Advances on measuring the operational complexity of supplier–customer systems

Sivadasan, Suja; Efstathiou, Janet; Calinescu, Anisoara; Huatuco, Luisa Huaccho

doi:10.1016/j.ejor.2004.08.032

Cited by 122 publications

(84 citation statements)

References 25 publications

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“…Two-echelon supply chain games and their information characteristics, in particular, are discussed in [27]. Finally, the papers [28][29][30] are concerned with measuring the operational complexity of supplier-customer systems, and initiated our serious interest in the field.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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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%

“…The main goal of the analysis is to show the time dependence of entropy ratio h = H/H u upon lead-time tolerance thresholds in days, denoted [b d , b u ], where b d denotes the lower bound, and b u the upper one, respectively. For a more detailed discussion and further details, we refer to [30].…”

Section: Small-sized Lubricant Shop Fdmentioning

confidence: 99%

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

Lukáš

Hofman

2016

Entropy

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“…The food sector has now become a global market with products sourced from all over the world to meet a growing demand for variety and consistency of products, regardless of seasonality. The resultant long and complex supply chains limit traceability and often involve multiple, specialised actors who generally do not have detailed knowledge of each other's processes and procedures (Sivadasan et al, 2006). Thus when a problem arises or is suspected at some point in the supply chain, identification of the source is frequently problematic, and the ability of actors to quickly and efficiently address the problem is often limited.…”

Section: Introductionmentioning

confidence: 99%

Food scares: a comprehensive categorisation

Whitworth

Druckman

Woodward

2017

BFJ

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to describe the development of a comprehensive categorisation of food scares. Design/methodology/approach -Following an initial desktop study, the categorisation was developed collaboratively with industry experts through a workshop and series of semi-structured interviews.Findings -The new categorisation developed is in Venn diagram format allowing overlapping categories. It is organised around the two major types of contamination (biological, and chemical/physical contaminants) and the two major causes of contamination (wilful deception, and transparency and awareness issues).Practical implications -The long and complex supply chains characteristic of current food production systems have resulted in a rising number of food scares. There is thus an increased emphasis on developing strategies to reduce both the number of incidents of food scares, and their associated economic, social and environmental impacts. The new categorisation developed in this study enables experts to address categories of food scares. Inclusion of the cause of contamination is particularly important as the method through which contamination occurs is key in devising food scare prevention strategies. Originality/value -The new categorisation, unlike previous categorisations, enables food scares to fall into multiple categories, as appropriate. Also, again in contrast to previous categorisations, it takes into account not only the physical problem of a food scare but also the mechanism through which it arises.

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“…Entropy as a measure of complexity in supply chains was first introduced by Frizelle and Woodcock (1995). Later, Sivadasan et al (2002), Deshmukh et al (1998) and Sivadasan et al (2006) studied their approach with an industry practice done by the Institute for Manufacturing (IFM) at Oxford and Cambridge universities (see website www.ifm.eng.cam.ac.uk/csp/projects/complexschain.html). Their papers present an entropy-based approach in more detail and defined structural (deals with variety (schedule) and operational (deals with uncertainty (deviation from the schedule)) complexity measures by extending Shannon information entropy.…”

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confidence: 99%

“…Later, Isik (2010) provided their approach and modified the complexity measures. In prior entropy-based works on complexity (Shannon, 1948;Shannon and Weaver, 1949;Frizelle Woodcock, 1995;Sivadasan et al, 2002;Sivadasan et al, 2006), it is argued that complexity (or entropy) is only a function of probabilities of different states. However, Isik (2010) argues that complexity is not only a function of probabilities of different states, but also each state can have different complexity levels of its own that needs to be considered.…”

mentioning

confidence: 99%

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

Isik¹

2011

Supply Chain Management

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Advances on measuring the operational complexity of supplier–customer systems

Cited by 122 publications

References 25 publications

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

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

Food scares: a comprehensive categorisation

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

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