TECHNICAL FIELD:
The present invention generally relates to the field of warehouse management, and
5 specifically to a system and a method for optimizing an order pickup path in a warehouse and
order batching to minimize an order pick up time.
BACKGROUND OF THE DISCLOSURE:
The following description of the related art is intended to provide background information
10 pertaining to the field of the disclosure. This section may include certain aspects of the art
that may be related to various features of the present disclosure. However, it should be
appreciated that this section is used only to enhance the understanding of the reader with
respect to the present disclosure, and not as admissions of the prior art.
15 The rise in the popularity and usage of digital platforms such as e-commerce platforms has
brought about a highly competitive market for companies. With a growing number of ecommerce platforms available, customers now have convenient access to a diverse array of
products from various platforms or vendors. Consequently, digital platforms find themselves
facing relentless pressure to ensure prompt and efficient delivery of orders to customers. In
20 this fiercely competitive landscape, speedy order fulfillment has become a critical factor in
customer satisfaction and retention. To stay ahead of the competition, digital platforms are
compelled to optimize their logistics and supply chain operations, streamlining their
processes to expedite the delivery of products. Embracing advanced technologies such as
automated warehouses, smart inventory management systems, and efficient last-mile
25 delivery solutions has become imperative in meeting the escalating demands of the ecommerce market. Overall, in this fast-paced digital era, the ability to swiftly deliver orders
has become a decisive factor for companies aiming to thrive in the highly competitive ecommerce landscape.
30 Further, to cater to customers faster, digital platforms must prioritize the swift and efficient
processing of orders. This entails optimizing every step of the order fulfillment process, from
the moment a customer places an order to the point of delivery. Digital platforms need to
3
invest in robust and scalable infrastructure that can handle high volumes of orders without
compromising speed or accuracy. Implementing automated order processing systems and
leveraging advanced algorithms can help streamline the workflow, minimizing manual errors
and reducing processing time. In the past, several solutions have been developed to address
5 the challenges of storing and locating items within store inventories, particularly in
warehouses or distribution centers. One notable solution is the implementation of barcode
and RFID (Radio Frequency Identification) systems. These technologies enable the
identification and tracking of individual items or batches using unique codes or tags. By
scanning these codes or tags, warehouse staff can quickly locate items within the inventory,
10 reducing the time spent searching for specific products. Additionally, automated storage and
retrieval systems (AS/RS) have been employed to efficiently move items within the
warehouse. AS/RS utilize robotic systems or conveyors to retrieve items from their designated
storage locations and transport them to a point of dispatch or staging area. However, the
current in-store picking process involves the utilization of Global Integrated Fulfillment (GIF)
15 systems, which prioritize and batch orders based on factors such as due time and order
placement time. However, this process is constrained by static limitations, such as the
maximum number of orders that can be included in one pick-walk by the retail store staff.
These batch orders are predetermined and categorized according to load number and due
times. Each batch order is further divided into different commodities or items, such as frozen
20 goods. A picker, or staff member responsible for gathering the items, receives a set of batch
orders and proceeds to navigate through the retail store to collect the items, filling a tote on
a first-come-first-serve basis. However, this method can result in inefficiencies as the picker
may have to cover unnecessary distances within the store to retrieve the items. Additionally,
due to the capacity constraint of the trolley used by the picker (typically limited to 8 totes per
25 trolley), the number of totes that can be collected per pick-walk is limited.
Further, the current classification of Stock keeping units (SKUs) of the present known solution
based on run rate may have an impact on their overall performance, especially when high
units of specific SKUs are included in a single order. However, in terms of order fulfillment
30 potential, this may have a lower impact. The current put away process lacks scientific
optimization and often involves storing items in empty locations spread across the
warehouse. This approach does not contribute to an optimal pick path distance, potentially
4
resulting in inefficient picking routes. Further, there is a challenge in terms of easily accessible
locations for high run rate Fast-Selling SKUs (FSNs) being scattered throughout the
warehouse. This means that pickers would have to travel long distances to complete a picklist,
which can lead to increased picking time and reduced efficiency. Additionally, if order
5 batching is based on item location overlaps, it could result in picklists containing items from
far ends of the warehouse. This would require pickers to travel longer distances to complete
the picklist, further impacting efficiency. In scenarios with high Units-to-Orders (U2O) and
high selection rates, using SKU-to-SKU affinity for putaway may not be effective. This is
because customers often build baskets across multiple verticals and categories,
10 encompassing a large number of SKUs. Affinity-based analysis may have limitations in
generating useful groups of SKUs, potentially leading to suboptimal putaway decisions.
Therefore, in light of these limitations, there is a need for a solution to overcome the
drawbacks of the current known solutions, such that picklists contains items from far end of
15 the warehouse to fulfil maximum number of orders. Hence, a system and a method for
optimizing an order pickup path in a warehouse associated with a digital platform is required.
OBJECTS OF THE INVENTION
Some of the objects of the present disclosure, which at least one embodiment disclosed
20 herein satisfies are listed herein below.
An object of the present disclosure is to provide a method for optimizing the order pickup
path in a warehouse associated with a digital platform.
25 Another object of the preset disclosure is to retrieve and analyze the historical orders to
determine the order per SKU value for each stock keeping unit (SKU).
Another object of the present disclosure is to automatically assign a rank to each SKU based
on its order per SKU value.
30
Yet another object of the present disclosure is to generate one or more SKU sets based on the
rank assigned to the SKUs and a pre-defined threshold rank, and determine the order
5
fulfillment potential for each SKU set by analyzing the percentage of historical orders that
include target SKUs similar to the SKUs in each SKU set.
Yet another object of the present disclosure is to optimize the order pickup path in the
5 warehouse based on the order fulfillment potential associated with each SKU set and to assign
placement locations to products within the warehouse, considering factors such as the order
fulfillment potential, distance between placement locations, and proximity to the pickup
zone.
10 Yet another object of the present disclosure is to minimize travel distances in pick up of the
order within the warehouse, reduce picking time of the order within the warehouse, and
enhance overall efficiency in fulfilling customer orders.
Overall, the invention aims to leverage historical order data and intelligent analysis to
15 optimize the order pickup path in a warehouse, thereby improving the speed and efficiency
of order fulfillment in a digital platform's ecosystem.
