The Fuzzy Model for Sectoral Resilience Analysis

YURY ALEKSEEVICH MALYUKOV1 ,ALEXEY OLEGOVICH NEDOSEKIN2,

ZINAIDA IGOREVNA ABDOULAEVA3,

ALEXEY VIKTOROVICH SILAKOV1

1Administrative Department,

Russian State University named after A.N. Kosygin (Technology. Design. Art),

Malaya Kaluzhskaya St., 1, 119071, Moscow,

RUSSIA

2Administrative Department,

"Institute of Financial Technologies",

Engels ave. 53, 194017, St. Petersburg,

RUSSIA

3Department of Medical Informatics and Physics,

North-Western State Medical University named after I. I. Mechnikov,

Piskarevsky pr. 47, 195067, St. Petersburg,

RUSSIA

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).

Key-Words: - sectoral economic resilience, 4x6 matrix, unfavorable impacts, matrix aggregate calculator

(MAC), balanced scorecard (BSC), public-private mobilization partnership (PPMP), anti-

risk/proactive management of resilience

Received: March 18, 2023. Revised: August 25, 2023. Accepted: September 10, 2023. Published: September 20, 2023.

1 Introduction

In the conditions of Russia's war efforts, a

mobilization economic program is necessary. It

assumes that specific sectors will emerge within

traditional economic industries that operate under

new rules, within the framework of a public-private

mobilization partnership (PPMP). During the

fulfillment of the state defense order through these

sectors, three criteria must be ensured: volume,

timeliness, and quality of production. In exchange,

the state must be ready to provide businesses with

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Yury Alekseevich Malyukov, Alexey Olegovich Nedosekin,

Zinaida Igorevna Abdoulaeva, Alexey Viktorovich Silakov

E-ISSN: 2224-2899

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guarantees for protecting both invested capital and

return on invested capital (ROE). As a whole,

sectoral resilience must be ensured, which we

understand to be the ability of sectors to function

with the required efficiency in the face of adverse

military, natural, or man-made conditions.

The issues of resilience of economic systems are

extensively discussed in works, [1], [2], [3], [4], [5],

[6], [7], [8], [9], [10], [11]. Additionally, for our

research, it is important to note that when modeling

resilience, the economic system must be constructed

to the level of a super-system and viewed as a

system of systems. This aspect of system modeling

is comprehensively discussed in works, [12], [13],

[14], [15], [16], [17], [18], [19], [20], [21].

The objective of this study is to propose a

fundamentally new scheme for analyzing industry

resilience, assuming that the set of negative

influences, the industry itself, and the set of

solutions for ensuring resilience are all subsystems

within a complex super-system that must be

comprehensively evaluated as a cybernetic system

that loses resilience under certain conditions and

seeks to return to its original stable state, i.e. regain

balance with the external environment.

The main difference between our approach to

the analysis of economic resilience and the cited

works is as follows. We consider not individual AE

scenarios weighted by significance level, but a

continuous spectrum of such scenarios, the

parameters of which are represented by fuzzy

numbers of a general form. In accordance with this

input condition, the response of the supersystem to

impacts is a continuous spectrum of ROE,

represented by a fuzzy number of a general form.

The sequence of sectoral resilience modeling is

as follows:

A. We identify the largest enterprises within the

sector and analyze them using the fuzzy-logical

technology of a matrix aggregate calculator (MAC),

[3], [5].

B. We build sectoral indices by the weighted

average method, where assets of companies on the

balance sheet act as weights. We apply the method

of intelligent filtering to suppress distortions.

C. We obtain forecasts for sectoral indices in the

form of fuzzy numbers and functions.

D. We formulate a draft state decision on supporting

sectors, to bring the ROE level in sectors to 20% a

year or higher.

E. We perform a comprehensive modeling of state

decisions according to the 4x6 matrix method.

Let's consider the 5 stages of modeling in order.

2 Assessment of Company Resilience

using the MAC Technology

Within the sector, dominant enterprises engaged in

the state defense order are selected. A detailed

analysis of resilience using the MAC technology is

described in, [5]. It is carried out based on the

following main indicators, assessed based on the

annual reports of companies:

MR –margin profitability (%),

OR – operational profitability (%),

NR – net profitability (%),

TAA – turnover of all assets (once a year),

TCA – turnover of current assets (once a year),

CL – common liquidity (dimensionless),

FL – financial leverage (dimensionless),

LD – loan dependency (dimensionless),

WACE – weight-averaged cost of equity (% a year),

WACL - weight-averaged cost of liability (% a

year),

LER – labor efficiency measured by revenue (USD

Th per 1 employee a year),

LENP - labor efficiency measured by net profit

(USD Th per 1 employee a year).

