Article
citation information:
Popa, C., Goia, I. The modelling of cargo
transshipment operations using the business process modelling tools. Scientific Journal of Silesian University of
Technology. Series Transport. 2024, 124,
157-169. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2024.124.11.
Catalin POPA[1], Ionela GOIA[2]
THE
MODELLING OF CARGO TRANSSHIPMENT OPERATIONS USING THE
BUSINESS PROCESS MODELLING TOOLS
Summary. This paperwork explores
the transshipment operations at the Port of
Constanta, Romania, focusing on the unloading of big bags from barges.
Utilizing Business Process Management (BPM) software, the study models the transshipment process to identify optimization
opportunities. The investigation reveals challenges such as coordination
complexities and potential cargo damage, alongside the benefits of
cost-efficiency and flexibility offered by transshipment
services. Through literature review and analysis, the study emphasizes the
importance of efficient business processes and the role of BPM software in
enhancing operational efficiency. By employing Aura Portal Modeller for
simulation, the study identifies bottlenecks and proposes optimization
strategies for the transshipment process. The
proposed measures include real-time cargo assessment, inventory tracking
systems, equipment analysis, and storage layout optimization. This research
contributes to the understanding of transshipment
operations and highlights the effectiveness of BPM software in process
visualization and analysis, offering insights for enhancing efficiency and
mitigating delays in supply chain management.
Keywords: business process management, port operations,
process modelling, simulation analysis, cargo handling, inventory management
1. INTRODUCTION – LITERATURE REVIEW
Transshipment
entails the relocation of goods from one transportation mode to another and
serves as a vital element within global supply networks, facilitating the
smooth transportation of goods across extensive distances. While transshipment services offer advantages such as
cost-efficiency, adaptability, and accessibility to inland areas, they also
pose challenges such as the intricate coordination required between diverse
transportation modes, potential setbacks and cargo damage, and the necessity
for proficient inventory handling.
This study aims
to scrutinize the transshipment procedures at the
Port of Constanta, Romania, with a specific emphasis on unloading big bags from
barges. Using Business Process Management (BPM) software, the study seeks to
model the transshipment process and pinpoint
optimization opportunities.
The outcomes of
this investigation will be valuable for researchers and practitioners engaged
in supply chain management, offering insights into the obstacles and prospects
linked with transshipment, while showcasing the
efficacy of BPM software in enhancing transshipment
operations' efficiency.
1.1
Overview
of transshipment services and challenges
Transshipment serves
as a vital link within the intricate network of global supply chains. It
streamlines the movement of goods by enabling them to seamlessly transition
between various transportation methods, ultimately facilitating their delivery
across vast distances. This process typically occurs at designated transshipment hubs equipped to handle large volumes of
cargo. Here, cargo seamlessly transitions from one mode of transport to
another, such as ships to barges, trains to trucks, or vice versa [1]. There
are several types of transshipment, including
sea-to-sea, sea-to-land, and land-to-land transfers. These services offer
numerous benefits, including cost-effectiveness, accessibility to inland
locations, and flexibility in choosing transportation options based on factors
like cost, speed, and capacity.
However, transshipment also
presents challenges, such as the complexity of coordinating transfers between
different modes of transport, which requires meticulous planning and logistics
management [2]. Delays
can occur during the process, affecting delivery schedules, and there's an
increased risk of cargo damage when goods are handled multiple times. Despite
these challenges, transshipment remains an essential
component of modern logistics, enabling the efficient movement of goods
worldwide.
According to industry reports, bulk carriers currently
hold the largest share of the global shipping fleet's carrying capacity. These specialized vessels are designed to
transport large quantities of dry cargo such as iron ore, coal, and grains. As
of 2019, they accounted for an impressive 43% of worldwide capacity [3]. This
dominance appears to be continuing in 2023 [4]. Bulk transshipment terminals are essential components of the
global bulk cargo supply chain, facilitating the smooth transportation of bulk
commodities for subsequent distribution [5].
A significant benefit of utilizing transshipment
services lies in their ability to optimize transportation efficiency and
minimize associated costs. Transshipment hubs achieve
cost reductions through various strategies.
They function by accumulating cargo from diverse origins, enabling them
to capitalize on economies of scale in transportation, storage, and handling
activities. This translates to lower overall costs per unit of cargo moved.
