Intelligent Inventory Management Research Articles

Blockchain-Based Intelligent Inventory Management for Hospital Supply Chain (HSC) with Deep Learning Techniques

A medication shortage may have far-reaching consequences, making it an important inventory management concern for healthcare organizations. Intelligent demand forecasting is essential to make cold chain businesses more competitive and save transportation and storage costs. Hence, this paper proposes a blockchain-based intelligent inventory management model (BC-IIMM) with deep learning techniques for hospitals to prevent running out of supplies, run their systems sustainably, and optimize their profits in the hospital supply chain (HSC).Preventing the contamination of pharmaceuticals requires a pharmaceutical cold chain monitoring system that uses IoT devices, blockchain, and cloud computing to track items across their entire supply chain reliably. One other perk is that both doctors and patients may benefit from drug recommendations made by a deep belief network (DBN) model. Further, to construct deep learning (DL), the proposed cold chain monitoring system produces large-scale, high-quality data. Holding cold chain items would become less expensive and cold chain inventory management decisions would be easier with it.Next, they provide a numerical result proving the suggested method works better than the baselines regarding refilling cost and shortage rate.

ROI Analysis of RFID-Based Intelligent Inventory Management Systems

This paper investigates the return on investment (ROI) of RFID-based intelligent inventory management systems. RFID (Radio Frequency Identification) technology has revolutionized inventory management by providing real-time visibility, improved accuracy, and enhanced operational efficiency. The study employs a mixed-methods approach, integrating both qualitative insights from industry experts and quantitative data from surveys and existing literature. Initial setup costs, ongoing maintenance expenses, and tangible and intangible benefits are thoroughly analyzed. The findings reveal that while the initial investment in RFID technology can be substantial, the benefits in terms of reduced labor costs, minimized inventory shrinkage, and improved inventory accuracy significantly outweigh the costs. ROI calculations, including payback period and net present value (NPV), demonstrate the economic viability of RFID systems. Comparative analysis shows that RFID systems outperform traditional inventory management systems, especially in sectors such as retail and healthcare. This paper provides a comprehensive framework for evaluating the ROI of RFID technology and offers practical recommendations for businesses considering its adoption.

Integrating IoT and blockchain for intelligent inventory management in supply chains: A multi-objective optimization approach for the insurance industry

This research aims to develop and optimize an intelligent supply chain framework tailored for the insurance industry by integrating an innovative inventory management policy supported by the Internet of Things (IoT) and blockchain technologies. Through an extensive literature review, key assumptions are established, needs are identified, and objectives are formulated. A multi-objective mathematical planning model is proposed to minimize cost and time within the supply chain, with optimization conducted under deterministic and fuzzy conditions. The model utilizes augmented epsilon constraint and weighted sum methods to address its multi-objectiveness. Noteworthy is the integration of advanced technologies such as Wireless Sensor Networks (WSN), Radio-Frequency Identification (RFID), blockchain, and online sales, distinguishing it from traditional approaches. Initial validation and testing in smaller dimensions demonstrate the model’s adaptability to precise methods, while larger dimensions necessitate the use of meta-heuristic algorithms for efficient problem resolution. Additionally, a comprehensive sensitivity analysis in the insurance industry on objective function coefficients reveals intricate relationships, offering valuable insights into optimization dynamics across diverse conditions.

Review on VEND IT!- A Smart Vending Machine

Abstract: Traditional vending machines, though reliable, have often lagged behind in terms of convenience, personalization, and sustainability. As society rely on digital connectivity and automation continues to grow, the need for a smarter, more responsive vending solution has become increasingly evident. In a world marked by constant technological innovation and evolving consumer preferences, the time has come to reimagine the traditional vending machine. "Vend It" is a visionary project that aims to reinvent the conventional vending machine, leveraging cutting-edge smart technology to create a seamless, efficient, and user-friendly vending experience. VEND IT! encompasses a comprehensive suite of innovations, ranging from intelligent inventory management to user-centric interface design, and remote monitoring and management capabilities.

Simulation of the Impact of Key Variables on Smart Supply Chains Efficiency in Iran’s Healthcare Industry

Background: The smartification of supply chains, which enables organizations to stay informed about crises in a timely manner and make appropriate decisions in response to the resulting pressures, has consistently been a crucial factor in the realm of organizational transformation. The healthcare industry in Iran faces more challenges and crises than other industries, especially due to its vital role in public health prevention and care.
 Objectives: According to the reasons described, this research aims to simulate and improve the efficiency of the healthcare supply chain when confronted with crises.
 Methods: In this study, the efficiency of the healthcare supply chain was simulated using the system dynamics approach and Vensim DSS.
 Results: The results indicated that healthcare supply chain efficiency in Iran is unsatisfactory and may confront challenges during crises. Therefore, this study places particular emphasis on examining scenarios for improving the current situation, which stems from the consensus of experts and stakeholders in this field. In the presented scenarios, a 2% improvement in the utilization of advanced intelligent technologies and a 5% improvement in intelligent inventory management were observed. Notably, the combined effect of these two scenarios led to an overall enhancement in the average efficiency of the healthcare smart supply chain. These improvements can increase the average efficiency levels within the pharmaceutical manufacturing segment up to 1.3%, 5.8%, and 7.7% in each of the aforementioned scenarios.
 Conclusions: It can be asserted that although the advancement of smart technologies and intelligent inventory management individually contribute to enhancing the efficiency of the healthcare supply chain in Iran, combining these changes can provide the groundwork for further increasing efficiency up to 7.7%.

