Re: Interpretive Structural Modeling Software Free Download Mega 1
Latrisha Adan
The main contributions of the current research are our proposing and demonstrating a novel integrated Fuzzy-ANP-ISM method to prioritize supply chain oriented key risk factors, and applying it (for the first time to our knowledge) in a real setting of green construction of residential megaprojects. This required formulation of specific parameters for this setting. Another new contribution is the inclusion of an infrequently used supply chain risk property, namely manageability, other than the severity of impact and the probability of occurrence. And finally, utilization of a new fuzzy environment for the Fuzzy-ANP body of knowledge, especially for that the scope of the questionnaire remains intact, through the fuzzy-fi-cation.
The following are the highlights of the remaining sections. Section 2 briefly reviews the theoretical and the empirical literature of the subject matter. At the end of Sect. 2, the research gap and implications of the literature are rendered. Section 3 starts by briefly explaining the ANP method, and continues with defining the fuzzy environment used, row sensitivity analysis, and the application of ISM in the ANP model proposed. At the end of Sect. 3, there is the procedure, entitled as the general guidelines, by which the research has been administered and explained in more detail in Sect. 4. Section 4 describes the model, introduces the case study and the expert panelist, explains the tools and techniques required to execute the analyses or reproduce, and renders the results. Section 5 interprets and discusses the resulting output. And finally, Sect. 6 concludes the overall research, including the managerial and theoretical implications, limitations, and the scope of the further research.
Those situations in the supply chain that might hinder organizations from continuing their businesses are known as supply chain risks (Heckmann et al. 2015). Supply chain literature also relates the definition of risk with concepts such as complexity, uncertainty, and resilience (Thom et al. 2016). Some relatively new sources of uncertainty and risk factors such as COVID-19, increasingly challenging climate change risks and economic and political instability factors render some of early risk management processes as incomplete and unable to fully and effectively cope with the total supply chain risk picture facing executives. Early studies of risk management, including SCRM, were conducted in a more stable environment than in current global conditions. For example, COVID-19 has caused global shortages of many key ingredients ranging from timber to computer chips.
This section introduces the methodological development of the integrated problem-solving method utilized in the current research. The basic principles of each method, necessary for understanding the data analysis, are explained and a step-by-step procedure is determined (in Subsect. 3.5) based on which data gathering and analysis are conducted in Sect. 4.
Relative importance weights of the initial super-matrix, namely local priorities, can be calculated using different optimization methods such as the eigenvalue method, the least squares, the logarithmic least squares, or the weighted least squares (Golany and Kress 1993; Nishizawa and Takahashi 2009; Saaty and Vargas 1984). Filling the weight vectors regarding the particular child nodes related to any parent node, will form the unweighted matrix, and transforming it to column-wise stochastic matrix results in the weighted matrix, which means the probability of entries in each column will sum up to 1 and the resulting weighted matrix should be raised to a sufficiently significant power until it converges into a stable limit matrix. The limit matrix indicates the priority of each alternative or criterion (Saaty and Vargas 2013). The mathematical explanation of this step can be described as the equation below.
The fuzzy set theory, introduced by Lotfi Zadeh (1965), discerns specific membership functions (MFs) for imprecisely defined classes of objects. Well-defined fuzzy sets are to deal with the imprecision triggered by the absence of sharply defined criteria of class membership and the values assigned range between zero and one. Fuzzy hybrid techniques of multi criteria decision-making, such as fuzzy ANP, are designed to assist the engineering professionals, particularly in construction project management, to handle uncertainties of the verbal statements or fuzzy ideas about the weights of the alternatives and or the criteria (Fayek 2020; Shafiee 2015).
To explain the need for sensitivity analysis when prioritizing risk factors, one might consider two schools of risk, risk as a subjective perception and risk as an objective construct with each requiring different risk management and mitigation strategies (Zhang 2011). For instance, in construction megaprojects, structural or known uncertainties are avoidable mainly through the reduction of complexity (Giezen 2012), while unpredictable uncertainties require crisis management (Lehtiranta 2011). Therefore, to attain agility and flexibility, managers are advised to be aware of inert unmanaged assumptions and or changing conditions of uncertainty in risk management practices where they can utilize predictive, adaptive, or hybrid methodologies of risk control (Costantini et al. 2021).