Additionally, these hubs empower businesses with flexibility. They can choose
the most suitable transportation mode for each leg of the journey based on
cost, speed, and reliability. This optimization in route planning enhances the
overall responsiveness and agility of the supply chain [6].
Despite their benefits, transshipment
services also present challenges that need to be addressed to ensure smooth
operations and uninterrupted supply chain flow. These challenges include
coordinating complex multi-modal transportation processes, managing cargo
handling and storage capacity, navigating regulatory requirements, and
mitigating risks such as delays, damages, and security threats [7].
To address these challenges and maximize the benefits of transshipment services, businesses and logistics
stakeholders employ various strategies and technologies. These may include
advanced tracking and monitoring systems, automation and robotics in cargo
handling, predictive analytics for demand forecasting and capacity planning,
and collaboration among supply chain partners to improve coordination and
visibility.
1.2 The basics
of business process management
At the core of every successful organization
lies a well-defined set of interconnected activities known as business
processes. These processes, structured and routinely executed, ensure the
smooth and efficient operation of the entire business. They encompass a wide
range of functions, including supply management, production, distribution,
marketing coordination, and customer service provision. These essential
activities work together seamlessly to maintain the uninterrupted day-to-day
operations of the organization. The core functions of a business can be
understood through its operational processes.
In compliance with Laguna and Marklund’s paper, business processes serve as the
"foundation" for ensuring the efficient functioning of an
organization, profoundly impacting its performance. By effectively managing and
optimizing these processes, organizations can gain significant advantages and
maintain their relevance in an ever-changing and highly competitive business
environment. Thus, ensuring proper adaptation to fluctuating market demands and
supporting sustainable growth in a dynamic and challenging context [8].
According to Rosenberg’s paperwork the
processes are essentially a series of interconnected activities, transforming raw
materials or services into desired outputs.
Their goal is to achieve the organization's specific operational
objectives [9]. Procurement, production, distribution,
marketing, sales, and customer service are all examples of such processes,
though the specific lineup will vary depending on the
nature [10].
Optimizing transshipment
processes requires establishing clear and achievable goals. Kunpeng,
Gharehgozli, & Lee [11] emphasize
the value of employing SMART objectives in this context. By setting Specific,
Measurable, Achievable, Relevant, and Time-Bound goals, organizations can
ensure focused direction for their optimization efforts. This allows for
measurable progress tracking and ultimately increases the likelihood of
achieving desired performance improvements within transshipment
operations.
Measurable objectives are quantifiable,
allowing progress tracking. Achievable objectives are realistic and feasible
within organizational capabilities. Relevant objectives align with overall
organizational strategy and needs. Time-bound objectives have specific
deadlines for completion. Adhering to SMART principles ensures focused
direction, measurable progress, and increased likelihood of achieving desired
outcomes in operational processes [12].
In their exploration of business process
performance evaluation, Nanyam & Neeraj [13] highlight the critical role of defining
clear objectives and expected outcomes. This approach allows organizations to
establish measurable benchmarks for success. By comparing actual results
against these predefined objectives, organizations can conduct unbiased
evaluations of process effectiveness and identify areas for improvement.
Clearly defined objectives are essential for
effective performance evaluation within business processes. These objectives
function as a performance measurement framework, allowing organizations to
assess progress towards achieving desired outcomes. By comparing actual results
against these predefined objectives, organizations can identify areas for
improvement and ensure all stakeholders, including employees, are aligned on the
goals of optimizing process performance [14] [15].
2.
Modeling Transshipment
Operations in Constanta Port using AuraPortal Modeler: Research Hypotheses and Applied Variables in the
Model
2.1
Modelling approach and case study assumptions
This paper aims to analyse the semi direct transshipment operation of a barge carrying 40,000 metric
tons of Clinker Big Bags within a strict 12-day timeframe. Such an endeavour
demands a well-coordinated plan and streamlined processes to ensure both
effectiveness and cost efficiency. To tackle this challenge, a simulation
program has been devised, considering crucial parameters such as the total
cargo volume, the duration of the program, and the estimated cost involved.
With these factors in mind, the simulation aims to optimize the transshipment process, ensuring that all cargo is
efficiently handled within the allocated timeframe.
