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Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models. [ABSTRACT FROM AUTHOR]

AbstractThis comparative study, constituting Part B of our extensive investigation into robust optimization (RO) in inventory management, builds on the foundational insights from Part A’s survey. It conducts a thorough analysis of various RO formulations and algorithms, emphasizing their practical application, efficacy, and computational considerations in inventory management contexts. This study is meticulously structured to address the pivotal inquiries identified in Part A, encompassing the effective representation of uncertainty, the selection of optimal decision criteria, the influence of decision rules on inventory management performance, computational challenges, and the adaptability of these methods to evolving technological and market conditions. This research juxtaposes theoretical findings with empirical data, offering a comprehensive evaluation of the strengths, limitations, and practical implications of each robust inventory model discussed in the literature. This investigation not only complements the survey in Part A but also serves as a standalone, in-depth contribution to robust inventory management. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly disrupted global Supply Chains (SCs), emphasizing the importance of SC resilience, which refers to the ability of SCs to return to their original or more desirable state following disruptions. This study focuses on collaboration, a key component of SC resilience, and proposes a novel collaborative structure that incorporates a fictitious agent to manage inventory transshipment decisions between retailers in a centralized manner while maintaining the retailers' autonomy in ordering. The proposed collaborative structure offers the following advantages from SC resilience and operational perspectives: (i) it facilitates decision synchronization for enhanced collaboration among retailers, and (ii) it allows retailers to collaborate without the need for information sharing, addressing the potential issue of information sharing reluctance. Additionally, this study employs non-stationary probability to capture the deeply uncertain nature of the ripple effect and the highly volatile customer demand caused by the pandemic. A new Reinforcement Learning (RL) algorithm is developed to handle non-stationary environments and to implement the proposed collaborative structure. Experimental results demonstrate that the proposed collaborative structure using the new RL algorithm achieves superior SC resilience compared with centralized inventory management systems with transshipment and decentralized inventory management systems without transshipment using traditional RL algorithms. [ABSTRACT FROM AUTHOR]

With the rise and rapid development of the e-commerce industry, the pressure borne by the supply chain of e-commerce enterprises is increasing, which greatly increases the complexity and uncertainty of inventory management in the e-commerce supply chain. The study integrates system dynamics and collaborative supply chain inventory management to construct a new model. The study first uses the concept of system dynamics to optimize the supply chain inventory management system; then the collaborative supply chain is used to construct a system model by combining the optimized management system. The results show that the collaborative supply chain inventory management model constructed by the study can better accelerate the product turnover rate, improve the overall operational efficiency and market competitiveness of the supply chain, and can provide new support for the development of e-commerce enterprises. [ABSTRACT FROM AUTHOR]

The article focuses on the role of Artificial intelligence (AI) in decision-making, emphasizing its potential as a decision support tool rather than an autonomous decision-maker. Topics include the impact of AI on business performance, the distinction between System-1 and System-2 thinking, and the need for caution in relying on AI for critical decisions due to potential biases and limitations.

Background: Efficient inventory management is critical for sustainability in supply chains. However, maintaining adequate inventory levels becomes challenging in the face of unpredictable demand patterns. Furthermore, the need to disseminate demand-related information throughout a company often relies on cloud services. However, this method sometimes encounters issues such as limited bandwidth and increased latency. Methods: To address these challenges, our study introduces a system that incorporates a machine learning algorithm to address inventory-related uncertainties arising from demand fluctuations. Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. The system is integrated with fog computing to facilitate the rapid dissemination of information throughout the company. Results: In experiments, we compare the proposed system with the conventional demand estimation approach based on historical data and observe that the proposed system consistently outperformed the conventional approach. Conclusions: This research introduces an inventory management system based on a novel deep learning architecture that integrates the attention mechanism with cloud computing to address the Newsvendor problem. Experiments demonstrate the better accuracy of this system in comparison to existing methods. More studies should be conducted to explore its applicability to other demand modeling scenarios. [ABSTRACT FROM AUTHOR]

