Semantic DMP Frameworks: MARE Approach

• Semantic DMP Frameworks (MARE) are ontology- and knowledge graph–centric architectures that unify disruption management processes across supply chains.
• The framework uses SPARQL-based reasoning to automatically monitor, assess, recover, and evaluate disruptions in real time.
• MARE integrates heterogeneous enterprise data with standardized ontologies, enabling actionable resilience analytics and process automation.

Semantic DMP Frameworks (MARE) encompass a class of semantic technologies and knowledge-driven methodologies for implementing and extending the Disruption Management Process (DMP) in complex operational domains—most notably supply chains. The MARE framework integrates ontologies, knowledge graphs, and SPARQL-based reasoning to offer an end-to-end, data-driven approach for monitoring, assessing, recovering from, and evaluating the impact of disruptive events across heterogeneous organizational data sources. This paradigm enables process automation, unified analytics, strategic coordination, and dynamic resilience measurement grounded in semantically explicit enterprise knowledge representations (Ramzy et al., 2022).

1. Conceptual Overview and Rationale

Semantic DMP Frameworks, as instantiated by MARE, are ontology- and knowledge-graph–centric architectures for operationalizing the four canonical DMP steps:

1. Monitor/Model
2. Assess
3. Recover
4. Evaluate

Whereas traditional DMP implementations operate in data silos, the semantic DMP approach provides a unified, formally encoded knowledge base (triple store) capturing the entirety of disruption events, supply plans, logistics, and recovery actions. Integration of live event feeds, operational progress, and recovery outcomes within the same semantic layer enables automatic, explainable assessments and facilitates the systematic tracking of resilience KPIs.

The core objectives of MARE include the full integration of heterogeneous data sources (orders, production scheduling, logistics, inventory, procurement), semantic modeling of disruptions and plans, automated detection of impacted supply nodes, recovery strategy generation, and quantitative resilience evaluation within a single extensible platform (Ramzy et al., 2022).

2. Semantic Modeling and Ontology Structure

MARE formalizes knowledge using two principal ontologies—published in OWL/RDF—supporting all DMP phases:

A. Disruption Ontology (ds:)

• Classes: ds:Disruption, ds:Cause, ds:Location
• Properties: ds:hasCause, ds:hasScope, ds:hasSeverity, ds:hasBeginDate, ds:hasEndDate, ds:hasLocation
• Example axioms include class hierarchies and property domains/ranges necessary for automated reasoning and compatibility with standard geospatial vocabularies.

B. Supply-Plan Ontology (sp:)

• Classes: sp:Order, sp:Plan, sp:Partner, sp:Product, sp:TransportMode, sp:Capacity, sp:Stock
• Properties: Relationships for product allocation, capacity, delivery, pricing, supplier group assignments, strategic stocks, and delivery status.

Enterprise data are semantically lifted to RDF triples, with mapping covering all business objects: orders, production capacity, partners, inventory, costs, and external disruptions. All modeled entities and relationships can be queried and updated in a standards-compliant triple store (e.g., Jena Fuseki, Blazegraph), supporting flexible integration and extensibility (Ramzy et al., 2022).

3. End-to-End Knowledge Graph Implementation

All relevant operational data undergo lightweight ETL and ontology mapping to be represented as RDF. For a disruption, a concrete triple might encode event cause, location (latitude/longitude), time window, and severity factor. Parallel data from ERP/SCM systems are mapped to the supply-plan ontology, including production plans, deliveries, capacity, supplier profiles, stocks, and transport modes.

Sample Turtle instantiations involve connecting orders to supply plans, plans to partners and products, disruptions to locations and severity scores, and so forth. These connections, once established in the central triple store, provide the substrate for all reasoning, impact analysis, and strategic response generation. This infrastructure enables live synchronization between operational systems and the DMP platform, supporting robust situational awareness (Ramzy et al., 2022).

