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International Journal of Scientific Research and Management (IJSRM)
||Volume||10||Issue||06||Pages||EC-2022-907-917||2022||
Website: www.ijsrm.net ISSN (e): 2321-3418
DOI: 10.18535/ijsrm/v10i6.ec04
Goli Mallesham, IJSRM Volume 10 Issue 06 June 2022 EC-2022-907
Leveraging AI in Embedded and Extended Warehouse Management
for Enhanced Efficiency
Goli Mallesham
Research Scientist Cap Adobe Bangalore
Abstract
Speed and accuracy of decision-making at the operational and tactical levels are critical in warehouse
management. This paper conceptually presents decision support systems (DSS) powered by artificial
intelligence (AI) at two levels – embedded warehouse management at the operational level and extended
warehouse management at the tactical level. For enhanced efficiency, suggestions are categorized at the
tactical level into system-front-end/back-end-heavy lifting, other back-end system suggestions, and system
extensions. Several AI technologies such as expert system rule engines, machine learning models, and natural
language understanding models can be applied at both levels. Efforts required for data preparation and model
training are highlighted.
Warehouse management takes place in a dynamic environment. New inventory arrives, and orders for
shipping out inventory are constantly issued. There is a large number of decisions to be made regularly to
coordinate the flow of materials in and out of a warehouse. Speed and accuracy of operational and tactical
decision-making are important in warehouse management. This paper begins by discussing decision support
systems (DSS) enabled by artificial intelligence (AI) for efficient decision-making at both the operational and
tactical levels. Subsequently, several AI technologies that can be applied to offer intelligence at both levels
are discussed. Throughout the paper, suggestions are made about how to apply these technologies to enhance
efficiency. Furthermore, the effort required in terms of data preparation and model training is discussed. The
pathways presented are only feasible with a supporting, intelligent IT infrastructure. Intelligence needs to be
built not only within the warehouse system but also extended out to the surrounding ecosystem. The paper
wraps up by either highlighting or reiterating the suggestions and insights to help stakeholders make the most
of the possibilities AI offers for decision-making in warehouse management. With the rapid growth of e-
commerce, flexible, adaptable AI-driven DSS could be the solution needed to help warehouse management
keep up with the ever-increasing pace and dynamism of the industry.
Keywords: Leveraging AI in Embedded and Extended Warehouse Management , Industry 4.0, Internet of
Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM)
1. Introduction to Supply Chain Optimization
The acute shortage of labor in warehouses,
aggravated by high turnover, has driven the
implementation of multiple levels of automation to
move to lights-out, 24/7 operations. Artificial
intelligence (AI) can be used in both embedded and
extended warehouse management systems to
address the increasingly complex challenges that
stem from operational, commercial, and social
changes and upheavals. Examples are the
uncertainty introduced by omni-channel logistics,
the ever-increasing number, variety, and velocity of
products that need to be handled, the variety of
warehouses, including temporary ones (pop-up
warehouses), and the variety of vehicles that
transport the products. In addition to AI, the
synergy between research, new technology such as
Goli Mallesham, IJSRM Volume 10 Issue 06 June 2022 EC-2022-908
blockchain, cloud and edge computing, IoT, etc.,
and standards (twin digital standards, digital supply
chain framework data model, semantic web
ontology, un/CEFACT core data model, etc.),
contribute significantly to address the challenges.
The ultimate goal is to optimize warehouse
operations while ensuring that the right products at
the right quantity and quality are delivered to the
right destination at the right time, minimizing costs
and still being socially responsible.
This paper presents some solutions. AI
technologies, including machine learning, deep
learning, and natural language processing, used in
both embedded and extended warehouse
management, are introduced. These AI technologies
address specific warehouse problems to enhance
efficiency. The advantages and disadvantages of
using AI in warehouse management are also
discussed. Furthermore, the use of standards and
new technology, including blockchain, cloud and
edge computing, the IoT, etc., in collaboration with
AI, to address warehouse challenges, are also
presented. Additionally, a case study involving the
use of AI in a warehouse that handles health-related
products is explored. The deployment of models—
in the cloud, at the edge, or in the embedded
1.1Background and Significance
Warehouse management has grown from internal
control and inventory oversight to logistics and
supply chain management since its inception. WMS
is a warehouse management system software that is
widely used. This is a software application that is
designed specifically to support the day-to-day
operations and tasks in a warehouse. Tasks may
include inventory receiving, put-away, picking,
packing, and shipping. WMS uses a central
database to provide highly configurable task logic
for equipment such as conveyor systems, carousels,
and other handling devices.
