Economic Modelling of the Manufacturing Machines Competitiveness
DASCHIEVICI LUIZA, GHELASE DANIELA
Faculty of Engineering and Agronomy,
Braila “Dunarea de Jos” University of Galati,
47, Domneasca St., Galati,
ROMANIA
Abstract: - The paper refers to a method of competitive management of the machines of the manufacturing
systems in the conditions that they execute small series works to order. In practice, order acceptance decisions
and production planning are usually taken separately. The sales department makes decisions regarding the
acceptance of orders, while the production department is responsible for production planning for the fulfillment
of accepted orders. In most cases, the decision to accept the order does not involving the production department
and the information about the planning of available production capacities is incomplete. In this context, the
purpose of the paper is to highlight the econometric correlations between technique-technology and
competitiveness, to study the role of the parameters related to them in the acquisition, and increase the
manufacturing machines' competitiveness.
Key-Words: - manufacturing machines system, monitoring and control of manufacturing process,
manufacturing machines' competitiveness, performance of the manufacturing system, profit
rate, market, customer requirements.
Received: August 15, 2023. Revised: December 17, 2023. Accepted: February 14, 2024. Published: April 10, 2024.
1 Introduction
Ensuring permanency competitiveness is the main
objective of any management system. The
specialized literature concerning competitiveness
does not involve the term innovation except at the
level of management policies, but the innovation-
competitiveness relationship studied in a technical
and technological context is much more important.
Innovation can be seen as a main component of
competitiveness that causes continuous
improvement, which is the engine of the total quality
system, the foundation of competitiveness. That's
why, in this context, innovation gets the certification
of analysis parameters and competitiveness
evaluation indicators. In an enterprise, the
managerial solutions and technical solutions are the
ways to stimulate innovation. A company's
technological strategy is the path it chooses for the
development and use of technologies. Technologies
represent a complex set of knowledge, means
(equipment), and procedures used in a local context
in advantageous economic conditions. In the
concrete conditions of companies, the priority is
precisely the technological strategy with all that it
implies. Even if the value of the investments is high,
the amortization is slow, a courageous approach to
the problem is necessary.
In conclusion, from the analysis of the literature,
[1], [2], [3], [4], [5], [6], an asymmetric or one-sided
approach to the competitiveness of industrial
enterprises, and especially those with mechanical
specifications, results, proving necessary to
reconsider in the context of "technical
competitiveness" inextricably linked to quality
through innovation.
The performance of the manufacturing system
depends on how it is managed. In several specialized
works, for example, [7], [8], [9], [10], references are
made to the relationships between the parameters of
the processing regimes and the technical
performance of the manufacturing system (purely
technical aspects), and in others, just as many, [9],
references are made to the relations between the
product made by the manufacturing system and the
market (that is, relations of an economic nature) and
which require us to intervene in the manufacturing
system to obtain economic effects favorable.
There is no known management algorithm for
the entire manufacturing system - the market, [9],
[11], [12].
2 Cost and Price Estimation
The main aspects of assessing the level of
performance of an enterprise that refers to the
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overall efficiency of economic activity are economic
efficiency, realized/planned performance, product
competitiveness, or company excellence.
The competitiveness of an enterprise is
determined by various factors that have a decisive
influence on the competitive power of the company,
such as production factors, managerial and
marketing capabilities, and financial, technical, and
creative resources.
The internal conditions available to an enterprise
contribute to its ability to obtain an advantage over
competitors, both in terms of costs and diversity,
quality, and renewal of the offer.
Any enterprise is made up of manufacturing
sections which, in turn, are divided into workshops.
This structure forms the manufacturing system of an
enterprise. Structurally, the workshop is made up of
independent manufacturing machines. To order a
product, manufacturing machines are grouped in the
manufacturing system specific to the product.
In industry, the manufacturing operations on
CNC machines (machine tools with numerical
control) allow controllable processing times, [4]. For
example, for a CNC turning machine, there is a
nonlinear relationship between the cost of the
manufacturing and the time of required processing,
[7]. The simultaneous objectives on a single CNC
machine are considered total manufacturing cost
(F1) and total weighted completion time (F2). Also,
the decisions for processing time are as critical as
decisions for job sequencing. F1 subject to a given
F2 level, is given an effective model for the problem
of minimizing. For this problem, in [7], it is deduced
some optimality properties and then, it is proposed a
heuristic algorithm to generate an approximate set of
efficient solutions.
