Article citation information:
Mańka, I., Mańka, A. Cost analysis and optimization in the logistic
supply chain using the SimProLOGIC program. Scientific Journal of Silesian University of Technology. Series
Transport. 2016, 93, 91-97.
ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2016.93.10.
COST ANALYSIS AND OPTIMIZATION
IN THE LOGISTIC SUPPLY CHAIN USING THE SIMPROLOGIC PROGRAM
Summary.
This article aims to characterize the authorial SimProLOGIC program, version
2.1, which enables one to conduct a cost analysis of individual links, as well
as the entire logistic supply chain (LSC). This article also presents an
example of the analysis of the parameters, which characterize the supplier of
subsystems in the examined logistic chain, and the results of the initial
optimization, which makes it possible to improve the economic balance, as well
as the level of customer service for a sample test task.
Keywords: logistic chain of supplies;
SimProLOGIC program; polioptimization; cost analysis in the logistics supply
chain; storage cost
1. INTRODUCTION
The issues concerning the cost optimization in
an LSC constitute the subject of topical research in many scientific centres
[8, 10, 11, 12]. The holistic/complete cost analysis of a supply chain is
referred to as supply chain costing in the specialist literature. In accordance
with the latest trends in this discipline, many scientists are abandoning the
approach, which promotes the competition between individual companies in favour
of the competition between entire supply chains [12]. This approach will be
effective only when it leads to the coordination of the entire chain,
taking into consideration the amount of supplies, dates, costs and the
effective flow of information.
2. TASKS PERFORMED IN THE
SIMPROLOGIC PROGRAM
It is possible to designate the most beneficial
conditions for the end customer, i.e., the lowest price, taking into account
the assumed level of customer service [1], only by means of the
polioptimization of the parameters of all elements of an LSC (the suppliers of
subsystems, the producers of finished goods, retail stores), as well as taking
into consideration transport, the choice of the means of transport, the
consolidation of supplies and the management of warehouses. The authors of
this article, based on the SimProLOGIC application, have concluded that the
number of variables having an influence on the final result of LSC
optimization, i.e., the retail price of goods, and the quality of provided
services (the availability of the goods for the customer and the minimization
of the stocks), i.e., market success or failure (not only one company, but the
whole chain of supplies), reaches up to 114 variables, taking into
consideration the minimum chain of supplies as shown in Figure 1. The aforementioned
number of variables, which characterize the minimum LSC, include:
-
the supplier of
subsystems (in a simplified way, only one) = nine variables,
-
transport, taking
costs into account = 18 variables,
-
the input
warehouse of the producer of finished goods = 14 parameters,
-
the production
department (excluding technical and quality parameters of the goods) = 12
parameters,
-
the output
warehouse = 14 parameters,
-
transport, taking
costs into account = 18 variables,
-
a retail store,
taking into consideration the level of customer service = 17 variables,
-
a simplified model
of the customer (the model of market demands) = 12 variables.
For such a great number of variables, it is
extremely difficult to find an optimal solution, taking into account an
objective function, which takes into consideration the minimum final costs,
i.e., the price of goods, while aiming to reach the required level of customer
service. Taking into consideration the aforementioned number of 114 parameters,
which influence the subtotals of the individual links of the supply chain,
it has been stated that it is sensible to develop software, which would
simulate LSC functioning and enable one to make a selection of the basic
parameters using logistic tools such as:
-
the permanent
order quantity method,
-
the permanent
order period method,
-
the consolidation
of supplies,
-
the choice of
means of transport and order quantity,
-
the selection of
warehouse space and size.
The most important component of the LSC
simulator under development, i.e., the SimProLOGIC program, is the
possibility of conducting many analyses and observations of the quality of
provided services, taking into account quality and quantity, for various input
parameters. The visualization of results for individual chains of an LSC
enables one to conduct quantity analyses. However, in the early stages of the
exploitation, it is possible to illustrate the operation of individual
subdivisions, including stoppages concerning the supplier of subsystems, the
waiting period before shipment and the prolonged duration of transport (the
likely change of the type of transport, the introduction of many vehicles,
the consolidation of supplies, the quantity of order changes), the
producer’s productivity/performance, and the effectiveness of warehouse
selection. It is also possible to make a qualitative and quantitative
evaluation of the quality of customer service by implementing the following
indicators:
- the annual (r index) and monthly (m
index) availability indicators of the level of customer service (POKd),
- the annual and monthly
reserve/resource indicators of the level of customer service (POKz),
- the annual indicator of the equity
of sale in relation to warehouse dimensions (SM).
These indicators were discussed in detail in
[1].
Fig.
1 The view of the main window of the SimProLOGIC program (version 2.1), which
contains the models of basic components of the LSC
Apart from the analysis of the
influence of individual parameters, which characterize the links of the LSC at
the level of customer service, the analysis of costs was also introduced to the
SimProLOGIC program, version 2.1. These costs result from the classical
approach to economic analysis within the management of the chain of supplies.
They take into consideration:
-
the
fixed costs of the company/entity,
-
the
fixed costs of warehousing (as a percentage of the value of goods annually),
-
the
variable costs dependent on the production volume,
-
the
variable costs associated with the loss of production capacity,
-
the
variable costs dependent on the quantity of stored resources,
-
the
costs of the product,
-
the
transport costs.
Apart
from the costs, the income concerns the sales of goods, a subsystem or the
income associated with the transport services and the profit margin of the
shop.