SUMMARY OF THE DISCLOSURE
This section is provided to introduce certain objects and aspects of the present invention in a
20 simplified form that are further described below in the detailed description. This summary is
not intended to identify the key features or the scope of the claimed subject matter.
In order to achieve the aforementioned objectives, one aspect of the invention relates to a
method for optimizing an order pickup path in a warehouse associated with a digital platform.
25 The method comprises retrieving, by a processing unit from a storage unit, a set of historical
orders placed via the digital platform, wherein each historical order from the set of historical
orders comprises one or more stock keeping units (SKUs). Further, the method comprises
determining, by the processing unit for each SKU from the one or more SKUs, an order per
SKU value based on the set of historical orders. The method further encompasses
30 automatically assigning, by the processing unit to the one or more SKUs, a rank based on the
order per SKU value of the one or more SKUs. Further, the method encompasses generating,
by the processing unit, one or more SKU sets based on the rank of the one or more SKUs and
6
a pre-defined threshold rank for the one or more SKU sets. The method further comprises
determining, by the processing unit, an order fulfilment potential of said each SKU set based
on a percentage of historical orders from the set of historical orders comprising one or more
target SKUs such that the one or more target SKUs are similar to one or more SKUs in said
5 each SKU set. Thereafter, the method comprises optimizing, by the processing unit, the order
pickup path based on the order fulfilment potential associated with said each SKU set.
Another aspect of the present disclosure relates to a system for optimizing an order pickup
path in a warehouse associated with a digital platform. The system comprises at least a
10 storage unit and a processing unit. The processing unit is configured to retrieve, from the
storage unit, a set of historical orders placed via the digital platform, wherein each historical
order from the set of historical orders comprises one or more stock keeping units (SKUs). The
processing unit is further configured to determine, for each SKU from the one or more SKUs,
an order per SKU value based on the set of historical orders. The processing unit is further
15 configured to automatically assign, to the one or more SKUs, a rank based on the order per
SKU value of the one or more SKUs. The processing unit is further configured to generate, one
or more SKU sets based on the rank of the one or more SKUs and a pre-defined threshold rank
for the one or more SKU sets. The processing unit is further configured to determine, an order
fulfilment potential of said each SKU set based on a percentage of historical orders from the
20 set of historical orders comprising one or more target SKUs such that the one or more target
SKUs are similar to one or more SKUs in said each SKU set. Thereafter, the processing unit is
further configured to optimize, the order pickup path based on the order fulfilment potential
associated with said each SKU set.
25 BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this
disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which
like reference numerals refer to the same parts throughout the different drawings.
Components in the drawings are not necessarily to scale, emphasis instead being placed upon
30 clearly illustrating the principles of the present disclosure. Some drawings may indicate the
components using block diagrams and may not represent the internal circuitry of each
component. It will be appreciated by those skilled in the art that disclosure of such drawings
7
includes disclosure of electrical components, electronic components or circuitry commonly
used to implement such components.
FIG.1 illustrates an exemplary block diagram of a system [100] for optimizing an order pickup
path in a warehouse associated with a digital platform, in accordance with exemplary
5 embodiments of the present disclosure.
FIG.2 illustrates a flow diagram of an exemplary method [200] for optimizing an order pickup
path in a warehouse associated with a digital platform, in accordance with exemplary
embodiments of the present disclosure.
The foregoing shall be more apparent from the following more detailed description of the
10 disclosure.
DESCRIPTION OF THE INVENTION
In the following description, for the purposes of explanation, various specific details are set
15 forth in order to provide a thorough understanding of embodiments of the present disclosure.
It will be apparent, however, that embodiments of the present disclosure may be practiced
without these specific details. Several features described hereafter can each be used
independently of one another or with any combination of other features. An individual
feature may not address any of the problems discussed above or might address only some of
20 the problems discussed above.
The ensuing description provides exemplary embodiments only, and is not intended to limit
the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of
the exemplary embodiments will provide those skilled in the art with an enabling description
25 for implementing an exemplary embodiment. It should be understood that various changes
may be made in the function and arrangement of elements without departing from the spirit
and scope of the disclosure as set forth.
8
Specific details are given in the following description to provide a thorough understanding of
the embodiments. However, it will be understood by one of ordinary skill in the art that the
embodiments may be practiced without these specific details. For example, circuits, systems,
processes, and other components may be shown as components in block diagram form in
5 order not to obscure the embodiments in unnecessary detail.
Also, it is noted that individual embodiments may be described as a process which is depicted
as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram.
Although a flowchart may describe the operations as a sequential process, many of the
10 operations can be performed in parallel or concurrently. In addition, the order of the
operations may be re-arranged. A process is terminated when its operations are completed
but could have additional steps not included in a figure.
The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example,
15 instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not
limited by such examples. In addition, any aspect or design described herein as “exemplary”
and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over
other aspects or designs, nor is it meant to preclude equivalent exemplary structures and
techniques known to those of ordinary skill in the art. Furthermore, to the extent that the
20 terms “includes,” “has,” “contains,” and other similar words are used in either the detailed
description or the claims, such terms are intended to be inclusive—in a manner similar to the
term “comprising” as an open transition word—without precluding any additional or other
elements.
25 As used herein, a “processing unit” or “processor” or “operating processor” includes one or
more processors, wherein processor refers to any logic circuitry for processing instructions. A
processor may be a general-purpose processor, a special purpose processor, a conventional
processor, a digital signal processor, a plurality of microprocessors, one or more
microprocessors in association with a DSP core, a controller, a microcontroller, Application
30 Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of
integrated circuits, etc. The processor may perform signal coding data processing,
input/output processing, and/or any other functionality that enables the working of the
9
system according to the present disclosure. More specifically, the processor or processing
unit is a hardware processor.
As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable
5 medium including any mechanism for storing information in a form readable by a computer
or similar machine. For example, a computer-readable medium includes read-only memory
(“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage
media, flash memory devices or other types of machine-accessible storage media. The storage
unit stores at least the data that may be required by one or more units of the system to
10 perform their respective functions.
As used herein, “similar” and “same” may be used interchangeably in this patent specification
and may intend to convey the same meaning. The use of these terms may not be interpreted
as implying any difference in meaning or scope.
