The BEIS Algorithmic Platform
Understand, Predict, Act. Dive deeper into the technology that powers systemic resilience and operational excellence across complex organizations.
See BEIS in ActionBuilt on a Foundation of Network Science & Emergent Intelligence
BEIS leverages cutting-edge concepts to provide a holistic and dynamic view of your business operations.
Dynamic Network Modeling
See your business as an interconnected system of domains, processes, resources, and their intricate relationships.
Emergent Score Calculation
Eb, Es, and Rs are calculated as emergent properties of this network, offering a holistic view of health and risk.
Resilience Quantification (Rs)
Our novel Resilience Score (Rs) measures your system's capacity to withstand, adapt to, and recover from disruptions.
AI-Powered (Advanced BEIS)
Leverage machine learning for predictive modeling and prescriptive analytics in more mature BEIS implementations.
This foundation enables a clear evolutionary path, allowing BEIS to grow from providing core visibility to offering advanced predictive and autonomous capabilities. Learn about our evolution.
Core Systemic Metrics: Understanding Eb, Es, and Rs
These unique scores provide a multi-dimensional view of your organization's operational health and resilience.
Performance BEI (Eb) – Systemic Friction
Eb quantifies the overall "drag," "resistance," or "inefficiency" in your system's ability to achieve its objectives. It highlights internal misalignments, bottlenecks, and process failures that impede performance.
Key Aspects & Quantification
Measures: Deviation from target performance, impact of bottlenecks, structural impediments in the network.
Conceptual Quantification:
- Weighted Node Underperformance: Aggregates underperformance of metrics and processes, weighted by criticality and impact on objectives.
- Path-Based Friction: Calculates friction along critical value streams based on cumulative underperformance and delays.
- Bottleneck Impact: Identifies and quantifies the systemic impact of critical bottlenecks.
The goal is to normalize this to a clear score, typically 0-1.
Shannon BEIS (Es) – Systemic Unpredictability
Es measures the overall stability and predictability of your system's behavior. It considers not just individual metric volatility but how it propagates and amplifies through the interconnected network.
Key Aspects & Quantification
Measures: Propagation of volatility, network state variability, sensitivity to external shocks.
Conceptual Quantification:
- Weighted Volatility Propagation: Models how individual metric volatility (e.g., entropy, std. dev.) propagates to connected nodes based on edge strengths.
- Network State Variability: Simulates or observes the range of states the overall network can enter given input volatilities.
- Sensitivity to External Shocks: Assesses fluctuation in key outputs in response to external factor volatility.
This score is also normalized, typically 0-1.
Resilience Score (Rs) – Systemic Adaptability
Rs is a proactive measure of your system's capacity to withstand, adapt to, and recover from disruptions while maintaining core functionality. This is a key differentiator of the BEIS platform.
Key Aspects & Quantification
Defines ability to: Absorb shocks, Adapt behavior, and Recover effectively.
Conceptual Quantification:
- Simulated Shock Impact & Recovery: Defines shock scenarios, simulates propagation, and measures impact magnitude (on Eb, Es) and recovery time.
- Network Structure Analysis: Assesses redundancy, modularity, connectivity, and centralization for inherent resilience.
- Resource Buffers & Flexibility: Considers availability of slack resources, cross-training, etc..
A higher Rs score (normalized 0-1) indicates better resilience.
Fueling Insights: The BEIS Data Ecosystem
BEIS thrives on diverse data sources to build its comprehensive network model and calculate accurate systemic scores.
A. Structural Data
Process maps, org charts, system architectures, strategic plans to define network topology and edge properties. Expert elicitation is key here.
B. Performance & State Data
Existing KPIs, process performance data (cycle times, error rates), resource performance (uptime, utilization). Includes qualitative assessments.
C. Time-Series Data
Essential historical data for quantitative metrics (M, P, R nodes) to calculate volatilities, Es, and for data-driven discovery. Frequency should match operational tempo.
D. Event & Shock Data
Logs of past internal disruptions and external shocks, with their quantified impacts, crucial for resilience (Rs) and Es calibration.
Phased Data Integration Strategy for MVP
- Start with Core Domains & Key Metrics.
- Expert-Defined Initial Network (qualitative strengths initially).
- Focus on Existing Time-Series Data for current BEIS prototype volatilities.
- Simplified Eb/Es (e.g., weighted node underperformance/volatility).
- Conceptual Resilience (Rs) before full simulation capabilities.
- Iteratively Add Detail: Incorporate Process (P) and Resource (R) nodes, refine edges.
This practical approach ensures value delivery from early stages while building towards the full vision.
Secure, Scalable, Future-Ready Technology
The BEIS platform is designed with modern architectural principles to ensure robust and reliable performance.
Cloud-Native Architecture
Leveraging cloud capabilities for scalability, flexibility, and global accessibility.
API-Driven Integration
Facilitates seamless connection with your existing enterprise systems and data sources.
Data Security & Integrity
Prioritizing the confidentiality, integrity, and availability of your critical business data.
Leveraging Advanced AI/ML
Our evolutionary roadmap heavily incorporates cutting-edge AI and Machine Learning techniques to unlock deeper insights:
Graph Neural Networks
For intricate risk propagation analysis and understanding complex network dependencies.
Reinforcement Learning
Enabling adaptive strategies, optimal decision-making, and system feedback loops.
Large Language Models
For enhanced intervention reasoning, narrative generation, and contextual understanding.
Agent-Based Modeling
To simulate complex stakeholder behaviors and systemic responses.