Complexity in Supply Chains: A New Approachto Quantitative Measurement of the Supply-Chain-Complexity

Isik, Filiz

doi:10.5772/15005

Cited by 34 publications

(31 citation statements)

References 19 publications

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“…Isik (2011) says that the most important criterion of optimization has long been focused on reducing costs in each sub-process that is part of an overall assembly process. Undoubtedly, new criteria related to the reducing of manufacturing lead times of assembly processes mean that new and different approaches have to be applied for managing the ASC including assessment methods for the evaluation of configuration complexity of such networked systems.…”

Section: Related Workmentioning

confidence: 99%

Configuration complexity assessment of convergent supply chain systems

Modrák

Márton

2014

International Journal of General Systems

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System designers usually generate alternative configurations of supply chains (SCs) by varying especially fixed assets to satisfy a desired production scope and rate. Such alternatives often vary in associated costs and other facets including degrees of complexity. Hence, a measure of configuration complexity can be a tool for comparison and decisionmaking. This paper presents three approaches to assessment of configuration complexity and their applications to designing convergent SC systems. Presented approaches are conceptually distinct ways of measuring structural complexity parameters based on different preconditions and circumstances of assembly systems which are typical representatives of convergent SCs. There are applied two similar approaches based on different preconditions that are related to demand shares. Third approach does not consider any special condition relating to character of final product demand. Subsequently, we propose a framework for modeling of assembly SC models, which are dividing to classes.

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Section: Related Workmentioning

confidence: 99%

Configuration complexity assessment of convergent supply chain systems

Modrák

Márton

2014

International Journal of General Systems

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“…Based on the shortcomings of the type I approaches, Isik (2010) (see also Isik 2011) proposes three extended entropy measures. Specifically, for each possible state i, all proposed quantifiers additionally consider a deviation value i that gives the difference ( i = a i − v i ) between the actual order quantity (a i ) and the desired target value (v i ).…”

Section: Entropy Approaches Of Type IImentioning

confidence: 99%

A new two-dimensional performance measure in purchase order sizing

Bock

Isik

2015

International Journal of Production Research

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Lack of knowledge about demand responses or about behavioural aspects of decision-making within procurement processes is a significant cost driver in modern supply chains. Very often, this lack of knowledge leads to a substantial increase in inventories and may endanger negotiated service levels. For instance, various studies reveal that decision-makers tend to anchor orders close to the average past demand although the target order size is significantly higher or lower. In order to improve this situation, feedback has to be systematically provided to the decision-makers. In combination with modern big data analytics and reporting instruments that enable exhaustive monitoring, effective indicators have to be applied in order to directly detect processes with significant potential for improvement. Hence, this paper proposes a new approach for measuring the intricacy in purchase order sizing that addresses self-awareness skills of decision-makers. By simultaneously analysing the amount and structure of occurring costs, processes with a significant and simple structured error pattern are identified. In order to identify these processes more reliably, a new approach that supplements former information-theoretic entropy measures by an additional cost value is proposed. By analysing costs and the structure of deviations from target values in a two-dimensional measure, a more comprehensive understanding of the considered order sizing process is pursued. In order to illustrate the application of the new approach and show limitations of one-dimensional measures, different scenarios that exemplify the new approach are presented.

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“…Fredendall and Gabriel [3] in this context point out that by "measuring the system's complexity, the managers can identify problems in the system that are hindering the production flow." Isik [4] specifies other negative consequences of complexity related to logistics activities as high operational costs, customer dissatisfaction, time delay in delivery, excess inventory, or inventory shortage. In general, complexity is not easy to measure, since it is difficult to define precisely.…”

Section: Introductionmentioning

confidence: 99%

Novel Complexity Indicator of Manufacturing Process Chains and Its Relations to Indirect Complexity Indicators

Modrák

Šoltysová

2017

Complexity

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Manufacturing systems can be considered as a network of machines/workstations, where parts are produced in flow shop or job shop environment, respectively. Such network of machines/workstations can be depicted as a graph, with machines as nodes and material flow between the nodes as links. The aim of this paper is to use sequences of operations and machine network to measure static complexity of manufacturing processes. In this order existing approaches to measure the static complexity of manufacturing systems are analyzed and subsequently compared. For this purpose, analyzed competitive complexity indicators were tested on two different manufacturing layout examples. A subsequent analysis showed relevant potential of the proposed method.

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Complexity in Supply Chains: A New Approachto Quantitative Measurement of the Supply-Chain-Complexity

Cited by 34 publications

References 19 publications

Configuration complexity assessment of convergent supply chain systems

Configuration complexity assessment of convergent supply chain systems

A new two-dimensional performance measure in purchase order sizing

Novel Complexity Indicator of Manufacturing Process Chains and Its Relations to Indirect Complexity Indicators

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