Corresponding Author: Muhammad Younus
E-mail: [email protected] �
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INTRODUCTION
The last mile delivery is a
crucial aspect of the supply chain, and its efficiency and accuracy are
essential for customer satisfaction (Xenou et al., 2022); (Modgil et al., 2021). The last mile delivery is the final stage of the
delivery process, where the product is delivered to the Customer's doorstep.
The last-mile delivery is often the most expensive and time-consuming part of
the delivery process. One of the critical challenges in last-mile delivery is
the accurate tagging of delivery zones within fulfillment centers (Gdowska et al., 2018). Delivery zone tagging techniques can be manual,
automated, or hybrid, each with advantages and disadvantages.
The effectiveness of these
techniques in improving the efficiency and accuracy of last-mile delivery is an
area of active research (Wu et al., 2021). This study analyzes delivery zone tagging
techniques for last-mile delivery orders within fulfillment centers. The
research will investigate the various methods used to tag delivery zones and
explore their effectiveness in optimizing last-mile delivery operations. The
study will be conducted through literature reviews and case studies of
fulfillment centers implementing different tagging techniques. The results of
this research will provide insights into the best practices for delivery zone
tagging and help fulfillment centers �improve their last-mile delivery operations (Tian & Zhang, 2021). It is of the utmost need to do urgent research on
the topic of examining delivery area/zone tagging strategies employed by
fulfillment centers for last-mile delivery orders. Optimizing the effectiveness
of delivery operations is essential in the face of rising e-commerce demand and
the exponential expansion of last-mile deliveries. Last-mile delivery
precision, efficiency, and effectiveness can all be improved by familiarity
with and application of effective area/zone tagging strategies. Researchers can
determine the best ways for specific contexts and operational restrictions by
analyzing and comparing alternative tagging methods, such as geofencing,
postcode-based zoning, and machine learning algorithms (Ni et al., 2019). The results of this study may have a profound
impact on last-mile delivery systems, leading to greater efficiency, shorter
wait times, lower prices, and happier customers. Research in this field is
essential for driving innovation and creating sustainable solutions for the
future because of the enormous influence last-mile delivery has on the
e-commerce business and the daily lives of consumers.
Numerous advantages and
avenues for study present themselves when fulfillment centers examine delivery
area/zone tagging strategies for last-mile delivery orders. First, fulfillment
centers can improve their operations and last-mile delivery efficiency by
proper tagging and categorization of delivery areas/zones. This modification
has the potential to decrease delivery times, boost customer happiness, and
enhance logistics efficiency �(Lagorio et al., 2016). In addition, researchers can learn more about the
spatial distribution of delivery requests by carefully analyzing tagging
methodologies in order to spot trends, improve route planning, and make more
efficient use of available resources. In addition, learning from the
experiences of others who have already applied such methods can illuminate the
way forward for the creation of smarter and more automated delivery systems as
we include cutting-edge technologies like AI and ML into fulfillment center
processes (Bijmolt et al., 2021). Ultimately, this study has the potential to change
last-mile delivery by increasing our understanding of delivery area/zone
tagging methodologies, leading to more environmentally friendly,
cost-effective, and customer-centric logistics solutions.
The effectiveness of delivery
zone tagging techniques in improving the efficiency and accuracy of last-mile
delivery is an area of active research. This study aims to investigate the
various methods used to tag delivery zones and explore their effectiveness in
optimizing last-mile delivery operations. The research will be conducted
through a literature review and case studies of fulfillment centers
implementing different tagging techniques. The results of this research will
provide insights into the best practices for delivery zone tagging and help
fulfillment centers improve their last-mile delivery operations.
