© Springer International Publishing AG, part of Springer Nature 2019
Natalia Kryvinska and Michal Greguš (eds.)Data-Centric Business and ApplicationsLecture Notes on Data Engineering and Communications Technologies20https://doi.org/10.1007/978-3-319-94117-2_13

Simulating and Reengineering Stress Management System—Analysis of Undesirable Deviations

Oleg Kuzmin1  , Mykhailo Honchar1  , Volodymyr Zhezhukha1   and Vadym Ovcharuk1  
(1)
Lviv Polytechnic National University, Bandera Street, 12, Lviv, Ukraine
 
 
Oleg Kuzmin
Email: oleh.y.kuzmin@lpnu.ua
 
Mykhailo Honchar
Email: mykhailo.f.honchar@lpnu.ua
 
Volodymyr Zhezhukha (Corresponding author)
Email: volodymyr.y.zhezhukha@lpnu.ua
 
Vadym Ovcharuk
Email: vadym.v.ovcharuk@lpnu.ua

Abstract

The article proposes a method for diagnosing the level of criticality of an undesirable deviation in the supply-production-marketing chain based on simulating the impact of a potential or actual incident that causes such a deviation on each link in the chain using a number of representative parameters (reliability of the supply chain, inventory management system, negative response from external marketing stakeholders, competitiveness of the marketing complex, quality of the production process, flexibility of the production process, level of planned processes violation). Given that in terms of their content the absolute majority of parameters for simulating the impact of a potential or actual incident that causes undesirable deviation for each link in the supply-production-marketing chain are fuzzy, it is feasible and necessary to use fuzzy set tools to solve the specified problem. Also, according to the results of the performed research, a set of heuristic rules for diagnosing incidents in the supply-production-marketing chain was proposed.

Keywords

ReengineeringStress managementStress management systemSupply-production-marketing chainUndesirable deviation

1 Introduction

One of the key conditions for a timely response to critical undesirable deviations that are substantial, extreme and exert significant negative impact on the enterprises operation is the availability of an effective stress management system. The multidimensional and diverse nature of these deviations necessitates the appropriate theoretical and practical training of managers engaged in such processes.

The policy for managing personal, group or corporate stress within the stress management systems at the business entities should ensure the implementation of complex key tasks such as:

  • Identification and determination of potential vulnerabilities in the production and economic activity of the enterprise from the point of view of emergence of critical undesirable deviations;

  • Development, implementation and formalization of the key elements of the stress management system;

  • Development of a list of potential critical undesirable deviations for each key business process of the company;

  • Development of a complex of potential measures for influence on critical undesirable deviations;

  • Establishment of criteria for the interpretation of critical undesirable deviations in such a way as to necessitate the use of managerial measures to influence them.

Critical undesirable deviations in the activities of business entities may take different forms determined by a number of parameters, namely: duration of the individual stages of deviations, the reasons behind their occurrence, level of influence on business processes, tools for managing them, etc.

The study of theory and practice makes it possible to conclude that the low level of process management within the stress management systems determines not only the increase in critical undesirable deviations, but also their negative impact on the activities of business entities. Therefore, it is important to have effective tools for simulating and reengineering such processes.

To build stress management systems at enterprises, it is expedient to use the simulation tools. It makes it possible to clearly identify the functional boundaries of each manager within this process, recipients and disseminators of information, timing for the implementation of the relevant work, level of responsibility and authority, etc. Simulation also provides for transparency of stress management systems since it allows you to understand the directions of documentation, information, communications, etc.

In addition, since in terms of their content the absolute majority of parameters for simulating the impact of a potential or actual incident that causes undesirable deviation for each link in the supply-production-marketing chain are fuzzy, it is feasible and necessary to use fuzzy set tools to solve the specified problem.

2 Paper Preparation

2.1 Features of Incident Criticality Simulation in the Stress Management Systems

In the analyzed context, taking into account the statement by A. I. Hizun [1, p. 48], it should be noted that the concept of critical undesirable deviation, which is essential, extreme and has a significant negative impact on the enterprise’s operation, is related to the concept of the incident. According to the author, the reason behind any crisis situation is “an incident with the highest level of criticality determined by the level of losses, the number of victims and other characteristics, that is, the incident/potential crisis situation”. Simulation of the incidents criticality in the stress management systems should be carried out in the supply-production-management chain. To formalize the above, we introduce a set of potential incidents $$P_{nv}$$:
../images/462031_1_En_13_Chapter/462031_1_En_13_Equ1_HTML.gif
(1)
where $$n$$ is the number of potential incidents in the supply-production-marketing chain, each of which can be represented as a function similar to [2–4]:
$$P_{{nv_{i} }} = \left\langle {P_{{nv_{i} }} ,P_{i} ,E_{i} , Z_{i} , R_{i} ,L_{ks} } \right\rangle ,$$
(2)
where
$$P_{{nv_{i} }}$$

is the designation of the i-th incident in the supply-production-marketing chain which may lead to a critical undesirable deviation;

$$P_{i}$$

a subset of parameters used to identify the i-th incident (or its prediction);

$$E_{i}$$

a subset of all possible fuzzy (linguistic) values of the i-th incident;

$$Z_{i}$$

a subset of actual values of the i-th incident at a certain time and space;

$$R_{i}$$

a subset of heuristic rules formed on the basis of fuzzy logic for each individual i-th incident;

$$L_{ks}$$

the level of criticality of the undesirable deviation in the supply-production-marketing chain caused by the i-th incident.

Taking into account formula (2) and the information described in the work [1, p. 49], the level of undesirable deviation in the stress management system can be considered critical, provided that $$L_{ks} \ge L_{{ks_{c} }}$$ where $$L_{{ks_{c} }}$$ is the average level of criticality of the undesirable deviation. Provided that $$L_{ks} < L_{{ks_{c} }}$$(the low level of criticality of the undesirable deviation caused by the i-th incident in the supply-production-marketing chain), the enterprise may decide on the influence on it using conventional methods or ignoring it as not causing significant damage to the production and economic activity of the business entity.

