ID, a parameter for hierarchy level, a parameter for strategic and operational impact, a parameter for succession difficulty, a parameter for disruption risk, and dependency IDs that represent the relationships between roles. Each role ID is associated with a person ID representing the individual currently holding the role. The person attribute matrix contains generative attribute parameters assigned to each person in the organization.These include values ​​representing the contribution to business success, mentoring activities, innovation contributions, social engagement, and institutional alignment. These generative attributes can be derived from various assessment instruments, including structured questionnaires, performance reviews, evidence of project participation, or rating grids.

[0072] Since generative attribute parameters can originate from heterogeneous measurement scales, the matrix preprocessing unit performs normalization and scale transformation operations. During preprocessing, the system converts heterogeneous values ​​into normalized scalar values ​​within a predefined reference interval. Normalization can be performed using min-max transformation, percentile transformation, or standardized scaling procedures that convert the input attributes into comparable numerical values. The normalization process ensures that generative attributes originating from different measuring instruments can be processed uniformly in subsequent computational steps.

[0073] After normalization, the vector generation unit creates a multidimensional generative vector for each person identifier stored in the person attribute matrix. The normalized generative attribute values ​​are arranged into an ordered numerical vector structure. Each generative vector represents the generative profile of the respective person and contains numerical components that represent the capacity to generate traditions, the capacity to mentor, the capacity to contribute to innovation, the contribution to social engagement, and the institutional orientation. These vectors are stored in memory along with the person identifiers so that they can be retrieved for network analyses.

[0074] In parallel with vector generation, the graph construction unit processes the interaction event matrix to create an institutional interaction graph that maps the relational architecture of collaboration and knowledge transfer within the organization. Each data record stored in the interaction event matrix contains an identifier for the originator, an identifier for the target, an identifier for the interaction type, a frequency parameter (number of interactions within a defined observation period), an intensity parameter (relative importance or duration of the interaction), start and end times (temporal duration of the interaction), and a parameter for knowledge criticality (intensity of knowledge exchange).

[0075] The graph construction unit transforms these interaction datasets into an adjacency structure, where nodes correspond to person identifiers and edges to relational interaction datasets. Each edge is assigned a weight derived from a combination of interaction frequency, interaction intensity, and knowledge criticality parameters. The weighting function yields a numerical value representing the strength of the knowledge-sharing relationship between two individuals. In some implementations, the weighting function can be calculated as a linear combination or multiplicative aggregation of normalized frequency, intensity, and knowledge criticality parameters. Edges can also be assigned direction attributes to represent directed relationships, such as mentoring or consulting interactions, where the flow of knowledge is predominantly unidirectional.

[0076] Before generating the final institutional interaction graph, the graph construction unit can apply filter operations that remove interactions whose frequency or knowledge criticality falls below predefined thresholds. This filtering ensures that only interactions representing meaningful knowledge exchange are included in the graph. The resulting adjacency structure thus represents the effective collaboration network through which institutional knowledge flows between individuals.

[0077] Once the graph structure is generated, the role criticality calculation unit processes the role matrix to determine the relative importance of each organizational role. For each role identifier, the system calculates a role criticality index based on a weighted aggregation of parameters for strategic impact, operational impact, difficulty of replacement, and disruption risk. Each parameter is assigned a configurable weight representing the relative importance of the corresponding dimension. The weighted aggregation can be calculated using a linear combination function, summing the product of each parameter value and its associated weight to obtain the role criticality index. The resulting index represents the role's importance in terms of institutional continuity.

[0078] After calculating the criticality indices of the roles, the system compares each index value with predefined threshold parameters to classify roles into categories such as critical, associated, or non-critical roles. Roles whose criticality index exceeds a predefined threshold are marked as critical roles and subsequently prioritized during the succession selection process.

[0079] Following role classification, the network metrics unit performs a structural analysis of the institutional interaction graph. The system calculates network metrics at the node level, including degree centrality, betweenness centrality, and proximity indicators. Degree centrality is calculated by determining the number or weighted sum of edges connected to each node in the graph. Betweenness centrality is calculated by determining the proportion of shortest paths between pairs of nodes passing through a given node. Proximity indicators are calculated by taking the reciprocal of the average distance between a node and all other nodes in the network.

