=Paper=
{{Paper
|id=Vol-2403/paper2
|storemode=property
|title=Assessing the Reviving Risks while using the
Manufacturing Resource Planning System at Agribusiness
Enterprises
|pdfUrl=https://ceur-ws.org/Vol-2403/paper2.pdf
|volume=Vol-2403
|authors=Olena Ponochovna,Volodymyr Piliavskyi,Petr Makarenko
|dblpUrl=https://dblp.org/rec/conf/icteri/PonochovnaPM19
}}
==Assessing the Reviving Risks while using the
Manufacturing Resource Planning System at Agribusiness
Enterprises==
https://ceur-ws.org/Vol-2403/paper2.pdf
Assessing the Reviving Risks while using the
Manufacturing Resource Planning system at agribusiness
enterprises
Olena Ponochovna1 [0000-0002-4377-0633], Volodymyr Piliavskyi2 [0000-0003-3311-0559], Petr
Makarenko1 [0000-0002-8967-9122]
1
Poltava State Agrarian Academy PSAA, Poltava, Ukraine
elena.ponotchovna@gmail.com, mpm0907_1949@ukr.net
2
International University of Business and Law, Kherson, Ukraine
v.piliavskyi@ukr.net
Abstract. The article proposes to use the mathematical apparatus of economic
analysis’ axiomatic theory for studying the reviving risks. Adapting of the
reviving risks’ evaluation method for simple global supply chains of financial
and production relations of agricultural enterprises has been conducted. The
methodical apparatus for measuring the logistic risks of optimal raw materials’
and final products’ stocks of agro-industrial enterprises has been provided. It is
proposed to implement the offered models and methods into Manufacturing
Resource Planning system, for rapid prevention and minimization of risks.
Keywords: Business Intelligence, Reviving, Manufacturing Resource Planning,
Universal Logistics System, Simple Global Supply Chain
Key terms. Mathematical Modeling, Mathematical Model
1 Introduction
Reviving (supply logistics) for the enterprise of agro-industrial production operates on
a principle of supply chain. This principle is related to transmitting the inventories,
raw materials, ingredients, final products, finances, information and services through
a network of suppliers, manufacturers, distributors, transfer companies and trade.
In a somewhat simplistic sense, the logistics chains consist of a supplier and a
consumer [1] or a set of partners in production and commercial activity [2]. Such
chains are presented as a structure that specifies all the links of material, financial and
other flows.
The process of logistics management covers up the supply of raw materials,
replenishment of inventories, monitoring the status of stocks in distribution network,
calculation of the filling point, forming of supply, production and marketing links. In
the US and Western Europe agribusiness enterprises, the MRP I (Material
Requirements Planning) [3] and MRP II (Manufacturing Resource Planning)
information systems are used to manage logistics. As experts [4] point out, MRP II
information systems today operate on-line with daily database updates.
The basis for building these productions, supply, sales and marketing Push-systems
has become the RP (Requirements / resource planning) logistic concept [5].
The production schedule plays a major role in MRP II. It is regulated and
controlled by the manufacturer within the production units of integrated formation.
The MRP II system operates under uncertainty of the external environment, the
probability to experience damage or lose a profit, possible failures and the threats of
risks. Therefore, calculation of risks, including reviving of the marketing component,
is an important component of assessing the enterprise’s potential. Reviving risks are
associated with the possible losses, while forming the stocks of agrarian raw materials
by the industrial producer and promoting the final products at the markets.
The problem of measuring the potential risks of marketing activities is extremely
relevant for agro-industrial food producing enterprises in terms of both − further
economic analysis theory development, and practical significance. The relevance of
this problem is first of all caused by the requirements of functional stability and
profitability, as the basis for quantitative assessment of the of universal logistics
systems’ competitiveness.
The article proposes to adapt the mathematical apparatus of economic analysis’
axiomatic theory [7] for assessing the reviving risks of agricultural enterprises.
Adapting of the reviving risks’ evaluation method has been conducted for simple
global supply chains, and simplifies its implementation into the MRP II information
systems of agribusinesses management.
2 Mathematical Approach and Modeling Technique
The risks of logistic supply cover up a many of initially uncertain factors. They
include:
- changes in the optimal amount of raw materials’ and final products’ stocks;
- refusal or termination of the activities by intermediaries or investors;
- force majeure circumstances, caused by normative and legal issues;
- termination of raw materials processing, or their insufficient supply;
- change of sales law, caused by changes related to innovations in qualitative
modification requirements, etc.