Every industry on average spends 70 percent on raw materials (inventory). Therefore, there is a need to know the raw material cost and also there is great importance to understand the inventory management system of this industry. The study helps a log to various departments to take steps to control the inventory process. In this competitive business world, each and every business organization need inventory management system for determining what to order, when to order, where and how much to order so that purchasing and storing costs are the lowest possible without affecting production and sales. Thus, inventory management control incorporates the determination of the optimum size of the inventory-how much to be ordered and when after taking into consideration the minimum inventory cost. The overall inventory management includes design and inventory control organization with proper accountability establishing procedure for inventory handling disposal of scrap, simplification, standardization and codification of inventories, determining the size of inventory holdings, maintaining record points and safety stocks, economic order quantity, Economic Order Quantity analysis and VALUE analysis and finally framing an inventory manual. [ABSTRACT FROM AUTHOR]

To accurately depict inventory management over time, this paper introduces a fractional inventory management model that builds upon the existing classical inventory management framework. According to the definition of fractional difference equation, the numerical solution and phase diagram of an inventory management system are obtained by MATLAB simulation. The influence of parameters on the nonlinear behavior of the system is analyzed by a bifurcation diagram and largest Lyapunov exponent (LLE). Combined with the related indexes of time series, the complex characteristics of a quantization system are analyzed using spectral entropy and C0. This study concluded that the changing law of system complexity is consistent with the LLE of the system. By analyzing the influence of order on the system, it is found that the inventory changes will be periodic in some areas when the system is fractional, which is close to the actual conditions of the company's inventory situation. The research results of this paper provide useful information for inventory managers to implement inventory and facility management strategies. [ABSTRACT FROM AUTHOR]

Poor planning and scheduling increase buying, storage, and obsolescence expenses. Material shortages increase labor, machine optimum time, etc. Industrial raw materials, semi-finished items, spares, and consumables have distinct consumption patterns, reorder points, purchase lead times, quantity limits, discounts, etc. To save money, machine learning predicts demand and prepares materials. This study employs ARIMA or Support Vector Machine (SVM) machine learning-based forecasting approaches to forecast materials for less inventory. Feature engineering eliminates seasonality, time series, and external demand and ignores data irregularities, missing figures, and disparities. This approach needs to adapt traits to factors, separate test and training data, and consider many future models to represent the best forecasts. Forecast reliability and consistency were examined for each model. Inventory management systems were evaluated for computational complexity and installation ease and found implementation issues. Both models' input data and resilience were examined using sensitivity analysis. Accurate prediction SVM and ARIMA predict material demand differently. Meaningful statistics show the optimal model. Performance differences between SVM and ARIMA enhance model selection. Thinking about the execution of high inventory system integration and computational complexity, response surface methodology chooses factorial variables with the highest or lowest responses. Analysis of variance, factor analysis, and effect modeling expansions demonstrated for the response. [ABSTRACT FROM AUTHOR]

Problem definition: Improving access to contraceptives is one of the most effective interventions to prevent unintended pregnancies and save the lives of women in least developed countries (LDCs), where the overwhelming majority of maternal deaths occur. However, access to reproductive health commodities is often limited in LDCs because of frequent stock-outs at last-mile health facilities. In this study, we evaluate and compare the effect of two distribution models on last-mile contraceptive availability and key public health outcomes (e.g., unintended pregnancies, maternal and newborn deaths). These distribution models are (i) the commonly used pull distribution model, in which health facilities are fully responsible for managing inventory, and (ii) an alternative model known as the informed push distribution model, which delegates inventory management tasks to external logistics providers. Methodology/results: We leverage the staggered transition from pull distribution to informed push distribution in Senegal, a country that redesigned its contraceptive distribution system. We conduct empirical analyses, including a triple differences estimation, on novel field data compiled from multiple sources to evaluate the effect of the transition. We find that the transition significantly reduces contraceptive stock-outs, frontline health worker workload, unintended pregnancies, and maternal and newborn mortalities and also improves client satisfaction, especially in health facilities with less mature inventory management practices and less developed road infrastructure. A comprehensive cost–benefit analysis shows that the aforementioned benefits are achieved in a cost-efficient manner at these facilities, making them prime candidates for the transition. However, for facilities with less mature inventory management practices but more developed road infrastructure, upgrading the inventory management system is a substantially more cost-efficient alternative than transitioning to a new distribution model without compromising the benefits. Managerial implications: Given the resource constraints faced by the public health sector in LDCs, it is imperative to understand how the operational and public health benefits of the transition to the informed push model vary based on facility characteristics. Our findings offer actionable insights for resource allocation by identifying health facilities that benefit the most from the transition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0488. [ABSTRACT FROM AUTHOR]