15 The present invention relates to a system and a method for optimizing an order pickup path
in a warehouse associated with a digital platform. As disclosed in the background section,
existing technologies have limitations and to address those limitations the present disclosure
discloses a novel solution to address the challenges in order fulfillment within warehouses.
Departing from traditional methods, the proposed solution focuses on the concept of Order
20 Fulfillment Potential (OFP) and class-based batching. By utilizing the OFP, each SKU is
classified based on its likelihood of successful order fulfillment. This classification drives the
identification of SKU locations within the warehouse. Unlike conventional approaches, where
SKUs are spread across the warehouse, the novel solution concentrates high fulfillment
potential SKUs in a designated area, occupying only a small portion of the warehouse space
25 but accounting for a significant portion of the picking volume. This strategic allocation reduces
the distance traveled by pickers, resulting in more efficient and faster order fulfillment. By
introducing this innovative solution, the present invention optimizes the layout and picking
process in warehouses, ultimately improving operational efficiency and customer
satisfaction. Therefore, the present invention provides a novel solution for optimizing an
30 order pickup path in a warehouse associated with a digital platform.
10
The present invention brings about a significant technical effect and advancement in the field
of order fulfillment in warehouses. By deviating from the traditional run rate-based
classification and adopting a class-based batching and a location-based batching approach,
the proposed solution introduces the concept of Order Fulfillment Potential (OFP) to classify
5 and identify the location of each stock keeping unit (SKU) within the warehouse. This
classification is based on the likelihood of successfully fulfilling customer orders associated
with each SKU. Through class-based batching, the invention groups orders that consist solely
of high fulfillment potential SKUs together, while orders containing a mix of SKU classes are
batched separately. As a result, high order potential SKUs occupy only a fraction such as 20%
10 of the warehouse space, yet they account for nearly for e.g. 50% of the overall picking volume.
This strategic allocation ensures that a number of pickers need to cover smaller distances to
complete their picklists, improving efficiency and reducing the time required for order
fulfillment.
The technical approach of the present solution represents a significant advancement in
15 optimizing the layout and picking process within a warehouse. By maximizing the utilization
of high order potential SKUs and minimizing the distances traveled by pickers, the invention
streamlines the order fulfillment workflow, enhances productivity, and minimizes operational
costs. This innovative solution not only improves the overall efficiency of the warehouse but
also contributes to a faster and more accurate order fulfillment process, leading to enhanced
20 customer satisfaction.
Hereinafter, exemplary embodiments of the present disclosure will be described in detail with
reference to the accompanying drawings so that those skilled in the art can easily carry out
the present disclosure.
25 Referring to Figure 1, an exemplary block diagram of a system for optimizing an order pickup
path in a warehouse associated with a digital platform is shown. The system comprises at
least one processing unit [102], and at least one storage unit [104]. Also, all of the
components/ units of the system [100] are assumed to be connected to each other unless
otherwise indicated below. Also, in Fig. 1 only a few units are shown, however, the system
30 may comprise multiple such units or the system may comprise any such numbers of said units,
11
as required to implement the features of the present disclosure. Further, in an
implementation, the system may be present in a server device to implement the features of
the present invention. The system may be a part of a service platform/ or may be independent
of but in communication with the service platform.
5
The system is configured for optimizing an order pickup path in a warehouse associated with
a digital platform, with the help of the interconnection between the components/units of the
system.
10 More specifically, in order to implement the features of the present disclosure, the processing
unit [102]is configured to retrieve, from the storage unit [104], a set of historical orders placed
via the digital platform, wherein each historical order from the set of historical orders
comprises one or more stock keeping units (SKUs). In an implementation of the present
invention, the set of historical orders may be retrieved by the processing unit [102] for a pre15 defined period of time. Further, in another implementation of the present invention, the set
of historical orders may be retrieved by the processing unit [102] for a particular geographical
region such as the set of historical orders is retrieved based on pincode associated with the
set of historical orders.
20 For ease of understanding, let’s consider an example, wherein the set of historical orders
retrieved by the processing unit [102] from the storage unit [104] comprises a set of 100
orders and each order of the set of 100 orders comprises one or more SKUs such as a SKU Q,
a SKU W, a SKU E, a SKU R, and a SKU T wherein said each order retrieved by the processing
unit [102] may not be comprising order for each of said one or more SKUs i.e., the SKU Q, the
25 SKU W, the SKU E, the SKU R, and the SKU T. But the complete set of 100 orders retrieved by
the processing unit [102] comprises one or more orders comprising said SKUs, i.e., the SKU Q,
the SKU W, the SKU E, the SKU R, and the SKU T.
It is important to understand that the inclusion of these specific implementations in the
30 patent specification does not limit the scope of the disclosure to only these variations. Other
implementations and modifications that fall within the general principles of the invention are
also considered to be part of the disclosure, as defined by the claims of the patent.
12
It should be noted that the specific implementation of the present invention may vary. In one
implementation, the set of historical orders may be retrieved by the processing unit [102] for
a pre-defined period of time. Further, the duration of said pre-defined period of time may
5 depend on various factors, including the requirements and specifications of the particular
system or application utilizing the invention.
Further, in an implementation of the present invention, each SKU from the one or more SKUs
may further be associated with one or more products. For example, a specific SKU, such as
SKU Z, may be associated with multiple products, such as product 1, product 2, and product
10 3. However, it should be understood that the aforementioned example is provided for
illustrative purposes only and does not limit the scope of the invention to the specific SKUproduct associations mentioned. Other SKUs may also be associated with different sets of
products within the context of the disclosure. Therefore, the scope of the present disclosure
should not be construed to be limited by the specific SKU-product associations described in
15 the present disclosure, but rather it should be determined by the claims and the full scope of
the disclosure.
Further, the processing unit [102] is configured to determine, for each SKU from the one or
more SKUs, an order per SKU value based on the set of historical orders. Further, in a
preferred implementation of the present disclosure, the order per SKU value comprises one
20 or more historical orders associated with said each SKU from the set of historical orders.