The effect of these factors is accompanied by a comprehensive reengineering of
logistics chain’s technological extension. It requires the use of a mathematical
apparatus for describing logistic chains, impacts on them, and the dynamic changes in
those chains.
The mathematical apparatus of the axiomatic theory of economic analysis (ATEA)
has been used for further research [6]. Within the post-neo-institutional economic
theory, the enterprise of agro-industrial production is viewed as a disjunctive set of
simple and elaborate global chains of financial and production relations. These chains
are the conservative structures. They are functionally oriented on producing and
selling certain types of competitive products on the market [7].
The competitive situation arises (under axiom А.1 of АТЕА [6]), when at least two
universal logistics systems start operating on the global market. The diagram of the
local chain of financial and production relations for such systems looks as follows:
Y X
B A C (1)
where: В - is an agrarian enterprise, А - processing enterprise, С - commodity
market, Y - raw materials, X - final product.
The agrarian enterprise B is a supplier of raw materials Y. Processing enterprise А
is a consumer (partially or completely) of raw materials Y. It also sells final product
X at the commodity market C.
The diagram (1) consists of two parts. Due to competition and force majeure
circumstances, there is a there is a possibility of loss of some part of the market,
which can be qualified as the risks of universal logistics systems’ operation in the
second part of the diagram (1).
Let’s assume that some parts of the universal logistics system include a number of
logistics risks. Distinguish the logistic risk of changing the optimal stock of raw
materials and final products (LR1) among them. For a logistic risk LR1 the analytical
apparatus for graphic research includes concepts and ratios, described in [8].
Let’s take a look at competitive change in the parameters of M (optimal stock of
raw materials), and T (time of stock turnover) the sales law (SL) M=P(T), when
y=P(t), t>0. The change is made in the direction of reducing the parameter of M to M
and, possibly, an increase of the parameter T to T . After change, the sales law will
look as follows: M P T .
Reduction of the parameter M is measured by:
M M1
M, M1 100% . (2)
M
It shows the percentage of the lost market share for the period T, i.e. M1 P T .
Changing the sales law and measuring the lost market share are illustrated in the
Fig. 1, a.
y y
y=P(t) y=P(t)
M M
MP T y=P(t) M g n;n 1 T y g n;n 1 t
M1 g n;n 1 T
M1
t t
0 T T mT, 0 (m+1)T, T T
a) b)
Fig. 1. Comparison of sales laws charts: a) initial y=P(t) and modified within the competitive
changes of sales market conjuncture y P t ; b) sales law y=P(t) at the last stage
[mT,(m+1)T] and the law of supply y gn;n 1 t at the first stage 0;T .
The maximal technological extension in explicit form (4) affects the change of the
diagram (1). The diagram of commercial extension of financial and industrial
relations’ logistics chain looks as follows (5).
Bn , A*Bn 1,..., A*B1, A*B, A,C . (3)
Y Y
i 1 A B j1 Y Y Y Y X
Bp
p
... i
i ... C B j
j
... A C . (4)
Expanding the logistic chain of financial, production, supply and distribution
structures is carried out to enhance the synergy effect and reduce the logistics costs.
Such changes will significantly affect the value added and the cost price of final
products X.
It should be noted that in the simple global chain of financial and production
relations of the agricultural enterprise (4) there is a sales law y=P(t), which is
characterized by the initial parameters M and T. М – submission and α – submission
of the product indicator X at maximum technological extension (3) look as follows:
M R ind X Y tec Mn , Mn 1,..., M1, M, m X . (5)
R ind X Y tec n , n 1 ,..., 1 , 0 , (6)
and at commercial extension (4):
M R ind X Y tec Mp , Mp1,..., M1 , M, m X . (7)
R ind X Y tec p , p 1 ,..., 1 , 0 , (8)
In ratios (6) and (8), the transformation coefficients α (0) and α '(0) are determined
by the equality:
M
0 0 . (9)
m X
The power of reviving in a simple global chain of financial and production
relations of an agricultural enterprise (3) is determined by the value:
rev
f tec ind X Y
n tec 1
. (10)
Ntec
f tec ind X Y 1
Let c’k (k=0,1,2,…,p) – be the cost of raw material per unit in the state of k. In this
state, the number of raw material units will be M’ k. СХ – the cost price per unit of the
final product, taking into account only the Y component. Then the value added at
each stage of the substance’s transformation can be determined by equation:
Mk
Ck 1 Ck 1 M Ck , k 1, 2,..., p,
k 1 , (11)
C C ,
0 0
Y X
and in the elementary structure A - by the equation:
M
CX CX C , (12)
m X 0
Expressions, ratios and equations (2) – (12) should be considered within the TVS
logistic methodology [6]. They are the basis for the analytical and graphic apparatus
of quantitative risk assessment, related to the functioning of a simple or a branched
global chain of financial and producing relations.