Efficient Algorithm for Base Stock Policy Optimization in Single-Product Assemble-to-Order Systems with Stochastic Lead Times Single-product assemble-to-order systems can be commonly observed in industrial settings. For example, firms targeting a niche market often choose to offer a single product; moreover, sole-product rollout is usually a preferable business strategy for manufacturers promoting a brand-new product or trying to penetrate a new market. However, maintaining an effective inventory management for those systems is a notoriously difficult problem, especially when the order lead times exhibit stochastic patterns. In "Single-Product Assemble-to-Order Systems with Exogenous Lead Times," Muharremoglu, Yang, and Geng investigate the case with a special type of stochastic lead time and utilize a certain analysis technique to develop an efficient algorithm for the performance evaluation of base stock policies. In addition, efficiently computable upper and lower bounds are proved based on the key idea of the algorithm. Extensive numerical studies show that the heuristics and the approximation methods developed from those bounds perform well in base stock optimization. We study a single-product assemble-to-order (ATO) system with exogenous lead times operated under a component base stock policy. Both demand and component lead times are random and are assumed to have finite supports. The challenge of evaluating a base stock policy in an ATO system with random lead times lies in the fact that one needs to compute the distribution of the minimum of n correlated random variables, where n is the number of components. The correlation arises because the replenishment quantities of different components are all contingent on the demand for the final product. We tackle this problem by first looking into two special cases, namely, the one with independent and identically distributed (i.i.d.) lead times and the one with sequential lead times. We give two algorithms for the i.i.d. lead time case, but both of them have exponential complexity in some system parameter. Then, we investigate the case of sequential lead times and utilize its particular structure to develop an algorithm with polynomial complexity. This is the first efficient algorithm for the performance evaluation of base stock policies in an assemble-to-order system with random lead times. Furthermore, using the method as an evaluation oracle in a steepest descent algorithm, we also obtain a polynomial time algorithm to optimize base stock for the case of sequential lead times. For the general case of exogenous lead times, we provide efficiently computable upper and lower bounds, which are identified based on the idea of comparing the level of correlation among component orders. Via extensive numerical studies, we test the performance of approximation methods developed from the identified bounds. We find that our proposed methods have advantages over other approximation methods in the literature, and both of them perform well as part of an approximated base stock optimization algorithm. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2009.0365. [ABSTRACT FROM AUTHOR]

The object of this research is the impact of the crisis caused by military actions in Ukraine on the restaurant business. The study aims to examine the adaptation strategies that restaurants use for survival and development in the face of sudden economic changes and increasing uncertainty. The problems addressed in the study include responding to the loss of customers, rising food prices, decreasing cash flows, and a shortage of qualified workers. The main findings of the study showed that restaurants that successfully adapt implement comprehensive changes: from digitalizing ordering and delivery processes to revising menus and optimizing resource use. These steps not only reduce costs but also improve operational efficiency, which is crucial for maintaining customer loyalty and attracting new customers. Specifically, restaurants that invested in digitalization (e. g., electronic menus, online ordering, and inventory management systems) reported a 30–40 % increase in sales compared to those that did not implement such changes. Restaurants that implemented strategies for staff retention and cost optimization reduced staff turnover by 25 % and increased productivity by 20 %. The results are explained by the implementation of innovative approaches and strategic rethinking of business models, which allow quick responses to changing market conditions and consumer needs. Effective use of digital technologies and optimization of internal processes have become key success factors. The data obtained have significant theoretical and practical value, as they can be used to develop recommendations aimed at improving the resilience and adaptability of the hospitality industry in crisis conditions. These results help understand how managers can use strategic planning to optimize business processes and ensure enterprise stability. The importance of the research is also highlighted by identifying future research directions that can help the restaurant business more effectively adapt to changing market conditions and global crises. [ABSTRACT FROM AUTHOR]