Warehouse management systems and embedded
warehouse management systems are two types of
warehouse management. The former is a specialized
software package designed for control over most
warehouse operations. A subsection of evolving
ERP systems would include these applications. ERP
systems, on the other hand, use warehouse
functions, which are referred to as embedded
warehouse management systems. Warehouse
functions are available that allow the majority of
warehouses with basic requirements to be managed.
Recent technological developments have increased
the use of artificial intelligence techniques,
especially machine learning, to enhance warehouse
management system capabilities. Despite the rich
literature on AI techniques for WMS enhancement,
limited works use AI techniques for extending
warehouse management functions and the
combination of embedded and extended functions.
Embedded and extended warehouse management,
providing sufficient intelligence in terms of
capabilities, can enhance warehouse efficiency to
the next level. AI techniques offer the advantage of
self-learning over traditional optimization methods
and can be utilized to address the problem of
warehouse management across multiple areas. AI
techniques not only provide opportunities for
enhancement in areas of warehouse
mismanagement but also boost the fusion of
embedded and extended WMS functions through
their appropriate design.
FIGURE 1: Networked scalability impact of AI.
1.2. Research Aim and Objectives
Given the promising potential of AI, this research
aims to explore how AI can be exploited in both
embedded and extended WM systems to enhance
their performance. The primary objectives are as
follows:
Goli Mallesham, IJSRM Volume 10 Issue 06 June 2022 EC-2022-909
1. Investigate the current state of AI application in
WM and identify the limitations of the existing WM
systems concerning not fulfilling the tasks that AI
can facilitate. 2. Develop AI-modified approaches
or entirely new AI solutions that, when
implemented in WM systems, can optimize
warehouse operations.
In achieving these research objectives, this study
first provides an overview of the tasks associated
with WM at a macro level. We then explore the
different AI technologies, methodologies, and
components that can be applied within both the
embedded and extended WM systems, elaborating
on their specific tasks and subtasks the AI
components can perform. Thereafter, we will
highlight the WM system tasks that may not be
currently achievable but have the potential to be
performed with the help of AI. Next, the directions
in which AI can utilize to contribute to WM system
tasks are explored, and suitable guidelines will be
presented for AI solution development.
2. AI Applications in Warehouse Management
Current, comprehensive inventories are pivotal for
efficient warehouse management, and AI has
remedied several warehouse-related problems.
There are labeling and locating technologies for
small and large-sized items. Automatic data capture
technologies take over manual data entry tasks and
increase the data's value. Sorting technology
automates the time-consuming task of sorting items
for shipment. Cranes, hoists, and forklifts help
move large items and pallets. A warehouse's
perpetual task is service rates and inventory
turnover.
Warehouse management concentrates on ensuring
that all functions performed in a material storage
area contribute, to the utmost possible extent, to
efficiency in serving the customer for minimum
cost. Five functions are crucial in all warehouses:
allocation, replenishment, aisle space assignment,
order picking, and shipping. AI can optimize or
assist in every one of these functions. Research
work has been done with Expert, a rule-based
system, simple office-like systems, neuron-like
systems, and other systems as solving approach
techniques for inventory allocation problems. The
rapid development of AI technologies shows that
much more sophisticated tools will soon be
available for allocation problems. The warehouse
can be seen as a complex dynamic system, which
requires a more intelligent approach for its control.
The potential use of AI technologies represents a
step in this direction.
2.1.AI Techniques and Algorithms
This paper describes the application of several AI
techniques and algorithms in embedded and
extended warehouse management to enhance their
efficiency. In particular, we describe our experience
in solving real-world problems by developing
commercial systems that support the automated and
semi-automated handling of large sets of diverse
orders in warehouses of different scales in several
countries. We also describe some open research
questions and our efforts to address them and
present some evaluation results and lessons learned
from our implementations. Finally, we offer some
conclusions and outline our plans for future work.
Given the real-time requirements of embedded
warehouse management for handling the movement
and processing of goods, the applicability of
traditional AI techniques and algorithms in this area
is rather limited. The extended warehouse
management system belongs to a higher level of
control and utilizes several techniques and
algorithms from AI to achieve greater efficiency.