METHOD
This study analyzes
delivery zone tagging techniques for last-mile delivery orders within
fulfillment centers. The research methodology used in this study is a
mixed-method approach that combines qualitative and quantitative research
techniques. The mixed-method approach is appropriate for this study as it
comprehensively analyzes the various delivery zone tagging techniques used
within fulfillment centers. The qualitative research component of this study
involves a literature review of existing research on delivery zone tagging
techniques. The literature review will provide insights into the various
tagging techniques used within fulfillment centers and their effectiveness in
optimizing last-mile delivery operations. The literature review will also
identify the challenges associated with delivery zone tagging techniques and
the best practices for implementing these techniques within fulfillment
centers. The quantitative research component of this study involves case
studies of fulfillment centers that have implemented different delivery zone
tagging techniques. The case studies will provide insights into the
effectiveness of different tagging techniques in improving the efficiency and
accuracy of last-mile delivery operations. The case studies will also identify
the factors that influence the effectiveness of delivery zones tagging
techniques, such as the volume of orders, the complexity of the street layout,
and the quality of the data used to train the system. The sampling method used
in this study is purposeful sampling. Purposeful sampling is a non-probability
sampling technique that involves selecting participants based on specific
criteria.
In this study, the
fulfillment centers selected for the case studies will be purposefully sampled
based on their size, volume of orders, and the tagging technique used within
the fulfillment center. Data collection for the qualitative research component
of this study will involve a systematic review of existing literature on
delivery zone tagging techniques. The literature review will use online
databases such as Google Scholar, Semantic Scholar, and PubMed. The search
terms used will include "delivery zone tagging," "last mile
delivery," "fulfillment centers," and "delivery
optimization." Data collection for the quantitative research component of
this study will involve case studies of fulfillment centers that have
implemented different delivery zone tagging techniques. The case studies will
be conducted through interviews with fulfillment center managers and employees,
observation of the delivery process, and analysis of delivery data. The data
collected will be analyzed using statistical methods such as regression
analysis and ANOVA.
Searching Criteria
for Research Data;
2023 OR 2022 OR
2021 OR 2020 OR 2019 OR 2018 OR 2017 Publication Year
All OA Open Access
Article Publication
Type
"Delivery area
in last mile" OR "delivery ZONE in last mile" OR "last mile
SORTING" or "fulfillment center sorting" OR "last mile
delivery"
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Figure 1. The visualization
shows the number of publications published each year
RESULTS AND
DISCUSSION
After Research and Development, the Following are Options to make
Possible Ops Delivery Zone Visibility for Checking.
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Figure 2. it shows journey of order in warehouse
Delivery
Area Input with Each Scanned Order
One potential solution for improving last-mile
delivery efficiency is implementing a delivery area input with each scanned
order. This technique involves assigning a delivery zone or area to each order
and inputting this information into the scanning device when the order is
received at the fulfillment center. This allows for more accurate tracking of the
order's progress and provides delivery drivers with a clear understanding of
the areas they will be servicing. Additionally, it can help fulfillment centers
optimize their delivery routes and improve overall efficiency. To determine
this technique's effectiveness, various delivery zone tagging techniques within
fulfillment centers can be analyzed to evaluate their impact on last-mile
delivery operations.
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Figure 3. it shows the way of input data against order
Smooth
Flow Implementation of this Solution
For Quick Scanning with Delivery Area Input Following Process
will be Followed
a.
Parcels will be Placed on the Sorting Table.
b.
Then Area Wise Sorting will be Done of Parcels.
c.
After Area Wise Sorting, Area Wise Scanning will be Done
of Parcels in Excel.
d.
When Scanning Completed Input Delivery Area of Parcels.
e.
Lastly, Upload Excel File on System. Then Orders will be
updated with the Delivery Area.
f.
Also, Instead of Uploading Excel File Area Wise after Each
Area Parcel is Scanned. What we can Do is After One Area is Scanned, Then Drag
& Drop Areas with Them and Continue Scanning from below of Next Area where
the last area Parcels ended. Lastly, After All, Areas are Scanned, and Drag
& Drop Areas are done, Upload it One time. In That way, Multiple Uploading
Area Wise will not be needed.
Benefits of this Solution
a.
The Benefit of the above Process Flow is;
b.
To Break down the Process of Scanning, Address Reading,
Delivery Area Input, and Physical Sorting. Because if we do all four Processes
simultaneously, it will take time, and Per Parcel Processing time will
increase. So doing one Process at a time will Increase Processing Speed.
c.
3 of 4 Processes Operations are already done, and it is fast.