Essential medicines or drugs are recognized as highly cost-effective components within contemporary healthcare, demonstrating significant potential for improving health outcomes. The provision of essential medicines directly impacts the functioning of healthcare facilities, resulting in financial hardship. This review aims to fill knowledge gaps by examining obstacles hindering access and utilization of essential medicines in India. This study conducted a comprehensive evidence synthesis, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to analyse articles on the availability and utilization of essential medicines in India. The search strategy included various databases and keywords. Published, peer-reviewed articles focusing on the National/State List of Essential Medicines and meeting specific criteria were considered. Data items included essential medicines, drug availability, utilization, and challenges. Data was extracted, synthesized, and analysed using thematic framework analysis. Out of 1,129 articles, 11 were selected for review. Studies consistently highlighted the inadequate availability of essential medicines in different Indian states. Availability of essential medicines varies in the range of 17-51 percent across major states of India. Stock-outs of medicines vary from 4 to 14 weeks. Governance issues including differential procurement mechanisms across states of India, hinder seamless availability of essential medicines. Other challenges included distribution and purchasing system inefficiencies, governance-related issues, and facility/user-level challenges impacting drug utilization. Disruptions in utilization were observed due to improper prescription practices and non-availability of affordable options. Accessibility and affordability also affected drug utilization. Issues with supply chain management and conflicting guidelines further contributed to the obstacles faced in ensuring availability and utilization of essential medicines in India. Ensuring the availability, accessibility, and affordability of essential medicines is of utmost importance. The public health system needs to strengthen its procurement and distribution management. Strengthening the logistics support for an efficient supply of essential medicines will reduce the time lag in receipt of drugs. Guidelines on essential drugs prepared by the National Health System Resource Centre need to be strictly adhered to and monitored in inventory management system. There is an urgent need to develop a sustainable model for achieving uniformity in the availability and utilization of essential medicines in India. [ABSTRACT FROM AUTHOR]

Competitive Revenue Management has been practiced in the airline and hotel travel verticals for over two decades. Leveraging the selling fares and rates of competitors as input into the revenue management systems improves inventory control recommendations that is reflective of prevailing market conditions. Recently, several class action lawsuits have appeared against casino-hotel operators who use automated revenue management software to recommend the best available rates. This has attracted the attention of the Department of Justice and the Federal Trade Commission who are investigating anti-competitive practices of casino-hotel operators. While Competitive Revenue Management is an established business process in the travel industry, this brings up the issue of the future of this revenue management practice in travel. [ABSTRACT FROM AUTHOR]

In contemporary business organizations, the pivotal role of automation in control processes is evident through Inventory Management Systems (IMS), which leverage advanced techniques and data analytics algorithms to optimize inventory levels, enhance accuracy, and minimize costs. However, existing security techniques for IMS, including access controls, firewalls, and audits, face challenges in effectively addressing the evolving threat landscape. These limitations, including struggles with dynamic user roles, susceptibility to data manipulation, and challenges in thwarting various cyber threats, necessitate innovative solutions for robust real-time management and security. Consequently, this work proposes a novel hybrid approach that integrates blockchain with RFID data, Generative Adversarial Networks, and Elliptic Curve Diffie-Hellman cryptographic techniques. In the developed hybrid approach, RFID readers are leveraged to collect inventory data, while the Generative Adversarial Network is specifically designed for processing the raw dataset, encompasses data filtering, normalization, and error correction tasks. The utilization of the Elliptic Curve Diffie-Hellman technique is integral for generating both private and shared keys, facilitating secure transmission between the IMS client and cloud-based servers. The blockchain module is engineered to enhance data security and protect shared secret keys, which is achieved through a two-layer mechanism involving encryption via the Advanced Encryption Standard algorithm and SHA-256 hashing function. Additionally, it incorporates the Artificial Algae Algorithm and an Elman Neural Network to ensure robust data access and integrity. To assess the effectiveness of the proposed hybrid approach, it is implemented on a publicly available dataset. The performance assessment involves a comparison with state-of-the-art security methods, considering key metrics such as encryption time, decryption time, key generation time, throughput, latency, and data confidentiality rate. Simulation results conclusively demonstrate that the proposed hybrid approach significantly reduces encryption time, decryption time, key generation time, and latency. Furthermore, it notably improves throughput and data confidentiality rates while aligning with stringent IMS security requirements.