Considering the designation of the i-th incident in the supply-production-marketing chain which could lead to a critical undesirable deviation $$\left( {P_{{nv_{i} }} } \right)$$, it should be noted that such incidents are proposed to be considered within the supply, production, and marketing processes of each individual entity. Given this, the set of identifiers for i-th incidents in the supply-production-marketing chain with $$n = 3$$ should be displayed as follows:
$$P_{nv} = \left\{ {\mathop {\bigcup }\limits_{i = 1}^{3} P_{{nv_{i} }} } \right\} = \left\{ {P_{{nv_{1} }} + P_{{nv_{2} }} + P_{{nv_{3} }} } \right\} = P_{{nv_{post} }} + P_{{nv_{vyr} }} + P_{{nv_{zb} }} ,$$
(3)
where $$P_{{nv_{1} }} = P_{{nv_{post} }} ,\;P_{{nv_{2} }} = P_{{nv_{vyr} }} , \;P_{{nv_{3} }} = P_{{nv_{zb} }}$$ are the identifiers of incident groups within the supply, production and marketing processes of the enterprise, respectively.
Considering a subset of parameters used to identify the i-th incident in the supply-production-marketing chain $$(P_{i} )$$, it should be noted that this subset must include key parameters $$P$$. Moreover, it is fair to assert that $$P_{i} \subseteq P$$ and
$$P = \left\{ {\mathop {\bigcup }\limits_{j = 1}^{m} P_{j} } \right\} = \left\{ {P_{1} ,P_{2} , \ldots ,P_{m} } \right\}, j = \overline{1,m} ,$$
(4)
where m is the total number of diagnostic parameters used to identify the i-th incident in the supply-production-marketing chain.

2.2 Parameters for Simulating the Impact of a Potential or Actual Incident on the Links of the Supply-Production-Marketing Chain

Study of theory and practice, as well as the results of the research carried out, makes it possible to identify the following main parameters of simulating the effect of a potential or actual incident which causes undesirable deviation on each link in the supply-production-marketing chain:
  1. 1.

    Reliability of the supply chain $$(N_{lp} )\,(j = 1)$$. The parameter, based on the review and generalization of literary sources, is one of the key indicators in the supply logistics and, to some extent, generalizes its effectiveness. Taking into account the results of research by V. Ermoshyn [5, p. 56], it should be noted that the reliability parameter of the supply chain indicates the effectiveness of supplying “the right products to the right place, at the right time, in the right condition and packaging, with the right documentation”. In a way, this parameter allows identifying both the flexibility of supply systems, the time to respond to changes in factors of the internal and external environment, production necessity planning, procurement plans development, ensuring operational schedule of supply, acceptance of raw materials, materials and components, checking their quality at the enterprise, etc.

     
The enterprise supply system is considered effective subject to absolute reliability of the supply chain. Within the scope of the research, we will use the indicator of the level of supply chain reliability, known in theory and practice (defined as the ratio of exactly completed deliveries by all parameters to their total required quantity) as the measure of the parameter.
  1. 2.

    Inventory management system $$(S_{uz} )\,(j = 2)$$. The problems in the field of supply can be evidenced by the untenable and inefficient inventory management system at the enterprise. Its typical features may, in particular, include constant shortage of stocks, on the one hand, and their surplus—on the other. Thus, the stock shortages are known to cause interruptions in the production process, and, consequently, decrease in sales volumes and decrease in revenue from the sales of products. In turn, the surplus of stocks also leads to a number of problems such as increase in costs for the storage of raw materials, materials or components, physical and moral aging of the latter at the enterprise, etc.

     
As the theory and practice studies show, today there are many methods, approaches and criteria in the field of optimizing inventory management processes. They, in particular, allow you to calculate the required and sufficient level of stock, the scope of the optimal order, etc., thus forming an integrated inventory management system. Within the analyzed aspect, it is expedient to highlight technological complexes for inventory management common in practice (for example, with fixed order scope, with its fixed frequency or with the established frequency of replenishment of stocks to a sufficient level), different models and methods of such management (for example, heuristic, technical and economic calculations, economic and mathematical calculations, etc.), etc. The level of the inventory management system development should be considered as the measure of this parameter. It is proposed to be established by an expert method taking into account the current trends in these processes.
  1. 3.

    Negative reaction from external sales stakeholders $$(R_{zs} ) \left( {j = 3} \right).$$ This parameter is suitable primarily for simulating the impact of incidents in the field of sales. As the study of theory and practice suggests, a significant indicator of the future crisis at an enterprise may include negative reaction to cooperation with the company under review primarily on the part of sales intermediaries and end users (however, potential negative issues can also be evidenced by negative assessments from intermediaries, creditors, financial institutions, shareholders, etc.). This negative reaction may be due to various reasons (e.g., deterioration in product quality, default by the company on its contractual obligations, regular changes in strategy, objectives and policies, frequent conflicts with business entities, etc.). In any case, negative reaction from external sales stakeholders means problems in the field of product sales, in particular, a significant decrease in its volumes, and thus a decrease in the revenue from the sales of products, increase in inventory balances, deterioration of turnover, etc. Within the scope of the study, we will consider the level of negative reaction as a measure of this negative reaction parameter on the part of external sales stakeholders; it is proposed to be calculated as the ratio of the number of external sales stakeholders that negatively assesses the cooperation with the enterprise-producer to the total number of such stakeholders.

     
  2. 4.

    Competitiveness of the marketing mix $$(K_{km} )\,(j = 4)$$. Identification of incidents in the field of sales can be done by identifying problems with the false forecast of demand for products, ineffective positioning of products in the market, inappropriate pricing policies, negative changes in the structure of the orders portfolio, imbalances in marketing policies, inadequate sales promotion, etc. The above should be generalized in the competitiveness parameter of the marketing mix.