For ease of understanding, continuing from the above example, wherein the set of historical
orders retrieved by the processing unit [102] from the storage unit [104] comprises the set of
100 orders and each order of the set of 100 orders comprises the one or more SKUs such as
25 the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T. Now, the processing unit [102]
determines the order per SKU value for each SKU from the one or more SKUs, i.e., the SKU Q,
the SKU W, the SKU E, the SKU R, and the SKU T, based on the set of historical orders i.e., the
set of 100 orders. Let's consider the orders from the set of 100 orders comprising the SKU Q,
the SKU W, the SKU E, the SKU R, and the SKU T are 10 orders, 80 orders, 40 orders, 25 orders,
30 and 35 orders, respectively. Now the order per SKU value determined by the processing unit
13
[102], wherein the order per SKU value for said SKUs, i.e., the SKU Q, the SKU W, the SKU E,
the SKU R, and the SKU T, comprises one or more orders associated with said each SKU, i.e.,
the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T, from the set of 100 orders, is as
shown in Table 1 below.
5
S. NO SKU Total Number of
Orders
Order Per SKU Value
1 SKU Q 100 10
2 SKU W 100 80
3 SKU E 100 40
4 SKU R 100 25
5 SKU T 100 35
TABLE 1
10 Further, the processing unit [102] is configured to automatically assign, to the one or more
SKUs, a rank based on the order per SKU value of the one or more SKUs. Further, in an
implementation of the present disclosure, the automatically assigning the rank by the
processing unit [102] to the one or more SKUs based on the order per SKU value of the one
or more SKUs may be done in a descending order based on the order per SKU value of the
15 one or more SKUs.
For ease of understanding, continuing from the above example, wherein the order per SKU
value for said SKUs, i.e., the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T, comprises
one or more orders associated with said each SKU, i.e., the SKU Q, the SKU W, the SKU E, the
20 SKU R, and the SKU T, from the set of 100 orders, as shown in Table 1 above. Now, the system
[100] as disclosed by the present disclosure will automatically assign a rank based on the order
per SKU value of said SKUs, i.e., the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T.
The rank automatically assigned by the system [100] to said SKUs, i.e., the SKU Q, the SKU W,
14
the SKU E, the SKU R, and the SKU T, based on their order per SKU value, is shown below in
table 2.
Further, the processing unit [102] is configured to generate, one or more SKU sets based on
the rank of the one or more SKUs and a pre-defined threshold rank for the one or more SKU
sets. In a preferred implementation of the present disclosure, the one or more SKU sets are
10 generated based on a comparison of the rank of the one or more SKUs with the pre-defined
threshold rank for the one or more SKU sets. In an exemplary implementation of the present
disclosure, the pre-defined threshold rank for the one or more SKU sets may be dynamically
generated by the system [100] based on at least one of a predefined system parameters
and/or a predefined user parameters.
15
For ease of understanding, continuing from the above example, wherein the rank
automatically assigned by the system [100] to said SKUs, i.e., the SKU Q, the SKU W, the SKU
E, the SKU R, and the SKU T, based on their order per SKU value, is shown above in table 2.
Let’s consider for ease of understanding that the pre-defined threshold rank for the one or
20 more SKU sets is 3. Now, the system [100] as disclosed by the present disclosure will generate
one or more SKU sets based on the rank of said SKUs, i.e., the SKU Q, the SKU W, the SKU E,
the SKU R, and the SKU T, and a pre-defined threshold rank, i.e., the rank 3, for the one or
more SKU sets. The system [100] as disclosed now will generate two SKU sets, such as a SKU
set A and a SKU set B, as shown below, wherein the SKU set A comprises the SKU W, the SKU
15
E, and the SKU T, and the SKU set B comprises the SKU Q and the SKU R, based on a
comparison of the rank of said SKUs, i.e., the SKU Q, the SKU W, the SKU E, the SKU R, and the
SKU T, with a pre-defined threshold rank, i.e., the rank 3 for the one or more SKU sets.
SKU set A (SKU (Order Per SKU Value) SKU set B (SKU (Order Per SKU Value)
SKU W (80) SKU Q (25)
SKU E (40) SKU R (10)
SKU T (35)
5
Further, the processing unit [102] is configured to determine, an order fulfilment potential of
said each SKU set based on a percentage of historical orders from the set of historical orders
comprising one or more target SKUs such that the one or more target SKUs are similar to one
10 or more SKUs in said each SKU set. In an exemplary, implementation of the present disclosure,
the one or more target SKUs may be at least one of the one or more SKUs in said each SKU
set, and/or one or more SKUs from the set of historical orders. Further, the order fulfilment
potential of said each SKU set is the number of historical orders completely fulfilled by the
one or more SKUs in said each SKU set.
15
It should be noted that the term "digital platform" includes a system and/or a method and/or
a software application that facilitates the exchange, sharing, or provision of digital content,
services, or products. Further, the term "digital platform" encompasses online marketplaces,
social media platforms, e-commerce websites, mobile applications, and other similar
20 technological infrastructures that enable digital interactions and transactions. The definition
provided above is intended to serve as a general description of a digital platform and is not
exhaustive. The specific features, functionalities, and characteristics of digital platforms may
vary depending on their design, purpose, and technological implementation. It is important
to note that the definition provided herein is for illustrative purposes only and should not be
25 construed as limiting the scope of the patent specification.
Further, in a preferred implementation of the present disclosure, the order fulfilment
potential associated with said each SKU set comprises an order fulfilment potential associated
with the one or more SKUs in said each SKU set. Further, in another implementation of the
present disclosure, the pre-defined threshold rank is associated with a pre-defined order
5 fulfilment potential of the one or more SKU sets.
For ease of understanding, continuing from the above example, wherein the SKU set A
comprises the SKU W, the SKU E, and the SKU T, and the SKU set B comprises the SKU Q and
the SKU R, based on the rank of said SKUs, i.e., the SKU Q, the SKU W, the SKU E, the SKU R,
10 and the SKU T, and a pre-defined threshold rank, i.e., the rank 3 for the one or more SKU sets.