The purpose of this paper is to assess the key factors that have the greatest impact on the inventory level, with a special emphasis on packaging products in the beverage industry in seven different countries. The research methodology includes: (1) Defining the pool of Key Inventory Factors, (2) Defining the final list of Key Inventory Factors, (3) Collecting data, (4) Analysis of the data (according to four different aspects) and recommendations. This study shows that these three factors: Holding costs, Information flow and Systems for order processing, inventory monitoring and inventory control (Systems for OPIMIC) have a significant correlation with the company's success. The paper offers additional analysis regarding the differences among them when it comes to their treatment in the Inventory Management Systems. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to delve into the effectiveness of third-party logistics (3PL) providers in inventory management for retail businesses operating in the e-commerce sector. Design/methodology/approach: The study focuses on analyzing a conducted case study. The case study aims to compare the currently functioning inventory management system with the system proposed by the 3PL provider for managing the location of SKUs in the warehouse. Findings: The research has allowed for a positive verification of the posed hypothesis (i.e., a 3PL company is capable of better inventory management for an e-commerce service recipient). The 3PL provider has the potential to implement an improved inventory management system based on location management and automated storage processes built upon the logistic service provider's know-how. Research limitations/implications: The primary limitation of the research lies in presenting results based solely on a single case study and the instance of a logistics operator with significant experience and an established market position. Originality/value: This article constitutes a significant contribution to the expanding spectrum of research concerning the role of third-party logistics (3PL) providers in enhancing inventory management processes, particularly in the context of e-commerce. The added value lies not only in confirming the positive affirmation of the research hypothesis but also in highlighting the immense potential that 3PL providers possess in implementing advanced inventory management systems based on location management strategies and warehouse process automation. [ABSTRACT FROM AUTHOR]

In this study, an online reinforcement learning-based approach and a reinforcement learning with prior knowledge approach are proposed to enhance decision intelligence in inventory management systems for handling nonstationary stochastic market demands in e-commerce environment with crowdsourcing resources. The proposed inventory control policies are designed to solve a multi-period inventory problem with the objectives of optimising inventory-related costs and service levels in the absence of prior information on demand patterns. An experimental analysis reveals that the proposed reinforcement learning-based inventory control policies achieve cost savings and higher service levels across various settings of cost ratios and lead times. [ABSTRACT FROM AUTHOR]

The recent applications of 4.0 technologies are gradually leading to the introduction and development of artificial intelligence in production systems. This study discusses the assessment of a Machine Learning model, based on Reinforcement Learning, that may allow the optimization of the inventory level at the machine level, thus improving the ordering system and inventory management. The model is applied to a real industrial case and the results, compared with those obtained by using the traditional optimization techniques, show an appreciable performance. It can be concluded that the model of Machine Learning developed can be successfully used for improving the order cycle of an enterprise. [ABSTRACT FROM AUTHOR]

This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective grey wolf optimization (MOGWO) method, reducing storage space while maximizing profit. Numerical outcomes are used to confirm the efficacy of the optimal solutions. The numerical analysis and tests for multi-objective inventory optimization are performed in the four practical scenarios. The inventory model's sensitivity analysis is performed to verify the optimal solutions further. Especially the proposed approach allows businesses to optimize profits while regulating the storage space required to operate in inventory management. The supply chain performance can be significantly enhanced using inventory management strategies and inventory management practices. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility. [ABSTRACT FROM AUTHOR]