Just one Process of Delivery Area Input is new, which will take significant
time, So with the help of Area Wise Scanning in Excel and no delivery Area
Input with Scanning. So, after Scanning is Done, we know the Parcels I have
Scanned are of DHA (Delivery Zone Example). Then lastly, I will input DHA
(Delivery Zone Example) in the Excel 1st row and then Drag Drop DHA (Delivery
Zone Example) value with each Row and Upload it. Our Process is Done. The extra
time taken by Delivery Area Input is minimized.
d.
Also, limiting the Ops Assistant is essential if we do all 4
Processes simultaneously. He needs to improve at all 4 Processes
simultaneously, Scanning, Address Reading, Delivery Area Input, and Physical
Sorting. So, with Process Breaking, we can deploy Ops Assistant to the Process
in which he is Good, increasing work efficiency.
Assign Orders to Shelves Barcode
In
the realm of last-mile delivery, the efficient management of orders within a
fulfillment center is essential for timely and cost-effective delivery to
customers. One potential solution is to utilize a barcode system to assign
orders to specific shelves within the fulfillment center. This approach enables
workers to quickly and accurately locate and prepare items for delivery,
reducing the time and effort required to fulfill orders. By analyzing delivery
zone tagging techniques within fulfillment centers, it becomes clear that using
barcodes is an effective way to streamline order management and improve the
overall efficiency of last-mile delivery operations. This approach can enhance
customer satisfaction by ensuring timely and accurate delivery while reducing
costs and increasing productivity within the fulfillment center.
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Figure 3. it shows the way to input data through App
Benefits
of this Solution
The Benefit of the above Process Flow is:
a.
Simple Process Flow.
b.
Give us Visibility of Order in a Specific Zone.
Gray Areas of this Solution
a.
The Gray Areas of the above Process Flow are;
b.
Development is Required for it on Our End.
c.
Human Intervention is Highly Required.
d.
If Space in Rack is Finished, then Ops Places Orders in Sack
Bags on Floor or Trolleys. This process will be skipped.
e.
If, Because of a Load or any issue, Parcels Processing is
delayed, then Ops Usually will skip this Process in the Morning.
f.
One Additional Scan Process is Added.
g.
More Manual Work Means More Human Error.
Auto Area Picking Algorithm in the Portal
In recent years, last-mile delivery has become
increasingly important in e-commerce. Fulfillment centers play a crucial role
in ensuring timely and accurate delivery of products to customers. To achieve
this, delivery zone tagging techniques identify and assign orders to specific
delivery areas. However, manually selecting and assigning delivery areas can
take time and effort. An auto area-picking algorithm has been developed and
implemented to address this issue in the Portal of fulfillment centers. This algorithm
utilizes machine learning techniques to assign delivery areas to orders based
on delivery location, product type, and order volume. By automating the
area-picking Process, this algorithm helps to increase efficiency and accuracy
in last-mile delivery, ultimately leading to improved customer satisfaction. In
this research article, we aim to analyze the effectiveness of the auto
area-picking algorithm in fulfillment centers and its impact on last-mile
delivery.
Benefits of this Solution
The Benefit of the above Process Flow is;
a.
No Human Intervention Required.
b.
Give us Visibility of Order in a Specific Zone.
c.
Less Manual Work Means Less Human Error.
d.
Skipping This Process will be Difficult Because Each Order
will be Passed Through at Booking.
e.
Offline Working Support Because It Runs on the Backend.
Gray Areas of this Solution
The Gray Areas of the above Process Flow are;
a.
Development is Required for it on Our End.
b.
Have to Continuously Correct the Formula for Optimizing the
Zone Picking Logic.
Auto Area Sorting Through Automation Machine
Delivery
zone tagging techniques play a critical role in last-mile delivery operations.
Properly tagging delivery zones help logistics companies optimize their
operations, reduce delivery times, and increase customer satisfaction. However,
manual sorting of delivery areas can be time-consuming and prone to errors,
leading to delays and mistakes in the delivery process. To address this issue,
automation machines such as Auto Area Sorting have been developed to automate
the sorting process of delivery zones. Auto Area Sorting uses machine learning
algorithms to recognize and sort delivery areas, eliminating the need for
manual sorting. This technology could revolutionize last-mile delivery
operations by improving efficiency and reducing errors. In this research
article, we will analyze the effectiveness of Auto Area Sorting and other
automation technologies in fulfillment centers for last-mile delivery orders
and explore the benefits and challenges associated with these technologies.