In [8], the authors show that they can generate
alternative reactive schedules considering the
manufacturing cost implications in response to
disruptions if they have the flexibility to control the
processing times of the jobs. They consider that
processing times of the jobs are compressible at a
certain manufacturing cost which are represented
with a convex function of the compression on the
processing time in a non-identical parallel
machining environment. By reassigning the jobs to
the machines and compressing their processing
times, in rescheduling, it is highly desirable to catch
up with the original schedule as soon as possible.
3 Economic Modelling Method
Proposed
Currently, the machines of manufacturing systems
are controlled by the programs of the machine tools
with the program command that composes the
system, [10]. Management is exclusively technical,
because there is no economic variable, although this
would represent the final goal of the processing
processes. The dynamics of the changes and the
general progress of the society are translated at the
company level through orders many in number,
small in volume, very varied, obtained through
frequent auctions with answers in short terms, which
do not provide time for the pertinent analysis of the
orders. As a result, it is no longer possible to manage
in the long term. A fluctuating type of management
(just like the market), online, with prompt, quick
reaction, but still ephemeral, must be imposed.
The manufacturing system receives contracts
following auctions (competitions) generated by
market requests. Obtaining maximum
competitiveness through instructions and
intervention on how to develop the manufacturing
process. The evaluation of competitiveness generates
a competitive management system. This competitive
management must enable the development of
competitive offers that will enter the auction. This is
done by using two learning techniques:
reinforcement learning technique to learn about the
market and unsupervised learning technique to learn
about the manufacturing system.
3.1 Modeling Algorithm
Starting from the contractual specifications, based on
the monitoring of the operation of the technological
systems, the consumption and costs of the
technological operations are established. A set of
data is formed and, through data-mining, the process
of discovering relationships and combinations,
generally knowledge, is carried out, and the results
found can be included in an automatic decision
support system.
The data mining methods that will be used are
unsupervised learning and supervised learning.
Through unsupervised learning, using the Principal
Components Analysis (PCA) technique, the pattern
of state variables of technological systems will be
discovered. According to the causal relationships
between variables, they are grouped using the
Cluster Analysis (CA) technique through
unsupervised learning. For each group of variables,
through supervised learning, knowing the data, on
the one hand, regarding the dimensions of the part,
the working regime, the precision characteristics,
and, on the other hand, those regarding the
consumption of time, materials, energy, the model
will be built econometric of the manufacturing
system. For example, cost and productivity could be
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DOI: 10.37394/232010.2024.21.4
Daschievici Luiza, Ghelase Daniela
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identified as functions that depend on the product
and the intensity of the manufacturing process.
The performance of the manufacturing system can
be evaluated by the profit rate P, according to the
relationship:
P = (p-c)q (1)
where: the price is noted with p, the cost is noted
with c and q is the productivity.
Once the productivity and expenses curve has
been drawn, the third variable, competitiveness, can
be added and on this basis the intensity of the
process can be adjusted, thus adapting the system to
obtain maximum profit. By adjusting the intensity of
the manufacturing process and through online
learning, the model of the manufacturing system
corresponding to the creation of competitive
products is adopted. It is a behavioral modeling
because it is based on monitoring the interactions
between the component elements of the
manufacturing system and the permanent circuit of
information from inside and outside the
manufacturing system.
3.2 Simulations of the Proposed Algorithm
To check the accuracy and applicability of the
competitive management concept needed by results
practically obtained, for a certain case.
Consequently, it will simulate and model a real
manufacturing system of the pilot enterprise, which
works in real conditions, on a real market, with real
parameter values and, based on this modeling, will
be realized as an experimental system.
In this frame, using the methodology will be
introduced the attributes and values generated
through online learning, which will determine the
state of the system and respectively solutions of that
carry out of the competitiveness optimum.
These will be applied to the model and will
determine the solutions for the management policy.
In the frame of the virtual enterprise, the
manufacturing system will be materialized by the
proof-of-concept stand. Through modeling, the
physic parameters obtained on the stand will be
introduced into the manufacturing process of the
virtual enterprise, thus working as a complex system.
The experimental stand will be realized by taking
into account the case of the most simple
manufacturing system, which contains one
technological system. The technological system will
be made up of machine tools with numerical control
(CNC) and will be equipped as one monitoring
system corresponding to a competitive management
algorithm. The experimental manufacturing
processes realized using the stand will be introduced
into databases. This database will be used for
implementation of the unsupervised online learning
methods. Estimating the obtained results will
conclude algorithm viability of competitive
management.