Now, the system [100] as disclosed by the present disclosure determines the order fulfilment
potential of said each SKU set, i.e., the SKU set A and the SKU set B, based on a percentage of
historical orders from the set of historical orders, i.e., the set of 100 orders comprising one or
more target SKUs, such that the one or more target SKUs are similar to one or more SKUs in
15 said each SKU set (the SKU set A and the SKU set B), the one or more target SKUs are the one
or more SKUs in said each SKU set (the SKU set A, or at least one of the SKU W, the SKU E, and
the SKU T comprised by the SKU set A or at least one of the SKU Q and the SKU R comprised
by the SKU set B. Further, the order fulfilment potential of said each SKU set, i.e., the SKU set
A and the SKU set B, is the number of historical orders, i.e., out of the set of 100 orders
20 completely fulfilled by the one or more SKUs (the SKU W, the SKU E, the SKU R, and the SKU
T) in said each SKU set (the SKU set A and the SKU set B).
Further, in a preferred implementation of the present disclosure, the order fulfilment
potential associated with said each SKU set comprises an order fulfilment potential associated
25 with the one or more SKUs in said each SKU set. Further, in another implementation of the
present disclosure, the pre-defined threshold rank is associated with a pre-defined order
fulfilment potential of the one or more SKU sets.
Thereafter, the processing unit [102] is configured to optimize, the order pickup path based
30 on the order fulfilment potential associated with said each SKU set. Further, the order pickup
path is a pathway within the warehouse to collect one or more products associated with the
one or more SKUs. Further, in a preferred implementation of the present disclosure, the order
17
pickup path is optimised based on assigning one or more placement locations in the
warehouse to the one or more products, wherein the assigning the one or more placement
locations is further based on the order fulfilment potential associated with said each SKU set,
and a distance between the one or more placement locations and a pick up zone in the
5 warehouse.
Further, in another preferred implementation of the present disclosure, the one or more
placement locations are assigned to the one or more products based on at least one of a
sequential order associated with said each SKU set and a sequential order associated with the
10 one or more SKUs in said each SKU set, wherein the sequential order associated with said
each SKU set is based on the order fulfilment potential associated with said each SKU set and
the sequential order associated with the one or more SKUs in said each SKU set is based on
the order fulfilment potential associated with the one or more SKUs in said each SKU set.
15 It should be noted that the term "warehouse" refers to a physical facility or storage space
used for the storage, organization, and management of goods, products, or materials. It
typically involves the systematic arrangement of items, inventory tracking, and logistical
operations related to the handling and distribution of stored items. Further, it should also be
noted that the definition provided above serves as a general description of a warehouse and
20 its functions within the context of this patent specification. However, it is essential to
acknowledge that warehouses can vary significantly in terms of size, design, operational
processes, and the types of goods they handle. The definition offered herein is not exhaustive
and is intended to provide a general understanding of the term within the scope of the
present disclosure.
25
Continuing from the above example, wherein the system [100] determined the order
fulfilment potential of said each SKU set, i.e., the SKU set A and the SKU set B, based on a
percentage of historical orders from the set of historical orders, i.e., the set of 100 orders
comprising one or more target SKUs such that the one or more target SKUs are similar to one
30 or more SKUs in said each SKU set (the SKU set A and the SKU set B), the one or more target
SKUs is the one or more SKUs in said each SKU set (the SKU set A, or at least one of the SKU
W, the SKU E, and the SKU T comprised by the SKU set A, or at least one of the SKU Q and the
18
SKU R comprised by the SKU set B). Now, the system [100] as disclosed by the present
disclosure may optimize the order pickup path based on the order fulfilment potential
associated with said each SKU set (the SKU set A and the SKU set B) by assigning one or more
placement locations in the warehouse to one or more products i.e., the one or more products
5 associated with the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T, based the order
fulfilment potential as determined above associated with said each SKU set (the SKU set A
and the SKU set B), a distance between the one or more placement locations and a pick up
zone in the warehouse such that the SKU set (either the SKU set A or the SKU set B) with a
higher fulfilment potential is placed near the pick up zone in the warehouse. Further, in
10 another implementation disclosure, the one or more products associated with the SKU set
(either the SKU set A or the SKU set B) from the one or more SKU sets (the SKU set A and the
SKU set B) with higher fulfilment potential associated with said SKU set (either the SKU set A
or the SKU set B) are placed near the pick up zone in the warehouse. Further, in another
implementation of the present disclosure, said each product from the one or more products
15 associated with the SKU set, for ease, let’s say the SKU set A with higher fulfilment potential
associated with said SKU set A is placed near the pick up zone in the warehouse.
Now, referring to Figure 2, an exemplary flow diagram of a method [200] is shown, for
optimizing an order pickup path in a warehouse associated with a digital platform, in
20 accordance with exemplary embodiments of the present invention. In an implementation the
method [200] is performed by the system [100]. Further, in an implementation, the system
[100] may be present in a server device to implement the features of the present invention.
Also, as shown in Figure 2, the method [200] starts at step [202].
25 At step [204], the method [200] comprises retrieving, by a processing unit [102] from a
storage unit [104], a set of historical orders placed via the digital platform, wherein each
historical order from the set of historical orders comprises one or more stock keeping units
(SKUs). In an implementation of the present invention, the set of historical orders may be
retrieved by the processing unit [102] for a pre-defined period of time. Further, in another
30 implementation of the present invention, the set of historical orders may be retrieved by the
processing unit [102] for a particular geographical region such as the set of historical orders
is retrieved based on pincode associated with the set of historical orders.
19
For ease of understanding, let’s consider an example, wherein the set of historical orders
retrieved by the processing unit [102] from the storage unit [104] comprises a set of 100
orders and each order of the set of 100 orders comprises one or more SKUs such as a SKU Q,
5 a SKU W, a SKU E, a SKU R, and a SKU T wherein said each order retrieved by the processing
unit [102] may not be comprising order for each of said one or more SKUs i.e., the SKU Q, the
SKU W, the SKU E, the SKU R, and the SKU T. But the complete set of 100 orders retrieved by
the processing unit [102] comprises one or more orders comprising said SKUs, i.e., the SKU Q,
the SKU W, the SKU E, the SKU R, and the SKU T.
10
It is important to understand that the inclusion of these specific implementations in the
patent specification does not limit the scope of the disclosure to only these variations. Other
implementations and modifications that fall within the general principles of the invention are
also considered to be part of the disclosure, as defined by the claims of the patent.
15
It should be noted that the specific implementation of the present invention may vary. In one
implementation, the set of historical orders may be retrieved by the processing unit [102] for
a pre-defined period of time. Further, the duration of said pre-defined period of time may
depend on various factors, including the requirements and specifications of the particular
20 system or application utilizing the invention.