Typically, at the core of both types of systems is a
knowledge-based system that utilizes a rule-based
system represented with production rules. In
embedded systems, the use of neural networks and
genetic algorithms is also becoming increasingly
popular. Furthermore, in extended systems, the use
of multi-agent systems is emerging as a powerful
approach. The increasing interest in using AI in
both types of systems is also illustrated by the
growing number of available implementations.
Goli Mallesham, IJSRM Volume 10 Issue 06 June 2022 EC-2022-910
FIGURE 2:Product design phase
2.2.Use Cases and Benefits
Embarking on an AI-driven path for EWM or
connecting an AI-powered EWX to an existing
EWM offers varied operational benefits. Both the
EWM domain and the adopters of embedded and
extended types of these technologies have gained
operational efficiency at scale as a common merit of
an AI/ML intervention, as presented in Figure 2.
Cross-industry use cases based on benefits along the
warehousing stakeholder value chain are depicted in
Figure 3 and are described in Table 1.
A. Supply Chain Director – Use Cases. Presented
with global demand patterns which are often
seasonal and based on leading economic indicators,
the EWM AI/ML solution can receive prescient
operational input. It can prepare the WMS in
advance and stockpile a selection of necessary items
based on the most probable demands. The EWM
can recommend optimized inventory replenishment
and allocation configurations for the DCs and store
networks. It can also ensure the safety stock levels
are adjusted perfectly, with the holding costs
minimized. Moreover, the EWM solution can
interact with the blockchain-based inventory system
implemented at the EWX level, to ensure overly
optimistic fraud-preventive reservations are
detected and addressed, releasing blocked
inventories for sale or redistribution to the other
DCs.
3.Embedded vs Extended Warehouse
Management
Artificial intelligence technologies in extended and
embedded warehouse management solutions can be
of great support in ensuring warehouse operations
are efficient. Extended WMS focuses on the
complete flow of goods, from the supplier's
warehouse to the recipient's warehouse. It includes
all the logistics business processes which begin
from the receipt of goods in the warehouse to the
issue of the goods from the warehouse, such as
transport, stock accounting, picking, packing,
inventory taking, and loading/unloading. Extended
WMS operates with a large number of items, orders,
and transports and is usually integrated with ERP
systems, providing necessary data for the WMS
operation.
Embedded WMS is focused on the optimization of
internal warehouse processes - receiving, putaway,
picking, packing, and dispatching - usually
performed in the customer's or third-party
warehouse. It is often implemented as a light
version of the Extended WMS, typically including
only a subset of its functions and working with a
limited scope of items and orders - usually not
exceeding the capacity of one warehouse or a few
warehouse locations. Neuronimbus, a leading AI
consulting and innovation company, deep dives into
AI technologies used in embedded warehouse
management solutions and the benefits derived
thereof.
3.1.Definition and Characteristics
Leveraging AI in embedded and extended
warehouse management for enhanced efficiency.
Today's warehouse management scope has
broadened from traditional in-and-out managing
and storage of goods to sales support, light
manufacturing support, reverse logistics support,
etc. The extended warehouse management area
focuses on the whole set of collaborative operations
from the goods' origin (e.g., production sites,
suppliers) to the customers and consumers, through
the distribution networks. These scopes, along with
the increasing dynamics and scale of operations in
the respective areas, impose the development and
deployment of intelligent tools for increased
efficiency and reliability. The intelligence, AI in the
presented cases, has to be embedded and extended,
Goli Mallesham, IJSRM Volume 10 Issue 06 June 2022 EC-2022-911
according to the two described above warehouse
management sub-areas.
Embedded intelligence is characterized by the
integration, closeness, and promptness of AI
techniques to support daily operational warehouse
management activities. The major characteristics of
the AI techniques for use in that area are relatively
low complexity, easy knowledge acquisition and
representation, fast knowledge processing and
feedback; easy knowledge reevaluation and
readjustment; high real-time operation reliability,
etc. Extended intelligence is characteristic of the use
of AI techniques that permit high-efficiency
knowledge processing and decision-making
support, based on massive data/knowledge
resources and repositories spread over distant
warehouse management systems or related business
systems. The major characteristics of the AI
techniques for use in that area are high complexity
to permit high-efficiency solutions; ability to
acquire, represent, store, and process knowledge
and get feedback distributed over several computing
entities; ability to operate with dynamic knowledge
distributed over several related operational or
business systems; generally high global operation
and decision-making reliability, etc.