Design and Development of Low Cost Automated Ration System Using IOT

Abstract: The Automated Ration Distribution System (ARDS) is a state-of-the-art technological system designed to improve the efficiency, fairness, and accuracy of ration distribution. The ARDS combines artificial intelligence, machine learning, and data analytics to automate the distribution process, lower errors, and ensure equitable access toessential food supplies. Among the primary characteristics are user-friendly interface, data analysis, biometric verification, and intelligentinventory management. The technology facilitates data-driven decision-making, optimizes resource distribution, lowers shortages, detects andeliminates fraud. By fostering fair access to essential goods and promoting transparency in the allocation of rations, the ARDS has the potential to revolutionize rationing, particularly in crisis- affected areas.

RESEARCH ON URBAN E-WASTE MANAGEMENT IN CHINA BASED ON SMART TECHNOLOGIES

The improper management of e-waste poses a serious risk to the environment, human health and socio-economic sustainability due to the increasing volume of e-waste globally, its complexity and hazardous components. The management of e-waste is widely recognized as critical around the world, and China has begun to pay attention to e-waste management as well. The development of smart digital technologies provides a new option to realize efficient e-waste management. This study systematically reviews the application of smart digital technologies in e-waste management, which mainly involves the collection and recycling process of e-waste. On the basis, the research topic of advanced digital technologies used in e-waste management literature were unfolded and discussed. To explore the potential application of smart digital technologies in urban e-waste recycling management, this study investigates key digital technologies and equipment for promoting e-waste management in cities, establishing a smart recycling system and an intelligent logistics recycling system for e-waste, using City X in China as an example. In this study, we try to propose an alternative for City X based on intelligent digital technology, which mainly covers the collection process of e-waste, an intelligent logistics recycling system to track and optimize the logistics of e-waste recycling, and an intelligent inventory management that can realize the digital management of e-waste. Our findings could improve the performance of urban e-waste recycling and provide reference for the promotion of smart urban e-waste management system.

INVENTORY DATA MINING SYSTEM USING FEDERATED MACHINE LEARNING STRATEGY

Inventory management is a particularly important process for keeping track of levels, orders, and transactions in the inventory retailing industry. A significant quantity of data about the stocked items in inventory is generated and gathered daily in the current retailing market. It is often a job to manage stocked goods effectively due to the growing volume of transaction data and their related relationships, but it must be conducted, especially when these inventory databases are scattered in various places. To collect all the inventory data from different distributed databases, it is time consuming, insecure, and inaccurate to implement intelligent management systems; it is essential to investigate the underlying dependencies of the body inventory items. However, current inventory management systems only have a limited ability for intelligent management because they depend on the statistical analysis of historical inventory data. There has not been much progress made in putting in place intelligent inventory management systems to use combined data-driven analysis to uncover hidden relationships. In this paper, we use distributed computing resources to process an enormous amount of inventory data and integrate federated learning, one of the most recent developments in data mining techniques. Federated Learning offers thorough aid for conducting various inventory management duties, including showing anomalous items and examining the aging of the inventory. Keywords: Data Mining, Distributed Database, Inventory, Federated Learning, Machine Learning DOI： https://doi.org/10.35741/issn.0258-2724.58.3.51

Intelligent inventory management with autonomation and service strategy.

The manufacturer's service to the customer is one of the critical factors in maximizing profit. This study proposes the innovative (Q,r) inventory policy integrated with autonomated inspection and service strategy for service-dependent demand. First, an advanced autonomated inspection makes the product error-free. Therefore, this makes customers more satisfied and increases profit. The proposed model decides the optimal investment for such autonomated inspection. Second, three types of services are considered in the study: unpaid, partially paid, and fully paid services. Each type of service has a different service level and the amount of the customer's payment. Our model finds the optimal service strategy based on the variable conditions along with the optimal quantity and reorder level of inventory policy. Numerical analyses are made for different service strategies, along with a sensitivity analyses for various critical parameters. Results show that the full paid service is 84.88% beneficial compared to the unpaid service, and the autonomated inspection policy is 5.02% beneficial compared to the traditional ones. The increase in unit servicing costs always increases the profit of the company.