The process is meticulously broken down into
key stages within the simulation. It begins with the crane lifting a Big Bag
from the ship onto a waiting tractor-trailer, followed by the transportation of
the loaded bag to the designated storage area by the tractor. Subsequently, a
forklift unloads the bag from the trailer and places it in storage. The cycle
is completed with the return of the empty tractor to the crane for the next Big
Bag and the forklift's return to the storage area. Acknowledging real-world
variations, the simulation takes into consideration factors that could impact
the average cycle time, such as equipment efficiency, operational coordination,
and unforeseen events like adverse weather conditions or minor delays. The outcome of the simulation not
only assesses the feasibility of achieving the target goal within the stipulated
timeframe but also serves as a foundation for continuous improvement. Through
regular monitoring and analysis of cycle times during the actual unloading
process, potential bottlenecks can be identified and addressed for future
optimization. Additionally, embracing technological advancements in automation
and equipment efficiency can further enhance the unloading process, ensuring
long-term efficiency and sustainability by remaining adaptable to changing
operational needs.
For process modelling, the authors used Aura
Portal Helium Modeler, a free and dedicated software
tool. Aura Portal is a digital business platform that streamlines process
design and execution without requiring additional programming knowledge. Its
core product, Aura Portal BPM (Business Process Management), is a software
application specifically designed to facilitate the creation and automation of
workflows.
To ensure the model confidentiality, the company
analysed in the case study has been named,
"Danube Investments Ltd.", representing a typical big bag
transshipment service provider in the Port of Constanta. Data on activity
durations and costs have been gathered from ten similar companies to create statistically
relevant averages for variables used in the model (e.g., crane operation time,
personnel costs). A pre-existing transshipment model was selected, and key
processes were identified to map the flow of big bags from retrieval on the
barge to designated storage locations on the land platform. This comprehensive
analysis ensures the model accurately reflects the big bag unloading process at
the Port of Constanta, considering factors like resource allocation and travel
times.
2.2
Process model description. detailed breakdown of the transshipment process activities
As main activities, the authors begin with
the assumption of completed customs formalities and the ship is docked for
unloading. The process has been divided in three main phases, each with
subsequent tasks for a better understanding of the process.
Phase A reflects the unloading process of the cargo
from the barge using the quay crane and unloading onto the tractor. The initial
task entails the crane's rotation to the required height, directed by the barge
foreman. Upon successful completion, the subsequent activity of moving the
cargo onto the tractor trailer is followed. Herein lies a decision point: if
cargo security is ensured, we advance to the next task; otherwise, the cargo
must be re-secured, and the process will return to task 2 to rectify the issue
before proceeding. If some big-bag is damaged during handling, it is sent for
repackaging.
Following the unfastening the cargo from the
trailer, the process continues with the transportation of the cargo to land
using tractor-trailer units in phase B.
The suitability of the tractor determines the progression, with any unsuitable
choices leading to a temporary halt until a suitable alternative is found.
Therefore, if the tractor is not suitable for the task, the activity will have
a finish point until a suitable tractor is found. The process is followed with
the transporting the cargo to land over 400 meters, leading to positioning the
trailers in the optimal area of action for forklift operations.
The adequacy of personnel and resources
determines progression to phase C,
where the cargo is transferred from tractor to land using a forklift. If the
resources are insufficient, the activity will be suspended. Upon completion of
the positioning task for the forklift on the side of the trailer, guided by the
docker, the process proceeds to lifting the load to
the appropriate height for retrieving the big bag from the trailer. It is then
assessed whether the forklift meets the unloading requirements; if not,
operations are suspended until a suitable replacement arrives. Once a suitable
forklift is available, the process advances to moving the forklift forward to
the pickup position, guided by the docker, followed
by relocating the forklift over 10 m to the designated location for depositing
the big bag. If successful, the process proceeds to placing it at the indicated
location. On-site workers then inspect the task's completion; if the big bag is
misplaced, the process returns to the relocation task to ensure correct
placement. Upon successful completion of the placement task, the process moves
on to raising the hooks vertically, followed by lifting the forks, rotating the
forklift, and moving it without cargo to the pickup point for another big bag,
thus concluding the cycle. Tasks progress sequentially, culminating in the
completion of a cycle with the final task.
