The Fuzzy Model for Sectoral Resilience Analysis

Abstract

The report describes a process of analyzing sectoral resilience using the strategic matrix model of 4x6. It presents the main measures at the government level that can contribute to the restoration of sectoral resilience in the event of unfavorable impacts such as military, natural, or technological incidents. Methods. The 4x6 matrix is an oriented graph, with nodes representing the matrix indicators distributed across the matrix cells, and edges representing the links between indicators. The model is dynamic and positioned in discrete time, with the unit of measurement being a year. The matrix models the industry as a cybernetic system with positive and negative feedback loops. Negative feedback loops are generated based on anti-risk management results. Positive feedback loops arise in two ways: a) reinvesting net profits in business and increasing equity; b) proactive decision-making. The report presents a simple example of a sectoral matrix consisting of 15 indicators connected by 22 links. It demonstrates the anti-risk and proactive management of industry resilience by the state, through public-private mobilization partnerships (PPMP). The paper examines the positive impact of the following measures on industry resilience: a) price regulation; b) return industrial mortgage; c) government supply chain factoring; and d) government leasing. The relationship between efficiency, resilience, risks, and opportunities is ambiguous. It is necessary to research the optimal zones where an acceptable value of all four factors can be preserved at the same time. Resilience is lost in both positive and negative senses; progress occurs in leaps, and new qualitative heights in business are achieved through repeated growth of all types of risk accompanying that business. In this case, stabilizing measures can hinder reaching new heights. The proposed modeling technology allows for the analysis of cross-industry interaction, including the creation of cross-industry syndicates (clusters).