Modular Production-System Approaches

• Modular/production-system approaches are formal frameworks that decompose complex systems into discrete, interoperable modules with clear interfaces.
• They employ combinatorial design models and meta-model mappings to optimize configuration, lifecycle management, and resource allocation.
• These methods enhance cross-disciplinary integration and improve reconfiguration efficiency in areas like robotics, manufacturing automation, and digital twin systems.

A modular or production-system approach encompasses a broad array of formal frameworks, methodologies, and meta-models for the design, implementation, reconfiguration, and evaluation of complex engineered systems through the assembly of discrete, interoperable modules. These approaches aim to both decompose monolithic systems into well-encapsulated subsystems and define explicit mechanisms for their composition, enabling efficient engineering, configuration management, lifecycle flexibility, and cross-disciplinary integration across domains such as manufacturing automation, robotics, large-scale infrastructure, and software for cyber-physical production systems.

1. Fundamental Concepts and Formal Models

The foundational notion is that a module, in an engineered system, is a self-contained subsystem encapsulating all necessary information—mechanical, electrical/electronic, and software—to implement specific functions or behaviors while exposing well-defined interfaces for integration. Modular approaches address both physical elements (e.g., material flow modules, robotic cells) and abstract units (e.g., software functions, control skills, process capabilities) (Regulin et al., 2022, Sidorenko et al., 2024, Köcher et al., 2022).

Formally, a modular system of $m$ parts (modules) $P(1),\ldots,P(m)$ is configured via the selection of design alternatives (DAs) for each part. The system $S = \{X_{1,j_1}, X_{2,j_2}, \ldots, X_{m,j_m}\}$, composed of DAs $X_{i,j}$ for each $P(i)$, is subject to constraints such as pairwise compatibility, quality, and resource budgets. Diverse combinatorial models underlie modular design, including:

• Basic Morphological Analysis (MA): Exhaustive enumeration of compatible DA assignments, enforcing binary pairwise compatibility constraints (Levin, 2012).
• Hierarchical Morphological Multicriteria Design (HMMD): Modules and alternatives are structured hierarchically; system evaluation uses vectors $N(S) = (w(S); n_1(S),\ldots,n_\tau(S))$, prioritizing worst-case compatibility and counts of high-priority DAs (Levin, 2013, Levin, 2012).
• Multiple-Choice and Knapsack Models: Select one DA per module/group under resource or performance constraints (Lee et al., 17 Mar 2025, Levin, 2012).
• Production-System Analogies: Modular/production-system approaches often align conceptually with rule-based systems (state machines, behavior trees), where module selection or activation is treated as the application of production rules, thereby synthesizing system trajectories through compositional state transitions (Levin, 2013, Sidorenko et al., 2024, Sriganesh et al., 2024).

2. Meta-Models and Information Integration

Formal meta-models are crucial to achieving consistency, traceability, and transferability across the multiple engineering stages (e.g., planning, detail design, control implementation) involved in modular system development.

• AutoMFM (Automated Material Flow Module Meta Model): Defines a five-class meta-model—General, Status, Function, Interface, and Control descriptions. Each class clusters domain-specific details (geometry, runtime states, functional logic, physical/software interfaces, control mappings) and establishes formal associations—e.g., control functions referencing logistic functions, I/O mapping to interfaces (Regulin et al., 2022).
• Consistency via Mappings to Execution Formats: Elements are mapped to AutomationML (AML) role and interface classes through formally defined mapping and interface-assignment functions, implemented as XSLT transformations to guarantee serialization consistency (e.g., $$R_1: C_{Control}.ControlFunction \to AML.CSRoleClassLib.ControlEquipment$$) (Regulin et al., 2022).

This strict ownership and clustering within a module effectively suppresses redundancy, ensures cross-discipline consistency, and supports orchestrated composition for plant-level reconfiguration and commissioning.

3. Design Methodologies and Multistage Synthesis

Multistage modular/production-system approaches address not just the design of a single composite configuration, but sequences/trajectories of system states over time or logic points:

• At each stage $t_i$ in a set $T = \{ t_1, \ldots, t_m \}$, Pareto-efficient modular system solutions $\mathcal{S}_i$ are generated using combinatorial synthesis (HMMD or multiple-choice models).
• Interstage compatibility functions $C(S_i^a, S_{i+1}^b)$ measure ease-of-upgrade or transition between neighboring solutions.
• The optimum system trajectory is found by maximizing a sum of local and transition scores, e.g., using dynamic programming:

  for i in 2..m: for b in 1..|S_i|: Best[i][b] = max_a [ Best[i-1][a] + f[i][b] + C[i-1][a][b] ] backtrack to reconstruct best trajectory

  This formalism is directly analogous to production-system models: design stages correspond to production or rule-application stages, and solution trajectories correspond to rule-firing sequences (Levin, 2013).

