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Inventory management for retail companies: A literature review and current trends

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Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature

• Review Article
• Published: 07 February 2023
• Volume 30 , pages 2605–2625, ( 2023 )

Cite this article

• Özge Albayrak Ünal   ORCID: orcid.org/0000-0001-7798-8799 1 ,
• Burak Erkayman   ORCID: orcid.org/0000-0002-9551-2679 1 &
• Bilal Usanmaz   ORCID: orcid.org/0000-0003-0531-4618 2  

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8 Citations

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Today, companies that want to keep up with technological development and globalization must be able to effectively manage their supply chains to achieve high quality, increased efficiency, and low costs. Diversified customer needs, global competitors, and market competition have led companies to pay more attention to inventory management. This article provides a comprehensive and up-to-date review of Artificial Intelligence (AI) applications used in inventory management through a systematic literature review. As a result of this analysis, which focused on research articles in two scientific databases published between 2012 and 2022 for detailed study, 59 articles were identified. Furthermore, the current situation is summarized and possible future aspects of inventory management are identified. The results show that the interest in AI methods has increased in recent years and machine learning algorithms are the most commonly used methods. This study is meticulously and comprehensively conducted so it will probably make significant contributions to the further studies in this field.

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Özge Albayrak Ünal & Burak Erkayman

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Albayrak Ünal, Ö., Erkayman, B. & Usanmaz, B. Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature. Arch Computat Methods Eng 30 , 2605–2625 (2023). https://doi.org/10.1007/s11831-022-09879-5

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Received : 13 August 2022

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Published : 07 February 2023

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DOI : https://doi.org/10.1007/s11831-022-09879-5

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AI in Inventory Management: Applications, Challenges, and Opportunities

2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

This paper delves into the multifaceted role of Artificial Intelligence (AI) in inventory management, encompassing its applications, challenges, and future opportunities. AI's integration into inventory management systems has revolutionized supply chain operations, enhancing efficiency, accuracy, and decision-making processes. The paper explores various AI applications, including demand forecasting, stock optimization, and automated reordering. However, it also acknowledges the challenges in AI implementation, such as data quality, interpretability, and model transparency. The research highlights the synergy between AI and emerging technologies like the Internet of Things (IoT), pointing towards new innovative solutions that were unimaginable in the past. In conclusion, the paper presents a balanced view of AI's transformative impact on inventory management, emphasizing both its current benefits and the hurdles that need to be overcome for its successful integration. Furthermore, this comprehensive analysis not only provides insights into the current state of AI in inventory management but also sheds light on its promising future, marked by efficiency and technological advancement.

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The field of inventory management stands on the brink of a transformative era, heralded by the confluence of Artificial Intelligence (AI) and the Internet of Things (IoT). This paper conducts an in-depth analysis, offering a visionary outlook by leveraging an extensive review of scholarly articles and existing literature on Artificial Intelligence (AI) and Internet of Things (IoT). The fusion of these technologies promises to revolutionize traditional practices by enabling real-time tracking, predictive analytics, and automated replenishment-ushering in unprecedented levels of efficiency and precision in inventory control. Furthermore, the study probes into the horizon of emerging trends, casting light on the progressive strides in machine learning, edge computing, and blockchain technology. Such advancements beckon a reimagined future for inventory strategies. However, this promising future is not without its hurdles. The research underscores critical impediments, including concerns surrounding data privacy, security, and technological constraints. Contributing to the scholarly discourse, this study amalgamates current research, offering a forward-looking perspective and elucidating the challenges ahead. It stands as an indispensable compendium for industry experts and academics alike, navigating the complex interplay of AI and IoT in inventory management.

This paper delves into the intricate process of integrating Artificial Intelligence (AI) into legacy inventory systems, a critical challenge in the realm of modern inventory management. It presents a comprehensive analysis, exploring the multifaceted barriers encountered in this integration, particularly in traditional industries. The study identifies and examines key technical, organizational, and financial challenges, offering a nuanced understanding of the complexities involved. Innovative solutions and strategies are proposed to address these challenges, drawing on a rich array of existing literature and real-world case studies. The paper highlights successful integrations of AI in various sectors, extracting valuable lessons and best practices. It contributes significantly to the existing body of knowledge by bridging theoretical research with practical applications, providing insights that are both profound and actionable. This research not only illuminates the path forward for traditional industries seeking to embrace AI in inventory management but also serves as a valuable resource for practitioners and researchers in the field. The findings and strategies outlined in this study offer a roadmap for successful AI integration, marking a pivotal step in the evolution of inventory management practices.

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In a global market that makes room for more competitors by the day, some companies are turning to AI and machine learning to try to gain an edge. Supply chain and inventory management is a domain that has missed some of the media limelight, but one where industry leaders have been hard at work developing new AI and machine learning technologies over the past decade. Many well-known companies are now using machine learning to optimize business processes in ways that might have been deemed science fiction 30 years ago, from customer service inquiries to planning for next month's shelf supply based on satellite data. Supply chain and inventory management is primed to embody the concept of smart automation over the next five to 10 years. In this paper, we have investigated the research made till date and proposed a way to improve the inventory management so that it can benefit the customer as well as organizations.

