A method for analysing operational complexity in supply chains

Wu, Yue; Huatuco, Luisa Huaccho; Frizelle, G.; Smart, Janet

doi:10.1057/jors.2012.63

Cited by 20 publications

(10 citation statements)

References 49 publications

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“…Entropy represents the most known quantitative measure of expected amount of information required to describe the state of a system, and builds a basic framework for the development of complexity theory [9,10]. Entropy is used for measuring the complexity of supply chains [11][12][13][14][15][16][17]. These papers bring manifold topics and approaches, and show also more or less connections with inventory control theory, in general.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies

Lukáš

Hofman

2016

Entropy

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This paper discusses a unified entropy-based approach for the quantitative measurement of operational complexity of company supplier-customer relations. Classical Shannon entropy is utilized. Beside this quantification tool, we also explore the relations between Shannon entropy and (c,d)-entropy in more details. An analytic description of so called iso-quant curves is given, too. We present five case studies, albeit in an anonymous setting, describing various details of general procedures for measuring the operational complexity of supplier-customer systems. In general, we assume a problem-oriented database exists, which contains detailed records of all product forecasts, orders and deliveries both in quantity and time, scheduled and realized, too. Data processing detects important flow variations both in volumes and times, e.g., order-forecast, delivery-order, and actual production-scheduled one. The unifying quantity used for entropy computation is the time gap between actual delivery time and order issue time, which is nothing else but a lead time in inventory control models. After data consistency checks, histograms and empirical distribution functions are constructed. Finally, the entropy, information-theoretic measure of supplier-customer operational complexity, is calculated. Basic steps of the algorithm are mentioned briefly, too. Results of supplier-customer system analysis from selected Czech small and medium-sized enterprises (SMEs) are presented in various computational and managerial decision making details. An enterprise is ranked as SME one, if it has at most 250 employees and its turnover does not exceed 50 million USD per year, or its balance sheet total does not exceed 43 million USD per year, alternatively.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies

Lukáš

Hofman

2016

Entropy

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“…Similar systems have been studied relatively well. For example, Wu et al (2013) propose a method for analyzing the operational complexity in supply chains by using an entropic measure based on information theory. The delayed feedback control (Holyst and Urbanowicz, 2000;Salarieh and Alasty, 2008), the sliding mode control (Dadras and Momeni, 2010), and other methods (Salarieh and Alasty, 2009;Du et al, 2010) have been used for controlling chaos in economic systems.…”

Section: Introductionmentioning

confidence: 99%

Robust controlling of chaotic behavior in supply chain networks

Nav

Motlagh

Makui

2017

Journal of the Operational Research Society

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The supply chain network is a complex nonlinear system that may have a chaotic behavior. This network involves multiple entities that cooperate to meet customers demand and control network inventory. Although there is a large body of research on measurement of chaos in the supply chain, no proper method has been proposed to control its chaotic behavior. Moreover, the dynamic equations used in the supply chain ignore many factors that affect this chaotic behavior. This paper offers a more comprehensive modeling, analysis, and control of chaotic behavior in the supply chain. A supply chain network with a centralized decision-making structure is modeled. This model has a control center that determines the order of entities and controls their inventories based on customer demand. There is a time-varying delay in the supply chain network, which is equal to the maximum delay between entities. Robust control method with linear matrix inequality technique is used to control the chaotic behavior. Using this technique, decision parameters are determined in such a way as to stabilize network behavior.

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“…Since Shannon's information entropy (Shannon 1948) enables a detailed analysis of the structure of occurring deviations from target values, several authors propose entropy-based measures for various problems in manufacturing or supply chain management (see, among others, Yao 1985;Ronen and Karp 1994;Frizelle and Woodcock 1995;Deshmukh, Talavage, and Barash 1998;Sivadasan et al 2002;Sivadasan et al 2006;Martínez-Olvera 2008;Smart, Calinescu, and Huaccho Huatuco 2011;Wu et al 2012). Due to an information theoretic definition of complexity, these approaches apply entropy values.…”

Section: Introduction and Literature Reviewmentioning

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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A method for analysing operational complexity in supply chains

Cited by 20 publications

References 49 publications

Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies

Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies

Robust controlling of chaotic behavior in supply chain networks

A new two-dimensional performance measure in purchase order sizing

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