In the presentation of the method, we start from
the fact that we will have a series of inputs,
considered as customer requirements, we will have a
series of restrictions given by the work regime and
the time of making the product, considered
independent variables and we will have a series of
variables dependent or output, one of which will be
the main objective variable, against which we
analyze the efficiency of the results. To understand
the problem, we will use an example and we will
first consider a random sequence from the database
of the drilling operation, Table 1, in which we find
the notations V1, with i=1,...,10.
Drilling operation is modeled by a Neural
Network technique or the best model provided by a
neuronal network is a practical modality to find out
causality relations between variables to be able to
determine the variable clusters [9]. The variables are
compared with each other with the help of the neural
network to obtain clusters of variables that are in
causal relationships. Obtaining the clusters is done
through a computer application, practically training
the network with the values from the database and
determining those variables between which there are
causal links.
Table 1. Example of experimental data regarding the process variables collected for the drilling process
Item
nr.
Type of
material
Number
of
holes
Speed
drilling
(mm/s2)
Advances in
drilling
(mm/s)
Number of
pieces
Drilling
time(s)
Power
consumption
(kw/hour)
Cost of
operation
(euro)
Amount
of wastes (Kg)
-
V1
V3
V4
V5
V6
V7
V8
V9
V10
1
OL 52
3
1,1
0,7
70
7242
6,04
0,026
13,12
2
OL 42
5
4,1
0,3
28
12033
3,76
0,0268
13,54
3
OL 42
5
2,05
0,25
59
6255
4,41
0,0315
15,87
4
OL 42
2
5,05
0,35
104
3404
37,86
0,108
54,52
-
-
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Daschievici Luiza, Ghelase Daniela
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Volume 21, 2024
We consider that the input data, the client's
requirements are V3 = 4, and V5 = 0.6, which you
don’t find in our experiment. We will choose from
the database those lines for which the common
distance will be the minimum, thus performing the
clustering of the lines with the minimum common
distance. In this way, a set of data will be selected
that has the quality that they will be in the vicinity of
the client's requirements and thus the mathematical
model will be a linear one, Table 2.
422
0,6
35
d V V
(2)
For the experimental implementation of the
modelling method proposed, an IT product was
developed and designed in the Visual FoxPro
programming environment, using the function
libraries in Matlab and C++.
4 Conclusion
The developed program was tested with numerous
sets of variables allowing the simulation of optimal
adaptive management based on the continuous
identification of the calculation model of the solution
table. The behavior of the program was good,
supporting the idea that this method is robust, giving
viable solutions for use.
Following the analysis of the solution table and
the use of the data set requested by the client in
practical form, small differences were observed
compared to the expressed requirements, actually
confirming the accuracy of the method and the
unlimited practical possibilities.
References:
[1] Rodney Anthony Stewart, IT enhanced project
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Pathways to improved performance and
strategic competitiveness, Automation in
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517.
[2] Francisco Restivo, An Agile and Adaptive
Holonic Architecture for Manufacturing
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Xu, Endong Xu, Resource-controlled
stochastic customer order scheduling via
particle swarm optimization with bound
information, Industrial Management & Data
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[4] M Srivastava, Condition –based maintenance
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[8] M. Selim Aktürk, Alper Atamtürk, Sinan
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[10] H. ElMaraghy, L. Monostori, G. Schuh,
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International Journal of Production
Economics, Elsevier, 2022, 243.
Table 2. Table Arranged According to the Shortest Distances
-
v2
v3
v4
v5
v6
v7
v8
v9
v10
d
price
1
12,5
3
1,1
0,7
70
7242
6,04
1093,75
13,12
1,004988
0,285
2
15,55
5
4,1
0,3
28
12033
3,76
1128,41
13,54
1,044031
0,285
3
11,6
5
2,05
0,25
59
6255
4,41
1323,17
15,87
1,059481
0,285
4
25,6
2
5,05
0,35
104
3404
37,86
4543,82
54,52
2,015564
0,285
5
9,55
7
4,05
0,8
49
3998
2,48
1042,74
12,51
3,006659
0,285
6
17,55
8
3,2
0,75
77
2459
13,17
6324,31
75,89
4,002812
0,285
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[12] A. Caggiano, Manufacturing System, CIRP
Encyclopedia of Production Engineering,
2019, pp.830–836.
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
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 conflicts of interest to declare
that are relevant to the content of this article.
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(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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WSEAS TRANSACTIONS on ADVANCES in ENGINEERING EDUCATION
DOI: 10.37394/232010.2024.21.4
Daschievici Luiza, Ghelase Daniela
E-ISSN: 2224-3410
30
Volume 21, 2024