Further, in an implementation of the present invention, each SKU from the one or more SKUs
may further be associated with one or more products. For example, a specific SKU, such as
SKU Z, may be associated with multiple products, such as product 1, product 2, and product
3. However, it should be understood that the aforementioned example is provided for
25 illustrative purposes only and does not limit the scope of the invention to the specific SKUproduct associations mentioned. Other SKUs may also be associated with different sets of
products within the context of the disclosure. Therefore, the scope of the present disclosure
should not be construed to be limited by the specific SKU-product associations described in
the present disclosure, but rather it should be determined by the claims and the full scope of
30 the disclosure.
20
Next, at step [206] the method [200] comprises determining, by the processing unit [102] for
each SKU from the one or more SKUs, an order per SKU value based on the set of historical
orders.
Further, in a preferred implementation of the present disclosure, the order per SKU value
5 comprises one or more historical orders associated with said each SKU from the set of
historical orders.
For ease of understanding, continuing from the above example, wherein the set of historical
orders retrieved by the processing unit [102] from the storage unit [104] comprises the set of
10 100 orders and each order of the set of 100 orders comprises the one or more SKUs such as
the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T. Now, the processing unit [102]
determines the order per SKU value for each SKU from the one or more SKUs, i.e., the SKU Q,
the SKU W, the SKU E, the SKU R, and the SKU T, based on the set of historical orders i.e., the
set of 100 orders. Let's consider the orders from the set of 100 orders comprising the SKU Q,
15 the SKU W, the SKU E, the SKU R, and the SKU T are 10 orders, 80 orders, 40 orders, 25 orders,
and 35 orders, respectively. Now the order per SKU value determined by the processing unit
[102], wherein the order per SKU value for said SKUs, i.e., the SKU Q, the SKU W, the SKU E,
the SKU R, and the SKU T, comprises one or more orders associated with said each SKU, i.e.,
the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T, from the set of 100 orders, is as
20 shown in Table 3 below.
S. NO SKU Total Number of
Orders
Order Per SKU Value
1 SKU Q 100 10
2 SKU W 100 80
3 SKU E 100 40
4 SKU R 100 25
5 SKU T 100 35
TABLE 3
21
Next, at step [208], the method [200] comprises automatically assigning, by the processing
unit [102] to the one or more SKUs, a rank based on the order per SKU value of the one or
more SKUs. Further, in an implementation of the present disclosure, the automatically
assigning the rank by the processing unit [102] to the one or more SKUs based on the order
5 per SKU value of the one or more SKUs may be done in a descending order based on the order
per SKU value of the one or more SKUs.
For ease of understanding, continuing from the above example, wherein the order per SKU
value for said SKUs, i.e., the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T, comprises
10 one or more orders associated with said each SKU, i.e., the SKU Q, the SKU W, the SKU E, the
SKU R, and the SKU T, from the set of 100 orders, as shown in Table 3 above. Now, the method
[200] as disclosed by the present disclosure will automatically assign a rank based on the order
per SKU value of said SKUs, i.e., the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T.
The rank automatically assigned by the method [200] to said SKUs, i.e., the SKU Q, the SKU
15 W, the SKU E, the SKU R, and the SKU T, based on their order per SKU value, is shown below
in table 4.
S. NO SKU Total Number of
Orders
Order Per SKU
Value
RANK of the SKU
1 SKU W 100 80 1
2 SKU E 100 40 2
3 SKU T 100 35 3
4 SKU R 100 25 4
5 SKU Q 100 10 5
TABLE 4
20
Next, at step [210], the method [200] comprises generating, by the processing unit [102], one
or more SKU sets based on the rank of the one or more SKUs and a pre-defined threshold rank
for the one or more SKU sets. In a preferred implementation of the present disclosure, the
one or more SKU sets are generated based on a comparison of the rank of the one or more
22
SKUs with the pre-defined threshold rank for the one or more SKU sets. In an exemplary
implementation of the present disclosure, the pre-defined threshold rank for the one or more
SKU sets may be dynamically generated by the system [100] based on at least one of a
predefined system parameters and/or a predefined user parameters.
5
For ease of understanding, continuing from the above example, wherein the rank
automatically assigned by the method [200] to said SKUs, i.e., the SKU Q, the SKU W, the SKU
E, the SKU R, and the SKU T, based on their order per SKU value, is shown above in table 4.
Let’s consider for ease of understanding that the pre-defined threshold rank for the one or
10 more SKU sets is 3. Now, the method [200] as disclosed by the present disclosure will generate
one or more SKU sets based on the rank of said SKUs, i.e., the SKU Q, the SKU W, the SKU E,
the SKU R, and the SKU T, and a pre-defined threshold rank, i.e., the rank 3, for the one or
more SKU sets. The method [200] as disclosed now will generate two SKU sets, such as a SKU
set A and a SKU set B, as shown below, wherein the SKU set A comprises the SKU W, the SKU
15 E, and the SKU T, and the SKU set B comprises the SKU Q and the SKU R, based on a
comparison of the rank of said SKUs, i.e., the SKU Q, the SKU W, the SKU E, the SKU R, and the
SKU T, with a pre-defined threshold rank, i.e., the rank 3 for the one or more SKU sets.
Next, at step [212], the method [200] comprises determining, by the processing unit [102], an
order fulfilment potential of said each SKU set based on a percentage of historical orders from
the set of historical orders comprising one or more target SKUs such that the one or more
target SKUs are similar to one or more SKUs in said each SKU set. In an exemplary,
25 implementation of the present disclosure, the one or more target SKUs may be at least one
of the one or more SKUs in said each SKU set, and/or one or more SKUs from the set of historical orders. Further, the order fulfilment potential of said each SKU set is the number of
historical orders completely fulfilled by the one or more SKUs in said each SKU set.
It should be noted that the term "digital platform" includes a system and/or a method and/or
5 a software application that facilitates the exchange, sharing, or provision of digital content,
services, or products. Further, the term "digital platform" encompasses online marketplaces,
social media platforms, e-commerce websites, mobile applications, and other similar
technological infrastructures that enable digital interactions and transactions. The definition
provided above is intended to serve as a general description of a digital platform and is not
10 exhaustive. The specific features, functionalities, and characteristics of digital platforms may
vary depending on their design, purpose, and technological implementation. It is important
to note that the definition provided herein is for illustrative purposes only and should not be
construed as limiting the scope of the patent specification.