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Grounded in advanced analytical techniques and close relationships with its partners, JD.com, the largest retailer in China based on revenue, is committed to an intelligent, integrated, and resilient supply chain that creates value for all stakeholders within the retail ecosystem. Despite challenges in the complex and sophisticated retail supply chain, JD.com has strengthened its supply chain agility, and attained shared value by focusing on supply chain efficiency, supply chain resilience, and customer-demand intelligence. Specifically, JD.com implemented an end-to-end inventory management system based on a dynamic programming model and a neural network model, leading to a reduction in costs and enhancements in operational efficiency. To be able to provide guaranteed service levels for consumers during supply chain disruptions, the company used intelligent techniques, including an emergency classification mechanism and a simulation model, to strengthen its collaboration with critical partners, attain resilience, and benefit society as a whole. For the upstream manufacturers, JD.com implemented the consumer-to-manufacturer system by incorporating state-of-the-art artificial intelligence algorithms and enabled manufacturers to accurately predict customer demand and produce more popular products according to customer preferences, hence creating value for the entire ecosystem. Billions of dollars in increased revenue and tens of millions of dollars in cost savings have been achieved to date since the implementation of the aforementioned techniques, and customer welfare and partner manufacturers' benefits have been significantly enhanced. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to explore how a common IT system is used to manage inventory, and to identify the factors and challenges that affect the decision and implementation of IT adoption in inventory management. Design/methodology/approach: The methodology of this research consists of two parts: a literature review and primary research. The literature review synthesizes the existing studies on IT adoption in inventory management and provides a theoretical framework for the analysis. The primary is based on qualitative research design which involves conducting in-depth interviews with four inventory managers who use different IT systems across various industries. The interviews are transcribed and coded, and the data are analysed using thematic analysis and cross-case comparison. Findings: The findings of this study show that inventory managers adopt IT mainly to reduce costs and increase efficiency, which aligns with the literature. However, they also encounter challenges such as skill competencies and resistance to change, which hamper the successful implementation of IT. Moreover, the findings reveal that the type of IT system and the industry context may have an impact on the outcomes of IT adoption. Specifically, the inventory managers who use ERP, such as SAP, experienced more benefits and fewer difficulties, and the inventory managers who work in manufacturing and retail industries face more complex and dynamic inventory situations than those who work in service and education industries. These findings suggest that IT adoption in inventory management is not a one-size-fits-all solution, but rather a context-dependent and system-specific process that requires careful planning and evaluation. Research implications and limitations: This study offers practical insights and recommendations for inventory managers and IT developers who seek to improve their inventory management practices and systems. It also highlights the importance of IT for enhancing supply chain management and operational performance, which is a key goal of inventory management. However, this study is limited by the small and self-reported sample and the focus on two common IT systems. Future research could use larger and more diverse samples, multiple data sources, and different IT systems. Originality: This study addresses the research question of how IT is used to manage inventory, and what factors and challenges affect the decision and implementation of IT adoption in inventory management. It adds to the literature on IT adoption in inventory management by comparing multiple case studies with different IT systems and industries. It reveals the common and unique factors and challenges that influence IT adoption and suggests practical implications for inventory managers and IT developers. [ABSTRACT FROM AUTHOR]

Stockouts constitute a major challenge in the retail industry. Stockouts are caused by errors related to manual stockkeeping and by the misplacement of items on shelves. Such errors account for up to 4% of lost sales. Real-time inventory management systems for misplaced items or missing stock detection in retail stores are limited. Accordingly, a conductive polymer-based interactive shelving system for real-time inventory management is developed. The system comprises an 80 × 48 sensor array fabricated by screen-printing a piezoresistive carbon-based conductive polymer layer onto gold interdigitated electrodes deposited on a flexible substrate. Each sensing pixel has dimensions of 5 mm × 5 mm and a sensing area of 4 mm × 4 mm. The sensor mat can detect the shape and weight features of stockkeeping units (SKUs), which can then be analyzed by a TensorFlow model for SKU identification. The developed system is characterized for functional resistance range, uniformity, repeatability, and durability. The accuracy of SKU identification achieved using shape features only and the accuracy of SKU identification achieved using both shape and weight features is 95% and 99.2%, respectively. The key novelty of the work is the development of a deep learning-embedded interactive smart shelving system for retail inventory management by using the shape and weight features of SKU. Also, the developed system helps to detect the SKU if they are stacked one over the other. Furthermore, multiple sensor mats implemented on various shelves in a retail store can be modularized and integrated for monitoring under the control of a single PC. Accordingly, the proposed retail inventory tracking system can facilitate the development of automated "humanless" shops. [ABSTRACT FROM AUTHOR]