Row sensitivity analysis (RSA) developed by Adams (2014) is a calculation technique for ANP models that provides scenarios in light of modifying the weights of the nodes. Changing the importance of a particular node, while modifying the weights of the remaining nodes proportionately concerning the original structure of the weighted matrix (described in Sect. 3.1), equips the decision-makers with the foresight of prediction and planning (theoretical explanations and a sample calculation can be found in the Appendix section).
ISM introduced by Warfield (1974) is an interactive learning process that provides structured hierarchies, reflecting the flow of the contextual relationship permeated between a set of elements (Farris and Sage 1975; Malone 1975; Warfield 1976). The systematic logical thinking administered by ISM is a widely used technique in multiple disciplines that equips decision-makers with a better comprehension of complex interdependencies between the elements of a system regarding the potential influence they may have on each other (Cherrafi et al. 2017; Hughes et al. 2020; Kumar and Goel 2021). During the ISM procedure (Jharkharia and Shankar 2004) some quantitative values namely driving powers can be assessed for each particular element, representing the number of elements it can directly or indirectly influence. For instance, in the context of risk management, the more a risk factor can act as a triggering source for the concomitant risks in a project, the more it can incur negative effects on the success of the whole project. However, these quantitative values can be interpreted as the degree of importance concerning their driving power, a criterion introduced in the current research as the influential rate, described in detail in Sect. 4.
Section four comprises six sub-sections. At first, the introduction of the alternatives and the criteria, the main components of the ANP model, is provided, followed by the description of the case and the introduction to demographic profiles of the panelists in the second sub-section. The third sub-section explains the process of pairwise comparisons. The fourth sub-section explains the fuzzy data analysis in detail. Results of the weighted matrix, consistency ratios, the limit matrix, and lastly the priorities of the criteria and alternatives are rendered in the fifth sub-section. Finally, the last sub-section renders the outputs of the row sensitivity analysis.
The proposed system consists of two types of components, namely the alternatives and the criteria. Following the subject matter, the alternatives are supply chain oriented key risk factors in the green construction of residential megaprojects, and the criteria are the features that assist in discerning the relative importance of the alternatives. The alternatives are imported from a former study (Alamdari et al. 2021) which has identified twelve all-inclusive items, through comprehensive literature review and semi-structured interview sessions with an international diverse panel of fifteen experts. In addition, the study has assessed the triggering interrelations between the alternatives, using the ISM method through a three-round DELPHI process with six individual diverse industry experts. The number of elements that each alternative can trigger is the driving power of that particular alternative. The alternatives of risk events and the driving powers (D.P.) attributed are represented in Table 6. On other hand, four criteria namely, impact, probability, manageability, which have been discussed in Sect. 2, and the influence rate, which has been discussed in Subsects. 2.3 and 3.4, are included in the ANP model. A brief description of the criteria used is represented in Table 7.
However, the ANP model proposed has two clusters, alternatives, and criteria, and the ultimate goal is the priority of the alternatives. The schematic ANP model which was formed in the SUPERDECISIONS software is represented in Fig. 2. The connections between the nodes are discussed in detail in Sect. 4.3.
The purpose of the pair-wise comparisons is to measure the relative importance between the pairs of nodes concerning parent nodes specified in the system. All the measurements accumulate and result in priorities. Besides that the alternatives should be compared concerning criteria, the relative importance of the criteria may change concerning different alternatives either, hence the feedback loop in the ANP method is requisite. Expert panelists answered sixteen sub-questions and overall made two hundred and forty comparisons, each. However, the main general quotation states as: which of the pair of the nodes given is more important concerning the given control criterion? Sub-questions provide a control criterion and a cluster of elements that should be compared. Table 9 illustrates the components of the sub-questions.
b1e95dc632