_files/image013.jpg)
Figure 5. it shows way to sort data in warehouse
Benefits
of this Solution
The Benefit of the above Process Flow is;
a.
No Human Intervention Required.
b.
Give us Visibility of Order in a Specific Zone.
c.
Less Manual Work Means Less Human Error.
d.
Skipping This Process will be Difficult Because Each Order
will be Passed Through at the Entrance.
e.
Decrease the need for Sorter Cost Saving.
Gray Areas of this Solution
The Gray Areas of the above Process Flow are;
a.
The cost of the Machine is High and will be Required in Every
Warehouse.
b.
Have to Continuously Correct the Formula for Optimizing the
Zone Picking Logic.
c.
Process Has to be Done Online if Any case, Offline Flow will
not Work.
d.
Some Development is Also Required for it on our end.
Scanner Fixed Placed on Shelves
In
recent years, the growth of e-commerce has led to an increased demand for
efficient and accurate last-mile delivery. One area of focus for fulfillment
centers is the development of effective delivery zone tagging techniques. To
improve the accuracy of these techniques, a possible solution is implementing a
fixed scanner placed on shelves. This technology would allow for real-time
inventory tracking and its location within the fulfillment center. By tagging
delivery zones with these location data points, delivery personnel could have
more precise information on the location of packages and optimize their routes
accordingly. This approach could improve efficiency and accuracy in last-mile
delivery, resulting in higher customer satisfaction and decreased delivery
times.
_files/image015.jpg)
Figure 6. it shows way of placing order in warehouse
Benefits
of this Solution
The Benefit of the above Process Flow is;
a.
Simple Process Flow.
b.
Give us Visibility of Order in a Specific Zone.
c.
Track of Order on Specific Shelve.
Gray Areas of this Solution
The Gray Areas of the above Process Flow are;
a.
No Human Intervention Required.
b.
Give us Visibility of Order in a Specific Zone.
c.
Less Manual Work Means Less Human Error.
d.
The cost of the Scanner is High and will be Required on Every
Shelf.
e.
One Additional Scanning Step of the Order will be added to
the Process.
f.
Some Development is Also Required for it on our end if we
want that in Info in Our Portal.
g.
Needed Staff to Press the Button of the Scanner, so Ideally,
Auto Start Scanner should be there. Otherwise, One Step is added to the
Process.
h.
If Space in Rack is Finished, then Ops Places Orders in Sack
Bags on Floor or Trolleys. So, This Process can be Skipped.
i.
If, Because of a Load or any issue, Parcels Processing is
delayed, then Ops Usually will skip this Process in the Morning.
Define Delivery Zone Against Rider Profile
At the time of Rider Enrollment, we can Define
the Delivery Zone where he will work on its profile in the Portal. In the
context of last-mile delivery, the accurate and efficient tagging of delivery
zones is critical to ensure delivery riders are assigned to appropriate areas.
One solution to this challenge is to define delivery areas or zones based on
the delivery rider's profile. This approach considers factors such as the
rider's experience, language proficiency, and knowledge of the local geography.
By considering these factors, delivery zones can be tagged more accurately, and
riders can be assigned to areas where they are best equipped to navigate and
communicate with customers. Here we aim to analyze different delivery zone
tagging techniques within fulfillment centers and assess the effectiveness of
defining delivery zones based on the delivery rider's profile.
Benefits of this Solution
The Benefit of the above Process Flow is;
a.
Simple Process Flow.
b.
Give us Visibility of Order in a Specific Zone.
c.
Less Manual Work Means Less Human Error.
d.
One Time Updation Required.
e.
No Additional Step or Process will be added.
Gray Areas of this Solution
The Gray Areas of the above Process Flow are;
a.
Development is required for it.
b.
We will not Get Delivery Zone Visibility Until the Rider is
Done Out for Delivery.
c.