     
The level of competitiveness should be considered as the measure of this marketing mix competitiveness parameter; it is proposed to be established by an expert method taking into account the current trends and industry development.
  1. 5.

    Production process quality $$(Y_{vp} )\,(j = 5)$$. This parameter should also be considered generalized during the simulation of the impact of incidents in the industrial sphere of the enterprise. Thus, problems can arise in case of inefficient production technology, deficit or surplus of resources taking into account anticipated expected production volumes, inappropriate level of production capacity, low level of control over individual parts of the production process, inappropriate priorities in the spheres of research and development, etc. It is proposed to consider the above in general within the parameters of the production process quality. Low quality of the production process can result, for example, in the failure of planned production volumes, inappropriate energy consumption, increase in equipment downtime, etc. The measure of this parameter, as in the previous case, should be such quality level, which is also proposed to be established in an organization using an expert method.

     
  2. 6.

    Production process flexibility $$(H_{vp} )\,(j = 6)$$. As theory and practice suggest, production process flexibility is important under the conditions of dynamic environment of the enterprise. It is considered from different aspects in the literature, while most authors hold an opinion that the flexibility indicates the ability of the production process to be promptly readjusted to manufacture new or modified products as a result of changing market requirements. Such a reorganization may include solving various problems, in particular, reequipping of technological processes, changes in the structure of production process or integrating new elements into it, introducing parallel technological channels, etc. It is obvious that the measure of the production process flexibility parameter should be the time to adjust it for the manufacture of a new or improved product (expert estimates should apply).

     
  3. 7.

    The level of planned processes violation $$(P_{zp} )\,(j = 7).$$ This parameter is suitable for simulating the impact of incidents in the field of supply, as well as in the field of production and marketing. Any deviation from the planned parameters may be promptly eliminated and significantly violate the established rules, mechanisms, policies, regulations, etc. The higher the level of violations, the higher the negative effects of crisis phenomena. This parameter must be both a measure and assessed by an expert method at the enterprise.

     

Each of the abovementioned parameters characterizes certain side of the incidents impact simulation in the supply-production-marketing chain. Obviously, under certain conditions, such parameters can complement each other and thereby increase the negative effect. Understanding how these parameters affect the entity’s activities allows you to identify potential problems, make predictions, take preventive measures, etc.

Each business entity has its priority in the identification of incidents in the supply-production-marketing chain within the framework of stress management systems. In many respects, such priority is determined by the activity of the enterprise, its stage of the life cycle, management parameters, etc. On the other hand, it should be borne in mind that different companies will react differently to existing or potential problems in the supply-production-marketing chain.

2.3 Justification of Feasibility of Using the Fuzzy Sets Theory for Diagnosing the Undesirable Deviation Criticality Level

In terms of their content the absolute majority of parameters for simulating the impact of a potential or actual incident that causes undesirable deviation for each link in the supply-production-marketing chain are fuzzy, it is feasible and necessary to use fuzzy set tools for further research. Based on the review and generalization of literary sources [6–10], the theory of fuzzy sets is widely used in solving various economic problems.

Given the above, a subset of parameters used to diagnose the i-th incident (or its prediction) within the framework of stress management systems $$P_{i}$$ should be formed on the basis of a set of parameters $$P$$, while:
$$\left\{ {\mathop {\bigcup }\limits_{i = 1}^{n} P_{i} } \right\} = \left\{ {P_{1} , \ldots , P_{n} } \right\}, \;P_{i} \subseteq P;$$
(5)
$$P_{i} = \left\{ {\mathop {\bigcup }\limits_{j = 1}^{{k_{i} }} P_{ij} } \right\} = \left\{ {P_{i1} , \ldots , P_{{ik_{i} }} } \right\},$$
(6)
where
$$n$$

is the number of potential incidents in the supply-production-marketing chain;

$$k_{i}$$

the number of parameters within the stress management systems associated with the i-th incident.

Thus, taking into account the results presented in the papers [1, 3, 11, 12], we get:
$$\left\{ {\mathop {\bigcup }\limits_{i = 1}^{n} P_{i} } \right\} = \left\{ {\mathop {\bigcup }\limits_{i = 1}^{n} \left\{ {\mathop {\bigcup }\limits_{j = 1}^{{k_{i} }} P_{ij} } \right\}} \right\} = \left\{ {\left\{ {P_{11} , \ldots ,P_{{1k_{i} }} } \right\}, \ldots ,\left\{ {P_{n1} , \ldots ,P_{{nk_{n} }} } \right\}} \right\}.$$
(7)
In our case, taking into account the above parameters in the supply-production-marketing chain (that is, $$n = 3, \;k_{1} = k_{2} = k_{3} = 3)$$ within the framework of stress management systems, we obtain the following: $$P_{11} = P_{1} , P_{12} = P_{2} , P_{13} = P_{7} , P_{21} = P_{5} , P_{22} = P_{6} , P_{23} = P_{7} , P_{31} = P_{3} , P_{32} = P_{4} , P_{33} = P_{7} .$$ Thus, formula (7) will be as follows:
$$\begin{aligned} \left\{ {\mathop {\bigcup }\limits_{i = 1}^{3} P_{i} } \right\} = \left\{ {\mathop {\bigcup }\limits_{i = 1}^{3} \left\{ {\mathop {\bigcup }\limits_{j = 1}^{{k_{i} }} P_{ij} } \right\}} \right\} & = \left\{ {\left\{ {P_{11} ,P_{12} ,P_{13} } \right\},\left\{ {P_{21} ,P_{22} ,P_{23} } \right\},\left\{ {P_{31} ,P_{32} ,P_{33} } \right\}} \right\} \\ & = \left\{ {\left\{ {N_{lp} ,S_{uz} ,P_{zp} } \right\},\left\{ {Y_{vp} ,H_{vp} ,P_{zp} } \right\},\left\{ {R_{zs} ,K_{km} ,P_{zp} } \right\}} \right\}, \\ \end{aligned}$$
(8)
where
$$P_{11} = N_{lp} ,P_{12} = S_{uz} ,P_{13} = P_{zp}$$

appropriate parameters for identifying incidents within the supply section $$P_{{nv_{1} }}$$;

$$P_{21} = Y_{vp} , P_{22} = H_{vp} , P_{23} = P_{zp}$$

appropriate parameters for identifying incidents within the production section;

$$P_{31} = R_{zs} , P_{32} = K_{km} , P_{33} = P_{zp}$$

appropriate parameters for identifying incidents within the marketing section.

