Article
citation information:
Naumov, V., Taran, I., Zhanbirov, Z., Mussabayev, B., Konakbai, Z. Assessing
the synergetic effect of selecting the optimal structure of a logistics chain. Scientific Journal of Silesian University of
Technology. Series Transport. 2024, 124,
109-126. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2024.124.8.
Vitalii NAUMOV[1], Ihor TARAN[2], Zhumazhan ZHANBIROV[3], Batyrbek MUSSABAYEV[4], Zarina KONAKBAI[5]
ASSESSING
THE SYNERGETIC EFFECT OF SELECTING THE OPTIMAL STRUCTURE OF A LOGISTICS
CHAIN
Summary. This study tackles a
critical challenge in logistics optimization: assessing the economic efficiency
not only of individual entities within a logistics chain, but also the
synergistic benefits that arise from their collaboration. We achieve this by
proposing a methodology that evaluates the economic efficiency of interactions
between participants in a logistics chain. This methodology goes beyond
individual efficiency and delves into how the overall economic benefit is
distributed among key stakeholders. These stakeholders include freight owners,
who initiate the delivery process, forwarders who manage and optimize
deliveries, carriers who physically transport goods, and freight terminals that
facilitate cargo handling and storage. To ensure the methodology’s relevance to
contemporary practices, we begin with a comprehensive review of recent
advancements in delivery chain optimization research. We propose to measure the
synergetic effect by considering delivery demand parameters, such as the weight
of the consignment and the distance it needs to travel. To validate our
methodology and gain practical insights, we conducted a series of experimental
studies specifically tailored to the Kazakhstani transportation market. By
analysing the share of the synergistic effect under varying delivery demand
parameters, we were able to identify trends and patterns.
Keywords: freight transportation, synergetic effect,
requests flow, logistics chain
1. INTRODUCTION
An efficient transportation system serves as
the lifeblood of any modern economy. It facilitates the seamless movement of
goods and people, fulfilling the critical needs of both consumers and
businesses. This paper delves into the intricate world of logistics, exploring
the fundamental role of the transportation industry and the challenges inherent
in designing and optimizing delivery systems.
Transportation bridges the gap between
production and consumption. By efficiently delivering goods, businesses can
reach wider markets, fostering economic growth. Reliable and cost-effective
transport empowers businesses to access raw materials, distribute finished
products, and connect with a wider network of employees and clients.
Furthermore, robust transportation systems underpin international trade,
enabling the smooth flow of goods across borders and fostering global economic
cooperation.
Within the transportation sector, road
transport plays a central, yet often underappreciated, role. It serves as the
foundation, handling the crucial “first mile” and “last mile” deliveries in
most journeys. This ensures seamless movement of goods throughout the entire
supply chain, even when other modes of transport, such as airplanes or ships,
are involved.
Behind the scenes, logistics companies act as
the masterminds, meticulously coordinating the intricate web of activities that
form the supply chain. Forwarding companies play a vital role in managing this
complex network. The effectiveness of their services directly impacts the
efficiency of the entire system. Their primary task is to meticulously plan and
organize the transportation process, ensuring goods arrive at their destination
on time, within budget, and in good condition. These efforts may be summarized
by selecting the proper structure of the logistics chain that ensures the minimum
total expenses for all entities participating in the delivery process.
This study aims to develop a
methodology for evaluating the economic efficiency of delivery processes within
a logistics chain. Furthermore, the methodology will assess how the resulting
synergetic effect is distributed among the different entities involved in the
delivery process.
The paper is structured as follows. Section 2
provides a concise review of recent research directions in delivery chain
optimization. Section 3 details the methodology employed to estimate the
synergistic effect for various delivery process participants: freight owners,
forwarders, carriers, and freight terminals. Section 4 presents the results of
experimental studies conducted to calculate the share of the synergistic effect
under varying delivery demand parameters (consignment weight and delivery
distance). Finally, Section 5 offers concise conclusions and outlines potential
avenues for future research.