4. Disruption Management Logic: SPARQL Reasoning and Recovery Strategies

Central to the semantic DMP approach is the orchestration of SPARQL queries and updates representing each DMP phase:

• Monitor/Model: New disruptions are inserted via SPARQL INSERT DATA statements using IoT feeds, incident reports, or managerial input.
• Assess: Disrupted supply plans and affected partners are identified by matching temporal, spatial, and severity envelopes between disruption events and existing plans using SPARQL INSERT, SELECT, or CONSTRUCT patterns. The system computes impact factors, reduces order quantities, and flags disrupted nodes semantically.
• Recover: MARE models four primary recovery strategies as parameterized SPARQL queries:
  • S1: Use of strategic stocks
  • S2: Alternative shipment modes
  • S3: Delayed replenishment
  • S4: Alternative supplier enablement
  • Each strategy is expressed as a SPARQL SELECT over the knowledge graph and returns viable fragments for plan repair, fully encoded in the RDF data model.
• Evaluate: Post-recovery, SPARQL analytics compute key performance indicators:
  • Recovery cost increase ($\Delta C$)
  • Recovery speed (number of late/on-time orders, total delay $\Delta T$)
  • Unsuccessful recoveries (count of unrecovered plans)
  • Customer-level impact (cost and delay per priority order)
  • Aggregate resilience index $$R_{\mathrm{disc}} = \frac{1}{N}\sum_{i=1}^N \frac{Q_{\mathrm{delivered},\,i}}{Q_{\mathrm{ordered},\,i}}$$

All such queries are concretely specified with parameters and aggregation logic attuned to business and operational metrics, and their results are persisted in the knowledge graph for longitudinal benchmarking and dashboarding (Ramzy et al., 2022).

5. Quantitative Resilience Analytics

MARE’s explicit resilience evaluation framework supports multiple dimensions:

• Continuous Resilience Curve: $R = \frac{\int_{t_0}^{t_f} P(t)\,dt}{t_f - t_0}$, where $P(t)$ is the fraction of demand fulfilled at time $t$; privacy- and audit-friendly as all quantities are RDF literals.
• Discrete Averaged Resilience: $R_{\mathrm{disc}}$ as above, allowing robust aggregate resilience indices per disruption event, scenario, or supply node.
• Cost, Delay, and Unsuccessful Recovery: Automated SPARQL aggregations yield precise cost and delay deltas, facilitating multi-objective trade-off analysis and reporting.

These metrics underpin dynamic dashboards and decision-support tools, enabling stakeholders to visualize recovery trajectories, compare strategy efficacy, and iteratively refine their operational and contingency planning (Ramzy et al., 2022).

6. Extensibility, Interoperability, and Integration

The ontology-centric design of MARE supports:

• Incremental Data Source Integration: Any ERP, MES, or WMS system exposing structured data can be mapped via ETL scripting to MARE’s RDF schemas.
• Ontology Extensions: New subclasses (e.g., disaster types, resilience KPIs, sustainability metrics) or relationships can be appended using OWL semantics without model disruption.
• API Ecosystem: Triple stores expose SPARQL and REST endpoints, facilitating interoperation with planning solvers (AnyLogic, Gurobi), BI tools (Grafana), or external analytical applications.
• Reasoning and Validation: Integration with OWL-RL or SHACL engines allows enforcement of domain-specific logic (e.g., severity range limits, delay propagation).

This suggests MARE’s architecture is well-positioned for composability, enterprise adoption, and research-driven advancement. Integration of richer analytics, predictive models, or optimization modules can be achieved without reengineering the semantic core (Ramzy et al., 2022).

7. Limitations and Outlook

While MARE provides a comprehensive foundation, several open challenges remain:

• Data Quality and Ontology Alignment: Semantic interoperability depends on consistent mapping and maintenance between enterprise data schemas and reference ontologies.
• Scalability: As data volume and variety grow across organizational boundaries, triple store performance and SPARQL reasoning scalability can become limiting factors.
• Advanced Reasoning: Automating nuanced human-like judgments about disruptions and strategy evaluation may require integration with probabilistic reasoning or machine learning components.

A plausible implication is that continued standardization of supply chain ontologies, advancement of scalable triplestore architectures, and systematic capture of domain-specific recovery strategies in semantic rules will further enhance the generality and effectiveness of Semantic DMP Frameworks (Ramzy et al., 2022).

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Reference

• "MARE: Semantic Supply Chain Disruption Management and Resilience Evaluation Framework" (Ramzy et al., 2022)