[0080] After calculating these structural metrics, the network metric calculation unit combines the network-based indicators with the generative vectors assigned to each individual to calculate the generative centrality values. The generative centrality value represents a composite metric that reflects both an individual's structural position in the knowledge network and the generative attributes associated with that individual. In one embodiment, generative centrality can be calculated by weighted aggregation of normalized centrality metrics and normalized components of the generative vector. The resulting metric quantifies the extent to which a given individual contributes to the creation, dissemination, and preservation of institutional knowledge.

[0081] In parallel with calculating generative centrality, the system processes the risk parameter matrix to assign risk indicators to person-role pairs. These risk indicators include, for example, the estimated probability of leaving within twelve months, the probability of leaving within 36 months, indicators of proximity to retirement, indicators of contractual risks, and indicators of external labor market risks. Using these risk parameters, the system can estimate the probability that individuals in key positions will leave the company within specific timeframes.

[0082] The unit for calculating operational readiness then determines the operational readiness of potential successors. The operational readiness for each person-role pair is determined by aggregating normalized generative vector values, generative centrality indicators, historical performance parameters, and role-specific experience indicators. The aggregated operational readiness is then compared to predefined thresholds to assign operational readiness categories such as immediate operational readiness, short-term operational readiness, medium-term operational readiness, or deferred operational readiness.

[0083] After classifying readiness levels, the succession selection unit identifies candidates for critical roles. The selection process utilizes the coverage parameter matrix stored in memory. This matrix defines coverage constraints, including the minimum number of successors for each critical role, the minimum number of successors in specific readiness categories, and limitations on the number of roles that can be assigned to a single individual as a potential successor. The succession selection unit identifies individuals whose readiness levels exceed predefined thresholds and whose generative centrality demonstrates a sufficient contribution to the institutional knowledge networks.

[0084] Within the pool of suitable candidates, the system performs a restricted selection process to identify the smallest combination of individuals that meets all coverage parameters defined for each role. This process can be implemented using combinatorial optimization or constraint satisfaction methods, which evaluate candidate combinations based on coverage constraints and role assignment requirements. The resulting sets of successor candidates represent the minimum number of individuals required to ensure the continuity of critical roles over defined on-call periods.

[0085] In some embodiments, the system also includes a network simulation unit that assesses the structural impact of potential staff departures. During the simulation, nodes associated with selected individuals are temporarily removed from the institutional interaction graph, and the network metrics calculation unit recalculates the centrality indicators for the remaining nodes. By comparing the generative centrality distributions before and after node removal, the system estimates the extent to which the pathways of institutional knowledge transfer would be affected by the departure of specific individuals.

[0086] The results of the successor selection process are ultimately stored in the succession planning memory. Each record contains the role ID, the successor's ID, the associated readiness category, the generative centrality value, and reference parameters describing the selection criteria. These stored records permanently map the organization's succession structure and enable the system to generate analytical reports that support succession planning decisions.

[0087] By integrating structured data matrices, relational graph creation methods, generative attribute modeling, network metric calculation, readiness assessment, and restricted successor selection techniques, the present invention provides a comprehensive computer system for evaluating institutional succession planning. The system transforms succession planning into a reproducible analytical process capable of quantifying institutional knowledge networks, identifying structural weaknesses, and determining candidate groups for succession that will ensure the continuity of critical organizational functions.

[0088] In an exemplary embodiment, the system device comprises a computing structure consisting of a processor assembly, a memory unit, a data interface controller, a matrix preprocessing unit, a vector generation unit, a graph construction unit, a role criticality calculation unit, a network metric calculation unit, a successor selection control unit, and a successor allocation memory unit.

[0089] The device has a storage unit that stores structured datasets. These describe organizational roles, the individuals holding those roles, generative attributes of the individuals, interaction datasets between the individuals, and risk variables related to potential departures. The storage unit manages these datasets in matrix form to enable efficient retrieval and processing by the system's computing units.

[0090] A matrix preprocessing unit transforms heterogeneous measurement scales within the generative attribute matrix into normalized scalar values. Generative attributes such as mentoring contribution, innovation participation, social engagement, and the creation of a sustainable legacy can initially be measured using various rating systems, including ordinal scales, percentages, or frequency counts. The matrix preprocessing unit converts these heterogeneous measurements into standardized numerical values ​​within a predefined reference interval, thus enabling a direct comparison between individuals and attributes.

