Abstract:
Engineering change is an everyday reality in modern manufacturing, yet the propagation of design changes into stable, production-ready plans remains slow, error-prone, and costly. Despite extensive investments in digital systems—PLM, ERP, and MES—manufacturers still struggle to synchronize design revisions with manufacturing execution, especially under complexity and rapid change. This white paper diagnoses the structural cause of this problem, synthesizes insights from academic research and industrial practice, and introduces context-aware production planning as the missing layer. Dirac is presented as the platform that fulfills this layer by maintaining a continuously correct production definition across engineering changes, enabling faster, clearer, and more resilient builds.

The Engineering Change Paradox

Change is constant in today’s manufacturing environments. Products evolve continuously due to iterative design improvements, supplier shifts, cost and quality pressures, regulatory updates, and customer requirements. Engineering Change Orders (ECOs) and Engineering Change Notices (ECNs) are the formal mechanisms by which these updates are governed.

Yet the paradox is stark: the engineering work to implement a change is often small, but the operational impact is vast. Studies in engineering change management consistently show that while technical processing of a design change may take days, the lead time for that change to land on the factory floor and be built reliably can stretch weeks or months. Most of this time is consumed not by engineering itself, but by coordination delays, waiting for approvals, reconciling disparate systems, and manually updating downstream artifacts.

During this gap, production runs with ambiguity: operators improvise, engineers issue clarifications, quality monitors for escapes instead of preventing them, and execution systems dutifully record plans that may already be obsolete. Over time, organizations normalize this state as unavoidable.

This normalization masks a structural problem: there is no system in the enterprise that owns the continuously correct definition of how products should be built under change.

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Where the Modern Stack Fails

The promise of digital manufacturing has driven billions in investment in enterprise software, yet the fundamental problem persists because the enterprise stack is architected around ownership of data rather than coherence of use.

PLM (Product Lifecycle Management) is the authority on design. It manages CAD, EBOMs, revisions, and engineering change processes.

ERP (Enterprise Resource Planning) manages supply chain, cost, procurement, and scheduling.

MES (Manufacturing Execution Systems) manages execution, traceability, and compliance on the shop floor.

Each system excels within its domain, yet none owns the integrated, operational definition of how the product is built. PLM dictates what the product is, ERP determines when and with what resources it should be built, and MES records what happened—but no system ensures the build definition is continuously correct and actionable under change.

Between these systems lie intermediate artifacts—EBOM, MBOM, Bill of Process (BOP), Routing, work instructions—that are often static, manually maintained, and updated asynchronously. When an ECO alters design or constraint, there is no deterministic way to propagate that change through these artifacts into a coherent build plan. This disconnect creates re-planning loops and ambiguity.

The Core Problem: Production Definition Drift

At the heart of manufacturing lies the production definition—the articulation of how a product is built, including sequence, geometry, tooling, parameters, inspections, and qualifications.

Traditional representations of production definition are snapshots:

• EBOM (Engineering Bill of Materials) reflects design structure
• MBOM (Manufacturing Bill of Materials) reorganizes for manufacturing
• BOP and Routing capture process sequences
• Work Instructions attempt to capture operator-level guidance

But these are not living systems. They are static translations, now formatted as an excel file or powerpoint, updated manually by a human only once change is detected and often after execution has already diverged.

Research in configuration management shows that information retrieval and configuration complexity are the central bottlenecks in manufacturing data environments. When data is fragmented across multiple representations and systems, retrieving the correct view for the task at hand becomes costly, error-prone, and slow. This complexity is compounded by overlapping representations, inconsistent semantics, and mismatches between systems of record.

The result is what practitioners know intuitively: production definitions drift from design as change accumulates, and reconciling that drift is costly.

Theoretical Insights: Object vs. Instance and Multi-View Configurations

Academic work on PLM configuration management distinguishes between object products (digital representations) and instance products (physical units), each with its own lifecycle. These lifecycles intersect with manufacturing life cycles and customer orders, creating a complex mesh of state transitions that must be reconciled. Designs evolve (object), inventory and resource constraints change (instance/manufacturing system), and orders come and go. At the intersection lies production planning.