15 Further, in a preferred implementation of the present disclosure, the order fulfilment
potential associated with said each SKU set comprises an order fulfilment potential associated
with the one or more SKUs in said each SKU set. Further, in another implementation of the
present disclosure, the pre-defined threshold rank is associated with a pre-defined order
fulfilment potential of the one or more SKU sets.
20
For ease of understanding, continuing from the above example, wherein the SKU set A
comprises the SKU W, the SKU E, and the SKU T, and the SKU set B comprises the SKU Q and
the SKU R, based on the rank of said SKUs, i.e., the SKU Q, the SKU W, the SKU E, the SKU R,
and the SKU T, and a pre-defined threshold rank, i.e., the rank 3 for the one or more SKU sets.
25 Now, the method [200] as disclosed by the present disclosure determines the order fulfilment
potential of said each SKU set, i.e., the SKU set A and the SKU set B, based on a percentage of
historical orders from the set of historical orders, i.e., the set of 100 orders comprising one or
more target SKUs, such that the one or more target SKUs are similar to one or more SKUs in
said each SKU set (the SKU set A and the SKU set B), the one or more target SKUs are the one
30 or more SKUs in said each SKU set (the SKU set A, or at least one of the SKU W, the SKU E, and
the SKU T comprised by the SKU set A or at least one of the SKU Q and the SKU R comprised
by the SKU set B. Further, the order fulfilment potential of said each SKU set, i.e., the SKU set
24
A and the SKU set B, is the number of historical orders, i.e., out of the set of 100 orders
completely fulfilled by the one or more SKUs (the SKU W, the SKU E, the SKU R, and the SKU
T) in said each SKU set (the SKU set A and the SKU set B).
5 Next, at step [214] the method [200] comprises optimizing, by the processing unit [102], the
order pickup path based on the order fulfilment potential associated with said each SKU set.
Further, the order pickup path is a pathway within the warehouse to collect one or more
products associated with the one or more SKUs. Further, in a preferred implementation of
the present disclosure, the order pickup path is optimised based on assigning one or more
10 placement locations in the warehouse to the one or more products, wherein the assigning
the one or more placement locations is further based on the order fulfilment potential
associated with said each SKU set, and a distance between the one or more placement
locations and a pick up zone in the warehouse.
15 Further, in another preferred implementation of the present disclosure, the one or more
placement locations are assigned to the one or more products based on at least one of a
sequential order associated with said each SKU set and a sequential order associated with the
one or more SKUs in said each SKU set, wherein the sequential order associated with said
each SKU set is based on the order fulfilment potential associated with said each SKU set and
20 the sequential order associated with the one or more SKUs in said each SKU set is based on
the order fulfilment potential associated with the one or more SKUs in said each SKU set.
It should be noted that the term "warehouse" refers to a physical facility or storage space
used for the storage, organization, and management of goods, products, or materials. It
25 typically involves the systematic arrangement of items, inventory tracking, and logistical
operations related to the handling and distribution of stored items. Further, it should also be
noted that the definition provided above serves as a general description of a warehouse and
its functions within the context of this patent specification. However, it is essential to
acknowledge that warehouses can vary significantly in terms of size, design, operational
30 processes, and the types of goods they handle. The definition offered herein is not exhaustive
and is intended to provide a general understanding of the term within the scope of the
present disclosure.
Continuing from the above example, wherein the method [200] determined the order
fulfilment potential of said each SKU set, i.e., the SKU set A and the SKU set B, based on a
percentage of historical orders from the set of historical orders, i.e., the set of 100 orders
5 comprising one or more target SKUs such that the one or more target SKUs are similar to one
or more SKUs in said each SKU set (the SKU set A and the SKU set B), the one or more target
SKUs is the one or more SKUs in said each SKU set (the SKU set A, or at least one of the SKU
W, the SKU E, and the SKU T comprised by the SKU set A, or at least one of the SKU Q and the
SKU R comprised by the SKU set B). Now, the method [200] as disclosed by the present
10 disclosure may optimize the order pickup path based on the order fulfilment potential
associated with said each SKU set (the SKU set A and the SKU set B) by assigning one or more
placement locations in the warehouse to one or more products i.e., the one or more products
associated with the SKU Q, the SKU W, the SKU E, the SKU R, and the SKU T, based the order
fulfilment potential as determined at step [212] associated with said each SKU set (the SKU
15 set A and the SKU set B), a distance between the one or more placement locations and a pick
up zone in the warehouse such that the SKU set (either the SKU set A or the SKU set B) with a
higher fulfilment potential is placed near the pick up zone in the warehouse. Further, in
another implementation disclosure, the one or more products associated with the SKU set
(either the SKU set A or the SKU set B) from the one or more SKU sets (the SKU set A and the
20 SKU set B) with higher fulfilment potential associated with said SKU set (either the SKU set A
or the SKU set B) are placed near the pick up zone in the warehouse. Further, in another
implementation of the present disclosure, said each product from the one or more products
associated with the SKU set, for ease, let’s say the SKU set A with higher fulfilment potential
associated with said SKU set A is placed near the pick up zone in the warehouse.
25
Thereafter, the method [200] terminates at step [216].
Therefore, the present disclosure introduces a novel solution that revolutionizes the order
fulfilment process within warehouses. This innovative approach involves a unique
30 classification and batching methodology, which significantly improves operational efficiency
and reduces supply chain costs. The classification process utilizes historical order fulfilment
patterns to identify the order fulfilment potential of each stock keeping unit (SKU). By
26
sequentially arranging SKUs based on their order fulfilment potential, an order fulfilment
pareto is established, providing valuable insights into the most efficient SKU sequences for
fulfilment. The order fulfilment pareto is then used to create SKU classifications for batching
and placement purposes.
