Fuzzy cognitive maps for decision-making in dynamic environments

Nacházel, Tomáš

doi:10.1007/s10710-020-09393-2

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…To accommodate the complexity of expert responses, cause-and-effect relationships and cognitive differences of the experts, a method which could replicate human cognitive behavior is well suited. Fuzzy cognitive map (FCM) is a powerful tool for modelling complex systems with several dependent variables (Nachazel, 2020) by incorporating and adapting human knowledge (Parsopoulos et al , 2003). It utilizes characteristics of fuzzy logic with neural networks for ensuring the accuracy of results The prime reason for deployment of FCM was deployed for statistical analysis for the study is its convenience, simplicity, adaptability and ability to model and simulate dynamic systems.…”

Section: Methodsmentioning

confidence: 99%

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A labelling system and automation comparison index for industry 4.0 system

Kumar

Asjad

Suhaib

2021

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Purpose This paper aims to put forward a labelling system capable of reflecting the level of different Industry 4.0 (I4.0)features present in a manufacturing system and further propose a comparative index to collectively estimate and compare the system automation level. Design/methodology/approach Data for the empirical study were collected from interactions with the practising managers and experts. A relationship among the six I4.0 features is developed with fuzzy cognitive maps. Findings The paper proposed a simple and easy-to-understand labelling system for I4.0 systems, which indicates the automation level in each of six dimensions of any manufacturing system. The system is further strengthened by a proposed automation comparative index (ACI), which collectively reflects the automation level on a scale of “0” to “1”. Thus, the labelling system and parameter could help in comparing the level of automation in the manufacturing system and further decision-making. Research limitations/implications Only seven industrial sectors are illustrated in the paper, but the proposed concept of the classification scheme and ACI find their applicability on a large spectrum of industries; thus, the concept can be extended to other industrial sectors. Furthermore, a threshold value of ACI is a differentiator between a I4.0 and other automated systems. Both aspects have the scope of further work. Practical implications The way and pace by which the industrial world takes forward the concept of I4.0, soon it will need a labelling system and a parameter to assess the automation level of any automated system. The scheme assesses the automation level present in a manufacturing system. It will also estimate the level of the presence of each of all six attributes of an I4.0 system. Both labelling system and ACI will be the practical tools in the hands of the practising managers to help compare, identify the thrust areas and make decisions accordingly. Originality/value To the best of the authors’ knowledge, this is the first study of its kind that proposed the labelling system and automation comparison index for I4.0 systems.

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Section: Methodsmentioning

confidence: 99%

“…FCMs are extensively used for decision analysis, operation research (Groumpos, 2010), engineering, social sciences and predictions (Nachazel, 2020). It is usually categorised as a neuro-fuzzy method (Parsopoulos et al , 2003).…”

Section: Methodsmentioning

confidence: 99%