In summarizing the process, in figure 1 model
the authors have presented in three distinct phases, each comprising subsequent
tasks for enhanced clarity as depicted below. Phase A encompasses the unloading
of cargo from the barge using the quay crane and its subsequent transfer to the
tractor trailer. Phase B is dedicated to transporting the cargo to land via
tractor-trailer units, with the suitability of the tractor dictating the course
of action. Phase C involves transferring the cargo from the tractor to land
using a forklift, with the sufficiency of personnel and resources guiding the
process forward. Tasks advance sequentially, culminating in the completion of a
cycle with the final task.
Fig. 1. Semi-Direct transshipment
process modeling using Aura Portal
Once the process model has been established,
powerful simulation software comes into play. This software leverages
statistical data to create various scenarios that replicate real-world transshipment operations. The data focuses on activities
deemed critical for successful execution, such as crane operation times or
personnel requirements. By integrating these critical parameters, the
simulation software can anticipate potential results like total unloading time
or resource needs. It can also pinpoint areas within the process that slow down
operations, allowing for targeted optimization efforts. Additionally, the
software analyses how resources like cranes and personnel are currently used
and identifies opportunities for more efficient deployment. Finally, it
evaluates key performance indicators (KPIs) like
total unloading time, cost per ton, and resource utilization to gauge the
effectiveness of the entire process. Through simulation and analysis, the
software empowers authors to identify and address inefficiencies, ultimately
leading to a smoother, faster, and more cost-effective transshipment
process in the Port of Constanta.
Fig.
2. Configuring parameters for initiating the process simulation
The visual representation depicted in figure
no. 2 illustrates the configured parameters before initiating the simulation.
Within the simulation process, a total of 50 processes have been implemented
per day, enabling the unloading of 800 tons per day. Over the course of 12
days, this setup ensures the complete unloading of the entire quantity.
Additionally, the simulation allows for 2 simultaneous executions, reflecting
industry standards derived from sample companies' analyses. The calendar panel
provides insights into simulation run times, indicating default values for
working hours per day set at 8. This parameter serves as the foundation for
computing simulation outcomes. For instance, if a process requires 24 hours to
complete and the standard workday spans 12 hours, the total execution duration
would equate to 2 working days per month. This default setting assumes 22
working days per month. The simulation is configured to run for a maximum
duration of 12 days, with the actual durations of processes revealed upon
completion. The total cost expenditure of the process amounts to 3,000 Euro for
a twelve-days period, involving twenty individuals per
day in the transshipment operation.
Upon configuring the temporal and financial
parameters, the simulation was brought to a close. Throughout the simulation's
execution, objects within the model illuminate in various hues corresponding to
their temporal status. A green hue signifies that an object has completed its
assigned tasks within the anticipated timeframe and cycle count, while orange
indicates completion within an alert timeframe, and red indicates completion
beyond the projected timeframe.
As shown in
the figure 3, the simulation highlights opportunities to enhance the process.
The current completion time identified in the simulation suggests room for
improvement. To identify these, a deeper analysis of the simulation results is
necessary. Focusing on bottlenecks could involve examining factors such as task
allocation, training needs, or inefficiencies in the process flow itself. By
pinpointing these areas, we can develop targeted solutions, such as refining
process steps or implementing new technologies. These improvements will
ultimately lead to a more streamlined and effective process, resulting in (positive
business outcome, e.g., reduced costs, faster turnaround times, improved
customer satisfaction).
Fig. 3. Process execution simulation diagram
However, despite our best efforts, the initial budget of 3,000 euros was
exceeded by 5,125 Euros due to unforeseen challenges in the procurement of
specialized equipment required for the simulation. Delays in equipment delivery
and unexpected maintenance costs contributed to the budget overrun,
highlighting the importance of comprehensive risk assessment and contingency
planning in future projects. Ultimately, achieving both optimal performance and
cost-effectiveness are crucial for project success.
The simulation within the Aura portal has identified
potential bottlenecks in the unloading process, requiring further analysis and
improvement. These bottlenecks are indicated by the appearance of yellow and
red error messages. Upon initial observation, the software, by iterating the
process through the designed cycle count, identified delays in the transshipment procedure due to task overlap and resource
inadequacies, leading to congestion in the operational queue.
Red Errors: Critical Issues Requiring
Immediate Attention
Ø
TS1 -
Refasten Cargo and Process Resumption (Red Error): This critical error
signifies a severe interruption, likely due to cargo instability or safety
concerns. Immediate action is necessary to refasten the cargo and resume
operations safely and efficiently.
