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1 INTRODUCTION

The Covid trend is characterized by an increase in the

number of cases in 2021 compared to the parallel

months in 2020. The first wave of Covid-19 in 2020

created great uncertainty and shattered the stable

rhythm of people's lives not only in Poland but all

over the world. Companies faced uncertain market

of rebuilding the Polish economy. Companies are

trying to get back to functioning and making profits

like before the pandemic. Covid has made a

revolution in the transport industry due to the sudden

increase in demand for courier services. Companies

providing courier services enjoyed much higher

revenues. Not all transportation sectors were so

spectacularly successful. An important element was

also looking for possibilities to improve the services

provided. Some Polish companies implemented new

projects in their businesses in order to best adapt to

examined enterprise the authors presented four key

areas, which the transport company should pay

attention to in order to analyze the provided services

and search for potential bottlenecks (Fig. 1).

Implementation of Logistics and Transport Processes in

an Enterprise Operating on Polish Territory in the Face

of COVID-19

Z. Łukasik

, A. Kuśmińska-Fijałkowska

, S. Olszańska

& M. Roman

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Figure 1. Key areas for improvement for a company

operating in Poland (own elaboration)

2 LITERATURE REVIEW

Implementing logistics projects involves large-scale

risk taking. It is important to apply activities that are

designed to maximize the use of the available capital.

implementation and know-how. The research

conducted by Zubkov demonstrates the essence of the

importance of the customer and its impact on the

transportation process. The quality of customer

service management is extremely important for the

efficiency of a transport company [19]. In any

transport or logistic process, it is important to collect,

analyze, and rank data. Modern transportation and

logistics systems should be intellectualized [20].

Processes should be designed holistically by

model for intelligent transportation system simulation

platforms was proposed [17]. The concept considers

routing based on increasing traffic safety while

reducing congestion. An important point concerning

modeling transit routes was presented by Qiang X. In

his research, Qiang X used a search method with an

increasing number of customers based on the concept

of comovement and minimizing transportation cost at

the same time. Using the critical path method, he

presented the most efficient results and identified the

these companies. Evangelista D.G.D. proposes the use

of a genetic algorithm to determine alternative routes.

The research investigated the creation of an

optimization model by means of an algorithm that

will offer an optimal route sequence for trucks [4].

Routing may involve the problem of assigning the

vehicle location and selecting the order of unloading,

as well as selecting appropriate travel routes in the

transportation network. Feed J. provides a solution in

the form of a developed optimization algorithm using

Monti C.A.U. [12], who addresses the problem of

multi-criteria truck movement. Monti C.A.U used a

mixed integer linear programming algorithm

considering truck scheduling, fleet reduction, and half

load transport reduction among others. Technical

constraints were also implemented to reflect the

accuracy of the model. The results of the measures

taken were: 72.92% fleet reduction, reduction in

unnecessary hours. There was a reduction in trips

with half loads to a result of 3.17%. A separate

given task are assigned to the next group. The

assigned tasks take into account the time of their

execution, and if a particular vehicle cannot perform

the task, then the task is assigned to another vehicle

[11]. Additionally, only if two conditions are met, i.e.,

execution time and payload, then the task can be

completed. The aspect of cost and consolidation is

also presented in the research by Kong Y., which

includes a number of costs that affect transportation.

It then identifies what constraints are present. The

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for cargo. The objective of the research is to develop a

transportation network model based on delay

minimization for a balance between the number of

vehicles and capacity. The algorithm is focused on

minimizing vehicle dormancy. A differential

evolutionary algorithm (MODE) was used to validate

the performance of the proposed algorithm. A

aspects of the four-index transportation problem is

included in the research of Skitsko V., who indicated a

genetic algorithm that solves the four steps of the

challenge using a special programming language [16].

When optimizing routes, the aspect of ecology and the

environmental impact of the executed order is

important. Operational parameters of speed,

combustion, tonnage, and exhaust emissions translate

into the quality of ecology. These topics are addressed

in the research of Pamučar D. The TSDSM system [13]

evaluate the performance of the network. In the

context of cost logistics and environmental protection,

the input data and parameters to the neural network

have been studied. The approach of environmentalism

and route improvement has been included in the

research of Liu Z. The adopted research methodology

considers the determination of routes with

environmentalism for transports containing

hazardous waste and the impact of these transports

[10]. Cargo consolidation also has an impact on

leads to increased efficiency and facilitates process

reengineering [5].

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4 PROCEDURES TO BE FOLLOWED DURING

IMPLEMENTATION OF THE LOGISTICS

PROJECT

of the study. Another major element is the method of

data collection. The data are collected directly from

the telemetry devices mounted on the truck tractor.

The use of recording devices in the vehicle allows for

high data accuracy. For safety purposes, a verification

of the data downloaded from the devices is performed

to exclude possible faults or errors in the

Finally, a comparison is made between the

performance results that were realized before the

alternative routes were introduced and after the

logistics project was implemented with the new travel

routes.

the routes (baseline option) completed in 2020. Figure

4 shows the length of each section in the original

version and after the improvements.

