Data Analytics and Artificial Intelligence for Inventory and Supply Chain Management

293 Pages·2022·7.533 MB·English

by  Dinesh K. Sharma, Madhu Jain

Inventory Optimization
Dinesh K. Sharma
Madhu Jain   Editors
Data Analytics
and Artificial
Intelligence
for Inventory and
Supply Chain
Management
Inventory Optimization
Series Editors
Nita H. Shah, Department of Mathematics, Gujarat University, Ahmedabad,
Gujarat, India
Mandeep Mittal, Department of Applied Mathematics, Amity Institute of Applied
Science, Amity University, Noida, India
Leopoldo Eduardo Cárdenas-Barrón, Department of Industrial and Systems
Engineering, Monterrey Institute of Technology and Higher Education, Monterrey,
Mexico
Inventory management is a very tedious task faced by all the organizations in any
sector of the economy. It makes decisions for policies, activities and procedures in
order to make sure that the right amount of each item is held in stock at any time. Many
industries suffer from indiscipline in ordering and production mismatch. Providing
best policy to control such mismatch would be invaluable to them.
The primary objective of this book series is to explore various effective methods
for inventory control and management using optimization techniques. The series
will facilitate many potential authors to become the editors or author in this book
series. The series focuses on an aspect of Operations Research which does not get
the importance it deserves. Most researchers working on inventory management are
publishing under different topics like decision making, computational techniques
and optimization techniques, production engineering etc. The series will provide the
much needed platform for them to publish and reach the correct audience.
Some of the areas that the series aims to cover are:
• Inventory optimization
• Inventory management models
• Retail inventory management
• Supply chain optimization
• Logistics management
• Reverse logistics and closed-loop supply chains
• Green supply chain
• Supply chain management
• Management and control of production and logistics systems
• Datamining techniques
• Bigdata analysis in inventory management
• Artificial intelligence
• Internet of things
• Operations and logistics management
• Production and inventory management
• Artificial intelligence and expert system
• Marketing, modelling and simulation
• Information technology
This book series will publish volumes of books which will be edited and reviewed
by the reputed researcher of inventory optimization area. The beginner and experi-
enced researchers both can publish their innovative research work in the form of
edited chapters in the books of this series by getting in touch with the contact person.
Practitioners and industrialist can share their real time experience bolstered with
case studies. The objective is to provide a platform to the practitioners, educators,
researchers and industrialist to publish their valuable work in the area of inventory
optimization.
This series will be beneficial for practitioners, educators and researchers. It will
also be helpful for retailers/managers for improving business functions and making
more accurate and realistic decisions.
·
Dinesh K. Sharma Madhu Jain
Editors
Data Analytics and Artificial
Intelligence for Inventory
and Supply Chain
Management
Editors
Dinesh K. Sharma Madhu Jain
Department of Business, Department of Mathematics
Management and Accounting Indian Institute of Technology Roorkee
University of Maryland Eastern Shore Roorkee, Uttarakhand, India
Princess Anne, MD, USA
ISSN 2730-9347 ISSN 2730-9355 (electronic)
Inventory Optimization
ISBN 978-981-19-6336-0 ISBN 978-981-19-6337-7 (eBook)
https://doi.org/10.1007/978-981-19-6337-7
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
Singapore Pte Ltd. 2022
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Dedicated to
Our Beloved Mothers
Dinesh K. Sharma
Madhu Jain
Foreword
Businesses and organizations that deal with the inventory, supply chain and logis-
tics primarily rely on the inventory management, space optimization, coping with
erroneous forecasts, customer satisfaction, and their vast network of suppliers and
partners to keep things moving effectively. This book offers an overview of state-of-
the-art, some perspectives and implementation of data analytics, optimization and AI
techniques for deterministic as well as stochastic modeling of inventory and supply
chains.
The chapters of this book contain the practical inventory and supply chain models
to ensure adoption of advanced techniques, new solutions, data analytics, intelli-
gent computing and nature-inspired optimization algorithms. These models lead
to better decision-making required in many industries and business organizations
for demand forecasting, planning and digital-execution tracking. The inventory and
supply chain models presented in each chapter provide valuable insights into how
things are produced, manufactured, and supplied to the customers by using soft
computing, data analytic and AI-driven tools.
Daniel I. Okunbor
Professor, Fayetteville State University
Fayetteville, NC, USA
vii
Preface
Intelligent computing can be used in supply chain management to tackle huge
volumes of data, understand relationships of demand vs supply, optimize the earn-
ings before interest, taxes, depreciation, and amortization for better decision-making
in an organization as an integrated end to end supply chain. Artificial intelligence
(AI)-driven tools can provide valuable insights for the inventory, logistics, warehouse
efficiency, on time delivery, forecasting of supply and demand. AI-based solution-
agnostic assessment and strategy will support the companies for better alignment
and inventory control, and capabilities to create a strategic intelligent road map for
the supply chain and logistics.
This book contains various inventory and supply chain models to tackle logistics
and optimization problems. Recent developments in the areas of nature-inspired opti-
mization and AI approaches to manage an end to end inventory and supply chains for
implementation and system integration have been presented. The models and perfor-