[0091] After normalization, a vector generation unit creates multidimensional generative vectors that are assigned to each person identifier stored in the system. Each vector contains ordered numerical values ​​that represent the generative attributes of the respective person. These vectors map each person's generative profile and serve as a numerical representation of their ability to generate, share, and expand institutional knowledge.

[0092] The system also includes a graph construction unit that generates an institutional interaction graph from interaction event matrices stored in memory. Each interaction event record contains parameters that identify the originator, the target, the interaction type, the interaction frequency, the interaction intensity, and the knowledge criticality associated with the interaction. The graph construction unit processes these records to create an adjacency structure in which nodes represent individuals and edges represent relational interactions weighted according to the interaction parameters.

[0093] The institutional network can encompass multiple levels of relationships, representing mentoring relationships, participation in joint projects, representational relationships, and technical advisory sessions. Each edge can also include attributes describing the direction, duration, and intensity of the interaction. The network structure thus enables a computer-aided representation of the flow of knowledge between individuals within the institution.

[0094] A role criticality calculation unit determines a role criticality index for each organizational role stored in the role matrix. The calculation is based on a weighted aggregation of parameters, including strategic and operational impact, difficulty of replacement, and failure risk of the respective role. Administrators can configure the weighting parameters for these dimensions according to organizational priorities.

[0095] The calculated role criticality index enables the system to identify roles that are crucial for institutional continuity. Roles exceeding predefined thresholds are marked as critical roles and prioritized in succession planning.

[0096] A network metrics calculation unit determines structural indicators for nodes in the institutional interaction graph. These indicators include degree centrality values, representing the number of connections to each node; betweenness centrality values, indicating the degree to which nodes act as intermediaries on shortest paths between other nodes; and proximity indicators, representing the relative proximity of nodes to other nodes within the graph structure.

[0097] The network metric calculation unit combines these structural indicators with the multidimensional generative vectors of each node to generate values ​​of generative centrality. Generative centrality represents a composite metric that reflects both an individual's structural position within the knowledge network and their generative attributes in terms of mentoring, innovation, and institutional contribution.

[0098] The system also includes a readiness calculation unit that determines the readiness of potential successors. This unit aggregates generative vector components with historical performance parameters and experience indicators to generate a numerical readiness rating for each person-role pair. These readiness ratings can be categorized as "immediate readiness," "short-term readiness," "medium-term readiness," and "deferred readiness."

[0099] A succession selection control unit processes role criticality indices, generative centrality values, readiness values, and risk parameters to identify candidate groups for successor roles in critical roles. The succession selection process considers coverage constraints stored in a coverage parameter matrix, including the minimum number of successors for each role and restrictions that prevent the excessive assignment of multiple roles to a single individual.

[0100] Using these restricted selection procedures, the system determines the minimum number of successors required to ensure the continuity of critical roles over defined time horizons.

[0101] The device may also include a network simulation unit configured to simulate the removal of nodes corresponding to selected individuals from the institutional interaction graph. Following node removal, the network simulation unit recalculates the generative centrality values ​​to estimate potential disruptions to knowledge transfer networks that might occur when certain individuals leave the organization.

[0102] Finally, a succession mapping storage unit stores records that link critical roles with selected successor candidates, readiness categories, and the generative centrality values ​​belonging to each candidate.

[0103] Through this integrated device architecture, the invention provides a reproducible computing system capable of analyzing institutional succession coverage, identifying structural weaknesses in knowledge transfer networks, and determining successor candidate sets using graph-based analysis methods.