The indicator of sectoral resilience, RI, is

estimated as a two-dimensional convolution using

the formulas from [5], and receives values from 0.1

(very low level) to 0.9 (very high level). The first

system of weights in the convolution is the

significance of factors in the evaluation. The second

system of weights in the convolution is nodal points

corresponding to qualitative gradations of the

indicators included in the evaluation. ROE is also

assessed as the ratio of net annual profit per

company to its capital.

Based on the assessment of RI and ROE for

companies, sectoral indices are constructed using

the weighted average method. If Xit is the

measurement of factor X for the i-th company in the

sector conducted in year t, and Ait is the assets of

the i-th company in year t, then the sectoral index

Ind_X (t) should be sought using the following

formula:

Ind_X (t) = 

󰇛󰇜󰇛󰇜 (1)

In Table 1 and Table 2, data on RI and ROE

indices are compiled, respectively, for five sectors

named according to the European classification,

[22]. In terms of dimensionality, sectoral indices

coincide with the corresponding indicators but are

presented in tables as decimal numbers.

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Table 1. Sectoral RI Indices

Year

Ind_ RI for sectors:

C11

DJ27

DK29

DL31

E40

2015

0.398

0.368

0.518

0.389

0.445

2016

0.356

0.371

0.490

0.424

0.448

2017

0.434

0.409

0.516

0.380

0.473

2018

0.469

0.458

0.476

0.395

0.461

2019

0.418

0.399

0.463

0.442

0.468

2020

0.310

0.376

0.422

0.421

0.438

2021

0.459

0.533

0.499

0.490

0.485

2022

0.506

0.581

0.498

0.417

0.476

Source: authors' research

Table 2. Sectoral ROE Indices

Year

Ind_ROE for sectors:

C11

DJ27

DK29

DL31

E40

2015

0.210

- 0.252

0.273

0.018

0.030

2016

0.027

0.028

0.627

0.107

0.344

2017

0.070

0.068

0.432

-0.001

0.134

2018

0.110

0.122

0.258

-0.219

0.114

2019

0.072

0.013

0.247

0.014

0.102

2020

- 0.085

0.115

0.133

0.104

0.080

2021

0.126

0.208

0.171

- 0.012

0.091

2022

0.183

0.165

0.181

0.066

- 0.037

Source: authors' research

3 Forecasting Sectoral Indices

The information contained in historical data is

sufficient to build a fuzzy forecast for the next

forecasting year. This forecast can be made in the

form of a fuzzy number using the following

formulas:

Min_I_X = 

󰇛󰇜 󰇛󰇜,

Av_I_X = 

󰇛󰇜 󰇛󰇜,

Max_I_X = 

󰇛󰇜 󰇛󰇜, (2)

Here, FI = FI (Min_I_X, Av_I_X, Max_I_X) is

a triangular fuzzy number with abscissas expressing

the minimum, average, and maximum values across

the I_X measurements for the entire observation

period, [5]. This is the forecast for the index for the

next year.

Table 3 provides data on triangular fuzzy

numbers within individual sectoral resilience indices

for sector C11 (as a separate sectoral example).

Table 3. Fuzzy sectoral resilience factors (C11)

Factor

Resilience

index

FI for C11 indices

Min_I_X

Av_I_X

Max_I_X

Z1

Ind_MR

0.178

0.301

0.368

Z2

Ind_OR

-0.021

0.079

0.155

Z3

Ind_NR

-0.055

0.044

0.104

Z4

Ind_TAA

0.557

0.745

1.106

Z5

Ind_TAE

2.672

4.136

9.909

Z6

Ind_CL

1.165

1.221

1.308

Z7

Ind_FL

1.005

1.304

1.512

Z8

Ind_LD

0.074

0.323

0.789

Z9

Ind_WACE

0.042

0.056

0.081

Z10

Ind_WACL

0.013

0.019

0.048

Z11

Ind_LER

1610

2533

4040

Z12

Ind_LENP

-128

106

411

RI

Ind_RI

0.310

0.419

0.506

ROE

Ind_ROE

-0.085

0.066

0.183

Source: authors' research

4 Development of State Regulatory

Policy

To have a basis for protecting capital and ROE, the

government must be confident in the effective

performance of companies within the framework of

the state defense order. Such efficiency is ensured

by the following necessary but not sufficient

criteria:

Ind_NR > 0.05, Ind_TAA > 1.5, Ind_FL > 1.6 (3)

In this case Ind_ROE > 0.2.