The same research calls for multi-view configurations that can represent business, product, and customer views of the product. What these frameworks expose is that manufacturing cannot treat configuration as a single flat model; different stakeholders need different structured views that are coherent with each other.

In practice, PLM provides one view (engineering structure), ERP a second (supply chain and finance), and MES a third (execution records). None of these views is structured for production planning—the specific intersection where build decisions are made.

This insight reveals two truths:

1. Configuration complexity is real and structural. It cannot be managed by a single artifact or siloed system.
2. Production planning requires a distinct view that reconciles design, constraint, geometry, and operational context into a coherent build definition.

Dirac’s core contribution is making this view computable, context-aware, and updatable autonomously under change.

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Context-Aware Production Planning: A New Layer

Context-aware production planning begins with the premise that a production step is only meaningful in the context in which it applies. Sequence, tooling, geometry, inspection requirements, variant applicability, and constraints all matter.

Unlike traditional systems that treat production steps as disconnected records or documents, context-aware planning makes context explicit, structured, and computable. A production step is not merely text or an identifier; it is a step-in-context.

This enables:

• Automated impact analysis of change — by evaluating the structural relationships, systems can determine where change matters.
• Part-aware sequencing — constraints derived from assembly geometry and physics inform valid execution paths.
• Variant-specific builds without redundancy — applicable steps can be filtered and validated automatically.
• Living work instructions — instructions update deterministically as change occurs, not reactively.

This concept directly addresses the structural failure mode exposed by both industrial experience and configuration management research: traditional production planning lacks the representational capacity to maintain a correct production definition under change.

Dirac: The Production-Definition Layer

Dirac is a context-aware production planning platform built to keep the production definition continuously correct. It sits between PLM, MES, and ERP, and integrates with all three.

At its core, Dirac treats the production definition as a structured, continuously updated system. Each production step is bound to:

• geometry
• sequence
• tooling and parameters
• inspection criteria
• variant applicability
• effectivity and qualifications

When an ECO arrives, Dirac performs structural impact analysis: it identifies which steps in the production definition are affected, surfaces the implications for tooling and inspection, and updates the work instructions before execution begins. This transforms ECO propagation from manual reconciliation to deterministic update.

The consequences are profound:

• Ramps stabilize faster because instructions reflect reality from the first build.
• Rework and scrap decrease because ambiguity is reduced.
• Quality escapes are prevented because inspection conditions evolve with the build definition.
• Timeline commitments are reliable because re-planning loops are shortened or eliminated.

From Static Artifacts to a Living System

The practical difference between traditional and context-aware planning can be appreciated in a simple comparison:

• Static Artifact Approach
  Updates occur when change is detected, often asynchronously. Work instructions are documents awaiting revision. Impact analysis is manual and slow. Execution systems may work from outdated information.
• Context-Aware Living Definition Approach
  Change triggers structural propagation. Work instructions are synthesized and updated as part of change implementation. Impact is computed, surfaced, and resolved before execution.

The first is reactive, slow, and reliant on tribal knowledge. The second is proactive, fast, and systematized.

Integration Without Disruption

Today, Dirac is designed to integrate with existing enterprise investments, not replace them. PLM remains the authority for design. ERP continues to own supply chain and costing. MES remains the authority for execution. Dirac fills the conceptual and operational gap between them by owning the production definition.

This integration-first approach allows organizations to adopt context-aware production planning without rip-and-replace initiatives. It complements existing governance, maintains compliance, and adds an operationally usable layer that ties design changes directly to production readiness.

Conclusion: The Future of Manufacturing Planning

Engineering change will always exist. The question is not whether change can be eliminated, but whether it must continue to be costly.

Traditional production planning collapses under complexity and change because it treats the production definition as a static artifact rather than a living system. As product complexity and change velocity increase, this approach becomes untenable.

Context-aware production planning offers a new paradigm: the production definition as a computable, continuously correct system. Dirac embodies this paradigm by ensuring that every change propagates deterministically through geometry, sequence, tooling, and inspection into a build definition that is ready to execute.

This is not merely an incremental improvement. It is the missing layer in the manufacturing stack: one that enables organizations to absorb change with clarity, confidence, and speed.

The future of high-velocity, high-complexity manufacturing depends on it.

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