We will not Get Delivery Zone Visibility of Orders on Backlog
in Shelves.
Data Entry Zone Value
In the world of last-mile delivery, accurately
tagging and categorizing delivery zones is critical to ensuring efficient and
timely delivery of orders. To this end, various tagging techniques have been
employed within fulfillment centers to improve delivery accuracy and speed. One
such technique involves data entry to input delivery area/zone values for each
order, which can then be used to direct orders to the appropriate delivery
zone. By analyzing the effectiveness of this technique, we can gain insights
into the impact of accurate delivery zone tagging on last-mile delivery
operations. This research article seeks to explore the use of data entry for
delivery zone tagging and evaluate its effectiveness in optimizing last-mile
delivery operations.
Benefits of this Solution
The Benefit of the above Process Flow is;
a.
Simple Process Flow.
b.
Give us Visibility of Order in a Specific Zone.
Gray Areas of this Solution
The Gray Areas of the above Process Flow are;
More Manual Work Means More Human Error.
Get Delivery Zone from Merchant at The Time of Booking
The last-mile delivery process has become
increasingly important as e-commerce continues to grow. One of the challenges
in the last-mile delivery process is accurately determining the delivery zone
for each order. This is critical for optimizing delivery routes and ensuring
timely delivery. In this context, a solution that has been proposed is to
obtain the delivery zone information from the Customer at the time of order
booking. This solution could improve the accuracy of delivery zone tagging and
reduce delivery time, as the delivery zone can be determined in real-time. In
this research article, we analyze different delivery zone tagging techniques
within fulfillment centers for last-mile delivery orders and evaluate the
effectiveness of obtaining the delivery zone from customers during order
booking.
Ways of Implementing Solution
The following are the ways of implementing this solution;
Delivery Address Standard
Delivery Address Standard Should be Shared with Customers on
the Merchant Website, and They Aware That It is Important for a Smooth Delivery
Process. Example Should show for Easiness of Customers.
_files/image017.gif)
Figure 7. it shows standard way of order address
Confirm
Current Location
When a Customer Inputs the Delivery Address in
Order Booking Form, Then Merchant Website Should Ask from Customer its Current
Location the Delivery Address. If Yes, Then Allow Location Service and Get the
Customer's Current Location. Prefilled that Data in Delivery Address Fields. If
no, then let him type the Address.
_files/image019.gif)
Figure 8. it shows pin point order address in Map
Delivery Address Validation API/Plugin
Address Validation API Will be Created by Us
for Merchants in Order to Get Clean and Clear Customer Addresses at the Time of
Booking by the Customer on the Merchant Website, which will help in the Correct
Lat Long Picking Process That Latlong will be Shared with Us at the Time of
Booking. It will be implemented on Merchant Website".
_files/image021.gif)
Figure 10. it shows the way to input address of order
Benefits of this Solution
The Benefit of the above Process Flow is;
a.
Simple Process Flow.
b.
No Human Intervention Required.
c.
Give us Visibility of Order in a Specific Zone.
Gray Areas of this Solution
The Gray Areas of the above Process Flow are;
a. Convince Customers to do it.
b. Development is required for it.
_files/image022.gif)
Figure 11. it shows movement of order through converyer belt
CONCLUSION
In conclusion, optimizing delivery zone
tagging techniques is crucial for improving the efficiency and accuracy of
last-mile delivery operations. The last mile delivery is the final stage of the
delivery process, where the product is delivered to the Customer's doorstep.
Lastlast-mile delivery is often the most expensive and time-consuming part of
the delivery process. Delivery zone tagging techniques can be manual,
automated, or hybrid, each with advantages and disadvantages. The effectiveness
of these techniques in improving the efficiency and accuracy of last-mile
delivery is an area of active research. The results of this research can inform
the development of more efficient and accurate delivery zone tagging techniques
and help fulfillment centers improve their last-mile delivery operations. This
study contributes to developing more efficient and accurate last-mile delivery
systems by analyzing the various tagging techniques and their effectiveness.
The study also highlights the importance of data quality in the effectiveness
of automated delivery zone tagging and the need for further research in this
area.
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