Inventory Management Strategies Research Articles

Empowering E-Commerce: Revolutionizing Inventory Management with Seamless API Integration

In today's dynamic e-commerce markets, effective inventory management is crucial for businesses to thrive. This paper explores the transformative potential of integrating Inventory Management APIs with e-commerce websites to streamline operations, enhance customer satisfaction, and maximize profitability. Beginning with an overview of the challenges posed by traditional inventory management methods in the digital age, we delve into the motivations behind adopting Inventory Management APIs and their alignment with the objectives of modern enterprises. Through a comprehensive literature review, we highlight the key dimensions of consumer behavior in online shopping and their implications for inventory management strategies. The methodology section outlines a systematic approach to developing an e-commerce website seamlessly integrated with an Inventory Management API, emphasizing agile principles and iterative development. Challenges and limitations associated with API reliability, data synchronization, scaling, and security are discussed, alongside strategies for mitigating these challenges. The results section showcases the tangible outcomes of the integration, including improved user experience, operational efficiency, and inventory accuracy. Looking ahead, the paper identifies future directions for geographical expansion, enhanced personalization, community features, mobile app optimization, integration of emerging technologies, and advanced analytics. In conclusion, the integration of Inventory Management APIs with e-commerce websites marks a significant milestone in redefining the standards for online retail, offering a comprehensive solution that combines user-friendly interfaces with robust inventory management capabilities. The findings underscore the importance of a holistic approach to e-commerce, addressing both customer-facing aspects and back-end complexities to drive sustainable growth in the digital marketplace.

Research on Inventory Sustainable Development Strategy for Maximizing Cost-Effectiveness in Supply Chain

This study addresses the challenges of the expanding global online retail market by developing an innovative inventory management strategy that promotes sustainable development. We propose an integrated planning model that utilizes a newly enhanced Parallel Chicken Swarm Optimization (PCSO) algorithm alongside the established Economic Order Quantity (EOQ) model and the (s, S) inventory policy. Our model demonstrates its effectiveness through simulation experiments, showcasing its ability to dynamically adjust inventory thresholds based on daily forecasted demand, which significantly optimizes replenishment strategies. Notably, our model achieves a total cost reduction while maintaining a high service level of 0.761467 and reduces computational time to 82.32 s, highlighting its efficiency and adaptability. The innovation of this research lies in the application of the PCSO algorithm to dynamically manage inventory in real-time, aligning with sustainable inventory management practices. This study significantly contributes to the field by enhancing the economic and environmental sustainability of supply chain operations.

Optimising Inventory Management Strategies for Cost Reduction in Supply Chains: A Systematic Review

This systematic review investigates and synthesises the complexities of optimising inventory management strategies for cost reduction in supply chains. The research utilised a systematic review methodology to comprehensively analyse inventory management strategies for cost reduction in supply chains, employing key search terms across databases such as PubMed, Scopus, and IEEE Xplore. A total of 50 journals were initially identified, followed by a rigorous screening process based on pre-defined criteria, including 23 relevant published articles written in English. Examining global supply chain dynamics and sector-specific challenges in local inventory management, the review proposes three key strategies: Just-in-Time (JIT) and lean inventory practices, advanced analytics and predictive modelling, and collaborative supply chain partnerships. Drawing on empirical evidence and sector-specific examples, the review demonstrates the effectiveness of these strategies in mitigating costs and enhancing overall supply chain efficiency. The policy implications underscore aligning business practices with proposed strategies, offering policymakers insights for formulating guidelines and incentives. Contributions to knowledge include a comprehensive framework for businesses to navigate contemporary supply chain challenges. At the same time, suggestions for further studies advocate for deeper explorations into emerging technologies and regional variations to refine and expand inventory management strategies.

Dynamics and Complexity Analysis of Fractional-Order Inventory Management System Model

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.