[0104] In one embodiment, the computer-implemented system for graph-based analysis of institutional succession planning is realized as an integrated electronic computing device. It comprises a processor unit connected to several hardware processing units and non-volatile memory. This memory is implemented using semiconductor memory circuits and stores structured data matrices, including a role matrix, a person attribute matrix, an interaction event matrix, and a risk parameter matrix. The role matrix stores electronically encoded role identifiers, organizational units, hierarchy level parameters, strategic and operational impacts, succession planning difficulties, disruption risks, and role dependencies. The person attribute matrix stores electronically captured person identifiers that are assigned to role identifiers.as well as generative attribute parameters such as contributions to the legacy system, mentoring, innovation, community, and institutional alignment. The interaction event matrix stores relational interaction data records with origin and target person identifiers, interaction types, interaction frequency, intensity, start and end times, and knowledge criticality. The risk parameter matrix stores person-role risk parameters such as the probability of leaving and proximity to retirement. It also includes contractual exposure parameters and external market exposure parameters; a matrix preprocessing unit, implemented as a dedicated arithmetic processing circuit and coupled to the storage unit,It converts heterogeneous parameter scales of the generative attribute parameters into normalized scalar values ​​within a predefined numerical reference interval through hardware-executed percentile transformations or min-max scaling operations; a vector generation unit, implemented as a vector processing circuit and coupled to the matrix preprocessing unit, constructs multidimensional generative vectors for each person identifier by arranging the normalized scalar values ​​into ordered numerical vector structures and storing the fixed-length numerical vectors in the memory unit; a graph construction unit, implemented as a graph processing circuit and coupled to the interaction event matrix, generates an institutional interaction graph represented by an adjacency structure stored in memory, in which nodes correspond to person identifiers and edges to relational interaction datasets.which are weighted by the interaction frequency parameter, the interaction intensity parameter, and the knowledge criticality parameter. Interaction datasets that meet predefined thresholds for interaction frequency and knowledge transfer are included, and directional attributes are assigned to edges representing mentoring, consulting, substitution, and cooperation relationships. A role criticality calculation unit, implemented as an arithmetic aggregation circuit and coupled to the role matrix, calculates a role criticality index for each role identifier by weighted summation of the strategic impact parameter, the operational impact parameter, the substitution difficulty parameter, and the disruption risk parameter, which are stored in the role matrix. A network metric calculation unit, implemented as a graph analysis circuit,The unit coupled with the graph construction unit and the vector generation unit calculates node relevance indicators, including degree values, betweenness values ​​determined from the proportions of shortest graph paths through nodes, and proximity indicators derived from the graph distance values ​​of the adjacency structure, and combines these indicators with the multidimensional generative vectors to generate generative centrality values ​​assigned to the nodes; a readiness rating unit implemented as an aggregation circuit generates readiness values ​​for person-role pairs using a weighted aggregation of normalized generative vector values ​​and historical performance parameters stored in the memory unit, and classifies the readiness values ​​into readiness categories, including immediate readiness, short-term readiness, medium-term readiness, and delayed readiness.using predefined threshold parameters; A successor selection control unit implemented as a constraint evaluation circuit, coupled with the role criticality calculation unit, the network metric calculation unit, and the risk parameter matrix, determines candidate sets for successors for roles whose role criticality index values ​​exceed a threshold by applying coverage constraints defined in a coverage parameter matrix, including parameters for the minimum number of successors, parameters for the minimum immediate readiness, minimum successors for defined readiness horizons, and maximum parameters for role assignment per person; a successor dependency calculation unit implemented as an analytical circuit determines dependency indicators representing the concentration of generative centrality values ​​among nodes,that are associated with a critical role; a network simulation unit implemented as a graph recalculation circuit temporarily removes nodes associated with selected person identifiers from the institutional interaction graph and recalculates the generative centrality values ​​to estimate the disruption of structural knowledge transfer, using exit probability parameters stored in the risk parameter matrix for multiple time horizons, including twelve-month and thirty-six-month horizons; and a successor mapping unit implemented in the memory circuit stores successor mapping records including role identifier, successor person identifier, readiness category indicator, and generative centrality value assigned to the successor, with all units interconnected via electronic communication channels within the computer.to enable the hardware processing of organizational data structures for determining institutional succession coverage.

[0105] The drawing and the preceding description contain examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0106] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A computer-aided system for graph-based analysis of institutional succession coverage. 102 storage units 104 Matrix preprocessing unit 106 Vector generation unit 108 Graph construction unit 110 Role criticality calculation unit 112 Network metric calculation unit 114 Successor selection control unit