Summarizing the above, identification of incidents in the supply-production-marketing chain involves simulating their impact on the corresponding element of the chain. If necessary, the impact of incidents on the entire chain can also be analyzed. Moreover, in each individual case, we should take into account appropriateness of parameters for identifying incidents in terms of sections (in other words, each individual section in the supply-production-marketing chain corresponds to its own set of parameters), as shown in Table 1.
Table 1

Parameters for diagnosing incidents in the supply-production-marketing chain

Parameters

Sections

Supply section

Production section

Marketing section

Reliability of the supply chain $$(N_{lp} )$$

X

  

Inventory management system $$(S_{uz} )$$

X

  

Negative reaction on the part of external sales stakeholders $$(R_{zs} )$$

  

X

Competitiveness of the marketing mix $$(K_{km} )$$

  

X

Production process quality $$(Y_{vp} )$$

 

X

 

Production process flexibility $$(H_{vp} )$$

 

X

 

The level of planned processes violation $$(P_{zp} )$$

X

X

X

Note developed by authors

2.4 Description from the Position of Fuzzy Sets of a Subset of All Possible Fuzzy (Linguistic) Values of Incidents

Then, it is necessary to describe a subset of all possible fuzzy (linguistic) values of the i-th incident, as well as a subset of heuristic rules formed on the basis of fuzzy logistics for each such incident from the position of fuzzy sets. This will eventually identify the level of undesirable deviation in the supply-production-marketing chain caused by the i-th incident.

Considering a subset of all possible fuzzy (linguistic) values of the i-th incident, it is appropriate to note that, in terms of their content, critical undesirable deviations within stress management systems are poorly-structures concepts with high levels of relativity. Therefore, it is proposed to use the combination of expert methods and fuzzy logic to solve the above problem. In particular, it is expedient to use the trapezoidal membership function known in the theory and practice from among the available $$L - R$$ functions (Fig. 1).
../images/462031_1_En_13_Chapter/462031_1_En_13_Fig1_HTML.gif
Fig. 1

Trapezoidal membership function in the theory of fuzzy sets

Note provided on the basis of [6, 13–15]

Thus, description of a subset of all possible fuzzy (linguistic) values of the i-th incident within the framework of stress management systems should be made taking into account the following equations [6, 13–15]:
$$\mu_{A} \left( x \right) = \left\{ {\begin{array}{*{20}l} {0, x < a_{1} ;} \hfill & {} \hfill \\ {\frac{{x - a_{1} }}{{a_{2} - a_{1} }}} \hfill & {a_{1} \le x < a_{2} ;} \hfill \\ 1 \hfill & {a_{2} \le x \le a_{3} ;} \hfill \\ {\frac{{a_{4} - x}}{{a_{4} - a_{3} }} } \hfill & {a_{3} < x \le a_{4} ,} \hfill \\ \end{array} } \right.$$
(9)
where $$a_{1} , a_{2} , a_{3} ,a_{4}$$—are the numerical parameters (actual values) of the trapezoidal $$L - R$$ function, in turn $$a_{1} \le a_{2} \le a_{3} \le a_{4}$$; $$a_{1}$$—are the left zero value of the function; $$a_{4}$$—right zero value of the function; $$a_{2} \,{\text{i}}\,a_{3}$$—points where the value of the membership function is maximum (trapezium vertex) (i.e. the certainty of the allegation of affiliation to a certain term-set).

Taking into account the peculiarities of the tasks solved in the context of simulating critical undesirable deviations in the supply-production-marketing chain, we should stress the appropriateness of using the following approach to the reflection of linguistic variables within the limits given in Fig. 1 of the trapezoidal membership function, i.e.: $$a_{1}$$—pessimistic assessment; $$a_{4}$$—optimistic assessment; $$a_{2} ,a_{3}$$—the most realistic assessment.

Thus, trapezoidal scale of all possible fuzzy (linguistic) values of the diagnosis of the i-th incident will involve a combination of linguistic variables and trapezoidal assessments (Table 2).
Table 2

Trapezoidal scale of all possible fuzzy (linguistic) values of the diagnosis of the i-th incident within the framework of stress management systems

Linguistic variables

Very low

Low

Average

High

Very high

Trapezoidal scales of assessments

(0; 0; 0.1; 0.3)

(0.1; 0.3; 0.3; 0.5)

(0.3; 0.5; 0.5; 0.7)

(0.5; 0.7; 0.7; 0.9)

(0.7; 0.9; 1.0; 1.0)

Note provided on the basis of [6, 13, 16, 17]

Considering the subset of heuristic rules generated by an expert method based on the fuzzy logic for a particular i-th incident $$R_{i}$$, it should be noted that for each such rule the following expression is true:
$$R = \bigcup\nolimits_{i = 1}^{n} {\left\{ {\bigcup\nolimits_{p = 1}^{{R_{i} }} {R_{ip} } } \right\}} = \left\{ {\bigcup\nolimits_{i = 1}^{n} {\left\{ {\bigcup\nolimits_{p = 1}^{{R_{i} }} {L_{ip} \to I_{ip} } } \right\}} } \right\} = \left\{ {\bigcup\nolimits_{i = 1}^{n} {\left\{ {\bigcup\nolimits_{p = 1}^{{R_{i} }} {R_{ip} = (L_{ip} \to I_{ip} )} } \right\}} } \right\},$$
(10)
where
$$R_{ip}$$

is p-th rule for the diagnosis of a potential i-th incident within stress management systems;

$$L$$

a unique state identifier for each i-th incident (and this state takes into account the number of diagnostic parameters of the i-th incident and the number of terms);

$$I_{ip}$$

element of the set of linguistic variables of the i-th incident.