Intelligent inventory management approaches for perishable pharmaceutical products in a healthcare supply chain

This study develops intelligent inventory management (IIM) approaches for managing perishable pharmaceutical products in a healthcare supply chain consisting of multiple regional hospitals and a central warehouse. The purchase price of the product is assumed to be age dependent, and the age distribution of the product is assumed to be known and is shared among all the supply chain members. Each agent representing a regional hospital or the central warehouse in the supply chain replenishes its stocks from its upstream agent. In IIM approaches, reinforcement learning methods, specifically Q-learning and Deep Q-network, are used to construct inventory policies. The IIM policies provide hospitals with near-optimal order quantities and remaining life distributions for products they order. Through many test instances, the performance of the IIM policies is compared to that of periodic review (R,s,S) policies obtained through a stochastic mixed integer programming model, solved using CPLEX and a genetic algorithm. The computational results demonstrate that the IIM policies are more cost-effective than the R,s,S policies, although the genetic algorithm has a speed advantage. Moreover, as compared with the R,s,S polices, the IIM polices have a lower possibility of product shortage, and thereby a higher service level for the patients, and a lower risk of product expiration. By implementing the IIM approaches, a healthcare system may also save storage space, in addition to having a lower total inventory cost. Sensitivity analyses are performed to derive managerial insights on the performance of the policies when the values of the key cost parameters change.

Intelligent Inventory Management for Cryptocurrency Brokers

Intelligent Inventory Management for Cryptocurrency Brokers

Enhanced Inventory Management Using Blockchain Technology Under Cloud Sector Enabled by Hybrid Multi-Verse with Whale Optimization Algorithm

Inventory cost control is an essential factor in supply chain management. If the supplier’s inventory is insufficient, then the chance to trade the product will be reduced. The manufacturer’s inadequate material inventory will have an effect in termination of production, delays, and a waste of resources and time. On the other hand, postponed transportation will certainly raise costs such as transportation costs and cancellation of orders. Therefore, the operation costs of enterprises will be more, which will lower profits. In conventional supply chains, inventory costs control is not feasible for the view of the entire supply chain. The main intent of this paper is to plan for intelligent inventory management using blockchain technology under the cloud sector. The inventory management of the supply chain includes “multiple suppliers, a manufacturer, and multiple distributors”. The proposed inventory management models consider some significant costs like “transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, and quality improvement cost”. This multi-objective cost function is minimized by a novel hybrid optimization algorithm; the concept of WOA is integrated to produce the new algorithm which is termed as Whale-based Multi Verse Optimization (W-MVO) algorithm. For securing the data of distributors, using blockchain technology in a cloud environment helps from the leakage of data to other unauthorized users. Once the cost is reduced in all aspects based on the proposed hybrid optimization algorithm, the distributer will store the concerning data in the blockchain under the cloud sector, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The valuable performance analysis over the conventional optimization algorithms proves the effective and reliable performance of the proposed model over the conventional models.

Inventory Management of Railway Logistics Park Based on Artificial Neural Network

In recent years, China has stepped up its support to the optimization and development of railways. Meanwhile, the development of modern information technology (IT) has enhanced the economic advantages of railway logistics. To intelligently manage the inventory of railway logistics park (RLP), this paper integrates artificial neural network (ANN) into RLP inventory management. Firstly, the functional demand of RLP inventory management was analyzed comprehensively, and the main factors affecting the inventory demand were divided into different categories. Then, the authors formulated the framework of intelligent inventory management for RLP, and put forward the strategy of continuous periodic inventory monitoring. Finally, a RLP inventory prediction model was constructed based on optimized genetic algorithm (GA) and backpropagation neutral network (BPNN), and proved effective through experiments. The research results provide reference for the application of ANN in inventory management and prediction in other logistics fields.

Retail Inventory Management When Records Are Inaccurate

Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. In this paper, we consider an intelligent inventory management tool that accounts for record inaccuracy using a Bayesian belief of the physical inventory level. We assume that excess demands are lost and unobserved, in which case sales data reveal information about physical inventory levels. We show that a probability distribution on physical inventory levels is a sufficient summary of past sales and replenishment observations, and that this probability distribution can be efficiently updated in a Bayesian fashion as observations are accumulated. We also demonstrate the use of this distribution as the basis for practical replenishment and inventory audit policies and illustrate how the needed parameters can be estimated using data from a large national retailer. Our replenishment policies avoid the problem of “freezing,” in which a physical inventory position persists at zero while the corresponding record is positive. In addition, simulation studies show that our replenishment policies recoup much of the cost of inventory record inaccuracy, and that our audit policy significantly outperforms the popular “zero balance walk” audit policy.

Towards the development of an intelligent inventory management system

This paper is concerned with the development of an intelligent inventory management system which aims at bridging the substantial gap between the theory and the practice of inventory management. The proposed system attempts to achieve this by providing automatic demand and lead time pattern identification and model selection facilities. The process of demand pattern identification together with the statistical tests used is discussed. The models incorporated cover deterministic demand models including: constant, quasi‐constant, trended and seasonal demand as well as stochastic demand models. This paper includes an empirical evaluation of the system on real data from the manufacturing and airline industries which shows that this system can lead to significant savings in inventory cost.