FIG 3:Multifaceted impact of AI in education.
3.2. Key Differences
This paper is inspired by the increasing relevance of
both chapters 2.1 Artificial Intelligence in
Warehouse Management and 2.2 The Extended
Warehouse Management of the book, and the fact
that the two technologies are typically addressed in
isolation given the disparate technology platforms
that currently support embedded warehouse
management and extended WMS. Enterprise
software platforms such as SAP and Oracle offer a
discrete WMS module, either as part of their core
ERP system or as an optional module tightly
integrated with the ERP. These are commonly
referred to as Extended WMS and typically require
significant lead time, investment, and customization
to implement.
Some key differences between these two types of
WMS are worth noting. Embedded WMS typically
runs on specialized technologies that enable real-
time control of warehouse automation equipment
(such as conveyor belts, sorters, robots, etc.) and
mobile devices for inventory transactions. These
technologies include programmable logic
controllers, industrial PCs, inventory transaction
processing engines, and wireless network
infrastructure. In contrast, extended WMS runs on
commercial, off-the-shelf computing architectures
and leverages well-established enterprise
transaction processing engines for inventory
transaction processing and integration with the
ERP. Furthermore, embedded WMS is designed to
support high-throughput transaction processing
scenarios, where a large number of inventory
transactions need to be completed in the shortest
time possible, such as goods movement inside a
high-volume distribution center. As a result, the
user interfaces for embedded WMS are typically
minimalistic and optimized for execution speed.
4. Integration of AI in Embedded and Extended
Warehouse Management
Warehouse management across the supply chain is
evolving with a rapid increase in the development
and deployment of extended warehouse
management systems. These systems use service-
Goli Mallesham, IJSRM Volume 10 Issue 06 June 2022 EC-2022-912
oriented architecture both in embedded technology
in the warehouse and at the enterprise level. When
implemented with optimized AI and delivered
through AI-based appliances, both the embedded
and extended warehouse management service
systems exhibit enhanced operational performance.
AI can easily be integrated into embedded
warehouse technology when delivered through
appliances. At the service-oriented enterprise level,
AI should be delivered through an on-demand
model with regular enhancements and updates. As
services evolve and new services are developed, the
AI infrastructure at the enterprise level needs to be
scalable and manageable, with low total cost for
service-oriented architecture. The governance of AI
at the warehouse needs to be extended to an
enterprise level, ensuring seamless integration of
embedded technology through standards defined in
consortia like ISA-95 and customization rules used
at the warehouse for specific enterprise services. At
both levels, the warehouse should leverage AI
through learning and prediction optimization
methods for enhanced operational performance.
4.1.Challenges and Solutions
Real-time embedded warehouse management and
extended warehouse management have their
challenges. For embedded warehouse management,
it is required to support the real-time, on-the-fly,
and just-in-time modes with inherently advanced
simplified modes of operation that preclude user
support. The real-time activities of the warehouse
cannot tolerate lengthy switching between various
AI subsystems or their modes of operation. The
extended warehouse management faces the
challenge of the inverse interface. While frequent
AI-driven automatic formulation of rules,
instructions, and task assignments for the
warehouse staff can significantly enhance
efficiency, the staff does not trust, and even resent,
an offered intelligent system that implements micro-
management. The more intelligence that is
implemented by such a system, the fewer chances
there are that the staff will accept and act upon it.
The solutions to the mentioned challenges imposed
by the embedded real-time warehouse management
include the creation of a very simple and shallow
multi-tier architecture of AI subsystems, where at
the top there is a light-touch type AIPS with
occasional staff interactions, specifying most
general policies and strategies. The warehousing
process enacted by this top AI subsystem must
evolve very slowly, with changes implemented in
the on-the-fly and just-in-time AI modes being very
infrequent. Rapidly evolving staff-tasking
interactions are implemented by the underlying
quicker-evolving AI subsystems that support
execution and lower-level control, with their
evolution mode being performed off-the-fly and off-
the-time-slice.
FIG 4: Portfolio management process.
5.Case Studies and Industry Examples
The section highlights case studies, industry
examples, or real-world implementations that are
relevant to the topic to help the reader better
understand the practical and applied aspects of the
concept.