Thus, taking into account the above parameters of diagnosing incidents in the supply-production-marketing chain, it is necessary to indicate the need for the formation of a set of specified rules for each individual section of the chain. Using the logic-linguistic connections within the fuzzy set theory and taking into account that $$I_{ip}$$ can acquire the values “very low”, “low”, “average”, “high” and “very high”, and also denoting the value of the output parameters as H—low, C—average, B—high and D—very high, we give a set of heuristic rules $$R_{ip}$$ for the diagnosis of incidents in the supply-production-marketing chain (Table 3).
Table 3

A set of heuristic rules $$R_{ip}$$ for diagnosing incidents in the supply-production-marketing chain

Rule number $$p$$

Indicators in the supply-production-marketing chain

Result

$$N_{lp} /Y_{vp} /R_{zs}$$

$$S_{uz} /H_{vp} /K_{km}$$

$$P_{zp} /P_{zp} /P_{zp}$$

1

VL

VL

VL

H

2

VL

VL

LW

H

3

VL

VL

AR

H

4

VL

VL

HG

C

5

VL

VL

VH

C

6

VL

LW

VL

H

7

VL

LW

LW

H

8

VL

LW

AR

H

9

VL

LW

HG

C

10

VL

LW

VH

C

11

VL

AR

VL

H

12

VL

AR

LW

H

13

VL

AR

AR

C

14

VL

AR

HG

C

15

VL

AR

VH

C

16

VL

HG

VL

C

17

VL

HG

LW

C

18

VL

HG

AR

C

19

VL

HG

HG

B

20

VL

HG

VH

B

21

VL

VH

VL

C

22

VL

VH

LW

C

23

VL

VH

AR

C

24

VL

VH

HG

B

25

VL

VH

VH

B

26

LW

VL

VL

H

27

LW

VL

LW

H

28

LW

VL

AR

C

29

LW

VL

HG

C

30

LW

VL

VH

C

31

LW

LW

VL

H

32

LW

LW

LW

H

33

LW

LW

AR

H

34

LW

LW

HG

C

35

LW

LW

VH

C

36

LW

AR

VL

H

37

LW

AR

LW

H

38

LW

AR

AR

C

39

LW

AR

HG

C

40

LW

AR

VH

C

41

LW

HG

VL

H

42

LW

HG

LW

H

43

LW

HG

AR

C

44

LW

HG

HG

C

45

LW

HG

VH

B

46

LW

VH

VL

C

47

LW

VH

LW

C

48

LW

VH

AR

C

49

LW

VH

HG

B

50

LW

VH

VH

B

51

AR

VL

VL

H

52

AR

VL

LW

H

53

AR

VL

AR

C

54

AR

VL

HG

C

55

AR

VL

VH

C

56

AR

LW

VL

H

57

AR

LW

LW

H

58

AR

LW

AR

C

59

AR

LW

HG

C

60

AR

LW

VH

C

61

AR

AR

VL

C

62

AR

AR

LW

C

63

AR

AR

AR

C

64

AR

AR

HG

C

65

AR

AR

VH

B

66

AR

HG

VL

C

67

AR

HG

LW

C

68

AR

HG

AR

C

69

AR

HG

HG

B

70

AR

HG

VH

B

71

AR

VH

VL

C

72

AR

VH

LW

C

73

AR

VH

AR

C

74

AR

VH

HG

B

75

AR

VH

VH

D

76

HG

VL

VL

H

77

HG

VL

LW

H

78

HG

VL

AR

C

79

HG

VL

HG

C

80

HG

VL

VH

B

81

HG

LW

VL

H

82

HG

LW

LW

H

83

HG

LW

AR

C

84

HG

LW

HG

B

85

HG

LW

VH

B

86

HG

AR

VL

C

87

HG

AR

LW

C

88

HG

AR

AR

C

89

HG

AR

HG

B

90

HG

AR

VH

B

91

HG

HG

VL

C

92

HG

HG

LW

B

93

HG

HG

AR

B

94

HG

HG

HG

B

95

HG

HG

VH

B

96

HG

VH

VL

B

97

HG

VH

LW

B

98

HG

VH

AR

B

99

HG

VH

HG

B

100

HG

VH

VH

D

101

VH

VL

VL

H

102

VH

VL

LW

H

103

VH

VL

AR

C

104

VH

VL

HG

C

105

VH

VL

VH

B

106

VH

LW

VL

H

107

VH

LW

LW

H

108

VH

LW

AR

C

109

VH

LW

HG

B

110

VH

LW

VH

B

111

VH

AR

VL

C

112

VH

AR

LW

C

113

VH

AR

AR

C

114

VH

AR

HG

B

115

VH

AR

VH

D

116

VH

HG

VL

B

117

VH

HG

LW

B

118

VH

HG

AR

B

119

VH

HG

HG

B

120

VH

HG

VH

D

121

VH

VH

VL

B

122

VH

VH

LW

B

123

VH

VH

AR

D

124

VH

VH

HG

D

125

VH

VH

VH

D

Note developed by the authors

Moreover, it should be noted that due to the same number of diagnostic parameters of these incidents, as well as the same formed set $$I_{ip}$$ (“very low”, “low”, “average”, “high” and “very high”) and the same approach to displaying the output parameters (H—low, C—average, B—high and D—very high), the number and contents of the heuristic rules $$R_{ip}$$ for the diagnosis of incidents in the supply-production-marketing chain will be the same irrespective of the elements of this chain, which somehow simplifies further calculations in this area within of stress management systems at the enterprise.