All further theoretical constructions will be conducted assuming that the
diversification tendencies of the global sales market within the effective marketing
policy in a simple global chain of agricultural enterprise’s financial and producing
relations (3) are predictable. Thus, it is possible to quantify the logistic risk LR1,
related to the change of sales law from y= P(t) to y P t .
Let’s assume that functioning of a simple global chain of financial and producing
relations of an agricultural enterprise (3) within the current sales law y=P(t) stops at
the moment of time, with further sale of an X product in the amount of M during the
period [mT,(m+1)T] (fig.1,b).
Producing the X product in the amount of M within the new sales law y P t
starts at the moment of time t=mT. This results in a tonne-modification of given M-
and α- product indicators of X ((3) and (4) accordingly). Modification is related to the
replacement of the value M to M . Reviving (supply logistics) also requires some
changes that will affect all equilibrium and supply laws during M M and T T
transitions. Note that the rebooting of a simple global chain of financial and
producing relations (3) with the replacement of the sales law from y=P(t) to y P t
happens from the period t=mT. To this moment, every manufacturing enterprise A*B k
(k=0,1,2,…n) has a raw material Y in the state of Yk and in the amount of Mk Mk
with excess of stock Mk Mk Mk . Regarding the sales law y P t , an
enterprise has excess of stock of the final product (the surplus)
m X m X m X . The system of redundant stocks of raw materials and final
products looks as follows:
Mn , Mn1,..., M1, M,m X . (13)
It is called the materialization of the investigated logistic risk LR1 of a simple
global chain of an agricultural enterprise’s financial and producing relations (3). It
should be noted that information regarding supply laws y=g n;n+1(t) in the simple global
chain (3) corresponds to the economic registration certificate (form ERC5), presented
in [8].
The cost of the risk LR1 essentially depends on how the distribution function of the
logistics management from the moment of time t=mT will be performed. To assess
the risk LR1, let’s look at two cases:
1. If the simple global chain of agricultural enterprise’s financial and producing
relations (3) will function according to the scheme (1), then the cost of risk LR1
should include the costs of long-term storage of excessive stocks (13) in the network
of warehouses of processing enterprises (Bn,A*Bn-1,…,A*B1,B). Herewith, there is a
transactional sales law y=P(t) acts during the last time interval [mT, (m + 1) T] of the
X
conditions of the contract for the supply of final product X within the link A C .
Expenditures on producing the excess stock of finished product X in quantity of
m X is calculated within the new sales law y P t during the first time interval
1
0;T . As a result we received m X g t , gn;n 1 t M
0 n;n 1
mn;n 1 n 1 T; t .
2. If a simple global chain of financial and production relations of the agricultural
enterprise (3) will function according to the scheme presented on (4), then the supply
of final product within the sales law y P t will start from the moment of time
X
t T . There is a transactional sales law y=P(t) acts within the link A C
during the first time interval [mT, (m+1)T] and the contract for supply of final
product X supply is being executed.
Thus, the costs related to the logistic risk LR1 will include the cost of maintaining
the excess stocks (13), and expenses on producing the initial stock of final product in
quantity m X
1
g
0 n;n 1
T , where,
gn;n 1 t Mmn;n 1 n 1 T; t ,
0t T.
In both cases, solving the issue of functioning of simple global chain of agricultural
enterprise’s financial and production relations (3) is related to the logistic risk LR1,
and should take into account the presence of excessive stocks (surpluses) of raw
materials and final products. These stocks exert financial pressure on the relevant
global chain. In this situation, reducing the pressure on warehouses of manufacturing
enterprises (Bn,A*Bn-1,…,A*B1,B), that store the above-mentioned excess stocks, is
possible only taking into account the specificity of the functional schemes, presented
in (1) and (3).
Unloading a simple global chain of financial and production relations of
agricultural enterprise (3) and its transition to a stable functioning mode should be
carried out in a spirit of reengineering. Reengineering modifies the global supply laws
and actively uses the TVS methodology.
Let us give the examples of the corresponding schemes of unloading a simple
global chain (3). For (1) at level M R ind X
Y tec and the time t, which fulfills the
inequality t T we receive:
Mn , Mn 1 Mn 1,..., M M, m X
Mn , Mn 1, Mn 2 Mn 2 ,..., M M, m X ... . (14)
Mn , Mn 1,..., M, m X Mn , Mn 1,..., M, m X .