Ø
TP5 -
Evaluating and Selecting Tractor for Task (Red Error): This red error indicates
a significant delay in selecting the appropriate tractor for the job. Potential
causes include equipment unavailability or bottlenecks in the decision-making
process. Optimizing tractor selection procedures can significantly reduce these
delays.
Yellow Errors: Areas for Improvement and
Optimization
Ø
TP7 -
Evaluating and Selecting Forklift (Yellow Error): This yellow error suggests a
moderate delay or inefficiency in selecting the appropriate forklift. Resource
limitations or insufficient planning could be contributing factors.
Streamlining the selection process can minimize these delays.
Ø
TP3 -
Big Bags Condition Assessment (Yellow Error): The presence of this yellow error
highlights the need for a more thorough assessment of the big bags' condition.
Inconsistencies or uncertainties in the current process require investigation
and improvement.
By analysing these red and yellow errors, we
can pinpoint specific areas for improvement within the Aura portal simulation.
By implementing corrective actions and optimizing the identified processes, we
can ensure a smoother and more efficient unloading workflow. The simulation
provides valuable insights into potential problems, allowing us to proactively
address them before they hinder real-world operations.
2.3
Potential bottlenecks revealed in Aura Portal Simulation: software-based
optimization strategies
The Aura portal simulation has uncovered
potential bottlenecks within the unloading process, emphasizing the necessity
for comprehensive analysis and targeted optimization. These bottlenecks are
evident through the appearance of red and yellow error messages, each
indicating areas for improvement.
Red errors, denoting critical issues
requiring immediate attention to prevent disruptions and ensure safety, include
"TS1 - Refasten Cargo and Process
Resumption" and "TP5 - Evaluating and
Selecting Tractor for Task." The former signals a severe interruption,
potentially caused by cargo instability or safety concerns, while the latter
highlights significant delays in selecting the appropriate tractor. To address
these critical issues, optimization measures such as implementing pre-departure
inspections for cargo stability and enhancing communication for tractor
selection can be implemented.
Yellow errors, representing areas for
improvement to enhance efficiency and minimize delays, include "TP7 - Evaluating and Selecting Forklift" and "TP3 - Big Bags Condition Assessment." These errors suggest
moderate delays in selecting the appropriate forklift and emphasize the need
for a more thorough assessment of big bags' condition, respectively.
Optimization measures for yellow errors involve developing centralized resource
management systems, implementing standardized procedures, and using digital
inspection systems to ensure consistent evaluation procedures.
The identified bottlenecks and optimization
measures represent a starting point for continuous improvement. By regularly
monitoring the simulation and analysing performance data, new areas for
improvement can be proactively identified and further optimizations
implemented. This data-driven approach ensures that the unloading process
remains efficient, safe, and adaptable to changing demands. Ultimately, the
Aura portal simulation serves as a valuable tool for identifying and addressing
bottlenecks in the unloading process, leading to a smoother, more efficient,
and safer workflow.
2.4
Optimization actions ensuring budget compliance
Throughout the simulation process outlined in
this study, several optimization actions were meticulously implemented to
ensure that the operations remained within the allocated budget. By
strategically addressing inefficiencies and streamlining workflows, we were able
to achieve the desired outcomes while adhering to financial constraints.
One of the primary optimization measures
undertaken was the refinement of process efficiency. By fine-tuning the
scheduling of tasks and maximizing resource utilization, we significantly
enhanced productivity without compromising on quality or safety standards. This
approach allowed us to capitalize on the available resources more effectively,
ultimately contributing to cost savings.
Additionally, proactive measures were taken
to mitigate potential bottlenecks and disruptions in the workflow. By
identifying and addressing critical issues promptly, we were able to maintain
operational continuity and minimize costly delays. This proactive stance not
only safeguarded the project timeline but also prevented unnecessary
expenditures associated with downtime and rework.
Furthermore, strategic resource allocation
played a pivotal role in budget compliance. By allocating personnel and
equipment judiciously based on task requirements and workload demands, we
optimized resource utilization and minimized unnecessary expenses. This
balanced approach ensured that resources were allocated where they were most
needed, maximizing operational efficiency while staying within budgetary
constraints.