The
exemplary structure of costs is shown in Figure 2. The solution shown in this
figure, which involves the selection of parameters associated with the supplier
of a subsystem, was developed using an iterative method, while the objective
function was to minimize the warehousing costs (1) and maximize the
overall balance (2) in the minimum possible duration (3). Assuming that the
fixed costs in the analysed task (4), warehousing costs (5) and product costs
(6), as well as the price of sale of a subsystem (7), are unchangeable,
the decisive factors are the working days (8), the size of the warehouse
(9) and the daily production volume (10).
The
presented example is based on the scenario, which concerns the production of
the selected kind of chocolate (see Fig. 1). By introducing the
corrections of baseline values of the size of the warehouse, i.e., 3,000 [j]
(the unit of the quantity of goods; to be precise, multipacks containing 25
items), and the daily production from the value of 500 [j] to 1,600 [j] and 130
[j] respectively (Fig. 2), we have achieved a warehousing cost reduction from
PLN21,686.86 to PLN5,515, while the income has increased from PLN23,013.14 to
PLN51, 944.72. This means that, by reducing the daily production to 26% of the
baseline value and decreasing the size of the warehouse assigned to this
subsystem, one has gained more than a 200% increase in income. The chart
also enables one to read the time after which one receives an overall positive
balance of the supplier’s operation, i.e., initially, it amounted to 296 days.
However, following the correction of only two measurements, the recovery/refund
period was reduced to 175 days. It is worth emphasizing that the selection of
these parameters is facilitated due to the possibility of conducting the
analysis of the whole process and the verification of the demand in the
preceding and the following links, as well as ultimately in a retail store and
in the model of a customer. The problems associated with the operation of an
individual link are perfectly visible in this case, due to the opportunity to
observe the indicator, which represents (using colours) the process of
production (green) and the time during which there is no production process in
a link because of the fact that the output warehouse is full, or because of
deficiencies in the input warehouse or days off from work.
It
is important that the presented result regarding the initial selection of
parameters for the subsystem supplier improved the results for this link
in the LSC. Nevertheless, there has been no significant improvement in the
economic parameters and the level of customer service. The reason for this is
the fact that the LSC should not be treated in terms of separate mechanisms,
but as a single system of correlated modules. This is why, at further stages,
one should adapt the values of further modules and subsequently return to the
first module in order to introduce further corrections, which will be necessary
because of the already modified demand and the frequency of supplies for the
producer of finished goods and the changes introduced in further links.
The current version of the SimProLOGIC program, i.e. 2.1., does not enable one
to select and polioptimize the parameters of the whole LSC. The parameters
should be selected in an iterative way using the simulation and observation of
the behaviour of the analysed and neighbouring links. The efficient method of the optimization
of the whole chain is its analysis, i.e., of the customer and the initial link,
in both directions. It enables one to characterize the real needs of the
customers and the initial evaluation of the flow of materials in the entire
LSC. Another verified strategy of optimization is to increase the performance
of production and the transport of all links, irrespective of costs, in order
to acquire the maximum source stream for the customer. Only in the preceding
link should one reduce the availability of resources in order to provide the minimum
level of customer service, thereby gradually reducing the performance of
further links in the direction towards the original link (here represented as a
supplier of subsystems).
(1) (2) (3) (4) (5) (8) (6) (7) (9) (10)
Fig. 2 The example of cost analysis
for the supplier of subsystems in the SimProLOGIC program (please note: number
formats are in Polish, e.g., 70 000 = 70,000)
Interesting
results also concern the analysis of the decreased number of days (in a week)
devoted to production in general or the production of subsystems for the
analysed finished goods. It has been stated that the additional working day,
namely, Saturday, reduces the profitability of the operations. Therefore,
it is possible to conduct a comparative analysis, which enables one to receive
an answer as to whether it is more beneficial to produce subsystems in smaller
quantities, rather than in accordance with production capacity or production
involving full capacity, but only from Monday to Thursday inclusive.
The
presented example concerns the first element of the modelled LSC. However,
these operations are realized in all links of the chain, thereby enabling one
to gain the intended final result, i.e., an increase in effectiveness and cost
reduction, while, at the same time, increasing income across the entire
logistic chain. The assumed level of customer service is ensured
simultaneously. In the given example, one can also see the didactic application
of the aforementioned software, which enables one to acquire the skills
and practical experience in the management of the supply chain in the chosen
examples, which constitute the library of the exemplary scenarios. These
scenarios require the student either to undertake action to reach the requested
level of customer service, to minimize the total cost or to search for the bottleneck
of the analysed LSC. It is also possible to present analyses, which require one
to take into account the management of the size of the warehouse or to take
decisions concerning the selection of the means of transport (the duration and
cost of transport) or the supplied volumes.
3. CONCLUSION
This
article has presented the subsequent version of the authorial SimProLOGIC program,
supplemented by a cost analysis of the whole LSC. The presented results
concerning the analysis and optimization of parameters, albeit only for an
individual link (the supplier of subsystems), show the potential offered by the
presented simulator in the field of the practical application of, and the
didactic process associated with, logistics and management. The presented
system encompasses several aforementioned methods of analysis used in the didactic
process at universities, which are associated with solutions to logistic
problems, thereby increasing its didactic value.
The course
of further work and development is associated with the expansion of the system
by a larger number of suppliers of subsystems and a larger number of retail
outlets. It is also planned to expand the customer models in order to make it
possible to take into account the variables of consumer behaviour and the
factors associated with chance. It also seems to be worthwhile to improve the
user’s interface to improve the clarity of the ongoing processes.
Simultaneously, we have been searching for new methods for solving problems
related to engineering tasks associated with logistics in order to enable one
to implement them in software, especially the methods concerning automatic multi-criteria
optimization (114 variables).
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Received 09.10.2016;
accepted in revised form 25.10.2016
Scientific Journal of Silesian University of
Technology. Series Transport is licensed under a Creative Commons Attribution
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