For (3) from the moment of time t T we have:
Mn Mn ,..., M M, m X
Mn , Mn 1 Mn 1,..., M M, m X ... . (15)
Mn , Mn 1,..., M, m X Mn , Mn 1,..., M, m X .
The analysis of M- modification presents the product indicator X, relative to the
raw materials Y (14) and (15). It shows that the transition of a simple global chain of
agricultural enterprise’s financial and production relations (3) to a stable functioning
mode will happen accordingly for n+1 і n+2 time intervals, lasting T . Herewith, in
both cases, a simple global chain of financial and production relations of the
agricultural enterprise (3) will undersupply the final product X to market C in an
amount of n 1 m X and m X n 1 m X with the equal deficit in an
amount of n 1 m X m X . It is obvious, that the cost of risk LR1 by the
scheme (3) will be greater than by the scheme (1). Thus, direct losses of agro-
industrial producing enterprises, integrated into a simple global chain of financial and
production relations (3), for the period of their overload will be less, than
CX n 1 m X m X . This makes up a part of the cost of logistic risk LR1,
without taking into account the cost of storing the excess stocks (13) in dynamics of
their unloading.
3 CASE Study of Defining the Logistics Risk LR1 Indicators in
the Supply of Raw Materials from the External Sources for LLC
«Globinsky Meat Factory»
Let’s see the example of calculation the risk in the logistic chains for Limited
liability company (LLC) «Globinsky Meat Factory» [10]. The input data for the
calculation was obtained from the internal sources (local enterprise accounting system
based on Database Management System, DBMS), and from the reporting
documentation of 2018.
The amount of annual processing of the meat factory is 300-310 thds. pigs.
According to the industrial technology accepted by the enterprise, 5000 of them
arrives for processing from two internal complexes of LLC «Globinsky pig complex»
every 7 days (total of about 250 – 256 thds. per year. The rest of the livestock – 50-54
thds pigs the meat factory purchases from external sources. The raw materials from
agricultural enterprises come for processing unevenly, in the amount of (М) and the
time (Т), therefore, a partial process of the livestock supply in a simple global chain
of financial and industrial relations is susceptible to randomness. According to the
internal reporting, a graph of the function X(t) was formed, describing the excess
stock and shortage of raw materials for annual processing (Fig.2). When constructing
a graph, it was considered that the meat factory has carried out the external purchases
of raw materials during 260 days.
The average meaning of the function X t is a constant value. If a period when the
demand for raw materials is not satisfied consider the refusal days and define them as
n1, then per year (n = 365) the assessment of the probability of supply failure P and
the probability of risk-free supply − Q count as:
n 96 45 n n1
P 1 0,386; Q 1 P 0,614.
n 365 n
The value of Q is called the reliability coefficient. Manager does not avoid the
logistical risk LR1, but seeks to reduce it to a minimum level, by correctly
determining the specified supply reliability of supply. If the rate of refusal is
increased by a constant value (shown by a dotted line in Fig. 2), then the duration of
the deficit will be limited to the time interval, supply reliability will increase.
While maintaining the amount of stock Mk Mk at a certain normative level, it
will correspond to some supply reliability Q(M k).
Fig. 2. Random changes in stock of raw materials of LLC «Globinsky Meat Factory» meat
processing enterprise from external suppliers
The data from the reporting documents was used for further work. The SMIDA
system [11] provides the following types of access to reporting data:
- direct access to tables and database records via API;
- access to web pages with reporting data with html-markup;
- access to reporting data in the form of a xml-document.
Common practice is placing the reporting documents in the form of pdf files, less
often − doc/docx, on the enterprise’s web pages.
Thus, the use of the proposed model for estimating the reviving risks in the
Manufacturing Resource Planning System provides the following data processing
scenarios:
a) full data integration at the middleware, using the API;
b) partial integration, using middleware applications conduct parsing of web pages
or xml-documents;
c) manual data integration, which requires preprocessing of downloaded reporting
documents in pdf / doc / docx formats.
Depending on the chosen scenario, DBMS resources (calculated SQL queries),
middleware applications or application software packages (MS Excel, MathCad,
Matlab and similar) can be used to conduct calculations.
An example of calculation the risks in logistics chains of external sources of raw
materials of LLC «Globinsky Meat Factory» is conducted in a manual mode, using
the MS Excel table processor.
Meat-processing enterprise determined the risk conditions by three scenarios (A, B,
C) of raw materials costs, obtained from the external sources. The excessive (beyond
the norm), average and deficient purchases of raw materials are being presented
(tabl.1).
