Performance measurement scenarios with fuzzy cognitive strategic maps

Glykas, Michael

doi:10.1016/j.ijinfomgt.2011.12.004

Cited by 19 publications

(7 citation statements)

References 79 publications

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“…At an early stage of this research trend, [36] proposed a combined Delphi method, a FCM and a multi-criteria technique TOPSIS model to facilitate policy makers a tool for measuring the impact of a decision. The importance of FCM in realistic and measurable objectives in strategic planning projects, through a series of FCM sample maps with causal relationships, to generate financial performance indicators has been discussed by [1]. Whereas [37] showed, how the FCMs can be used to create futuristic data-driven scenarios incorporating text mining and fuzzy association rules with the objective to improve the sustainability of electric vehicles.…”

Section: Previous Workmentioning

confidence: 99%

“…That is why financial performance should be addressed and clear terms and with an appropriate level of detail. The other categories are the financial position, risk management strategies and practices, risk exposures, accounting policies, basic business and corporate governance information [1,2]. Due to the dynamics of both, financial markets and business models, the creation of flexible financial models has become one of the main challenges for investments [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

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In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios.

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

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“…Carvalho (2013), focuses on FCM as tools to model and simulate complex social, economic and political systems on point of views; discussing the structure, the semantics and the possible use in the qualitative systems. Glykas (2012) presents the application of a fuzzy cognitive map (FCM) framework and its associated modelling and simulation tool to strategy maps (SMs) and resolve limits of SMs. He used combination of BSC and FCM for placement of different performance measurement scenarios using the fuzzy cognitive strategic (FCSM).…”

Section: Making Fcm Framework For Technology Strategy Mapsmentioning

confidence: 99%

Causality analysis of the technology strategy maps using the fuzzy cognitive strategy map

Yousef¹

2014

Afr. J. Bus. Manage.

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The main purpose of this study is to find out the causality relationships between the strategic management of technology (SMOT) objects in the selected Iranian high technology companies, based on the balanced scorecard (BSC) and fuzzy logic approaches. Evaluations of critical technological indicators in the selected high technology-based companies illustrated they have used different cognitive procedures in their strategic management of technology studies, which have previously been discussed throughout the SMOT literature. Technology strategy maps try to make convergences between the objects of SMOT using benefits of the technology balanced scorecard (TBSC) in the high technology environment. Technology strategy maps empower high technology companies in both technology and business areas, based on the four perspectives of proposed TBSC. The first step in our evaluations is based on-field studies questionnaires responded to by 150 personnel from different industries. The next step is based on the empirical collected data from 24 high technology companies; causal and effect relationship analysis between each of these objects was calculated and mapped using the fuzzy cognitive map (FCM). Obtained fuzzy cognitive strategy map (FCSM) simply explains the causality relationships between the objects of the SMOT, which were not well understood in the traditional technology strategy maps.

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“…Improvements are aimed at two points. The first is the building of strategy maps, which are critical to identify cause-effect relationships between strategic objectives (Glykas, 2012). The second is to overcome organizational inertia and lack of adaptation to a changing environment.…”

Section: Introductionmentioning

confidence: 99%

Designing a balanced scorecard using a scenario approach

González

Quezada

Palominos

2019

ARLA

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Purpose The purpose of this paper is to design a method or process that relates dynamic environmental conditions with the strategic objectives present in a company’s strategy map, thereby allowing rapid analysis and the generation of new strategies that the company can adopt in a timely manner in order to maintain and/or improve its competitive position in the market. Design/methodology/approach One training session and three workshops were run involving the company staff. The intervention team reviewed and analyzed the results achieved and prepared the final presentation. Finally, they were asked to evaluate the intervention methodology with respect to usefulness and ease of use. Findings The developed methodology allows companies to obtain a broader vision of their future prospects. The executives’ participation in the process provided them a vision and understanding of the most relevant and sensitive variables of their environment. Research limitations/implications The proposed methodology is easy to use. It provides a simple and agile mechanism to adapt the strategy of an organization to the changes in the market and environment. Social implications This process will provide social benefit for the global community, from both a methodological perspective and a practical one, as it will provide a tool that will positively impact competitiveness and quality of life for workers and the wider community. Originality/value This paper allows the identification of the possible scenarios and their incorporation into a strategy map, which may help companies deal with the changes in their competitive environment. The strategy maps generated for each of the scenarios provide useful information with respect to presented events.

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Performance measurement scenarios with fuzzy cognitive strategic maps

Cited by 19 publications

References 79 publications

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

Causality analysis of the technology strategy maps using the fuzzy cognitive strategy map

Designing a balanced scorecard using a scenario approach

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