Inventory Optimization Research Articles

Optimizing pharmaceutical supply chain management through AI-driven predictive analytics: A conceptual framework

The pharmaceutical supply chain is a complex, multi-layered system that faces unique challenges, including fluctuating demand, stringent regulatory requirements, and logistical constraints. This paper explores the role of AI-driven predictive analytics in optimizing pharmaceutical supply chain management, presenting a conceptual framework that enables companies to leverage advanced data analytics for improved decision-making, risk management, and operational efficiency. Key AI techniques, such as machine learning, data mining, and predictive demand forecasting, are discussed as tools for addressing critical supply chain issues, including inventory optimization, demand variability, and regulatory compliance. The proposed framework details essential components—data collection, processing, integration with existing systems, and real-time decision-making—and outlines critical success factors for effective implementation. By offering insights into the transformative potential of predictive analytics, this paper provides actionable recommendations for pharmaceutical companies seeking to build resilient and responsive supply chains. Future research directions are also proposed, emphasizing the need for adaptable predictive models and the ethical implications of AI applications in pharmaceuticals. Keywords: Pharmaceutical Supply Chain, AI-driven Predictive Analytics, Machine Learning, Demand Forecasting, Inventory Optimization, Regulatory Compliance.

Research on supply chain efficiency optimization algorithm based on reinforcement learning

Supply chain efficiency is critical to enterprises and can affect their competitiveness. The supply chain faces an uncertain and complex external market environment, facing the problem of supply chain efficiency optimization; the traditional optimization method is ineffective, which can better face the current environment and deal with problems. It has advantages in optimizing supply chain efficiency and has been widely used. This paper first expounds on the importance of supply chain management status, the limitations of traditional supply chain management methods, and reinforcement learning in the application of supply chain optimization. Then, through experiments, reinforcement learning, supply chain optimization problems, and the analysis of related algorithm design, the optimal algorithm focuses on inventory management optimization. Finally, this paper points out the future research directions and development trend of the supply chain efficiency optimization algorithm based on reinforcement learning.

Lifecycle product inventory optimization: a hybrid approach with Grey-Wolf and Ant-Colony methods

Lifecycle product inventory optimization: a hybrid approach with Grey-Wolf and Ant-Colony methods

Revolutionizing Supply Chain : The Synergy of Warehouse Management and Data Analytics

This article examines the transformative impact of integrating data analytics with warehouse management in modern supply chains. It explores how this synergy is revolutionizing inventory management, order fulfillment, and customer service across industries. The article discusses key elements of effective warehouse management, including optimal space utilization, efficient inventory control, and timely order processing. It then delves into how data analytics elevates these functions through inventory optimization, real-time monitoring, and predictive analytics. Case studies from e-commerce and manufacturing sectors illustrate real-world applications and benefits. Finally, the competitive advantages gained through enhanced operational efficiency, cost reduction, and improved service levels are analyzed, highlighting the critical role of this integration in shaping the future of supply chain management.

Integrated order acceptance and inventory policy optimization in a multi-period, multi-product hybrid production system

Integrated order acceptance and inventory policy optimization in a multi-period, multi-product hybrid production system

Pricing in spatial classification system in non-symmetric market demand based on the calculations of double interval grey numbers

PurposeThe contemporary landscape of supply chains necessitates a comprehensive integration of multiple components encompassing production, distribution and customer engagement. The pursuit of supply chain harmony underscores the significance of pricing strategies within the framework of dual-channel distribution, particularly when confronted with the dynamics of asymmetric demand performance.Design/methodology/approachThis paper delves into a nuanced decision-making challenge anchored in a dual-channel distribution context featuring a retailer and two distinct products. Notably, the retailer’s decision-making process employs the computational framework of dual grey numbers, a robust tool for handling uncertainty.FindingsThis study revolves around applying game theory to manufacturers. Each manufacturer presents its aggregated price proposition to the retailer. Subsequently, the retailer identifies the optimal pricing configuration among the manufacturers' aggregate prices while adhering to constraints such as spatial classification and inventory costs. This article’s contribution extends to delineating the retailer’s capacity to discern the influence of product market potential and the aggregate product cost on the overall demand.Originality/valueThe model’s innovation lies in its harmonious fusion of spatial classification, pricing strategies and inventory control. Notably, this novel integration provides a platform for unraveling the intricate interplay between non-symmetric market potential, production costs and cross-sensitivity. The investigation is underscored by the utilization of the double interval grey numbers, a powerful computational approach that accommodates the inherent uncertainty pervasive in the domain. This study fills a gap in the existing literature by offering an integrated framework unifying spatial allocation, pricing decisions and inventory optimization.