5 Further, by implementing the present solution as disclosed by the present disclosure, an
increase in picking productivity and a reduction in supply chain costs can be achieved. The
present solution incorporates an optimal putaway system that ensures orders can be picked
based on class-based classification, minimizing the distance travelled during the picking
process. A key aspect of the present disclosure is creating order fulfilment potential for SKUs
10 and utilizing this technique to determine SKU placement in high Units-to-Orders (U2O)
warehouses. Simultaneously, orders are batched based on the order fulfilment potential of
the SKUs, addressing the challenges related to U2O and order penetration mix. This approach
goes beyond the limitations of current off-the-shelf processes, which often neglect the
complexities associated with high U2O and order penetration mix, placement, and batching
15 in warehouse operations. Through its novel classification and batching approach, the present
invention offers significant advancements in optimizing warehouse efficiency and enhancing
order fulfilment capabilities.
While considerable emphasis has been placed herein on the preferred embodiments, it will
be appreciated that many embodiments can be made and that many changes can be made in
20 the preferred embodiments without departing from the principles of the invention. These and
other changes in the preferred embodiments of the invention will be apparent to those skilled
in the art from the disclosure herein, whereby it is to be distinctly understood that the
foregoing descriptive matter to be implemented merely as illustrative of the invention and
not as limitation.
We Claim:
1. A method for optimizing an order pickup path in a warehouse associated with a digital
platform, wherein the method comprises:
5 - retrieving, by a processing unit [102] from a storage unit [104], a set of historical
orders placed via the digital platform, wherein each historical order from the set
of historical orders comprises one or more stock keeping units (SKUs);
- determining, by the processing unit [102] for each SKU from the one or more
SKUs, an order per SKU value based on the set of historical orders;
10 - automatically assigning, by the processing unit [102] to the one or more SKUs, a
rank based on the order per SKU value of the one or more SKUs;
- generating, by the processing unit [102], one or more SKU sets based on the rank
of the one or more SKUs and a pre-defined threshold rank for the one or more
SKU sets;
15 - determining, by the processing unit [102], an order fulfilment potential of said
each SKU set based on a percentage of historical orders from the set of historical
orders comprising one or more target SKUs such that the one or more target SKUs
are similar to one or more SKUs in said each SKU set; and
- optimizing, by the processing unit [102], the order pickup path based on the
20 order fulfilment potential associated with said each SKU set.
2. The method as claimed in claim 1, wherein the order pickup path is a pathway within
the warehouse to collect one or more products associated with the one or more SKUs.
3. The method as claimed in claim 2, wherein the order pickup path is optimised based on
assigning one or more placement locations in the warehouse to the one or more
25 products, wherein the assigning the one or more placement locations is further based
on the order fulfilment potential associated with said each SKU set, and a distance
between the one or more placement locations and a pick up zone in the warehouse.
4. The method as claimed in claim 3, wherein the order fulfilment potential associated with
said each SKU set comprises an order fulfilment potential associated with the one or
30 more SKUs in said each SKU set.
5. The method as claimed in claim 4, wherein the one or more placement locations are
assigned to the one or more products based on at least one of a sequential order
28
associated with said each SKU set and a sequential order associated with the one or
more SKUs in said each SKU set, wherein the sequential order associated with said each
SKU set is based on the order fulfilment potential associated with said each SKU set and
the sequential order associated with the one or more SKUs in said each SKU set is based
5 on the order fulfilment potential associated with the one or more SKUs in said each SKU
set.
6. The method as claimed in claim 1, wherein the order per SKU value comprises one or
more historical orders associated with said each SKU from the set of historical orders.
7. The method as claimed in claim 1, wherein the one or more SKU sets are generated
10 based on a comparison of the rank of the one or more SKUs with the pre-defined
threshold rank for the one or more SKU sets.
8. The method as claimed in claim 7, wherein the pre-defined threshold rank is associated
with a pre-defined order fulfilment potential of the one or more SKU sets.
9. A system for optimizing an order pickup path in a warehouse associated with a digital
15 platform, wherein the system comprises:
a storage unit [104]; and
a processing unit [102], wherein the processing unit [102] is configured to:
- retrieve, from the storage unit [104], a set of historical orders placed via the
digital platform, wherein each historical order from the set of historical orders
20 comprises one or more stock keeping units (SKUs);
- determine, for each SKU from the one or more SKUs, an order per SKU value
based on the set of historical orders;
- automatically assign, to the one or more SKUs, a rank based on the order per SKU
value of the one or more SKUs;
25 - generate, one or more SKU sets based on the rank of the one or more SKUs and
a pre-defined threshold rank for the one or more SKU sets;
- determine, an order fulfilment potential of said each SKU set based on a
percentage of historical orders from the set of historical orders comprising one
or more target SKUs such that the one or more target SKUs are similar to one or
30 more SKUs in said each SKU set; and
- optimize, the order pickup path based on the order fulfilment potential
associated with said each SKU set.
29
10. The system as claimed in claim 9, wherein the order pickup path is a pathway within the
warehouse to collect one or more products associated with the one or more SKUs.
11. The system as claimed in claim 10, wherein the order pickup path is optimised based on
5 assigning one or more placement locations in the warehouse to the one or more
products, wherein the assigning the one or more placement locations is further based
on the order fulfilment potential associated with said each SKU set, and a distance
between the one or more placement locations and a pick up zone in the warehouse.
12. The system as claimed in claim 11 wherein the order fulfilment potential associated with
10 said each SKU set comprises an order fulfilment potential associated with the one or
more SKUs in said each SKU set.
13. The system as claimed in claim 12, wherein the one or more placement locations are
assigned to the one or more products based on at least one of a sequential order
associated with said each SKU set and a sequential order associated with the one or
15 more SKUs in said each SKU set, wherein the sequential order associated with said each
SKU set is based on the order fulfilment potential associated with said each SKU set and
the sequential order associated with the one or more SKUs in said each SKU set is based
on the order fulfilment potential associated with the one or more SKUs in said each SKU
set.
20 14. The system as claimed in claim 9, wherein the order per SKU value comprises one or
more historical orders associated with said each SKU from the set of historical orders.
15. The system as claimed in claim 9, wherein the one or more SKU sets are generated based
on a comparison of the rank of the one or more SKUs with the pre-defined threshold
rank for the one or more SKU sets.
25 16. The system as claimed in claim 15, wherein the pre-defined threshold rank is associated
with a pre-defined order fulfilment potential of the one or more SKU sets.