2. LITERATURE REVIEW
The field of transportation process
optimization is rich with diverse challenges. Researchers grapple with issues
like managing fleets, controlling costs, and developing optimal strategies for
both customer service and vehicle routing [1-4]. Ensuring quality control
throughout the transportation process, understanding market behaviour
to predict demand [5-7], and allocating resources efficiently are all crucial
aspects [5, 8]. Additionally, mitigating risks associated with transportation
decisions and maximizing supply chain reliability are key areas of research [9,
10]. This complex field draws on a multitude of academic disciplines.
Operations research, economics, and engineering all contribute valuable
approaches to finding optimal solutions, as evidenced by the variety of models
and algorithms developed. Notably, a recent area of focus involves optimizing
supply chains while considering factors like risk, uncertainty, and
sustainability [11].
The ever-changing technological
landscape constantly presents new research opportunities. Studies like [12]
highlight how the evolving business environment challenges the effectiveness of
current optimization methods. This research also identifies key trends and
knowledge gaps relevant to both practitioners and academics, exploring future directions
for optimization research in emerging markets and evolving freight transport
organizations [12]. Similarly, Köhler and Brauer delve into the transformation of freight transport,
outlining new analytical needs and potential modelling approaches for the
future [13].
The transportation and logistics
industry is undergoing a digital revolution fuelled
by the Internet of Things and Big Data. The COVID-19
pandemic and the shift to remote work further accelerated this trend, pushing
major players like Maersk, MSC, and Hapag-Lloyd to
embrace online platforms. This digital shift has a ripple effect, forcing even
smaller transport companies to adapt. Research by [14] explores the impact of
online freight platforms (OFPs) on traditional
logistics service providers (TLSPs). The authors
found that OFPs don’t necessarily threaten TLSPs, but rather offer manufacturers new options for
outsourcing deliveries. Similarly, authors of [15] examine the interaction
between information systems and the performance of international freight
forwarders. Looking ahead, digitalization and automation are expected to
continue shaping the industry with advancements in artificial intelligence and blockchain technology, as evidenced by works like [16] and
[17]. These developments hold promise for real-time shipment tracking and
increased supply chain transparency.
Sustainability is emerging as a
second major trend within the transportation and logistics industry. Sustainable practices act as a crucial bridge
between modernization and responsible industry functioning. Research by [18]
exemplifies this by developing a model that integrates resilience and agility
into designing sustainable agri-food supply chains.
This model considers uncertainties in the environment. The ultimate goal of sustainability
efforts in freight transport is to minimize overall costs while reducing
negative environmental and social impacts. To achieve this, research by Pamucar et al. [19] recommends maximizing the potential of
alternative transportation modes, like rail, to lessen the negative
consequences of road freight transport such as emissions, noise, and
congestion. The authors propose a transportation planning strategy for freight
companies based on fuzzy sets to rank these alternative modes effectively. Similar
studies in [20] further explore ways to encourage a shift towards rail and
other alternatives to road transport. Focusing on eco-friendly solutions, [21]
proposes an effective algorithm for routing vehicles specifically within the
context of sustainable transport. This algorithm, based on restrictive
inheritance, helps determine environmentally friendly routes. By implementing
such solutions, the transportation industry can contribute to sustainable
development by minimizing the environmental and social impact of transport.
Another key direction for the
transportation and logistics industry involves integrating passenger and
freight movement within cities, often referred to as “urban co-modality”.
Research by Ma et al. [22] explores the potential benefits of such integration
by modelling a public transport system that accommodates both passengers and
goods. Their study examines how this co-modality can impact existing
forwarding, trucking, and passenger services across the urban transport system.
Notably, the authors identify scenarios where co-modality can improve profits
for forwarders, carriers, and transit operators, while also increasing consumer
surplus for both freight customers and passengers compared to traditional
separated systems. Beyond modelling, the paper [23] presents a practical
framework for developing and evaluating an innovative service called Integrated
Demand-Responsive Transport (I-DRT). This service combines passenger and
freight transportation with a demand-responsive approach, meaning it adapts to
real-time needs. The study utilizes Osterwalder’s
business model canvas to outline the infrastructure, vehicles, personnel,
costs, revenue streams, and partnerships required for I-DRT implementation. A
pilot project in Misano Adriatico,
Italy, demonstrated the service’s potential. The results suggest that
addressing challenges related to legislation, policy, and stakeholder
participation is crucial for achieving more robust and sustainable long-term
outcomes. Further supporting this trend, a comprehensive review by [24]
explores existing practices and approaches used to integrate passenger and
freight transport in urban areas. This review highlights the numerous positive
impacts of integration, including reduced traffic congestion, improved resource
utilization, and increased overall sustainability within cities.