Neuronal Models for the Optimization of Inventory, Waste and Informational Flow

The present study aims to identify and put into practice certain solutions for economic improvement using the Lean Six Sigma (LSS) methodology within companies from the wood processing industry. The main objectives refer to estimating those effects, which are generated by the informational flows in terms of the decision-making process and to identify those areas corresponding to the manufacturing process, which can be improved by using this methodology. The research strategy is based on the use of a questionnaire during 2019-2021, which has addressed to over 150 most important companies from the wood processing industry. In developing the neuronal models for the improvement of stock, the informational flow of the management accounting, costs and for the reduction of the overstock risk in relationship with the waste management was discovered by applying the LSS methodology that the issues were related to inadequate quality records, formality, procedures, and management systems.

Impact of an enterprise controlled substance management system on labor and inventory costs.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To evaluate the impact of an enterprise controlled substance (ECS) management system with integration of analytics software on labor requirements and inventory cost within a health-system pharmacy. A prospective, pre-post observational study was designed to assess the impact of implementing a solution that connects disparate systems with the integration of analytics software. Three study modules were implemented over approximately 18 months. The intervention consisted of implementation of new CS vaults, a centralized server, an automated central medication inventory system, and inventory optimization analytics software. The number of transactions and time spent on CS reports were compared before and after implementation to determine labor and inventory efficiencies. Both of the study facilities had a decrease in CS daily stockouts and an increase in inventory turns compared to baseline, while the total number of transactions (vends) at the central vault and decentralized dispensing cabinets increased. The addition of analytics allowed for establishment of informed changes to periodic automated replenishment levels. Additionally, both facilities saw a reduction in the number of expired medications, and there was subsequently a reduction in the total reverse distributor costs. Finally, both facilities had a reduction in the amount of time spent on manual tasks associated with reconciling and managing discrepancies. An inventory management system integrated with an advanced analytics tool provided a reduction in the time spent on receiving, storing, and reconciling CS records while the number of transactions increased. The ECS solution enhanced the visibility of the chain of custody while closing the loop between reporting and receiving inventory, eliminating or reducing the frequency of manual processes.

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

Revolutionizing Retail: An Empirical Study on the Impact of Generative AI in Omnichannel Strategies

This article examines the transformative impact of Generative AI (Gen AI) on omnichannel retail, exploring its role in enhancing customer experiences and streamlining operations across digital and physical platforms. Through a comprehensive analysis of current literature, industry case studies, and empirical data, the study investigates six key applications of Gen AI in retail: personalization and dynamic content creation, virtual shopping assistants, inventory and fulfillment optimization, marketing content generation, customer insights and sentiment analysis, and immersive AR/VR experiences. The findings reveal that Gen AI significantly improves personalization, operational efficiency, and cross-channel integration in retail. However, the research also identifies challenges in implementation, including data privacy concerns and the need for seamless technology integration. This article contributes to the growing body of knowledge on AI in retail and provides practical insights for retailers seeking to leverage Gen AI in their omnichannel strategies. The paper concludes by discussing future research directions and the potential long-term implications of Gen AI on consumer behavior and global retail practices.

Resilience in Cyclicality: Impact of Inventory Management Efficiency on Operational Profitability

ABSTRACTThe cyclical and non‐cyclical nature of the consumer goods industry presents unique dynamics that require discernment in analyzing working capital (WC). These firms often deal with considerable inventory levels that create costs and liquidity challenges, particularly during economic downturns. This paper investigates the impact of inventory days (InvD) on operating margin (OM) in both demand‐stable and demand‐sensitive businesses. The empirical analysis employs system GMM regression with a sample of publicly traded US consumer goods firms from 2010 to 2022. The findings reveal an inverted U‐shaped relationship between InvD and OM among cyclical companies, indicating that these firms require a comparatively lower optimal inventory level (108 days) to maximize profitability at 3%. The results highlight the importance of strategic inventory management (IM) under varying economic and demand conditions and balancing the risks of stock‐outs against the costs of overstocking to optimize profitability. This paper contributes to the literature on the impact of inventory dynamics on operating profit in stable and volatile markets. It estimates the optimal inventory level that enhances profitability and predicts the optimal inventory dynamics for maximum profit.