Table 1. Risk conditions, depending on the daily cost of purchasing pigs in live (wet) weight of
meat processing enterprise
Stocks of raw The magnitude Name of risk Raw materials costs under
materials of the risk gradations procurement scenarios
(Purchases) (probability of (thds. UAH)
undesirable A B C
outcome)
Excessive 0,1 Min 1100 1300 1500
Average 0,4 Avg 800 900 1000
Deficient 0,7 Max 400 500 300
As a result, the following absolute risk measures have been calculated: rank, mean
absolute deviation, dispersion, standard deviation, variance and semi-variance.
Additional indicators – semi-variance derivatives – deviation and coefficient of
variation have also been calculated (tabl.2).
Table 2. The results of estimating the reviving of raw materials from external sources for LLC
«Globinsky Meat Factory»
Absolute risk measures Designation The value of risk indicators LR1
A B C
Rank Rg 700000 800000 120000
Expected average costs R 710000 840000 760000
Mean absolute deviation MAD 142000 168000 152000
Variance σ 85720 103520 225920
Standard deviation ε 292,8 321,7 475,3
Semi-variance SV 9610 46240 148120
Deviation, % SV/ε2 11,21 44,67 65,56
Coefficient of variation ν 0,412 0,383 0,625
Thus, the risk level LR1 for partial purchases of raw materials from external
sources for LLC «Globinsky Meat Factory, according to the coefficient of variation,
is the highest in C logistics system. Such type of raw material supply scenario is not
recommended to use.
4 Conclusions
The article uses the mathematical apparatus of the axiomatic theory of economic
analysis (АТЕА) [6] for quantitative measurement of the logistic risk of changing the
raw materials’ and final products’ optimal stocks. The use of the proposed method
allows reducing losses and measuring the lost market share, related to changes in
sales stocks.
The structures and financial consequences of a simple global chain’s of financial
and production relations functioning, under emergence of a logistic risk, have been
reviewed. The changes of the optimal stock of raw materials and final products have
been researched. The results of the research can be used in MRP II information
systems. It is an effective motivation for logistics management, as planning of the
needs for productive resources allows enterprises to gain some benefits in improving
the quality of their marketing potential (minimal levels of raw materials’ and final
products’ stocks, and reducing the duration of the order fulfilment cycle). It also
reduces the logistics costs of storing and maintaining the stocks, due to coordination
of supplies within the logic chain. Thus, according to data [4,9], MRP II allows to
reduce the amount of stocks by 17% (in terms of value), the costs of raw materials’
purchase by 7%, as well as significantly increase the profitability of production.
The proposed estimation of quantitative measurement of a logistic risk improves
the level of information systems’ use, such as MRP II. This allows bettering meet the
consumer demand by reducing the duration of production cycles and the time of raw
materials’ surpluses turnover, better supply organization, more efficient use of all
kinds of resources, and to reduce the total costs' production, supply and sales.
References
1. Sumets, O. Logistics: Theory, Situation, Practical Problems / Part 1. Logistics as an
Instrument of a Market Economy. Hi-Tech Press, Kiev (2010). (In Ukrainian)
2. Supply chain and logistics terms and glossary.
http://www.iwla.com/assets/1/24/2010_Glossary_of_Terms_10.7.11.pdf, last accessed
2019/02/24.
3. Burcher, P.: Material Requirements Planning. Wiley Encyclopedia of Management. 1-2
(2015).
4. Kurbel, K.: MRP II: Manufacturing Resource Planning. Progress in IS. 61-93 (2012).
5. Moses, S., Sangplung, W.: Resource planning for just-in-time make-to-order
environments: A scalable methodology using tabu search. Cogent Engineering. 4, (2017).
6. Shcholokova, T.: Bases of Axiomatic Theory of Economic Analysis Journal of Applied
Mathematics. Statistics and Informatics (JAMSI) 5, 61-97 (2009).
7. Kobzev, V. Logistic management. 3nd edn. Noulidg, Lugansk (2014). (In Ukrainian)
8. Shcholokova, T.: Bases of organization of logistic audit of universal logistic systems.
Synergy 2, 33-51 (2017). (In Russian)
9. Johnson, P., Flynn, A.: Purchasing and supply management. McGraw-Hill Education, New
York, NY (2015).
10. Meat processing plant “GLOBINO”. http://eng.globino.ua/mjasokombinat, last accessed
2019/05/07
11. Stock market infrastructure development agency of Ukraine (SMIDA).
https://smida.gov.ua/, last accessed 2019/05/07