As shown in information in Table 3, in the analyzed context, we should mention the existence of 125 heuristic rules $$R_{ip}$$ for the diagnosis of incidents in the supply-production-marketing chain.

2.5 Integrated Models for Incident Groups in the Supply-Production-Marketing Chain

Summarizing the above, in stress management systems, an integrated model for incident groups within the supply chain will be as follows:
$$\begin{aligned} P_{{nv_{1} }} & = \left\langle {P_{{nv_{1} }} ,P_{1} ,E_{1} , Z_{1} , R_{1} ,L_{{ks_{1} }} } \right\rangle \\ & = \left\langle {P_{{nv_{post} }} , \left\{ {P_{11} ,P_{12} ,P_{13} } \right\}, \left\{ {\left\{ {E_{111} ,E_{112} ,E_{113} ,E_{114} ,E_{115} } \right\} } \right.,} \right. \\ & \quad \left. {\left\{ {E_{121} ,E_{122} ,E_{123} ,E_{124} ,E_{125} } \right\},\left\{ {E_{131} ,E_{132} ,E_{133} ,E_{134} ,E_{135} } \right\}} \right\}, \\ & \quad \left. {\left\{ {Z_{11} , Z_{12} , Z_{13} } \right\},\left\{ {R_{11,} R_{12,} R_{13, \ldots ,} R_{11,} } \right\},L_{{ks_{1} }} } \right\rangle \\ & = \left\langle {P_{{nv_{post} }} ,\left\{ {N_{lp} ,S_{uz} ,P_{zp} } \right\},\left\{ {\left\{ {E_{{P_{{nv_{post} }} }}^{{N_{lp1} }} ,E_{{P_{{nv_{post} }} }}^{{N_{lp2} }} ,E_{{P_{{nv_{post} }} }}^{{N_{lp3} }} ,E_{{P_{{nv_{post} }} }}^{{N_{lp4} }} ,E_{{P_{{nv_{post} }} }}^{{N_{lp5} }} } \right\}} \right.} \right., \\ & \quad \left. {\left\{ {E_{{P_{{nv_{post} }} }}^{{S_{uz1} }} ,E_{{P_{{nv_{post} }} }}^{{S_{uz2} }} ,E_{{P_{{nv_{post} }} }}^{{S_{uz3} }} ,E_{{P_{{nv_{post} }} }}^{{S_{uz4} }} ,E_{{P_{{nv_{post} }} }}^{{S_{uz5} }} } \right\},\left\{ {E_{{P_{{nv_{post} }} }}^{{P_{zp1} }} ,_{{}} E_{{P_{{nv_{post} }} }}^{{P_{zp2} }} ,E_{{P_{{nv_{post} }} }}^{{P_{zp3} }} ,E_{{P_{{nv_{post} }} }}^{{P_{zp4} }} ,E_{{P_{{nv_{post} }} }}^{{P_{zp5} }} } \right\}} \right\}, \\ & \quad \left\{ {Z_{{N_{lp} }} ,Z_{{S_{uz} }} ,Z_{{P_{zp} }} } \right\},\left. {\left\{ {R_{11,} R_{12,} R_{13, \ldots ,} R_{1125,} } \right\},L_{{ks_{1} }} } \right\rangle . \\ \end{aligned}$$
(11)
Similarly, an integrated model for the groups of incidents within the production processes will be as follows:
$$\begin{aligned} P_{{nv_{2} }} & = \left\langle {P_{{nv_{2} }} ,P_{2} ,E_{2} , Z_{2} , R_{2} ,L_{{ks_{2} }} } \right\rangle \\ & = \left\langle {P_{{nv_{vyr} }} , \left\{ {P_{21} ,P_{22} ,P_{23} } \right\},} \right.\left\{ {\left\{ {E_{211} ,E_{212} ,E_{213} ,E_{214} ,E_{215} } \right\},} \right. \\ & \quad \left. {\left\{ {E_{221} ,E_{222} ,E_{223} ,E_{224} ,E_{225} } \right\},\left\{ {E_{231} ,E_{232} ,E_{233} ,E_{234} ,E_{235} } \right\}} \right\}, \\ & \quad \left. {\left\{ {Z_{21} , Z_{22} , Z_{23} } \right\},\left\{ {R_{21,} R_{22,} R_{23, \ldots ,} R_{11,} } \right\},L_{{ks_{2} }} } \right\rangle \\ & = \left\langle {P_{{nv_{vyr} }} ,\left\{ {Y_{vp} ,H_{vp} ,P_{zp} } \right\},\left\{ {\left\{ {E_{{P_{{nv_{vyr} }} }}^{{Y_{vp1} }} ,E_{{P_{{nv_{vyr} }} }}^{{Y_{vp2} }} ,E_{{P_{{nv_{vyr} }} }}^{{Y_{vp3} }} ,E_{{P_{{nv_{vyr} }} }}^{{Y_{vp4} }} ,E_{{P_{{nv_{vyr} }} }}^{{Y_{vp5} }} } \right\}} \right.,} \right. \\ & \quad \left\{ {E_{{P_{{nv_{vyr} }} }}^{{H_{vp1} }} ,E_{{P_{{nv_{vyr} }} }}^{{H_{vp2} }} ,E_{{P_{{nv_{vyr} }} }}^{{H_{vp3} }} ,E_{{P_{{nv_{vyr} }} }}^{{H_{vp4} }} ,E_{{P_{{nv_{vyr} }} }}^{{H_{vp5} }} } \right\},\left\{ {\left. {E_{{P_{{nv_{vyr} }} }}^{{P_{zp1} }} ,E_{{P_{{nv_{vyr} }} }}^{{P_{zp2} }} ,E_{{P_{{nv_{vyr} }} }}^{{P_{zp3} }} ,E_{{P_{{nv_{vyr} }} }}^{{P_{zp4} }} ,E_{{P_{{nv_{vyr} }} }}^{{P_{zp5} }} } \right\}} \right\}, \\ & \quad \left\{ {Y_{vp} ,H_{vp} ,P_{zp} } \right\},\left. {\left\{ {R_{21,} R_{22,} R_{23, \ldots ,} R_{2125,} } \right\},L_{{ks_{2} }} } \right\rangle . \\ \end{aligned}$$
(12)
An integrated model for the groups of incidents within the marketing processes will be as follows:
$$\begin{aligned} P_{{nv_{3} }} & = \left\langle {P_{{nv_{3} }} ,P_{3} ,E_{3} , Z_{3} , R_{3} ,L_{{ks_{3} }} } \right\rangle \\ & = \left\langle {P_{{nv_{zb} }} , \left\{ {P_{31} ,P_{32} ,P_{33} } \right\}, } \right.\left\{ {\left\{ {E_{311} ,E_{312} ,E_{313} ,E_{314} ,E_{315} } \right\},} \right. \\ & \quad \left\{ {E_{321} ,E_{322} ,E_{323} ,E_{324} ,E_{325} } \right\},\left. {\left\{ {E_{331} ,E_{332} ,E_{333} ,E_{334} ,E_{335} } \right\}} \right\}, \\ & \quad \left. {\left\{ {Z_{31} , Z_{32} , Z_{33} } \right\},\left\{ {R_{31,} R_{32,} R_{33, \ldots ,} R_{31,} } \right\},L_{{ks_{3} }} } \right\rangle \\ & = \left\langle {P_{{nv_{zb} }} ,\left\{ {R_{zs} ,K_{km} ,P_{zp} } \right\},\left\{ {\left\{ {E_{{P_{{nv_{zb} }} }}^{{R_{zs1} }} ,E_{{P_{{nv_{zb} }} }}^{{R_{zs2} }} ,E_{{P_{{nv_{zb} }} }}^{{R_{zs3} }} ,E_{{P_{{nv_{zb} }} }}^{{R_{zs4} }} ,E_{{P_{{nv_{zb} }} }}^{{R_{zs5} }} ,} \right.} \right.} \right. \\ & \quad \left\{ {E_{{P_{{nv_{zb} }} }}^{{K_{km1} }} ,E_{{P_{{nv_{zb} }} }}^{{K_{km2} }} ,E_{{P_{{nv_{zb} }} }}^{{K_{km3} }} ,E_{{P_{{nv_{zb} }} }}^{{K_{km4} }} ,E_{{P_{{nv_{zb} }} }}^{{K_{km5} }} } \right\},\left. {\left\{ {E_{{P_{{nv_{zb} }} }}^{{P_{zp1} }} ,E_{{P_{{nv_{zb} }} }}^{{P_{zp2} }} ,E_{{P_{{nv_{zb} }} }}^{{P_{zp3} }} ,E_{{P_{{nv_{zb} }} }}^{{P_{zp4} }} ,E_{{P_{{nv_{zb} }} }}^{{P_{zp5} }} } \right\}} \right\}, \\ & \quad \left\{ {R_{zs} ,K_{km} ,P_{zp} } \right\},\left. {\left\{ {R_{31,} R_{32,} R_{33, \ldots ,} R_{3125,} } \right\},L_{{ks_{3} }} } \right\rangle . \\ \end{aligned}$$
(13)
Simulation of stress management systems in the supply-production-marketing chain enables us to identify the level of influence of potential incidents on each of these areas. If necessary, it is also possible to determine the level of influence of these incidents on the chain as a whole. Therefore, under these conditions, the set of heuristic rules will include the following components:
$$P_{{nv_{1} }} = P_{{nv_{post} }} ,P_{{nv_{2} }} = P_{{nv_{vyr} }} , P_{{nv_{3} }} = P_{{nv_{zb} }} .$$
(14)
The proposed method for diagnosing the level of criticality of the undesirable deviation in the supply-production-marketing chain, based on simulating the effect of a potential or actual incident that causes such a deviation on each link in a given chain using a number of representative parameters, has been applied in a number of domestic companies. For example, at PJSC “Lviv Locomotive Repair Plant”, certain actual incidents in Q4 2017 in the supply and marketing chain were diagnosed as having caused critical undesirable deviations (Table 4).
Table 4