This study examines an optimal pricing policy for managing deteriorating products in a stochastic inventory system. Customer arrival is formulated as a Poisson process with a fixed rate, and customer demand is probabilistically expressed based on the valuation of the product. Typically, customer interest in items decreases over time because of the declining quality of deteriorating products. A dynamic pricing policy is needed to increase retailer profitability under stochastic scenarios. This paper combines the Hamilton–Jacobi–Bellman (HJB) equation with the Particle Swarm Optimization (PSO) approach to realize an optimal solution for the dynamic pricing strategy with specific boundaries using an analytical approach. Furthermore, an artificial intelligence-based High-Order Neural Network (HONN) model is implemented as an intelligent strategy to approximate the complex dynamic behaviors of the stochastic inventory system. The present study aims to fill the research gap by comparing the analytical solution obtained from the hybrid optimization using HJB–PSO and the intelligent strategy obtained from the HONN model. The proposed approaches bring greater accuracy and efficiency to inventory management tools. The performance difference between the proposed algorithms indicates that the error does not exceed the desired limit (≤ 1%), indicating that the analytical and numerical solutions found can be used interchangeably in practice. Furthermore, all validation tests ensure that the proposed decision support system can aid policymakers in managing stochastic inventory systems with minimal effort and adopting strategies to ensure resilience and customer satisfaction. [ABSTRACT FROM AUTHOR]

Background: The South African retail sector faces numerous inventory management-related problems that have detrimental effects on both inventory and warehouse performance. Some of the negative effects include the inability to fulfil orders, poor-quality control and inventory management operating systems, and ineffective warehouse security and layout design. It is necessary to develop and identify interventions for mitigating these problems. Additionally, literature on inventory and warehouse management in the South African retail sector is currently limited. The study tests a model for linking inventory management to warehouse performance in the retail industry in South Africa. Method: The study used a quantitative survey method involving 203 supply chain professionals selected from retail outlets in the Gauteng and KwaZulu Natal provinces. Data were analysed using structural equation modelling based on the partial least squares technique. Results: Three inventory management practices: inventory investment and ABC analysis contributed positively to inventory performance. Inventory performance contributed positively to warehouse performance. Conclusions: Inventory management and performance are important drivers of warehouse performance in the retail sector. However, a correct mix of inventory management practices is essential. The study offers several contributions. Theoretically, the study provides insights into the contribution of inventory management to warehouse performance in retail environments. Specifically, the study identifies the inventory management practices that are important in optimising both inventory and warehouse performance in retail supply chains. [ABSTRACT FROM AUTHOR]

Background: Circular supply chain management encourages manufacturers to take advantage of used materials and industrial wastes, ensuring social and economic benefits of enhanced environmental sustainability in production. With the development of digital technologies, transactions can be recorded within distributed or decentralized ledger technology. To improve the reliability of digital transactions on the internet, manufacturers need to apply security software such as blockchain technology across a network. Blockchain technology in the supply chains brings transparency, traceability, and security to online transactions, increasing customer attractiveness to the used product and recycling process. Secure transactions might bring significant benefits to the future of the retail industry. The manufacturer exploits the retailer as an online store. Consumers access an online store for purchase, return, and comment on the manufacturer's products. Methods: This research presents a novel meta-heuristic algorithm, specifically, adaptive particle swarm optimization (adaptive PSO), to determine the optimal policy for product price and quantity of materials imported from suppliers for maximizing total profits with sustainability. The adaptive PSO algorithm evaluates the fitness function involving discrete random variables. The supply chain model captures realistic conditions by considering the inherent uncertainties of customer demand. Two scenarios in business settings have been tested on the same customer demand. The proposed framework is intended to yield a more sustainable, resilient, and regenerative system, securing the data of the shared community. Results: The swarm intelligence algorithm implements profit optimization to determine the unit price of the product and the number of materials to be imported. The numerical experiments are conducted to demonstrate the efficacy of the optimal strategy and to evaluate the effect of the number of used products returned to the recycling center. An effective circular supply chain management is realized based on a secure database shared across a network of participants. Key findings significantly contribute to the intelligent decision support system for optimizing inventory management under various stochastic scenarios. Conclusions: Sharing, reusing, repairing, and remanufacturing help companies transition to a circular economy, minimize waste, diversify sources of supply, and maintain production continuity. Transactions on the blockchain framework become transparent and traceable. Based on the circular economy and blockchain platform, this study deals with an adaptive particle swarm optimization algorithm (APSO) to determine the optimal policy for product unit price and quantity of materials imported from suppliers to maximize total profits. The presented methodology can provide better supply chain visibility and traceability under highly realistic stochastic environments. [ABSTRACT FROM AUTHOR]