The transportation and logistics
industries are navigating a turbulent economic landscape shaped by geopolitical
tensions and global downturns. These disruptions necessitate that transport
companies re-evaluate their operational practices. To thrive in this
environment, effectively combining traditional approaches with modern modelling
and analysis techniques is crucial. This allows for the development of
efficient strategies for managing and optimizing transportation processes. The
paper [25] exemplifies this by exploring the synergy between business analytics
and modelling in freight transport. The study establishes updated criteria for
evaluating business intelligence in this context and applies the IF-AHP method to assess the implementation of data analytics
and modelling in logistics. Additionally, the research [26] provides an
empirical analysis of factors contributing to volatility in the freight
transportation market. Technological advancements also play a key role. For
instance, the study [27] proposes an architecture for telematics tools along
with a methodology that merges delivery planning with transportation demand
modelling. This allows for calculating performance indicators used in the
preliminary assessment of delivery scenarios. Understanding shipper
decision-making is another crucial aspect. The research [28] utilizes latent
class modelling to analyse the heterogeneity of
freight shippers’ preferences when choosing transportation modes. This research
sheds light on the thought processes behind shipper and agent choices.
Optimization remains a critical focus. The paper [29] proposes a mathematical
algorithm for route construction, leveraging real-world data and demonstrating
its effectiveness in solving large-scale instances. Similarly, the authors of
the study [30] aim to create a tool that generates cargo loading plans and
route sequences for efficient pallet distribution, tackling a combined vehicle
routing, and loading problem. Furthermore, the paper [31] proposes a two-stage
model to optimize procurement of road-rail transshipment and truck routing,
fostering synergies between these transportation modes. By embracing these
advancements and fostering a data-driven approach, the transportation and
logistics industries can navigate economic challenges and develop more
efficient and resilient operations.
The research presented utilizes a
diverse range of methodologies to tackle various challenges in transportation
and logistics. Optimization problems are frequently addressed through mixed
integer linear programming [2, 4] and hybrid approaches combining goal
programming with genetic algorithms [4]. Additionally, game theory proves
valuable in analysing strategic interactions between
different players within the transportation ecosystem [3, 14, 31, 32]. For modelling and analysis, researchers leverage techniques
like discrete event modelling [5] to simulate real-world scenarios and
functional analysis [6] to understand complex relationships. Cluster modelling
[6, 28] helps identify groups with similar characteristics, while fuzzy logic
approaches and fuzzy stochastic programming [8, 18] allow for incorporating
uncertainty into decision-making. Statistical methods, including structural
equation modelling [10, 15] and regression analysis [15], are employed to
identify relationships between variables. Furthermore, cluster, variance, and a
posteriori analyses [15] provide deeper insights into data. Decision-making
support tools are explored as well, with research by Pamucar
et al. [19] utilizing an order priority approach based on fuzzy sets of images.
Qualitative methods also play a role. Studies based on in-depth interviews [20]
offer valuable insights from industry professionals, while SWOT
analysis [21] provides a framework for strategic planning. Looking towards the
future, research by [25] highlights the potential of the intuitionistic fuzzy
analytical hierarchy process for decision-making. Additionally, empirical
analysis and panel regressions used in [26, 33] offer valuable insights into
market trends. Finally, research by [29, 30] demonstrates the effectiveness of
operational research techniques for solving complex optimization problems
related to vehicle routing and cargo loading. This rich tapestry of
methodologies ensures a comprehensive understanding of the transportation and
logistics landscape, allowing researchers to develop effective solutions for
the challenges faced by the industry.