Mechanism Study on Innovative Influences of AI and Blockchain on Supply Chain Logistics: Case Study of JD and Alibaba

This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba. The successful cases of JD and Alibaba in supply chain management show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness. Applying AI in supply chain logistics, such as demand forecasting, inventory optimization and transport route planning, can provide accurate data analysis and decision support. It can help reduce inventory costs, shorten transportation time and optimize resource allocation in the supply chain. However, AI and blockchain technologies still face several challenges, such as data privacy and security issues, technology costs and standardization. Future research and practice will continue to work on solving these challenges, further optimizing and improving the application of AI and blockchain in supply chain logistics, and promoting innovation and development in supply chain logistics.

The Role of Complex Systems in Predictive Analytics for E-Commerce Innovations in Business Management

This review explores the incorporation of complex systems theory into predictive analytics in the e-commerce sector, particularly emphasizing recent advancements in business management. By analyzing the intersection of these two domains, the review emphasizes the potential of complex systems models—including agent-based modeling and network theory—to improve the precision and efficacy of predictive analytics. It will provide a comprehensive overview of the applications of emergent predictive analytics techniques and tools, including real-time data analysis and machine learning, in inventory optimization, dynamic pricing, and personalization of customer experiences. In addition, this review will suggest future research directions to advance the discipline and address the technical, ethical, and practical challenges encountered during this integration phase.

Optimal inventory decisions of infrequently needed spare parts for maintenance with inaccurate demand forecasts and limited storage space

Abstract In modern manufacturing workshops, maintaining adequate inventory for spare parts is an effective strategy to satisfy maintenance requirements. However, due to the stochastic nature of failures, spare parts often have characteristics of infrequent usage and inaccurate forecasted demand. Additionally, the multitude diversity of spare parts and limited storage space in warehouse poses challenges to inventory decisions regarding the types and quantity of spares. Thus, this paper investigates the inventory decision optimization problem of infrequently needed spare parts for maintenance within limited storage space and inaccurate forecasted demand. This paper constructed an integer programming model based on chance constraints to address the uncertainty of the storage space occupied by the spare parts. The main contributions of this paper are as follows. First, we investigate the inventory decision optimization problem within limited storage space and inaccurate forecasted demand, which are rarely considered in the optimization problem of the spare parts for maintenance. Second, we proposed a method for simplifying the computation of chance constraints based on a transformation of the Central Limit Theorem. Third, we proposed a heuristic iterate algorithm to search the optimal inventory decision by minimizing the overall downtime and storage costs. Using data from the workshop of an automotive enterprise, a case study is conducted by comparison with the Genetic Algorithm.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global market.

AI-Driven Personalized Shopping: Enhancing Retail Experiences through Body Type Analysis and Real-Time Inventory Integration

Abstract: This article introduces an innovative AI-driven personalized shopping system integrating body type analysis, real-time inventory management, and smart recommendations to transform the retail experience. The system significantly improves fit satisfaction, shopping efficiency, and inventory optimization by leveraging advanced computer vision, machine learning, and deep learning technologies. User trials involving 5,000 participants show a 40.3% increase in fit and style satisfaction, 37% reduction in shopping time, and 28% increase in conversion rates compared to traditional methods. The system's ability to provide highly accurate, personalized recommendations at scale addresses key challenges in e-commerce, potentially revolutionizing the retail industry by enhancing customer satisfaction, reducing returns, and optimizing inventory management.

THE MODELLING OF CARGO TRANSSHIPMENT OPERATIONS USING THE BUSINESS PROCESS MODELLING TOOLS

This paperwork explores the transshipment operations at the Port of Constanta, Romania, focusing on the unloading of big bags from barges. Utilizing Business Process Management (BPM) software, the study models the transshipment process to identify optimization opportunities. The investigation reveals challenges such as coordination complexities and potential cargo damage, alongside the benefits of cost-efficiency and flexibility offered by transshipment services. Through literature review and analysis, the study emphasizes the importance of efficient business processes and the role of BPM software in enhancing operational efficiency. By employing Aura Portal Modeller for simulation, the study identifies bottlenecks and proposes optimization strategies for the transshipment process. The proposed measures include real-time cargo assessment, inventory tracking systems, equipment analysis, and storage layout optimization. This research contributes to the understanding of transshipment operations and highlights the effectiveness of BPM software in process visualization and analysis, offering insights for enhancing efficiency and mitigating delays in supply chain management.