The Effect of Inventory Management Strategies on Business Growth in Manufacturing Companies: A Case of Gisakura Tea Factory Ltd

The Effect of Inventory Management Strategies on Business Growth in Manufacturing Companies: A Case of Gisakura Tea Factory Ltd

Enhancing Economic Development through Inventory Management Optimization in Agro-based Industries in Bihar: A Comparative Study of EOQ and EPQ Models

Inventory management is crucial for optimizing operational efficiency and cost-effectiveness in agro-based industries, particularly in regions like Bihar,known for their agricultural resources. This study compares the effectiveness of two inventory management models, the Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ), in addressing the unique challenges faced by Bihar's agro-based industries. Data from Taruwar Agro Industries Private Limited in Ashiyana, Patna, use to calculate EOQ and EPQ values for three product categories: Tomato Chili, Peri Peri, and Punjabi Tadka. The study employs relevant formulas to compute optimal order quantities and total inventory costs, assuming constant demand, quality, price, holding costs, and ordering costs. Both the EOQ and EPQ models offered viable approaches to inventory optimization. EOQ focused on minimizing holding and ordering costs, while EPQ integrated production-related costs to optimize production flow. The study underscored the importance of aligning inventory management strategies with operational capabilities, market demand, and financial objectives. This study contributes to advancing theoretical understanding and practical applications of inventory management in agro-based economies like Bihar. Recommendations for empirical validation, contextual adaptation, technology integration, and long-term impact assessment are provided to enhance inventory management practices. Integration of the BCG matrix into decision-making processes offers a comprehensive approach to prioritizing inventory management strategies and guiding strategic implications.

Research on Seasonal Orderliness and Linear Correlation of Vegetable Sales Data

The key to the study of seasonal orderliness and linear correlation of vegetable sales data is to understand how sales patterns are affected by seasonal factors and the linear relationship between these patterns and other variables. Seasonal ordering reflects cyclical changes in sales data over time that are influenced by factors such as climate, traditional festivals, and consumption habits. For example, certain vegetables may sell better in the winter while others in the summer. By analysing historical sales data, time series models such as Autoregressive Moving Average (ARMA) can be used to forecast future sales trends, which in turn support inventory management and pricing strategies. In addition, linear correlation analysis helps to identify the relationship between sales volume and factors such as price, cost and market demand, as well as the interactions between different vegetables, including complementary and substitution effects. This information is crucial for supply chain optimisation, promotional campaign planning and risk management. With the development of big data and analytics, researchers can more accurately model and predict market behaviours to support strategic decision-making for vegetable producers and retailers. Research that integrates seasonal ordering and linear correlation will provide insights into the complex dynamics of the vegetable market and provide data to support market participants in developing strategies to adapt to the changing environment.

Developing a fuzzy logic-based carbon emission cost-incorporated inventory model with memory effects

Inventory control is a widely discussed topic in the real world, and recently, it has become closely linked to concerns about carbon emissions and global warming. Global warming is a pressing issue, mainly due to a lack of awareness and action. Traditional inventory models, which typically use integer-order differential equations, overlook the memory aspect of the system. Addressing inventory management is essential in our efforts to combat global warming. This paper introduces a novel approach by incorporating carbon emission costs within a fuzzy environment. Fractional Calculus, a powerful mathematical tool, is employed to capture the memory effect of the system. This approach distinguishes between long memory, characterized by a strong memory effect, and short memory, associated with a poor memory effect, through the use of fractional derivatives and integrals. Numerical results are analyzed based on these memory concepts. Entrepreneurs often find it difficult to determine exact values for known parameters in the real world. Therefore, this study considers the uncertain nature of ordering costs, the rate of deterioration, and demand rates as triangular fuzzy numbers. The optimal average cost and ordering intervals are determined using a solution methodology. A sensitivity analysis is performed to demonstrate how different system parameters influence the outcomes within a fuzzy environment. Notably, it is observed that profit tends to be higher under conditions of strong memory compared to poor memory effects. Moreover, it's worth emphasizing that profits are notably more favorable when employing the signed distance method compared to the graded mean integration method, especially in situations marked by strong memory effects. This study highlights the importance of considering sensitive parameters in the model, especially under conditions of strong memory effects. Such parameters require careful attention in the pursuit of effective inventory management strategies to mitigate carbon emissions and combat global warming.

Influence of Inventory Management Strategy on the Performance of Retail Chains in Nairobi Central Business District.