The presented literature review
emphasizes that efficient transportation systems require strong interaction
between all participants in the transport market. This collaboration allows for
considering the inherent randomness of demand and technological processes.
Additionally, it helps eliminate roadblocks caused by poorly defined goals at
the tactical planning stage.
3. THE PROPOSED APPROACH TO ESTIMATE THE
SYNERGETIC EFFECT
Our proposed
methodology builds upon the research presented in [34] on optimal delivery
chain structure selection. While we demonstrate the approach for calculating
the synergistic effect for each participant type within the context of four
basic logistics chain structures, the methodology itself is inherently
scalable. The framework can be readily extended to accommodate a wider range of
alternative structures or additional delivery process participant types without
requiring any fundamental modifications.
3.1. Alternative structures of a logistic chain
Within a logistics system,
individual supply chains involve a set of potential structures for delivering
goods. These structures can be analysed by
considering the key players involved in the flow of materials. The starting
point of any supply chain, acting as the source of the material flow, is the
freight owner, also known as the consignor. The destination point is another
cargo owner, the consignee. Therefore, both the beginning and end points of the
delivery chain involve freight owners. Physically, the movement of materials
(the material flow processing) is handled by a carrier company. The
organization and planning of this flow are often managed by a freight
forwarder, who may utilize resources such as freight terminals when necessary.
Within the logistics chain, freight
forwarders act as organizers of technological processes. They play a crucial
role by concentrating information flows and ensuring smooth communication
between all parties involved in moving goods. When a cargo owner needs to
deliver goods, it typically contacts a freight forwarder who then coordinates
the entire delivery process. This is known as the 1F-structure
[34], where one forwarder and one carrier are involved. Notably, cargo
terminals are not utilized in this specific scenario. The process starts with
the shipper informing the freight forwarder about the need for delivery. The
forwarder then identifies a suitable carrier capable of transporting the
shipment to the consignee (receiver). Bilateral agreements are then
established: one between the forwarder and the shipper, and another between the
forwarder and the carrier. The shipper pays the forwarder for their services,
and the forwarder uses these funds to pay the carrier. Depending on the
specific arrangement, the carrier might be responsible for delivering the
shipment from the shipper to the border, and then from customs to the
destination. This type of logistics chain is commonly used for road
transportation when the shipment weight matches the capacity of a single
vehicle.
The 2F-structure
represents a more complex variant within the logistics chain where two freight
forwarders are involved [34]. Upon receiving a shipment request from a shipper,
the initial freight forwarder locates a carrier to deliver the goods to the
border. They then send the request to a partner forwarder, who arranges onward
delivery to the consignee using a regional carrier in their area. This
structure necessitates four bilateral agreements: shipper and initial freight
forwarder, initial freight forwarder and carrier delivering to the border, the
two freight forwarding companies, partner forwarder, and regional carrier
completing the final leg of the delivery. The financial flow involves the
shipper paying the initial forwarder. From this payment, the initial forwarder
then compensates both the regional carrier and the partner forwarder for their
respective services. The partner forwarder, in turn, uses their received
payment to cover the costs of the regional carrier they utilize.
The cargo terminal plays a central
role in the logistics chain with the 1T-structure
[34]. Upon receiving a request from a shipper, the freight forwarder assesses
the economic feasibility of using the cargo terminal. If this option proves
cost-effective, the forwarder then identifies carriers for two legs of the
journey: one to deliver the cargo to the terminal and another for international
export to the destination. This process involves establishing four bilateral
agreements: between the forwarder and the shipper, between the forwarder and
the regional carrier delivering to the terminal, between the forwarder and the
cargo terminal, and between the forwarder and the international carrier for
export. The freight forwarder utilizes funds received from the shipper to pay
for the services of both carriers and the cargo terminal itself. The 1T-structure is particularly suited for situations where
initial cargo deliveries occur by road, followed by consolidation based on the
destination at the terminal, and finally, onward shipment using a main
transport mode like rail. In some cases, the cargo terminal may even handle the
export of the consolidated shipment, offering comprehensive logistics services.