Results of diagnosing criticality of some actual incidents at PJSC “Lviv Locomotive Repair Plant” in Q4 2017

Links of the supply-production-marketing chain

Incidents

Fuzzy (linguistic) values of influence

Level of criticality (identified by a set of heuristic rules $$R_{ip}$$)

Supply

Delays in the supply of brake equipment

Reliability of the supply chain $$(N_{lp} )$$

Inventory management system $$(S_{uz} )$$

The level of planned processes violation $$(P_{zp} )$$

High

High

High

High

Noncompliance with the standards of purchased contactors of the MK series

Reliability of the supply chain $$(N_{lp} )$$

Inventory management system $$(S_{uz} )$$

The level of planned processes violation $$(P_{zp} )$$

Average

Average

Average

High

Marketing

Different decrease in demand for the thermal treatment of shafts by external customers

Negative reaction on the part of external sales stakeholders $$(R_{zs} )$$

Competitiveness of the marketing mix $$(K_{km} )$$

The level of planned processes violation $$(P_{zp} )$$

Low

Low

Average

Low

Negative feedback on the quality of wheeled pairs repair

Negative reaction on the part of external sales stakeholders $$(R_{zs} )$$

Competitiveness of the marketing mix $$(K_{km} )$$

The level of planned processes violation $$(P_{zp} )$$

High

High

High

Average

Note established by the authors

Thus, the results of diagnosing the criticality of certain actual incidents at PJSC “Lviv Locomotive Repair Plant” in Q4 2017 make it possible to conclude that it is expedient to interpret delays in the supply of brake equipment, as well as negative feedback on the quality of wheel pairs repair as critical. The foregoing suggests the possibility of practical use of the proposed method for diagnosing the level of criticality of undesirable deviation in the supply-production-marketing chain.