Operations strategy is the value-adding process used by the company to guarantee that customers receive value through the conversion of resources into finished goods and services. Operations management procedures include quality control, supply chain management, inventory management, risk management, and information technology. The main objective of this study was to establish the influence of inventory management strategy on the performance of retail chains in the Nairobi Central Business District. The study was anchored on the systems theory. The study adopted a descriptive research design and the target population was 34 retail chains within the Nairobi CBD area. The sample population involved 80 managers who were in charge of the retail chains in Nairobi CBD. Self-administered structured questionnaires were used to collect primary data. Data was analyzed using descriptive and inferential statistics. The study found that inventory management strategy has a positive significant influence on the performance of retail chains in the Nairobi Central Business District. This is because an increase in a unit of inventory management strategy leads to an improvement of the performance of the retail chains. The study recommended that retail chains should implement inventory management practices that aim to reduce deadstock and minimize inventory holding costs. The study also recommended that organizations should utilize inventory forecasting techniques to accurately predict demand and adjust inventory levels accordingly. The study further recommended that retail chains should explore vendor-managed inventory (VMI) arrangements with key suppliers to maintain optimal stock levels. The study recommended that retail chains should regularly assess and update inventory turnover strategies to meet customer demand effectively.

Modeling of effective management of logistics supply of an industrial enterprise

Introduction. The economy and industry, which show a tendency to grow even in wartime, will require improved logistics and improved infrastructure. The implementation of such measures is based on scientifically based solutions in the effective management of high-tech (HT) production within the logistics management of integrated supply chains. The purpose of the paper is to determine an effective approach to inventory management at a high-tech industrial enterprise within the framework of developing a scientifically based inventory management strategy using the analysis of existing models. Results. The paper presents an analysis of the main models of operational inventory management of a high-tech enterprise: a single-product statistical model with deterministic demand, a model in the case of accounting for unsatisfied requirements, a model of inventory management based on random demand, a model in the case of loss of unsatisfied requirements, a model of a production system with periodic checks in case of random demand. It is emphasized that inventory management consists in establishing the moments and volumes of the order for their occupancy and distribution along the chains of the supply subsystem within the framework of strategic management. There is an orientation towards cost minimization. The whole inventory management process is imagined as a component of the supply-production-marketing system, which is managed on the basis of a complex logistics approach.Accounting for the features of each subsystem - supply, production, sales, as well as optimal management of resources, is based on cost minimization and leads to high-quality satisfaction of demand for ready-made innovative products with sufficient economic effect. Conclusion. Inventory management consists in establishing the moments and volumes of the order for their filling and distribution among the links of the supply subsystem. The listed inventory management models are intended for practical use, others are of interest because they allow you to understand the essence and identify the main properties of the inventory management system. Accounting for the specifics of each subsystem (supply, production, sales), as well as competent management of their resources, flows (financial, material, information), based on cost minimization, ultimately leads to the main goal of the IT of an industrial enterprise - high-quality satisfaction of demand for finished products with maximum economic effect.

WORKING CAPITAL MANAGEMENT STRATEGIES OF NIGERIAN MANUFACTURING FIRMS AND THE EFFECTS ON CORPORATE PERFORMANCE

The study examined the working capital management strategies of Nigerian Manufacturing Firms and effect on Corporate Performance. Working capital management was decomposed into five variables being account receivable management (ARM), account payable management (APM), inventory management (INVM), cash conversion cycle (CCC) and cash conversion efficiency (CCE) which serve as the explanatory variables of the study while Corporate Performance was proxied by Return on Assets (ROA). The data for the study were collected from the Financial Statement and Annual Accounts of the selected firms covering a data panel framework of 20 years period (2000 to 2020) and nine (9) firms specifically were selected. The analysis was based on the ARDL regression technique was used to estimate the regression model while the probability values of the ARDL regression estimates were used to test the hypotheses at 0.05 level of significance. The result showed that: Account receivable management strategy have long run and no short run effect on return on asset of quoted manufacturing firms in Nigeria; Account payable management strategy have long run and no short run effect on return on asset of quoted manufacturing firms in Nigeria; Inventory management strategy have long and short run effect on return on asset of quoted manufacturing firms in Nigeria; Cash conversion cycle strategy have long and short run effect on return on asset of quoted manufacturing firms in Nigeria; and Cash conversion efficiency strategy have no long and short run effect on return on asset of quoted manufacturing firms in Nigeria. Further analysis revealed that account receivable, account payable inventory management and cash conversion efficiency had causal effects on return on assets. However, there is no causal relationship between cash conversion cycle strategy and return on asset of quoted manufacturing firms in Nigeria. The study thus concluded that working capital management strategies have significant effect on the financial performance of firms, and that the degree of effect differs by period, type of strategy and financial performance indicator. It was recommended among others that management of firms should firms increase the supplies to customers and allow credit to its customers, and firms should also explore the use of short-term credit in financing their business. The study contributed to knowledge by employing long run panel technique in investigating the working capital and firm performance nexus in Nigeria.