The 2T-structure
is a common option for deliveries involving long distances and the use of a
main transport mode [34]. In this scenario, the freight owner initiates the
process by contacting a freight forwarder. The freight forwarder, after evaluating
various logistics chain options, selects the 2T
structure as the most efficient solution. The forwarder then takes charge of
coordinating the entire process. They first arrange for a regional carrier to
transport the goods from the shipper’s location to a nearby cargo terminal.
Agreements are then established with the terminal, the main carrier (e.g.,
shipping line or railway company), and a partner forwarder in the recipient’s
region. The partner forwarder mirrors these actions in their region, arranging
for a regional carrier to deliver the goods from the receiving terminal to the
destination. Contracts are also established with the terminal and the regional
carrier. Additionally, a separate agreement exists between the two forwarders.
Financially, the freight forwarder in the sender’s region uses the payment
received from the freight owner to cover the costs of all involved parties:
regional and international carriers, the local terminal, and the partner
forwarder’s services. The recipient region’s forwarder, in turn, uses the funds
received from the first forwarder to pay for the local terminal and carrier
services. There can be variations in this payment structure. Sometimes, the
sender’s terminal might directly pay the regional carrier there, while the
recipient’s terminal handles the final delivery costs.
3.2. The method to calculate the synergistic
effect
The use of the most effective
structures of cargo delivery chains is possible through the interaction of
transport market entities within a single system. Therefore, the effect of
choosing the optimal supply chain options is a synergistic effect.
The effect of a management decision
on choosing a delivery chain structure is assessed relative to other
alternative options. For a given request for transport services, the effect 
relative
to the 
-th option is
determined as follows:
, (1)
where 
is total
costs of delivery process participants for -th structure, [EUR/request]; 
is total
costs of delivery process participants for the optimal structure,
[EUR/request].
For a set of alternative structures,
the effect of choosing the optimal option can be assessed as an arithmetic
mean, but it is more correct to estimate the average considering the weight of
each of the alternative options, assessed by the corresponding value of the
total costs:
, (2)
where 
is the
effect of choosing the optimal structure for a given request for transport
services, [EUR/request]; 
is the
set of alternative structures, 
.
Using the models developed in [34]
to calculate the costs of the subjects of the delivery process, the total costs
of servicing one request for the -th type of a
logistics chain can be estimated based on the average number of requests
received over a given period:
, (3)
where 
is the
total costs of delivery process subjects for the -th structure of a logistics chain, [EUR]; 
is the
expected value of the time interval between requests in a flow [hours/request];
is the
duration of the period during which the process of receiving requests for
transport services is considered, [hours].
Then formula (2) can be written as:
, (4)
where 
is the
total costs of subjects of the delivery process
during the simulated period for the optimal structure of a delivery chain,
[EUR].
Let us consider expression (4) for
the case when the 1F structure is the optimal one for
a given request:
, (5)
, (6)
Finally, from (6) we obtain:
. (7)
Generalizing (7) for the case where
the 
-th logistics chain
structure is optimal (
), we obtain the following relationship for
determining the synergistic effect per request:
. (8)
The synergistic effect for the
entire logistics system servicing a flow of requests for deliveries, that
arises due to the justification by a freight forwarder of the most effective
logistic chain structures, can be determined as the sum of effects obtained for
the requests in the flow.
3.3. Estimating a part of the synergetic effect
for the participants
The share of the synergistic effect
attributable to a specific participant in the delivery process is assessed
based on the effect per request using the following formula:
, (9)
where 
is the
share of the synergistic effect attributable to the 
-th entity of the delivery chain; 
is the
synergistic effect from servicing a request
attributable to the -th participant, [EUR/request]; 
is the
set of the delivery process participants, 
: 
–
freight owner, 
–
freight forwarder, 
–
carrier, 
–
freight terminal.
The synergistic effect of the -th participant is
estimated as the weighted average value of the difference in expenses according
to the total expenses of all entities that participate in the delivery process
similarly to (4):
, (10)
where 
is the
expenses of the -th participant in the delivery process during
the simulated period when the -th structure of the logistics chain is used,
[EUR/request]; 
is the
expenses of the -th participant for
the optimal structure of the delivery chain, [EUR/request].
