Unraveling complexity in assembly supply chain networks

Márton

2013

Entropy

Abstract:In this paper, we present a methodological framework for conceptual modeling of assembly supply chain (ASC) networks. Models of such ASC networks are divided into classes on the basis of the numbers of initial suppliers. We provide a brief overview of select literature on the topic of structural complexity in assembly systems. Subsequently, the so called Vertex degree index for measuring a structural complexity of ASC networks is applied. This measure, which is based on the Shannon entropy, is well suited for the given purpose. Finally, we outline a generic model of quantitative complexity scale for ASC Networks.

Section: Specifications Of Asc Network Complexity Measurementioning

confidence: 94%

Development of Metrics and a Complexity Scale for the Topology of Assembly Supply Chains

Márton

2013

Entropy

“…Their methods are devoted to measuring topological complexity and are also easily applicable in manufacturing process domain. The authors of the work [42] developed two practically oriented complexity indicators to assess assembly supply chain networks. It is also worth mentioning a generic static complexity measure that is easily employed in manufacturing systems environment [43].…”

Section: Related Workmentioning

confidence: 99%

Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity

Šoltysová

2023

Applied Sciences

In principle, modular or integral character of manufacturing lines depends on the topological designs of products and determined operation tasks. On the other hand, in specific situations there is an articulated need for modular design in smart manufacturing systems since modular layouts are a crucial step towards agile production via smart manufacturing. The aim of this paper is to explore how the modular layout relates to manufacturing lead time (MLT) and to operational complexity of smart manufacturing systems. For this purpose, topologically different models of alternative process layouts were simulated and tested, while MLT values were obtained using Tecnomatix Plant Simulation. The obtained positive findings of this research could be useful not only in selection of the most suitable process design from the alternative ones, but especially in deepening the knowledge and bettering understanding of the concept of optimal network modularity.

“…Competitive properties of the SDC measure can be found by its comparison with possible concurrent measures. Possible alternative complexity indicators were already mutually compared in previous studies [19,20], where so called Index of vertex degree (Ivd) met optimality criteria for assessment of network complexity in the best way. Index of vertex degree has been introduced by Bonchev and Buck [21] and is expressed for Graph G consists of a set of V vertices, {V}≡{v1, v2, … , vV}, by formula:…”

Section: Comparison Of Sdc Against To Concurrent Measurementioning

confidence: 99%

Axiomatic Design Based Complexity Measures to Assess Product and Process Structures

Šoltysová

2018

MATEC Web Conf.

Definitions of complexity often depend on several circumstances, such as the nature of investigated complex system, the kind of complexity, the conceptual framework used for a study, the theoretical approach taken, and the like. In this paper, two complexity measures that are based on Boltzmann’s entropy concept and AD theory are proposed and described. The first measure quantifies product variety complexity and the second one focuses on process structure complexity. Such complexity techniques will be used to determine product platform complexity and related process complexity for early stage of design decision-making. The method focused on product platform complexity assumes that the distribution of FR-DP couplings offers a suitable complexity concept, which prescribes that coupled designs should be decoupled, if possible, since uncoupled design is ideal and a decoupled design is less good, while a coupled design is the least satisfactory. Analogically, the same principle is used for the purpose to quantify topological process complexity by transforming input components into process variables and product modules including final product into design parameters. Subsequently, relevant properties of these measures will be analysed by computational experiments. Finally, practical findings for mass customization practice will be presented.