Manufacturing Cycle Insights for Global Pharma giant

A major global pharma manufacturer needed to cut Manufacturing Cycle Time (MCT) across products and plants but was constrained by siloed systems and static analytics that obscured true bottlenecks. Traditional data warehouses provided aggregate KPIs but not the relationships between idle, inventory, test, and processing times that actually drive MCT.

We implemented a knowledge graph that modeled batches, work orders, process steps, test queues, equipment, and policies as connected entities. This made MCT fully decomposable and comparable across plants, enabling analysts and operations leaders to pinpoint systemic delays and validate improvements with traceable, queryable evidence.

The client faced four core obstacles to MCT improvement:

Data silos and inconsistent definitions across MES, LIMS, ERP, and QMS made end-to-end traceability difficult. Analysts relied on spreadsheets and manual joins, masking waiting-time accumulations and cross-plant differences.

Limited visibility into relationships between process steps meant root causes were inferred, not proven. Idle, inventory, test, and processing times were tracked, but their dependencies (queues, approvals, equipment availability) were not.

Cross-plant comparisons lacked normalization and lineage, preventing clear benchmarking for identical SKUs produced in different sites with varying calendars, shifts, and routing.

Legacy dashboards aggregated metrics but could not represent paths, constraints, or causal structures, making it hard to test hypotheses or run what-if scenarios with confidence.

We built a graph-native MCT analytics solution that connected operational data and surfaced causal drivers:

Knowledge Graph Architecture: A manufacturing ontology capturing Product, SKU, Batch, Work Order, Operation, Equipment, Test, Queue, Release/Hold, Plant, Shift, and Calendar, with relationships like precedes, blocked_by, queued_at, performed_on, and waits_for_approval.

Data Integration Pipeline: Ingested MES (ISA-95), LIMS, ERP, and QMS data; harmonized time stamps, calendars, and state transitions; computed standardized MCT components per SKU/plant.

Graph Analytics and Causal Insights: Applied path analysis, critical-path detection, centrality, and queue aging to locate systemic delays; used matched cohort comparisons across plants to attribute impact to specific steps, tests, and approvals.

Decision Apps and Governance: Delivered a graph explorer, MCT decomposition views, cross-plant benchmarks, and what-if simulations; all insights retained lineage to source systems for auditability.

The client gained end-to-end traceability of MCT across products and plants, with clear decomposition into idle, inventory, test, and processing time. Analysts moved from spreadsheet assembly to root-cause investigation, accelerating improvement cycles.

Graph traversal made hidden queues and approval waits visible as critical-path contributors, guiding interventions like rebalancing test capacity and revising release windows. Cross-plant benchmarks enabled consistent definitions and fair comparisons, revealing best practices to replicate.

With lineage-backed insights, operations leaders could justify changes, run what-if analysis before execution, and monitor outcomes with comparable, repeatable metrics.

Success hinged on a graph-native design and tight alignment to MCT:

Domain Ontology First: Modeling real manufacturing entities and state transitions ensured accurate MCT decomposition and comparability.

Normalization and Lineage: Standardized calendars, shift rules, and definitions enabled cross-plant benchmarking with audit-ready traceability.

Actionable Analytics: Critical-path and queue aging surfaced high-leverage delays; what-if scenarios supported confident change management.

Change Adoption: Embedded decision apps in existing workflows and trained CI, QA, and operations teams for rapid uptake.

Extensible Foundation: The graph now supports adding maintenance events, environmental factors, and supplier variability for broader cycle-time control.