Let us consider expression (10) for
freight owner for the case when the 1F-structure is
optimal for the given demand parameters:
, (11)
where 
is the
synergistic effect obtained by cargo owners, [EUR/request]; 
, 
, 
, and 
are
expenses of freight owners during the simulated period when 1F-, 2F-, 1T- and
2T-structures of delivery chain used, [EUR/request].
Summing up the synergistic effect of
all participants in the delivery process, we obtain the following expression:
(12)
,
(13)
.
The following equalities are true by
definition:
. (14)
Substituting (14) into (13), we
obtain the following:
(15)
.
Or, similar to the transformations
in (5), equality (15) can be shown in the following form:
(16)
Since the expression on the right
side of equation (16) in accordance with (8) is the synergistic effect in the
logistics chain from using the 1F-structure as the
optimal one, it can be argued that the sum of the synergistic effects of the
delivery chain entities when choosing the optimal 1F-structure
is equal to the synergistic effect for the entire chain.
It is easy to verify that equality
(16) is satisfied for cases when the 2F-, 1T-, and 2T-structures of the
delivery chain are optimal.
Thus, the sum of the effects of the
delivery process participants is equal to the synergistic effect of choosing
the optimal delivery option for the logistics chain as a whole:
. (17)
The described approach allows us to
calculate the share of synergetic effect for each type of the delivery
participants, such that the sum of shares equal to 1.
4. RESULTS OF EXPERIMENTAL STUDIES AND
DISCUSSION
To evaluate the synergistic effect
for each participant within a given logistics chain structure, a simulation
model was developed based on the proposed methodology for calculating this
effect. This model determines the corresponding share of the synergistic effect
for each participant. Implemented in the C# programming language, the
simulation model leverages a library available from a publicly accessible
repository [35].
The simulation experiment
incorporated numerical parameters reflecting the Kazakhstani cargo
transportation market. These parameters included fuel costs, operator wages,
tariffs for storage and transportation services, etc. The results of this
experiment, investigating the impact of request flow parameters on the
distribution of the synergistic effect among delivery process participants, are
presented in Fig. 1-4.
Fig. 1. Dependence of freight
owners’ share in the synergistic effect on demand parameters
Fig. 2. Dependence of a freight
forwarder’s share in the synergetic effect
from demand parameters
Fig. 3. Dependence of a carrier’s
share in the synergetic effect from demand parameters
Fig. 4. Dependence of a freight
terminal’s share in the synergistic effect
from demand parameters
Analysis of the results of a
simulation experiment conducted for the expected values of the consignment
weight in the range from 1 ton to 25 tons, as well as the values of
the average delivery distance in the range from 100 km to 2500 km,
allows us to draw the following conclusions:
-
across a considered range of request flow
parameters, freight owners generally experience the largest share of the
synergistic effect (Fig. 1);
-
forwarders see the smallest share of the
synergetic effect; within the considered parameters’ range, their maximum share
remains low at around 0.7% (Fig. 2);
-
the distribution of the synergistic effect
is not linear; it exhibits a non-linear relationship with the parameters
characterizing the request flow for forwarding services;
-
for specific combinations of request flow
parameters, some participants, including freight owners, might even experience
a negative share of the synergistic effect; this can be attributed to negative
individual effects for a specific participant when servicing a request within
the overall optimal chain structure for the entire delivery system;
-
the share of the synergistic effect
captured by freight owners is minimized when request flow parameters approach
the lower limit of the considered range; conversely, as average delivery
distance and average consignment weight increase, the freight owners’ share
also increases;
-
for forwarders, the share of the
synergistic effect is maximum when servicing requests that are characterized by
the delivery of small weights over short distances; with the increase in
expected values of the consignment weight and the delivery distance, the share
of the synergistic effect of forwarders decreases;
-
the share of the synergistic effect
attributable to freight terminals is maximum when servicing the flow of
requests, which is characterized by delivery distances close to the lower limit
of the considered range;
-
for carriers, the share of the synergistic
effect from the interaction of the delivery process entities is maximum if the
flow of requests with small expected values of the consignment weight is
serviced.