Inventory classification with artificial intelligence: Conceptual framework

In today's world where competition is increasing harshly, it is important to achieve sustainable profits and keep costs competitive. Under these conditions, the importance of supply chain elements is increasing day by day. Management of inventory costs, which constitute a large volume among cost items, affects the performance of companies. The first step to focus on to keep inventory costs under control and improve is inventory classification. Inventory classification, which is at the top of the supply chain elements and closely affects the subsequent phases, it is critical in determining supply chain performance. Thanks to inventory classification, material groups are determined and stock strategies for these groups are clarified. Incorrect inventory classification causes materials to be assigned to incorrect groups, which negatively affects inventory costs and subsequent phases of the supply chain, causing an increase in costs. The most used methods for inventory classification are ABC analysis, multi-criteria inventory classification and optimization. However, the increasing momentum in artificial intelligence studies in recent years has also closely affected inventory classification. The advantages brought by artificial intelligence methods have also created distinctive contributions to inventory classification studies. This study provides a conceptual framework that examines artificial intelligence methods in the field of inventory classification.

Artificial Intelligence Enabled Supply Chain Management: Unlocking New Opportunities and Challenges

This paper delves deeper into the potential of Artificial Intelligence (AI)-enabled Supply Chain Management (SCM) as a groundbreaking technology capable of revolutionizing supply chain operations and ushering in a new era of possibilities. In today's dynamic business landscape, where agility and efficiency are paramount, AI plays a pivotal role in redefining how supply chains operate. The journey commences with an in-depth exploration of AI's fundamental concepts and its manifold applications within SCM, shedding light on its adaptability across various aspects of the supply chain, from demand forecasting to inventory optimization. Moreover, this paper illuminates the myriad benefits that AI brings to SCM practitioners. These advantages encompass heightened operational efficiency through real-time data analysis, cost reduction through predictive maintenance and optimized routing and a superior customer experience resulting from improved demand prediction and personalized service offerings. However, acknowledging the transformative power of AI in SCM, we must also acknowledge the hurdles in its implementation. This paper underscores the significant challenges that organizations may face while integrating AI into their SCM processes, ranging from data quality issues and concerns regarding privacy and security to the need for domain-specific human expertise. To address these hurdles effectively, the paper proposes a comprehensive framework. This framework encompasses a holistic strategy that aligns AI initiatives with organizational goals, governance and ethics considerations to ensure responsible AI deployment, and a clear roadmap that guides the implementation journey from inception to full integration. In conclusion, this paper offers valuable insights into the opportunities and challenges that AI-powered SCM presents in the ever-evolving business landscape. By providing practical recommendations, it equips organizations with the knowledge and tools needed to successfully harness the potential of AI in their supply chain operations, ultimately paving the way for enhanced competitiveness and sustainability in the future.

Perancangan Kebijakan Persediaan e-Commerce dengan Pertimbangan Permintaan Probabilistik dan Performance Score

Julis, a local company focused on the production and sale of perfumes through e-commerce platforms, is one of the SMEs facing such challenges. Julis has encountered difficulties in managing its product inventory, leading to high inventory costs and low customer order fulfillment rates (fill rate). These issues risk hindering the company's growth and diminishing consumer trust. This research aims to design optimal inventory policies for Julis by considering probabilistic demand and the company's performance score. This performance score is a measure of performance that integrates two key criteria: profit, calculated based on total inventory costs, and fill rate. With the right inventory policies, Julis is expected to improve inventory efficiency, reduce costs—particularly ensuring inventory costs meet the target of less than 40% of the company's total revenue or income—and achieve a minimum fill rate target of 95%, which is considered crucial for maintaining customer satisfaction and supporting business growth.The approach used in this research is an inventory optimization model that combines probabilistic methods in determining Order Quantity (Q) and Reorder Point (ROP). This design aims to address demand uncertainty, which often poses a major challenge in inventory management within the e-commerce sector. Additionally, this research introduces the integration of inventory classification based on performance score, enabling the company to prioritize certain products based on relevant performance criteria. As a result, each product category can be managed more efficiently and strategically according to business priorities. The results of this research indicate that the implementation of the designed inventory policies can significantly improve Julis' performance score. Specifically, in terms of cost efficiency, these policies can reduce inventory costs and increasing the fill rate, which directly positively impacts the company's profitability. Furthermore, the integration of inventory classification based on performance score has also proven effective in helping Julis identify the most critical products to prioritize in its inventory management.