It should be noted that PJSC “Lviv Locomotive Repair Plant” is one of the leading enterprises of Ukrainian railway, and this justifies its selection as an object for diagnosing the possibility of implementation the method of undesirable deviation criticality level diagnostic in the supply-production-marketing chain. Today, this is one of the leading enterprise in Ukraine in repairing locomotives. The Company is equipped with the current techniques and the breakthrough technologies are being widely used. The plant carries out the medium repairs and overhaul of the trunk electric locomotives, both direct and alternating current, repairs of industrial railway transport and the quarry electric locomotives traction units, as well as repairs the traction motors, auxiliary electrical machines, wheel sets, etc.

PJSC “Lviv Locomotive Repair Plant” is a successful modern enterprise, which is developing dynamically and efficiently provides its services in the rolling stock repairs and modernization of the whole post-Soviet area railways. For the consistently high quality and benefits achieved in the Ukrainian business of the repair and maintenance of the railway rolling stock, the plant was repeatedly awarded with the “Star of Quality” and as the “Enterprise of the Year” of the National Rating of Goods and Services Quality of Ukraine, as well as with the International Economic Rating “League of the Bests”. In recent years, the plant won the winner’s fame of the “Face of the City” contest in the category “Lviv Industrial”.

At PJSC “Lviv Locomotive Repair Plant”, the certification center TUV SUD Management Service GmbH has realized the certified audit of the quality management system for compliance with the requirements of ISO 9001:2015. Based on the audit results (report number 707084328), a positive decision according the certificate issue has been taken. The Certificate is in force from 01/05/2018 to 01/04/2021.

A more thorough analysis of the undesirable deviations criticality in the supply-production-marketing chain at PJSC “Lviv Locomotive Repair Plant” made it possible to single out the following factors of risk:

  • the market conditions change;

  • failure to provide the plant activities with the sufficient amount of raw materials, materials and energy;

  • a lack in training the plant employees to produce new types of competitive products;

  • deterioration of the general economic situation in Ukraine, the tax law instability.

Key problems that make a significant influence on the PJSC “Lviv Locomotive Repair Plant” activities from the position of critical undesirable deviations are the problems of political, economic and production-technological character. In particular, the political instability in the country and management change of the branch made a negative impact on the stability and sustainability of production due to the insufficient planning and financing the orders volumes of Ukrzaliznytsia. Too harsh pricing policy of the monopolist-customer has a harmful impact on the production and solvency; because of the lack of working capital, the timely manufacture kitting-up gets broken, the time schedules and technologies of repairs works deform, and this leads to quality deterioration and the production costs increase. Besides, in such conditions the company is not able to renew the fixed assets according the plan and fully, to absorb new technologies and new types of overhaul and modernization. The financial plan of PJSC “Lviv Locomotive Repair Plant” has proposed a complex of measures for reducing possible risks, which would allow to minimize these risks, as the financial plan is based on the calculations and on the company’s abilities to improve the financial, organization and production situation. At this stage the Company requires the support of the Government of Ukraine in matters of the plant reconstruction by supporting the potential investors.

At the same time, it should be mentioned that the production and services in electric locomotives overhaul at PJSC “Lviv Locomotive Repair Plant” is profitable and have perspectives of the further demand growth on the market of Ukraine and CIS countries. Business operations financing is realized according to the principle of planned self-financing. The funds receipted from the sold production are being allocated by the enterprise on its own to cover the items of production costs, wage fund, taxes, contributions to the social security funds, losses, etc. The Company, in accordance with the agreements terms, must receive cash for the performed products (services) within a period not exceeding 10 days after the works are completed. Most often it happens that the customers postpone their payments. That is why the Issuer is making every effort to replenish the working capital in order to provide payments of wages, taxes, compulsory payments, and so on. To do this, the Issuer uses the banking services to get the short-term credits. It is also provided for the Company to use the funds to purchase the fixed assets, intangible assets and for the other purposes that contribute to the production development, improve social conditions of the Company’s employees and their material welfare. Renovation of the production fixed assets, implementation of the new breakthrough technologies, mastering the new types of repairs require the commitment of significant financial resources in the form of profitable investment projects. The separate investment projects of production diversification by means of electric locomotives modernization development are promising.

The implementation results of the proposed method of diagnosing the level of undesirable deviation criticality in the supply-production-marketing chain allowed PJSC “Lviv Locomotive Repair Plant” not only to simulate the influence of the potential or current incident, which stipulates for such a deviation, to the each link of the mentioned chain using the range of representative parameters, but in so doing to increase the volumes of sales and improve the financial indicators due to the appropriate decision-making (Table 5).
Table 5

Information about the product sales of PJSC “Lviv Locomotive Repair Plant” in the 1st quarter of 2018

#

The main types of the enterprise products

Output volume

Sales volume

In monetary terms, thousand UAH

In percent to the total output (%)

In monetary terms, thousand UAH

In percent to the total sales volume (%)

1

Services in the rolling stock repair and modernization

14,397

54.13

20,475

67.69

2

Services in the wheel sets repair

8234

30.96

7275

24.05

3

Services in the traction motors repair

2549

9.58

1674

5.53

4

Other services

1218

4.58

824

2.72

Note constructed by the authors on the basis of PJSC “Lviv Locomotive Repair Plant” financial statements

3 Conclusion

The proposed method for diagnosing the level of criticality of an undesirable deviation in the supply-production-marketing chain enables the analysts to simulate the impact of a potential or actual incident that causes such a deviation on each link in the chain using a number of representative parameters (reliability of the supply chain, inventory management system, negative response from external marketing stakeholders, competitiveness of the marketing complex, quality of the production process, flexibility of the production process, level of planned processes violation). Simulation of stress management systems in the supply-production-marketing chain enables us to identify the level of influence of potential incidents on each of these areas. If necessary, it is also possible to determine the level of influence of these incidents on the chain as a whole.