JOURNAL ARTICLE

Integrated Operations Management for Distributed Manufacturing

Abstract

In traditional manufacturing operations management systems, the four pillars of ISA-95 (production, quality, maintenance, inventory) are each implemented as separate software systems. Each system independently manages its own data, operational decision-making, and resources. If the heterogeneous data from these disparate systems could be successfully integrated, manufacturing operations could be greatly improved by allowing decisions related to these systems to be integrated across multiple distributed operating units and made closer to the factory floor with better contextualization of the connected systems. The challenge of integrating these systems is magnified for loosely coupled and/or geographically distributed enterprises and contracted third parties that provide production, storage, transportation, and/or other capabilities. This paper describes some requirements and associated research challenges for balancing two objectives driving integrated operations management for distributed manufacturing: (1) integrating heterogeneous data and related decisions to enable better informed decision-making, and (2) making and executing operational decisions closer to data sources and control actuation. This second objective enables systems to react faster, while also reflecting the realities of distributed enterprises. The research goal is to develop and standardize model-based approaches for design, decision-support, and execution of operations management functions. From this perspective, the main focuses of this paper are operational control, reliability management, and maintenance activities. All authors of the Work are U.S. Government employees and prepared the Work on a subject within the scope of their official duties. As such the Work is not subject to U.S. copyright protection.

Keywords:
Computer science Scope (computer science) Process management Factory (object-oriented programming) Distributed manufacturing Systems engineering Manufacturing engineering Engineering

Metrics

5
Cited By
0.66
FWCI (Field Weighted Citation Impact)
18
Refs
0.77
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Scheduling and Optimization Algorithms
Physical Sciences →  Engineering →  Industrial and Manufacturing Engineering
Manufacturing Process and Optimization
Physical Sciences →  Engineering →  Industrial and Manufacturing Engineering
Business Process Modeling and Analysis
Social Sciences →  Business, Management and Accounting →  Management Information Systems
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