Figure 4. Reduction of the distance covered on the routes

carried out in the studied company

The authors indicate that the largest regression

occurred in sections no. 8 – 45 km, and no. 5, where

the difference is 40 km. Also, sections no. 1 and no. 10

exhibit a significant difference of the covered

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Another key aspect is to answer the question how

significantly the introduced changes have affected the

operational parameters: vehicle efficiency, payload

utilization factor, and time utilization factor. (Figs. 5,

6, 7).

number of tkm/h was 200. In 2021, the value increased

to 849.23 tkm/h. There was also a high increase in

section no. 7 in April, where there was an increase

from 264 tkm/h to 760 tkm/h. The upward trend in

vehicle performance is very good news for the

company as it means that the vehicle has done more

transport work per hour.

With the help of the data collected from the routes,

the authors made calculations that indicate the

coefficient of payload utilization (Fig. 6.) This is one of

capacity utilization (Fig. 6). Detailed characteristics

are presented in Table 1. As it results from the

analysis, the highest load capacity utilization took

place on section no. 9. Section no. 4 is also

characterized by a high level of results.

The authors analyzed the data in terms of working

time utilization and prepared a graphic representation

of the indicators of working time utilization on

particular sections (Fig. 7).It is observed that the year

2021 achieves higher levels of working time

utilization (Fig. 7). Sections no. 4 and no. 9 show the

highest increase. In January there is an increase from

52% to 65% in 2021. Section no. 7 also shows a large

increase. In February of 2020, the value is 33%. On the

indicator has a low volume, it may suggest that the

driver used his working time mainly for activities:

− Technical - related to the operation and

preparation of the vehicle for work;

− Supervision - related to the supervision of loading

and unloading;

− Emergency - related to the use of time to solve

unexpected activities that prevent further transport

operations in a safe manner.

Figure 6. Coefficient of vehicle capacity utilization

January and March at low levels. April is at 75% while the other 2021. More freight was transported making

months are around 80%. the indicator higher than in 2020.

2 January with the highest value The value reaches a high in February in both 2020 and 2021 is

of 96% while March with the January at 92%, March and April characterized by a decrease in productivity

lowest value of 63%. at an equal level of 96%. compared to the other months.

4 High performance in each Relatively low amplitude of index Section no. 4 is very effective as both 2020

month analyzed. fluctuations, all routes above 90% and 2021 scores remain high.

Highest value of 99% in January. with the highest value of 96% in

February and April.

6 Section no. 6 is characterized by The payload utilization rate is 2021 is definitely more stable and at a

big increase.

January deviates from the other

months with a score of 16%.

9 The section that reaches the The value for this section is as Section no. 9 is the most efficient, which

highest values and is stable. high as in 2020. January will means that on this route the payload of the

reach a very good value of 98%, semi-trailer was used almost 100%.

while April will reach the lowest

value at 83% which is also very good.

3, 5, 8, 10 No freight was transported on the indicated sections in both 2020 and 2021.

its decision by taking care of the highest standard for the current supplier and ensuring the availability of sets of

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IMPLEMENTED PROCESSES IN THE

TRANSPORT COMPANY

When considering the transport of cargo in Poland

through 2019, the period since the beginning of the

COVID-19 outbreak has resulted in a decline in tons

of cargo transported. The biggest collapse for the

transportation industry occurred in August 2020. The

year 2021 also started at a very low level with 20,818

thousand tons (Fig. 8.). Since March, an improvement

in the amount of cargo transported has been visible.

Figure 10. Quantity of cargo transported expressed in [t]

January and February 2020 represented the period

before the first COVID-19 cases (Figs. 9, 10.). In the

investigated company operating in Poland, the

difference in transported loads expressed in tons in

particular sections is relatively small. The value for

January in section no. 9 remains at the same level.

Also, sections no. 4 and no. 2 in February are

characterized by a difference of merely 1 ton. The

authors noticed the biggest deviation for January in

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Figure 12. Quantity of cargo transported expressed in [t]

The next stage consisted in comparing March and

April when the first cases of COVID-19 appeared in

2020 and March and April in 2021, when there were

further increases in the incidence of Covid-19. In Figs.

11 and 12, the authors noticed significant

disproportions in tons transported. In March, the

biggest disparities are seen in section no. 1, where

there was an increase from 5 to 20 tons in 2021, and in

section no. 7, where starting with a result of 4 tons in

7 CONCLUSIONS

The comparative analysis and data collection on the

designated routes in the company operating in Poland

required the authors to implement a project of

modified routes. The authors emphasize the

importance of the fundamental rules of project

development, which determine the success of project

realization. Subsequently moving through the eight

stages of the procedure associated with the

implementation of the logistic project allows to

maintain the reliability and reality of the reproduced

The company followed the new routes proposed

by the authors. The main benefit achieved by the

implementation of the routes is the reduction of the

distance covered on four sections, i.e., sections no. 1,

5, 8, and 10. The total number of reduced kilometers is

129 km, which is the value for one month. Converting

the reduced kilometers into 12 months, the total value

of reduced kilometers will reach 1548 km. On an

annual basis this is a significant saving in kilometers

travelled. The alternative routes had a stimulating

effect on the transport performance defined as vehicle

efficiency. The highest increase was observed in four

sections, incidentally section no. 1 had an increase of

649.23 tkm/h in April 2021. The vehicle payload

utilization on section no. 9 is 98% utilized. The highest

increase in payload utilization was observed in

section no. 7. The reduced distance also had a positive

under multiple resource constraints. European Journal

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