Fig. 5-7 present diagrams
illustrating the distribution of the synergistic effect across participants in
the delivery process. These diagrams are generated for various combinations of
the expected values of delivery distance and average consignment weight.
Examining Fig. 5, we observe the
distribution of the synergistic effect for a consignment weight of 1 ton.
Carriers capture the greatest share of this effect, with this share reaching a
maximum at the highest considered average delivery distances. In contrast,
freight owners experience a peak share for delivery distances around 600-700
km. Interestingly, freight terminals achieve their
maximum portion of the synergistic effect at the expected value of a delivery
distance of 100 km.
Fig. 5. Distribution of the
synergistic effect between entities of the delivery process
for an average parcel weight of 1 ton
The diagram in Fig. 6 shows an
average cargo volume of 7 tons. In this case, the carrier’s contribution to the
overall synergistic effect is reduced compared to the scenario shown in
Fig. 5. With an average of 7 tons, the carrier only benefits when the
average delivery distance is 100 km or more. In this scenario, most of the
synergistic effect goes to cargo owners. This synergistic effect for freight
owners increases as the average delivery distance gets longer. Conversely,
cargo terminals see a decrease in their share of the synergistic effect as the
average delivery distance rises.
Fig. 6. Distribution of the
synergistic effect between entities of the delivery process
for an average parcel weight of 7 tons
Fig. 7. Distribution of the synergistic
effect between entities of the delivery process
for an average parcel weight of 25 tons
Analysis of Fig. 7 reveals that
increasing the expected value of the consignment weight does not alter the
observed distribution of the synergistic effect among delivery participants.
Cargo owners continue to capture the largest share, while freight terminals
receive a negligible contribution. Notably, the share for carriers remains
negative. Furthermore, the trend of increasing cargo owners’ share and decreasing
freight terminal share persists with rising average delivery distance.
5. CONCLUSIONS
Recent
research has focused on optimizing supply chains while considering risk,
uncertainty, and sustainability. As technology evolves and the industry
changes, scholars continue to develop innovative solutions for a more efficient
and sustainable transportation system. The proposed methodology enables
researchers to evaluate the effect of selecting the proper structure of a
delivery chain for all participants of the delivery process.
The
results of the conducted experimental studies allowed us to state that the
combined effects experienced by individual participants in the delivery process
are equivalent to the synergistic effect achieved by selecting the optimal logistics
chain structure for the entire system. Analysis of the simulation experiment
reveals that cargo owners exhibit limited values for their share of the
synergistic effect, while forwarders capture the smallest portion.
Interestingly, certain request flow parameter combinations can lead to negative
shares of the synergistic effect for one or more participants. This phenomenon
can be attributed to situations where a specific participant experiences
negative effects when servicing a request that is deemed optimal for the entire
delivery chain.
The
findings of our research offer valuable guidance for optimizing logistics
chains within the Kazakhstani market, and potentially other markets with
similar characteristics. Ultimately, the results of this study go beyond the
Kazakhstani context. They offer a broader contribution by demonstrating how to
maximize economic efficiency within logistics chains through a focus on
collaboration and the equitable distribution of synergistic benefits. This
knowledge can empower stakeholders across the logistics industry to make
informed decisions when configuring their supply chains.
Future
research efforts can be directed towards a more comprehensive understanding of
the factors influencing the distribution of the synergistic effect. This could
involve investigating the impact of additional demand parameters beyond those
explored in this study. For instance, factors such as order frequency, delivery
time windows, and shipment urgency could be examined to determine their influence
on the share of the synergistic effect for each participant type. Additionally,
expanding the scope of the analysis to encompass a broader range of alternative
logistics chain structures would be valuable. This could be achieved by
incorporating a more detailed consideration of the technological processes
performed by various entities within the chain. By analysing how these
processes interact and contribute to the overall efficiency, researchers could
gain deeper insights into how different chain structures influence the
generation and distribution of the synergistic effect.
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Received 05.05.2024; accepted in revised
form 16.07.2024
Scientific
Journal of Silesian University of Technology. Series Transport is licensed
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