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Artificial Intelligence (AI) is the revolutionary invention of human intelligence. Artificial Intelligence is nothing but the duplication of human in which machines are programmed to rationally think and behave like humans developed for very many purposes including business decision making, problem-solving, business data analysis and interpretation and information management. The application of AI in business endeavours decides the competitive advantage, market leadership, robust operating efficiency of corporates and other business houses. Exploiting the application of AI in the manufacturing and distribution process enables the organisations to reach the pinnacle in their business graph. Businesses are operating in the international market which is highly multifaceted and challenging to serve the world as a sole market for their products, services and their products and without the integration of technology into their business processes, they cannot assure the sustainable growth. The management of the process of transforming the raw materials into the final product is called Supply Chain Management (SCM) and the effective movement and storage of goods, services and information are called Logistics Management (LM). This article analyses the applications of Artificial Intelligence in Supply Chain and Logistics Management (SC&LM)

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https://www.ijert.org/inventory-management-using-machine-learning https://www.ijert.org/research/inventory-management-using-machine-learning-IJERTV9IS060661.pdf A major requirement for small/medium-sized businesses is Inventory Management since a lot of money and skilled labor has to be invested to do so. E-commerce giants use Machine Learning models to maintain their inventory based on demand for a particular item. Inventory Management can be extended as a service to small/medium sized businesses to improve their sales and predict the demand of various products. Demand forecasting is a crucial part of all businesses and brings up the following question: How much stock of an item should a company/business keep to meet the demands, i.e., what should the predicted demand of a product be? Among its many benefits, a predictive forecast is a key enabler for a better customer experience through the reduction of out-of-stock situations, and for lower costs due to better planned inventory and less write-off items. We discuss the challenges of building an Inventory system and discuss the design decisions.

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Purdue Hospitality and Tourism Management graduate student wins best paper award for innovative technology research

Written By: Rebecca Hoffa, [email protected]

Evita Ma poses with the fellow Purdue HTM attendees at the 29th Annual Graduate Education and Graduate Student Research Conference in Hospitality and Tourism in January. (Photo provided)

As Starship robots deliver food to hungry Boilermakers across Purdue University’s West Lafayette campus, their presence is often welcomed like that of a neighborhood pet — students are often seen helping them when they get stuck or smiling and moving out of their way when they meet them on the sidewalk. When Alei (Aileen) Fan , associate professor in the White Lodging-J.W. Marriott, Jr. School of Hospitality and Tourism Management (HTM) and an expert in service innovation and experience design, helped one along its way one day, she was met with a low-tone, male voice saying, “Thank you.” This contradicted the “cute” vision Fan had in her mind of the robots being like her dog at home.

When Fan relayed the experience to her PhD student Chang (Evita) Ma , the College of Health and Human Sciences graduate student was inspired to investigate deeper how the combination of appearance and voice impacted the consumer’s overall experience in the service encounter.  

Evita Ma poses with her best paper award at the 29th Annual Graduate Education and Graduate Student Research Conference in Hospitality and Tourism. (Photo provided)

These efforts culminated in winning a best paper award at the 29th Annual Graduate Education and Graduate Student Research Conference in Hospitality and Tourism in January.

“I’m really honored,” Ma said. “As far as I know, for the past three years or so, no one from our school has gotten the award, so it was a pleasure to have that. It’s very competitive — over the 130-some presentations, I was selected as one of the four winners.”

The study looked at how people’s reactions differed when comparing matched, or congruent, voice and appearance and mismatched, or incongruent, voice and appearance. The researchers found that depending on the robot’s function, people tended to be either more or less accepting of when a robot’s voice does not match its appearance.

In a utilitarian scenario where the consumer really only desires the robot to perform a job function and doesn’t care as much about having an emotional connection, people often prefer the congruent voice and appearance because they feel the incongruent one is not well-designed or not capable of performing their desired outcome. When consumers are in a hedonic scenario where they don’t care as much about the functions the robot is performing but simply wish to have a connection or engage with the robot, the congruency doesn’t matter as much, and some of the surprising elements, such as the mismatched voice and appearance, may attract people to engage with it.

“We as human beings actually view things holistically,” Ma said. “We don’t separate the different parts.”

Considering how these distinct factors work together to shape consumer perceptions could ultimately shape the characteristics of future service robots to improve service encounters across the industry.

“It’s a timely topic,” Fan said. “Whether you like it or not, technology takes up a lot of our lives. This research provided practical guidelines for industries and companies on how to design different robots to better serve our customer.”

This study resulted as a product of Ma’s study “Decoding the Shared Pathways of Consumer Technology Experience in Hospitality and Tourism: A Meta-Analysis,” which examined the literature currently available to investigate the different types of technology and how humans interact with them. Ma ultimately noticed a gap in papers that combined elements — many simply looked at appearance or voice independently.

“Very few of the papers actually combine all the different subtle elements together; they just focus on one single perspective like appearance or voice,” Ma said. “So, I began looking at: What is the combination between those? Our initial idea was we wanted to know how the combination of design elements of service robots impacts the customer’s reactions and feelings of the service in different scenarios.”

Prior to coming to Purdue, Ma spent four years gaining industry experience at the Hospitality Financial and Technology Professionals nonprofit in Hong Kong, where she made many connections on the technology side of the industry and solidified her interest in coming to a very technology-focused university to earn her PhD.

“Purdue has such a strong background in terms of technology and engineering, so that was also one of the reasons I wanted to keep focusing on that for my research area,” Ma said. “After taking a class with Dr. Fan, I decided to join her team.”

Beyond research, Ma has fully immersed herself in the teaching and engagement areas of graduate student life as well. Ma teaches two 200-level marketing courses to undergraduate HTM students, where she’s maximized opportunities for the students to engage in experiential education. She’s also working on curriculum development with HTM faculty and has become involved in several graduate student organizations, including the Purdue Graduate Student Government, where she is a senator.

After graduating from the program, Ma plans to pursue a faculty position that allows her to combine her passion for research with her love for teaching and service.

“She’s really the star student,” Fan said. “When we evaluate a PhD student, there are three aspects: research, teaching and service. Evita is excellent in all of these.”