The requirements (3) lead to the following measures

of state sectoral regulation:

 Fixing prices for essential goods;

 State supplier factoring;

 State leasing;

 State reverse mortgage of industrial non-current

assets.

All data collected as a result of the preliminary

analysis is placed in a 4x6 matrix as shown in

Figure 1. The 4x6 matrix is a system of six

strategically interrelated maps, each with four

strategic perspectives highlighted:

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Theats Risk

E BSC E

R E R

P R P

A P A

A

Opp-s Chances

E Decisions E

R E R

P R P

A P A

A

Fig. 1: 4x6 Matrix

Source: [1]

Map labels:

 Threats - Threats map;

 Opp-s - Opportunities map (as in the SWOT

matrix);

 BSC - Balanced scorecard map;

 Risk - Risk map;

 Chances - Chances map;

 Decisions - Decisions map.

 Strategic perspective labels:

 A - Resources;

 P - Processes;

 R - Industry relations with its key stakeholders

(consumers, suppliers, banks, employees,

government, etc.);

 E - Effects - the expected results of the industry's

activities.

Fig. 2: Simple example of an industry 4x6 matrix

Source: authors' research

The expanded 4x6 matrix is shown in Figure 2.

Table 4 summarizes the node labels of the

corresponding graphic, and Figure 2 summarizes the

edge labels of the graphic. The indicators on the

strategic maps are denoted using the XYZ principle,

where X is the code for the strategic perspective, Y

is the code for the map, and Z is the indicator

number within a cell of the matrix.

Table 4. Indicators of the 4x6 matrix

№

Indicator

code

Indicator name

Unit of

measurement

1

RT1

Sectoral demand

compression

index

% year-on-year

2

RO1

Sectoral demand

expansion index

% year-on-year

3

EB1

Return on equity

(ROE) index

% a year

4

RB1

Net profitability

index

%

5

PB1

Labor efficiency

index

Thousand USD

revenue per

employee per

year

6

PB2

Asset turnover

index

Once a year

7

AB1

Weighted

average cost of

capital (WACC)

index

% a year

8

AB2

Financial

leverage index

Dimensionless

9

ER1

Integral sectoral

index

From 0 to 1

10

EC1

Integral sectoral

chance

From 0 to 1

11

RD1

Sectoral decision

factor 1:

increase in net

profitability

%

12

PD1

Sectoral decision

factor 2:

increase in asset

turnover

Once a year

13

PD2

Sectoral decision

factor 3:

increase in labor

efficiency

Thousand USD

revenue per

employee per

year

14

AD1

Sectoral decision

factor 4:

increase in

financial

leverage,

decrease in

weighted

average cost of

capital

Leverage –

dimensionless,

weighted

average cost of

capital - % a

year

15

AD2

Sectoral decision

factor 5:

Leverage –

dimensionless,

weighted

average cost of

capital - % a

year

Source: authors' research

The contents of Figure 2, Table 4, and Table 5 lead

to the following explanatory observations:

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 The industry in the 4x6 matrix model represents

a cybernetic system with the following basic

properties:

 The industry's goal is to achieve steady

growth in ROE. The business owner receives

their income last in the value chain. This

implies that all other stakeholders have

already received their share of the profit and

are satisfied with it.

 The industry is open to the world, making it

susceptible to adverse effects (AE) both in a

negative (Threats) and positive

(Opportunities) sense. The impact of AE on

the industry could result in a temporary loss

of resilience. The industry has a certain level

of sensitivity to AE (this thesis is not

explained in detail in this article).

 The industry aims to achieve equilibrium with

the environment and maintain homeostasis.

Therefore, it responds to AE resilience, and

the response is formed by the industry's

governing subsystem (the state). In response

to a temporary loss of resilience, the

government forms anti-risk and pro-

opportunity decisions. In the first case,

management is carried out within a negative

feedback loop (returning the system to its

previous state); in the second case,

management involves transitioning the

industry system into a qualitatively new state.

 The relationships in Table 5 may have the

following content:

 Traditional functional-algorithmic

relationships;

 Fuzzy connections;

 Production-type connections of IF-THEN.