Claims

A computer-implemented system for graph-based analysis of institutional succession coverage, the system comprising: a storage unit that stores structured data sets, including: • a role matrix consisting of role identifier values, organizational unit identifiers, hierarchy level parameters, strategic impact parameters, operational impact parameters, replacement difficulty parameters, disruption risk parameters, and role dependency identifiers; • a person attribute matrix comprising person identifiers linked to role identifiers and generative attribute parameters, including legacy system contribution parameters, mentoring contribution parameters, innovation contribution parameters, community contribution parameters, and institutional alignment parameters;• an interaction event matrix comprising relational interaction data records, including originating person identifier, target person identifier, interaction type identifier, interaction frequency parameters, interaction intensity parameters, interaction start timestamp, interaction end timestamp, and knowledge criticality parameters; and • a risk parameter matrix comprising person role risk parameters, including exit probability parameters, proximity to retirement parameters, contractual risk parameters, and external market risk parameters; a matrix preprocessing unit connected to the storage unit and configured to convert heterogeneous parameter scales stored in the person attribute matrix into normalized scalar values ​​within a predefined reference interval;a vector generation unit coupled to the matrix preprocessing unit and configured to create a multidimensional generative vector for each person identifier by arranging the normalized scalar values ​​of the generative attribute parameters into an ordered numeric vector; a graph construction unit coupled to the interaction event matrix and configured to generate an institutional interaction graph represented by an adjacency structure in which the nodes correspond to person identifiers and the edges to relational interaction datasets weighted by the interaction frequency parameter, the interaction intensity parameter, and the knowledge criticality parameter;a unit coupled to and configured with the role matrix for calculating role criticality, which calculates a role criticality index for each role identifier using a weighted aggregation of the strategic impact parameter, the operational impact parameter, the difficulty of replacement parameter, and the disruption risk parameter stored in the role matrix; a network metric calculation unit coupled with the graph construction unit and the vector generation unit, configured to calculate node relevance indicators, including graph degree values, mediation values, and proximity values ​​derived from the adjacency structure, and subsequently combines the node relevance indicators with the multidimensional generative vectors to generate generative centrality values ​​associated with the nodes;A successor selection control unit coupled with the role criticality calculation unit, the network metric calculation unit, and the risk parameter matrix is ​​configured to identify successor candidate sets for roles with role criticality index values ​​exceeding a threshold by applying coverage constraints defined in a coverage parameter matrix, including minimum successor number parameters and readiness level constraints. System according to claim 1, wherein the graph construction unit generates the adjacency structure using interaction data sets whose interaction frequency parameter exceeds a predefined interaction threshold and whose knowledge criticality parameter exceeds a predefined knowledge transfer threshold. System according to claim 1, wherein the matrix preprocessing unit converts heterogeneous measurement scales of the generative attribute parameters into normalized values ​​by means of percentile transformation or min-max scaling operations, and wherein the vector generation unit stores each multidimensional generative vector as a fixed-length numeric vector that is assigned to the corresponding person identifier in the storage unit. System according to claim 1, wherein the graph construction unit assigns direction attributes to the edges of the institutional interaction graph representing mentoring, consulting, substitution, and cooperation relationships, and wherein the role criticality calculation unit calculates the role criticality index using a weighted sum expression that includes weighting parameters associated with the strategic impact parameter, the operational impact parameter, the replacement difficulty parameter, and the disruption risk parameter. System according to claim 1, wherein the network metric computation unit computes betweenness centrality values ​​by determining the proportion of shortest graph paths passing through each node of the institutional interaction graph, and wherein the network metric computation unit computes proximity indicators for nodes of the institutional interaction graph using graph distance values ​​derived from the adjacency structure. System according to claim 1, further comprising a readiness assessment unit configured to generate readiness values ​​for person-role pairs using a weighted aggregation of normalized generative vector values ​​and historical performance parameters stored in the memory unit. System according to claim 6, wherein the readiness calculation unit classifies the readiness values ​​into readiness categories such as immediate, short-term, medium-term and delayed readiness based on predefined threshold parameters, and wherein the successor selection control unit determines a minimum successor quantity for each role by selecting combinations of persons that meet the coverage constraints stored in the coverage parameter matrix, including: parameters for the minimum number of successors, parameters for the minimum immediate readiness and parameters for the maximum role assignment per person. System according to claim 1, further comprising a dependency computation unit configured to calculate dependency indicators representing the concentration of generative centrality values ​​among nodes associated with a critical role. System according to claim 1, wherein the risk parameter matrix stores exit probability parameters for multiple time horizons, including a twelve-month and a thirty-six-month time horizon. The system according to claim 1 further comprises a network simulation unit configured to temporarily remove nodes associated with selected person identifiers from the institutional interaction graph and recalculate the generative centrality values ​​to estimate the disruption of structural knowledge transfer; and a successor mapping storage unit configured to store successor mapping records containing the role identifier, the successor person identifier, the readiness category indicator, and the generative centrality value associated with the successor.

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