> VISION: COGNITIVE LOGISTICS // TRANSITION_LOGIC

THE EVOLUTION
ROADMAP.

> PROTOCOL: ILS-to-IPS Transition Logic

Engineering the shift from Integrated Logistics Support (ILS) to Integrated Product Support (IPS).

In many programs, logistics engineering still relies on document-driven processes, periodic file-based data exchanges, and reactive decision cycles.

The sector often treats digitization as transformation. But without connected data, feedback loops, and traceable decision logic, digital outputs remain operationally weak.

"The Missing Middle is not a hardware problem. It is a data architecture and trust problem."

The gap between clean analytical models and real-world constraints.

Engineering decision-centric sustainment architectures to close that gap, supporting the industry transition from ILS to IPS.

Execution Phases

01 // INTEROPERABILITY & DIGITAL THREAD

Connecting Static Snapshots

In many sustainment programs, data is still exchanged as deliverable packages (XML exports) that behave like static snapshots. Phase 1 establishes an interoperability layer that connects these outputs into a unified, decision-ready data layer while staying compatible with existing standards.

The scope excludes authoring standards deliverables, focusing instead on turning their outputs into auditable decisions.

> TARGET_OUTCOME:

Cross-domain visibility across documentation, maintenance, and supply data without breaking current workflows.

02 // DECISION-CENTRIC ANALYTICS

From Compliance to Computation

Standards define what outputs should exist, but not how decisions should be computed when demand is intermittent and data is sparse. Phase 2 focuses on hybrid probabilistic and machine learning approaches that translate uncertainty into auditable recommendations for inventory posture.

> TARGET_OUTCOME:

Readiness-driven recommendations that are explainable, measurable, and technically defensible.

03 // ASSURED DECISION SUPPORT AT SCALE

Closing the Trust Loop

Closing the data loop is not enough; we also need to close the trust loop. Phase 3 focuses on explainability, provenance, and audit trails so that AI-assisted sustainment recommendations remain transparent, reviewable, and safe to deploy.

> TARGET_OUTCOME:

A trust layer that scales decision support without sacrificing engineering accountability.