mance analysis presented facilitated valuable insights for the retailers and managers
to improve business operations and make more realistic and better decisions. This
book offers a number of smartly designed strategies related to inventory control and
supply chain management for the optimal decision.
This book consists of 15 chapters that bring interesting aspects of modeling
and applications of artificial intelligent computing and optimization techniques in
inventory and supply chain management. The subject presented in the book will be
beneficial for academicians, researchers and practitioners to get ideas about recent
advances in the modeling and data analytics for the optimal decision of inventory,
logistics and supply chain. In different chapters of the book statistical, optimization
and AI techniques for the stochastic inventory control and supply chain management
are presented. Chapter 1 deals with Markov decision processes for a supply chain
(SC) model based on a two-tier queueing-inventory system that delivers packages
of fixed-size items from its inventory stored in a distribution center to a retail
shopping mall (RSM). Chapter 2 presents a state of art and literature survey on the
inventory models with imperfect production systems. The prominent works related
to nature-inspired optimization algorithms and their applications in inventory control
are presented. Chapter 3 is concerned with multi-objective mathematical model for
socially responsible supply chain inventory planning by framing four objectives
ix
x Preface
related to cost, local development, steadiness in employment and investment in green
technology. The proposed model has the capability to obtain the optimal number of
products to be manufactured and re-manufactured, number of inventories, number
of employees to recruit and lay-off within a certain region in each quarter of the year,
decision on time to invest in green technology. Chapter 4 facilitates an overview
of critical areas of supply chain where AI can help in improving the flexibility
of nature-inspired optimization techniques, assuring delivery to the final mile,
providing personalized solutions to the stakeholders in upstream and downstream
supply chains and many more. The inventory model with price-dependent demand
and imperfect production has been studied in Chap. 5. The markdown policy is
adopted to lower the on-hand inventory and to enhance the total revenue or the
rate of sale of goods. Chapter 6 is devoted to study the inspection process for the
inventory queueing system with retrial orbit, balking and defective items. Chapter 7
investigates how a poultry farmer can manage the farm so that any type of viral flu
can be tackled along with profit-maximizing, order quantity, etc.
In Chap. 8, the inventory model is developed to analyze the issues of pavement
cracks based on Deep Learning to detect and classify five types of road problems
including longitudinal, traverse, block, alligator cracks and potholes. In Chap. 9,
Economic Production Quantity model is developed to propose an optimal strategy
to decarbonize the environment through reusing policy of waste plastic from a dump
yard in a rhino brick industry where each unit of production contains some propor-
tion of plastic material. The cost analysis of supply chain for deteriorating inventory
items with shortages in fuzzy environment is done in Chap. 10. Chapter 11 deals
with the inventory problem in multi-echelon setup for scrutinizing the execution of
a single manufacturer and multiple retailers’ supply chain. The impacts of product
type on inventory cost and lot size on the lead time are studied. Chapter 12 describes
a production inventory model with flexible production system under online payment
and preorder discount facility. Chapter 13 discusses a joint effect of preservation
technology investment and order cost reduction under different carbon emission
regulation policies by developing a sustainable inventory model in an inflationary
environment. Chapter 14 examines the impact of corporate credibility on inventory
management decisions using the concepts of AI. Chapter 15 is devoted to analyze a
bidirectional neural network dynamic inventory control model for reservoir opera-
tion. Both forward and backward dynamic inventory control operations based on the
previous states of the reservoir and the current states of the input are considered. The
subject matter presented in the book will enrich the knowledge in the direction of
optimal control and future design of the inventory systems and supply chains that will
be of great importance not only from theoretical point of view but will strongly reflect
the practical and managerial implementations in the concerned commercial/industrial
organizations, production/manufacturing systems, business/service sectors and many
more areas.
Princess Anne, USA Dinesh K. Sharma
Roorkee, India Dr. Madhu Jain
Acknowledgements
This book is the collection of the works of eminent researchers who have contributed
towards various chapters of the book. We are thankful to all the authors for their
valuable contributions. We would like to express our gratitude to all reviewers for
their help and professional support. We greatly appreciate the cordial support of
Prof. Neeta Shah and Dr. Mandeep Mittal for inviting us to publish in book series
Inventory Optimization. We humbly acknowledge a lifetime gratitude to Prof. G. C.
Sharma, Agra for the unconditional support, valuable suggestions and continuous
encouragement.
We would like to thank Department of Business, Management and Accounting
at the University of Maryland Eastern Shore, Princess Anne, Maryland, USA, our
research group at the Modeling and Data Analytics Lab, Department of Mathematics,
IIT Roorkee, India, for technical support. Our special thanks to Ms. Nidhi Sharma,
Mayank Singh and Praveendra Singh who have supported unstintingly with great
enthusiasm and efficiency during the book preparation. We would like to thank the
team at Springer, in particular Project Coordinator Ramesh Kumaran and Publishing
Editor Nupoor Singh for their help and support for publishing this book.
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