4. Assembly-, Disassembly-, and Lifecycle-Oriented Extensions

Recent modular/production-system methodologies explicitly integrate Design for Assembly (DFA) and Design for Disassembly (DFD) principles into architectural synthesis:

• Extension of Modular Function Deployment (MFD): Assembly-oriented module drivers (e.g., weight, tolerance, connection count, accessibility), coded interface taxonomies, and assembly feasibility indices (AFI) guide clustering and interface design:

$$AFI = \frac{ \sum_{i=1}^N \left( 1 - \frac{d_i}{d_{max}} \right) }{ N }$$

$ARS = \frac{ \sum_{i=1}^N z_i }{ 2N }$

• Lifecycle Modularization: Integration of DFD with metrics such as Disassembly Index (DI) and combined Modularity Scores,

$$M = w_{asm} \left( 1 - \frac{T_{asm}-T_{min}}{T_{max}-T_{min}} \right) + w_{dis} DI$$

supports early-phase assessment and reconfiguration for sustainability (Monetti et al., 13 Oct 2025, Monetti, 3 May 2025).

These approaches, validated through workshops and case studies, show that embedding production-lifecycle drivers yields architectures with improved assembly/disassembly efficiency and ability to adapt to evolving operational and sustainability requirements.

5. Standardization, Automation-Ready Integration, and Digital Twin Alignment

Information models and data exchange standards are central to automating modular/production-system engineering:

• AutomationML Integration: CAEX-based AML formats support serialization of complete module descriptions—including geometry, logic, interfaces, and control mapping—enforcing a cross-discipline, discipline-independent data structure for modules. AutoMFM, via AML, enables generation of simulation models, PLC code, and documentation from a single source-of-truth module configuration (Regulin et al., 2022).
• Digital Twin and OPC UA Alignment: The modular architectural paradigm extends to digital twin systems, with each module (and its submodels) represented in data structures supporting automated planning, execution, and live reconfiguration (Xia et al., 2023, Häbig et al., 2023).

Combined, these allow plug-and-produce, versioned evolution, and reliable cross-tool interoperability of modular production systems.

6. Impact, Evaluation, and Industrial Practices

Quantitative and qualitative analyses document the impact and best practices for modular/production-system approaches:

• Data redundancy reduction: Surveys in material-flow systems report 10–60% of engineering data duplicated across disciplines; a rigorously modular approach can eliminate nearly all redundancy, with corresponding reductions in cross-discipline inconsistencies (Regulin et al., 2022).
• Effort allocation: Analysis of real system engineering found 22% of exchanged information to be between electrical and software disciplines, and 20% between planning and software, with software engineering alone comprising ~37% of the total effort.
• Payback curves: After integrating the modular meta-model into the toolchain, the amortized return (reduction in rework) was found to be approximately tenfold the integration cost, once several modules had been instantiated or reconfigured (Regulin et al., 2022).
• Benchmarking modularity and maintainability: Industry analyses (SWWMAT4aPS) define and use maturity indices for modularity in design, test & QA, and operation/maintenance. High-performing organizations strictly layer modules and employ advanced code-configuration, version-controlled libraries, and state-machine patterns for modular PLC software (Vogel-Heuser et al., 2022).
• Plug-and-play extensibility and resilience: In robotics, strictly partitioned, versioned modules with standardized message-passing enable rapid integration of new functionality and graceful degradation (sliding-mode autonomy) in fielded multi-agent systems (Sriganesh et al., 2024).

7. Outlook and Advanced Research Directions

Active research topics include:

• Automated combinatorial optimization: Efficient algorithms for multi-stage, multi-objective modular design—combining surrogate-based performance models, NSGA-II, and decision-support heuristics—for large-scale systems facing economies-of-scale and parameter heterogeneity (Lee et al., 17 Mar 2025).
• Skill- and capability-based production orchestration: Ontology-driven approaches distinguish "capabilities" (what is achieved) from "skills" (how it is done), enabling production recipes to remain decoupled from plant configurations and supporting dynamic skill rebinding in orchestrated processes (Köcher et al., 2022, Sidorenko et al., 2024).
• Integrated assembly/disassembly design: Quantitative production-readiness metrics influence both the evolution of modular product architectures and the continuous improvement during mass production and end-of-life reconfiguration (Monetti et al., 13 Oct 2025, Monetti, 3 May 2025).
• Cognitive and AI-augmented orchestration: LLM agents and digital twin representations plan, explain, and execute modular production tasks by mapping high-level capabilities to available distributed skills, minimizing manual intervention and raising system flexibility (Xia et al., 2023).
• Dynamic, containerized, and mobile modularity: Dynamic resource allocation and mobile production units (Mobile Production Containers) enable hyperconnected, scalable, and adaptive modular supply chain ecosystems (Liu et al., 11 Nov 2025, Malladi et al., 2019).

Technical focus is increasingly on formalizing modularity through domain-extended meta-models and robust interface standards, as well as automating system (re)configuration, verification, and cross-disciplinary traceability using semantic and algorithmic means. The modular/production-system paradigm is now integral to the digital transformation of engineering processes spanning mechanical, electrical, software, and operational domains in both discrete manufacturing and process industries.