Table 5. Connections between indicators in the 4x6

matrix

№

Link code

(Source node-

Target node)

Content of the link

1

e1

(RT1-RB1)

Compression of industry demand

leads to a decrease in net profitability

(NP)

2

e2

(RT1-PB2)

Compression of industry demand

leads to a decrease in asset turnover

(AT)

3

e3

(RO1-RB1)

Expansion of industry demand leads

to an increase in net profitability

(NP)

4

e4

(RO1-PB2)

Expansion of industry demand leads

to an increase in asset turnover (AT)

5

e5

(RB1-EB1)

Net profitability (NP) directly

influences ROE (DuPount formula)

6

e6

(PB2-EB1)

Asset turnover (AT) directly

influences ROE (DuPount formula)

7

e7

(AB2-EB1)

Financial leverage (FL) directly

influences ROE (DuPount formula)

8

e8

(PB1-RB1)

Growth in labor efficiency measured

by revenue leads to an increase in net

profitability

9

e9

(EB1-ER1)

Decrease in ROE leads to an increase

in overall risk

10

e10

(EB1-EC1)

Increase in ROE leads to an increase

in overall opportunity

11

e11

(ER1-RD1)

Increase in overall risk leads to the

start of Solution 1

12

e12

(ER1-PD1)

Increase in overall risk leads to the

start of Solution 2

13

e13

(ER1-AD1)

Increase in overall risk leads to the

start of Solution 4

14

e14

(ER1-AD2)

Increase in overall risk leads to the

start of Solution 5

15

e15

(AD1-AB2)

Solution 4 leads to a decrease in

financial leverage (FL)

16

e16

(AD1-AB1)

Solution 4 leads to a decrease in

WACC 3

17

e17

(AD2-AB2)

Solution 5 leads to a decrease in

financial leverage (FL)

18

e18

(AD2-AB1)

Solution 5 leads to a decrease in

WACC 3

19

e19

(AB1-RB1)

Decrease in WACC_Z leads to an

increase in net profitability (NP)

20

e20

(EC1-PD2)

Increase in overall risk leads to the

start of Solution 3

21

e21

(PD1-PB2)

Removal of morally outdated funds

leads to an increase in asset turnover

(AT)

22

e22

(PD2-PB1)

Increase in motivation quality leads

to an increase in labor productivity

Source: authors' research

5 Example of Modeling within a 4x6

Matrix

Let's consider an example of an abstract industry

segment - a group of companies united by certain

characteristics (such as geographical, sectoral,

product-related, etc.). Let's assume that the level of

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information disclosure of these companies allows

for the synthesis of a consolidated financial

statement with a sufficient level of detail to identify

all the necessary indicators for model calculations.

Table 6. Reporting and forecasting years based on

the results of modeling the industry segment

Indicator

Value EUR

Year 1

Year

2.1

Year

2.2

Year

2.3

Revenue

(million)

1000

900

850

800

Current

operational cost

(million)

800

720

680

640

Gross Margin

(million)

200

180

170

160

Fixed

operational cost

(million)

70

Operational

profit (million)

130

110

100

90

Non-operational

income

(million)

0

Current

investment cost

(million)

30

Financial cost

(million)

70

Profit before tax

(million)

30

10

0

-10

Profit tax

(million)

6

2

0

Net profit

(million)

24

8

0

-10

Own capital

(million)

300

Borrowed

capital (million)

700

Fixed assets

(million)

800

Current assets

(million)

200

Total assets =

Total liabilities

(million)

1000

Ind_MR (%)

20%

Ind_NR (%)

2%

1%

0%

-1%

Ind_TAA

(times per year)

1.000

0.900

0.850

0.800

Ind_WACC

(% per annum)

7%

Ind_FL

(dimensionless)

2,33

Ind_ROE

(% per annum)

8%

2,7%

0%

-3,3%

Source: authors' research

All calculations will be carried out in euros. From a

modeling perspective, the choice of currency for the

consolidated financial statements does not have a

significant impact.

Let's call Year 1 - the reporting year for the

company, Year 2.1 - the forecast year under

scenario 1, Year 2.2 - the forecast year under

scenario 2, and Year 2.3 - the forecast year under

scenario 3. Each of the scenarios is modeled outside

the 4x6 matrix using its own modeling tools. The

modeling results are presented in Table 6.

From Table 6, it can be seen that:

• The industry segment is formally operating on the

breakeven point, which is determined by the

annual consolidated revenue of 850 million euros.