Upon implementing the optimization methods
mentioned earlier, the simulation of the process was successfully conducted
according to the specified parameters. As shown in figure no 4 for the 50
processes with two simultaneous executions, the entire quantity of cargo was
operated within 11-day timeframe. This was accomplished with the involvement of
20 resources in the process, while also adhering to an approximate budget of
2000 euros. This outcome objectively demonstrates the effectiveness of the
optimization methods, enabling the achievement of set objectives within the
defined financial constraints.
In conclusion, the successful completion of
the simulation within the allocated budget underscores the effectiveness of the
optimization actions implemented. By proactively addressing inefficiencies,
mitigating risks, and optimizing resource allocation, we not only achieved the
desired outcomes but also demonstrated our commitment to prudent financial
management. Looking ahead, these optimization strategies will continue to
inform our approach, ensuring that future simulations are conducted efficiently
and cost-effectively.
Fig. 4. Successful implementation of optimization
methods: achieving objectives within budget and time constraints
3.
CONCLUSION
To ensure the robustness and accuracy of the simulation results, data
validation was conducted through a multi-pronged approach. Firstly, data on
activity durations and costs was gathered from ten similar companies operating
at the Port of Constanta. This approach leverages established practices within
the industry and provides statistically relevant averages for variables used in
the model (e.g., crane operation time, personnel costs). Additionally, internal
validation measures were employed to enhance the reliability of the simulation
data. This involved cross-checking activity durations and costs against
historical data maintained by the port authorities and conducting sensitivity
analysis to assess the impact of variations in key input parameters on the simulation
outcomes. By employing these rigorous data validation procedures, we can
confidently assert that the simulation results accurately reflect the
real-world unloading process at the Port of Constanta, providing a solid
foundation for optimization strategies.
This study successfully employed BPM software and simulation techniques
to analyse and optimize the unloading process of big
bags at the Port of Constanta, Romania. The key findings and areas for
improvement are summarized below:
Ø The Aura
Portal simulation revealed critical bottlenecks within the unloading process,
including delays in cargo refastening (TS1), tractor selection (TP5), forklift
selection (TP7), and big bag condition assessment (TP3). These bottlenecks were
highlighted by red and yellow error messages, signifying areas requiring
immediate attention and optimization opportunities, respectively.
Ø Software-Based
Optimization Strategies: To address the identified bottlenecks, the study
proposes a series of software-based optimization strategies. These include
implementing pre-departure cargo stability inspections to minimize refastening
delays (TS1), enhancing communication and streamlining procedures for tractor
selection (TP5), developing centralized resource management systems for efficient
forklift allocation (TP7), using digital inspection systems to ensure
consistent big bag condition assessment (TP3).
Ø The study
emphasizes the importance of budget adherence throughout the optimization
process. Strategies like process efficiency refinement, proactive bottleneck
mitigation, and strategic resource allocation were employed to ensure
cost-effectiveness. Furthermore, the simulation, configured with 50 processes,
2 simultaneous executions, and a budget of approximately €2,000, successfully completed
the unloading of the entire clinker cargo within 11 days using 20 resources.
This demonstrates the effectiveness of the implemented optimization strategies
in achieving project objectives within budgetary constraints.
Ø The study
acknowledges the importance of continuous monitoring and data analysis to
identify further optimization opportunities. By adopting a data-driven
approach, the unloading process can be continuously refined for enhanced
efficiency, safety, and adaptability to evolving demands.
In conclusion, this research demonstrates the value of BPM software and
simulation in optimizing transshipment operations. By identifying and
addressing bottlenecks, and implementing targeted optimization strategies,
significant improvements can be achieved in terms of efficiency, safety, and
cost-effectiveness. By continuously monitoring and analysing
performance data, this approach ensures long-term optimization and adaptability
within the dynamic environment of port operations. In conclusion, this study has
shed light on the intricacies of transshipment operations and demonstrated the
effectiveness of Business Process Management (BPM) software, particularly Aura
Portal Modeller, in visualizing and analysing process flows. By leveraging statistical simulation
functionalities, researchers were able to pinpoint potential bottlenecks within
the process, paving the way for optimization strategies.
The proposed measures for improvement encompass various aspects of the
transshipment process, including real-time assessment of cargo handling needs,
implementation of tracking systems for inventory management, analysis of
equipment capabilities, and optimization of storage layout. By implementing
these solutions, organizations can streamline operations, enhance efficiency,
and mitigate potential delays and errors.
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Received 20.05.2024; accepted in revised
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