Optimization and inventory management under stochastic demand using metaheuristic algorithm.

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.

Research on the Inventory Management in the Modern Business

Inventory management plays a pivotal role in modern business operations, impacting operational efficiencies, financial security, and competitive advantages. This essay explores the multidimensional field of inventory management, delving into seminal research articles. It shows how accounting methods and strategic decision-making interact, shaping operational pathways. Operations research guides inventory management strategies' effectiveness assessment. Creating a culture of continuous improvement based on empirical observations is equally crucial. Innovations, like structured Markov Decision Processes (MDPs) with convex cost functions, bridge theory and real-world applications. The essay also examines forecasting errors' implications when relying too heavily on predictions, highlighting optimal control techniques as resilience tools in volatile environments. Contemporary inventory strategies balance precision and adaptability, focusing on supply/demand synchronization. Empirical case studies exemplify efficient responses to market changes. Variability management in inventory frameworks offers insights into resource allocation across diverse product lines. Lastly, the essay deconstructs the strategic implications of asymmetric sales changes on inventory strategies. In a globalized, technologically dynamic age with shifting market forces, prudent inventory management is imperative. This essay showcases its significance with empirical wisdom and academic rigor, illuminating quantitative precision, strategic insight, and operational excellence in modern business environments.

A comprehensive analysis of linear and non-linear deterioration rates in dual-warehouse inventory systems

This study presents an inventory system where items have an initial linear deterioration rate that transitions to a non-linear pattern, addressing a critical aspect of contemporary supply chain management. Our research introduces a dual warehouse inventory model comprising a rented facility and utilizing an owned warehouse. This unique approach is geared towards products experiencing a shift in deterioration rates, starting linearly and progressing non-linearly. The model operates under a constant demand rate, allowing for partial backlogging during shortages. Key to our analysis are distinct holding costs assigned to each warehouse, reflecting real-world scenarios. We derive conditions that minimize the total cost, supporting these with numerical examples spanning various parameter settings. A comprehensive sensitivity analysis evaluates the influence of each parameter’s alteration, keeping others constant. This analysis illuminates the model’s robustness and adaptability to different operational contexts. The findings offer significant managerial implications, providing valuable insights for effectively managing inventory in scenarios where product deterioration rates change over time. Our approach distinguishes itself by integrating linear and non-linear deterioration patterns within a dual warehouse system, a novel contribution to inventory management literature. The practical relevance of our model is underlined by its applicability in diverse supply chain environments, making it an essential tool for contemporary inventory management strategies.

Research on Supply Chain Inventory Management Strategies of Manufacturing Enterprises in the Context of Digitization

Research on Supply Chain Inventory Management Strategies of Manufacturing Enterprises in the Context of Digitization

K-Means Algorithm Method for Clustering Best-Selling Product Data at XYZ Grocery Stores

This study aims to utilize the K-means clustering algorithm in data mining to categorize sales data at XYZ Grocery store. The research is essential for understanding sales patterns and enhancing inventory management strategies. The research methodology involves implementing the K-means clustering algorithm to generate centroid values for each cluster, thereby creating groups of products based on their sales performance. The findings of this study are expected to provide insights into sales trends at the store. While the abstract provides a general overview, specific results and contributions of this research are not detailed. Further studies could offer a more in-depth understanding of the practical applications of these findings in improving store management and inventory control.

Использование методов кластеризации для анализа продаж автозапчастей на СТО грузовых машин

Clustering methods are widely used to divide goods into groups depending on sales volumes in order to build an optimal purchasing planning and inventory management strategy. Cluster analysis methods do not provide an unambiguous partition of the original set of objects, therefore, in the work, existing clustering methods were analyzed to study sales of auto parts at truck service stations. To solve the problem, the following methods were chosen: k-means, hierarchical agglomerative clustering and DBSCAN. Before using the k-means method, the elbow method found the optimal number of clusters. The DBSCAN method is based on object density and automatically determines the number of clusters. The initial data for cluster analysis was information on sales of spare parts at truck service stations for 3 years; clustering was applied to data by year. The DBSCAN algorithm showed unsatisfactory results, since most of the goods (86%) were identified in one cluster, while others contained units of goods. The k-means method gave the best partitioning result, each group has a different volume. The distribution of goods in clusters changes over three years, so managers should study the change in the affiliation of goods to one group or another. The obtained clustering results will help determine the real needs of spare parts at truck service stations and build an optimal procurement strategy.