Case Study 1: Picking Optimization in an AGV-
based EWM System. In this case study, we combine
a cloud-based extended warehouse management
system (EWM) with multiple sets of automated
guided vehicles (AGV). The commercial EWM
system covers typical warehouse management
functions from goods receiving to shipping. We
program the AGV sets for part of the picking work
and replenishment transports. A picking route
optimization algorithm operates the AGV sets.
AGV navigates through the warehouse based on a
combination of natural features for localization and
digital maps. The cloud logistics system controls the
AGV: assigns order-based transport tasks, releases
Goli Mallesham, IJSRM Volume 10 Issue 06 June 2022 EC-2022-913
the transport system when ready, and tracks
transport execution. The automatic data exchange of
the cloud-based logistics system with the AGV sets
includes order information, AGV availability, bin
contents, and order totes transported by the AGV.
Case Study 2: Picking Optimization in a Swarm
Robotics-based EWM System. In this work, we
present a collaborative mobile robotics system for
warehouse order processing. Instead of centralized
coordination, we aim at loosely coordinating the
activities of a heterogeneous set of entities:
commercially available automated guided vehicles
(AGVs) and proprietary small autonomous ground
robots (AGRs) serving as a swarm. We introduce
two types of functions assigned to the robots:
picking goods to be shipped and replenishing goods
to stock. For the picking function, we explore
different strategies: dispatching independent robots
to randomly selected goods and sending
information-constrained robot groups to specific
goods areas. The swarm AGRs are dynamically
grouped based on the quality of information
exchange between them. The collaborating robots
are evaluated in terms of their performance and
communication requirements. The demonstrated
robotic warehouse system is modestly sized, but it
scales appropriately; the presented configurations
can easily be mapped to larger installations.
6. Conclusion
The evolution of eWM and the features that are
embedded or extended to cater to quicker, efficient
deliveries to service the superfast internet shopping
order-delivery cycle are highlighted. An attempt is
made to draw attention to the key inclusions that
have the potential to bring about marked changes in
the way warehouse operations are managed. The
advancements in embedded AI models warrant
periodic model refreshes to maximize operational
efficiency through improved decision-making. AI is
on a self-evolutionary track enabled by embedding
into the operational process. The evolutionary
extended models with feedback should ensure
guaranteed convergence. The onus of the
evolutionary process lies in the hands of the data
custodian to preempt data drift and data shock,
through data readiness for AI models, to initiate
ground-breaking intelligence in warehouse
operations.
The superfast changing world of internet shopping
with overarching orders for same-day or one-day
deliveries is putting immense pressure on
fulfillment centers for efficient cycle-time
operations. The warehouse management processes
must keep pace with this accelerated demand for
speedy deliveries. eWM with its embedded AI
models is on a keel to revolutionize warehouse
operations. Our readiness to leverage AI for
intelligence infusion into warehouse operations will
be an enabler for operational efficiency strides. We
are at a point in time where AI is not just an enabler
but is on the cusp of becoming a self-evolving
intelligence with feedback mechanisms through
extended models.
6.1 Future Trends
In the coming years, we anticipate several trends in
the area of embedded and extended warehouse
management that will result from the increasing
proliferation of AI at different layers of warehouse
software. Firstly, the traditional divide between
embedded and extended WMS will blur as more
functions from extended WMS will be augmented
into embedded WMS, enabled by lighter and more
specialized cloud-based extended WMS, and fast-
evolving edge computing capabilities. As such, all
warehousing companies, regardless of the scale of
their operations, will be able to benefit from full-
featured WMS. Secondly, the higher levels of
embedding of AI will shift the focus of the
enhanced efficiency derived from the advanced
WMS from execution support to strategy
optimization in the long run.
In the short term, due to the prohibitive costs of
embedded AI or edge AI implementations, as well
as the still-existing inflexibilities of embedded
WMS, the focus of embedded AI WMS will be on
Goli Mallesham, IJSRM Volume 10 Issue 06 June 2022 EC-2022-914
the execution areas that require very quick, or even
real-time, responses. Slotting optimization and real-
time adaptive wave management are examples of
such areas. As the costs of embedded AI
implementations come down, and the capabilities of
embedded WMS go up, it is set to become the
primary area of growth in WMS-related AI
enhancements, due to the focus on execution
support for mid-scale and large-scale operations.
Moreover, the fast response times of embedded AI
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