As major disasters vary in both severity and incidence, making appropriate humanitarian responses critical, disaster relief supply chains increasingly incorporate both social and technical aspects into their operations. Power relations and their implications appear to have been overlooked in existing research into humanitarian supply chain management systems; thus, this research examines the extent to which power influences the decision-making process of the purchasing function. We use socio-technical systems theory to uncover the role of power in this context. As a result of this research, a new framework is developed to provide a concise yet holistic approach to understanding power using a real-life case study from the humanitarian realm. The outcomes suggest that local culture can determine power relations in disaster relief operations via vested interests and lobbying with foreign donors. [ABSTRACT FROM AUTHOR]

In the current global market, managing supply is not a straightforward process and it becomes even more complex as uncertainty and disruptions occur. In order to mitigate their impact, the selection of suppliers of strategic items should have a more holistic view of the operations in the supply chain. We propose an integrated model for supplier selection, considering inventory management and inbound transportation. We approach this problem, incorporating stochastic demand and suppliers' imperfect quality. Imperfect quality triggers additional costs, including external failure and holding costs. Supply disruptions also affect the suppliers' lead time, resulting in delivery delays. We develop a methodology to address this challenge with simulation-optimisation. A genetic algorithm determines supplier selection decisions, while inventory decisions are computed analytically. Discrete-event simulation is used to evaluate the overall performance, as well as to update the lead time dynamically, according to the disruptions. Finally, sensitivity analysis providing managerial insights reveals that criteria in supplier selection should be given a different priority depending on the characteristics of the items, and the effectiveness of disruption mitigation strategies depends on the disruption characteristics. [ABSTRACT FROM AUTHOR]

The digital revolution has transformed the marketing landscape, offering unprecedented opportunities for rural enterprises to expand their reach and increase profitability. In the present context, as the world continues to embrace digitalisation, rural businesses must leverage innovative marketing strategies to tap into new markets and thrive in the competitive global economy. This abstract explores the significant opportunities in digitalisation for marketing rural enterprise products in the current context. The advent of e-commerce, social media, and mobile applications has paved the way for rural enterprises to access global markets, breaking geographical barriers. By embracing these platforms, businesses can showcase their products to a broader audience, leading to increased sales and visibility. Advanced data analytics and artificial intelligence enable rural enterprises to understand customer preferences better. Leveraging this information, businesses can craft personalised marketing campaigns, offering products tailored to individual customer needs, leading to improved customer satisfaction and loyalty. Digitalisation has also impacted supply chain management in rural enterprises. By implementing digital tools like IoT devices, blockchain technology, and cloud-based inventory management systems, these businesses can streamline operations, optimise inventory levels, and reduce production costs, ultimately leading to increased efficiency and profitability. Social media platforms provide a cost-effective means for rural businesses to promote their products and engage with customers directly. Through social media marketing, rural enterprises can build brand awareness, gather valuable feedback, and establish a loyal customer base. Digital financial services like mobile banking and online payment gateways have significantly simplified financial transactions for rural enterprises. This ease of access to financial services empowers them to conduct business more efficiently and access credit, fostering their growth and sustainability. Digitalisation has opened doors to remote learning opportunities. Rural enterprises can enhance their skills and knowledge through e-learning platforms, accessing training modules on various subjects, such as marketing, product development, and quality control. In conclusion, the abstract underlines that embracing digitalisation in marketing offers rural enterprises the potential to overcome traditional limitations and thrive in the modern business landscape. However, this transformation is not without challenges, as the adoption of digital technologies requires adequate infrastructure, digital literacy, and cybersecurity measures. Nevertheless, by seizing the opportunities presented by digitalisation, rural businesses can realise exponential growth and contribute significantly to their communities' economic development. [ABSTRACT FROM AUTHOR]