Inventory Management at Makerere University Business School and Its Impact on the Revenue Performance of Small and Medium Enterprises

Numerous studies have explored the effectiveness of implementing operations management techniques in inventory management and its impact on revenue performance. This study aims to ascertain the significance of inventory management, its various approaches, and the correlation between managing inventory effectively and revenue performance. Employing a secondary data collection method, the researcher reviewed and analyzed pertinent documents from various authors. The research findings reveal that a majority of small and medium-sized enterprises struggle to adopt new inventory management methods, thereby adversely affecting their revenue performance. When inventory management isn't integrated seamlessly, treating it as a separate entity, these enterprises struggle to ensure optimal resource utilization. However, the study also notes the presence of both positive and negative relationships between inventory management strategies and sales performance. In conclusion, the study recommends that SMEs enhance their inventory management techniques to positively impact business revenue. This improvement is crucial for streamlining operations and maximizing financial outcomes. Keywords: Inventory management, Business revenue, Applying operations, Management techniques, Sales performance.

Optimizing the Hospital Blood Bank Stock in Korea: A Comparative Analysis of the Uniform 5-Day Stock Index and a Novel Blood Stock Index.

Maintaining optimal blood inventory levels in hospitals is important to prevent blood shortage and wastage. We aimed to provide an efficient blood inventory management strategy for hospital blood banks nation-wide by comparing the current use of 5-day issuable stock (IS) with Lim's IS as a novel target IS. The average and CV of daily usage (DU) were calculated from information entered into Korea's Blood Management System by 194 participating hospitals in 2019 and 2020. Using these data, Lim's IS was calculated by determining the simulated annual average blood shortage day nearest to 1 for each blood group in each hospital. The 5-day IS (5IS) was estimated by multiplying the average DU in 2018 by five to count the shortage days in 2019. The average DU (0.3-231.3 units) and corresponding CV (0.33-7.14) in the participating hospitals were inversely proportional (r=-0.699 to -0.695). The hypothetical averages of 5IS and Lim's IS were 27.0±41.2 and 24.7±20.8, respectively (P=0.006). The shortage days for 5IS and Lim's IS were 8.9±22.7 and 1.0±1.9, respectively (P<0.001). While 5IS was unacceptable for universal application, Lim's IS remained near one shortage day and is considered more efficient than 5IS. Hospitals should implement indicators that consider DU and its variations. This is the first study to introduce Lim's IS as an indicator of optimal blood inventory, and the data are expected to provide guidance for effective blood inventory management nationwide, particularly during blood shortages.

A Novel Approach to Identify Industrial Logistics Decarbonization Opportunities: Method Development and Preliminary Validation

This article explores how different types of inventories affect the costs of decarbonizing transportation in manufacturing companies. For these companies, it is difficult to find affordable ways to reduce emissions from transportation given their resource scarcity. Additionally, they handle numerous inventory items that have varying transportation needs based on their order frequency and value, which necessitates the development of tailored inventory management strategies. One tool to do so efficiently is the ABC/XYZ analysis, which classifies items into nine different inventory categories. These groups have different economic importance and predictability, which impacts total logistics costs. Our literature analysis contends that lower-carbon transportation alternatives yield varying abatement costs contingent upon the specific inventory categories. Subsequently, we empirically validate this proposition through discrete-event simulations in two case studies involving Austrian manufacturing enterprises, employing combined road-rail transportation as an illustrative decarbonization measure. Statistical tests substantiate the significance of the XYZ dimension in influencing carbon emission abatement costs during the transition from road to rail transportation. In conclusion, our study offers a novel perspective on decarbonization efforts, underscoring the importance of leveraging established management tools to inform strategic decarbonization decisions. This research holds promise for catalyzing progress in overcoming entrenched challenges associated with decarbonization initiatives within industrial logistics.