• The modeling considers market contraction

scenarios in the range of 10-20% from the level of

the reporting year.

• The segment's borrowed capital has been formed

at an average weighted interest rate of 10% per

annum.

• Anticrisis measures for the industry segment are

not included in scenarios 1-3, as the asset and

capital structure remain unchanged.

5.1 Adverse Effects (AE) Dimensions

Let's include a simplified AE model in the matrix,

which considers the expected market contraction as

a triangular fuzzy number Z = (-10%, -15%, -20%),

as shown in Table 6. However, here we model the

complete range of scenarios, with the expectations

of the impacts distributed unevenly and tending

towards the center of the interval.

In this case, the factor Z remains in the

"basement" of the matrix model, and it is linked to

the "basement" factor Revenue through a regular

functional relationship:

Revenue (Year 2) = Revenue (Year 1) * (1 - Z) (4)

5.2 Industry Risk Assessment before

Decision

The industry's response to the fuzzy market

contraction Z is represented by the industry-specific

ROE index in a triangular form as Ind_ROE =

(min=-3.3%, av=0%, max=2.7%). A norm of

N1=0% per annum corresponds to the breakeven

point. The risk of the industry segment incurring

losses under this AE scenario can be estimated using

the following formulas:

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Risk =













󰇛󰇛󰇜

 󰇛󰇜󰇜



󰇛󰇜󰇧󰇛󰇜

 󰇛󰇜󰇨



(5)

where

R = 

󰇛󰇜󰇛󰇜

 

(6)

 =

























(7)

and δ is an infinitely small value. In this case, the

uncertainty of the form "zero over zero" in formula

(5) is resolved using one of L'Hopital's rules:



󰇛󰇜

 = -1 (8)

Calculating using formulas (5), (6), (7), and (8)

gives Risk = 0.550. We can fuzzify this value by

introducing the linguistic normalization that has

already become a tradition:

High Level: Risk < 0.1 - acceptable non-decreasing

risk;

Middle Level: 0.1 < Risk < 0.2 - borderline risk;

Low Level: Risk > 0.2 - unacceptable risk; (9)

Thus, the qualitative value of the integral risk

falls on the Risk map in the matrix, while the

original quantitative value is moved to the

"basement". Since the risk is unacceptable, the red

alert light is triggered, indicating that an anti-risk

decision is necessary and mandatory. If the yellow

light had turned on instead (indicating borderline

risk), the decision could have been delayed.

However, in this case, the decision is urgent as the

fate of the industry segment depends on it.

5.3 Solution Dimensions

The following comprehensive solution, undertaken

by the government in relation to the industry

segment, is being considered:

 Replace BC = 200-300 million euro of

borrowed capital with own funds. This will

reduce Ind_WACC and corresponding financial

costs.

 Sell FA = 200-300 million euro of non-

current assets with an expected discount to the

book value d=10-20%. This will increase the

turnover of all assets, scale up in the market,

even with losses, while also paying off certain

loans – and again lower WACC.

If we were in a scenario paradigm of modeling,

we would have to "split" the three initial scenarios

of AE, overlaying all the options of the proposed

solution on them. However, since we are in the

paradigm of fuzzy sets and soft computing, it is

sufficient for us to connect the indicators of interest

in a fuzzy form, creating a similar Table 6

computational scheme based on formulas in fuzzy

notation, for borrowed capital and fixed assets,

respectively:

BC (Year 2) = C (Year 1) - C; (10)

FA (Year 2) = FA (Year 1) - FA *(1-d) (11)

Losses associated with the sale of fixed assets

are attributed to non-operational income, with a "-"

sign. These losses reduce the size of equity capital,

which is also reflected in the modeling. In turn,

profits, if any, are distributed as dividends to the

owners of companies in the segment and do not

affect the size of equity capital.

5.4 Modeling Results

The modeling results are presented in Table 7.

Table 7. Modeling Results

Value EUR

Indicator

Value

Year 1

Value Year 2

min

av

max

Revenue

(million)

1000

800

850

900

Current

operational

cost (million)

800

640

680

720

Gross Margin

(million)

200

160

170

180

Fixed

operational

cost (million)

70

Operational

profit (million)

130

90

100

110

Non-

operational

income

(million)

0

-60

-37,5

-20

Current

30

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2023.20.177

Yury Alekseevich Malyukov, Alexey Olegovich Nedosekin,

Zinaida Igorevna Abdoulaeva, Alexey Viktorovich Silakov

E-ISSN: 2224-2899

2044

Volume 20, 2023

Value EUR

Indicator

Value

Year 1

Value Year 2

min

av

max

investment cost

(million)

Financial cost

(million)

70

26

23,75

22

Profit before

tax (million)

30

-26

8,75

38

Profit tax

(million)

6

0

1,75

7,6

Net profit

(million)

24

-26

7

30,4

Own capital

(million)

300

440

512,5

580

Borrowed

capital

(million)

700

20

237,5

220

Fixed assets

(million)

800

500

550

600

Current assets

(million)

200

Total assets =

Total liabilities

(million)

1000

700

750

800

Ind_MR (%)

20%

Ind_NR (%)

2%

1%

0%

-1%

Ind_TAA

(times per year)

1.000

1.143

1.133

1.125

Ind_WACC

(% per annum)

7%

4%

3%

Ind_FL

(dimensionless)

2,33

0,59

0,46

0,38

Ind_ROE

(% per annum)

8%

-5,9%

1,4%

5,2%

Source: authors' research

In the case of Table 7 data, Ind_ROE = (-5.9,

1.4, 5.2)% per annum, and the corresponding risk is

Risk = 0.323, it is significantly reduced but still

unacceptable. From this, the following

recommendations for adjusting the initial industry

solution arise:

 Do not sell FA at a discount higher than 10%;

 Negotiate with banks to reduce the interest rate

on the loan or restructure the debt with reduced

current interest payments. This will not solve

the situation in a strategic sense, but it will

allow for "riding out the storm in the library"

(an analogy from the movie "The Day After

Tomorrow"), postponing radical decisions until

the moment when the market recovers (if it

recovers).

6 Conclusion

The 4x6 strategic matrix is a universal tool for

modeling enterprises and industries for completely

different purposes, including analyzing industry

resilience. The conclusions obtained in such

modeling cannot be obtained within any other

model representations.

The approach incorporated into our modeling

system is fuzzy-logical and allows for the possibility

of complementing it with probabilistic components

depending on the type of uncertainty being studied.

In all cases, the uncertainty of the industry's existing

conditions must be classified and appropriately

described.

The 4x6 matrix reproduces the order of industry

management by the state, while the industry as an

object of management is seen as a cybernetic

system. The feedback arising in the course of

management is negative (if the management is anti-

risk) or positive (if the management is pro-

opportunity Sometimes the decisions that are made

can contradict one another.

For example, a strategy of maintaining the

status quo in the context of AE may hinder the

discovery of new market opportunities and effective

management. Industry segments responsible for

activities in the face of different types of challenges

may be fundamentally different. If specialized inter-

industry syndicates are well-suited to the conditions

of a particular period, then it is advisable to create

special inter-industry clusters for the conditions of

market expansion (according to the experience of

Uzbekistan, [23]).

The main directions of development of the

approach proposed in the article are as follows:

- Transition from a 4x6 matrix to a 7x6 matrix,

increasing the number of strategic perspectives.

- Taking into account specific anti-risk and

prochance decisions in the model, which involves

modeling real options.

In all cases, the activities of such new economic

entities are successfully modeled using the 4x6

matrix and other adjacent technologies, such as

industry-specific R-lenses, [24].

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DOI: 10.37394/23207.2023.20.177

Yury Alekseevich Malyukov, Alexey Olegovich Nedosekin,

Zinaida Igorevna Abdoulaeva, Alexey Viktorovich Silakov

E-ISSN: 2224-2899

2045

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DOI: 10.37394/23207.2023.20.177

Yury Alekseevich Malyukov, Alexey Olegovich Nedosekin,

Zinaida Igorevna Abdoulaeva, Alexey Viktorovich Silakov

E-ISSN: 2224-2899

2046

Volume 20, 2023

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

- Alexey O. Nedosekin developed the fuzzy model.

- Yury A. Malyukov has written the paper.

- Zinaida I. Abdoulaeva conducted calculations

forecasting sectoral indices and more.

- Alexey V. Silakov created an information base for

the calculations.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself:

No funding was received for conducting this study.

Conflict of Interest:

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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DOI: 10.37394/23207.2023.20.177

Yury Alekseevich Malyukov, Alexey Olegovich Nedosekin,

Zinaida Igorevna Abdoulaeva, Alexey Viktorovich Silakov

E-ISSN: 2224-2899

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