Traditional Inventory Management 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.

Impact of uncertainty ascribed to defective products on supply chains

ABSTRACT Traditional inventory management has considered when and how much to order against the uncertainty of demand. Although many studies on inventory management have presumed that quality of products received from manufacturers is perfect, defective products would be, in actual, contained in the procurement to retailers. The inclusion of defective products causes the uncertainty in supply. The retailer does not necessarily require being responsible for all the loss by overstock and shortage of products due to the uncertainty ascribed to the inclusion of defective products. The purpose of this paper is to investigate the impact of the uncertainty ascribed to the inclusion of defective products on a supply chain. This paper first has formulated a simple supply chain model with defective products composed of the manufacturer and retailer. The model has made it clear how the uncertainty ascribed to the inclusion of defective products has an impact on the order quantity and respective profits. Then, this paper has discussed a method of assessing a latent loss as a monetary value. Combining contract techniques and game theory, this paper has proposed a contracting method that the manufacturer compensates the retailer for the latent loss to maintain a supply chain partnership.

Implementation and evaluation of an EHR-integrated perpetual inventory system in a large tertiary hospital oncology pharmacy.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. This study aimed to compare the impact of an electronic health record (EHR)-integrated perpetual inventory management system (EIMS) versus a traditional inventory management system (TIMS) on inventory accuracy, visibility, and turnover in a large academic tertiary hospital. The quasi-experimental study was conducted over 12 months (a 6-month preimplementation period and a 6-month postimplementation period, with an 11-month washout period) at Houston Methodist Hospital. The EIMS was implemented following the use of TIMS. A total of 114 matched inventory items from both systems were identified and compared. The primary outcome was inventory accuracy, calculated as cycle count accuracy. The secondary outcomes were inventory visibility and monthly inventory turnover rate. Analysis demonstrated a 6.02% absolute increase in inventory accuracy (P < 0.001) with use of the EIMS versus the TIMS. After adjusting for inflation, there was an increase in the captured cost of goods sold from $4.16 million to $5.16 million. The monthly inventory value, adjusted for inflation in the prices of studied inventory items, increased from $2.05 million to $2.33 million. The monthly inventory turnover rate increased from 2.03 to 2.23 turns per month (P = 0.305) when compared to the pre- and post-implementation periods. Inventory visibility increased from 133 inventory items to 264 inventory items after EIMS implementation, indicating a 98% visibility increase compared to preimplementation levels. This study found that implementing an EIMS significantly increased pharmacy inventory accuracy and inventory visibility, which are essential for optimizing patient care and pharmacy financial management.

Machine Learning Enabled Inventory Control for Deteriorating Items with Carbon Emission and Trade Credit under Learning and Forgetting

This research introduces a novel approach to inventory control, leveraging machine learning techniques to optimize inventory decisions for deteriorating items while accounting for carbon emissions and trade credit policies. In contemporary supply chain management, sustainability considerations have become increasingly vital. The carbon footprint associated with inventory management practices necessitates innovative solutions to minimize environmental impact. Moreover, trade credit offers financial flexibility but requires careful management to maintain profitability. To address these complex challenges, our proposed framework integrates machine learning algorithms into inventory control to enhance decision-making precision. The model incorporates dynamic learning and forgetting effects, allowing the system to adapt to changing demand patterns over time. This adaptability is particularly critical when dealing with deteriorating items that exhibit non-constant demand rates. Carbon emissions are assessed throughout the supply chain, and environmentally conscious decisions are made to minimize the carbon footprint. Additionally, trade credit terms are optimized to strike a balance between financial constraints and inventory performance. Our approach demonstrates superior performance in terms of minimizing costs, reducing carbon emissions, and enhancing supply chain resilience compared to traditional inventory management methods. Real-world case studies and simulations validate the effectiveness of our machine learning-enabled inventory control system, showcasing its practical applicability. This research contributes to the advancement of sustainable supply chain management by providing a comprehensive framework that combines AI-driven inventory control, carbon emission reduction, and trade credit optimization, ultimately fostering environmentally responsible and financially viable inventory decisions.

Reducing carbon footprint and promoting resource sustainability in the retail industry through the prevention of dead stocks

In the retail industry, majority of the traditional inventory management methods commonly focus on minimizing business costs but fail to consider implications on environmental sustainability. Existing inventory classification methods have failed to consider the interaction among the different performance indicators for a timely recognition of the potential dead stocks and dead stocks in the inventory. This study aims to identify and effectively classify potential dead stocks and dead stocks through an enhanced inventory classification method that integrates the remaining product shelf life as an indicator for timely recognition of dead stocks. The study also aims to provide guidelines in setting up decision rules needed in objectively classifying inventory based on each performance indicator through an automated algorithm with decision rules. Using data sets taken from open databases that provide real-world data, results show that the automated algorithm can effectively detect the presence of dead stocks and distinguish such inventory from potential dead stocks in the system which existing inventory classification methods are unable to do. One-Way ANOVA Tests performed showed that the proposed classification method can consistently classify inventory across different retail data sets while Monte Carlo simulation was used to simulate the amount of inventory that becomes dead stocks in a multi-period inventory management system. Results show that 17% of these potential dead stocks, if undetected, become dead stocks in the following period. Over a ton of carbon emissions is prevented with lesser dead stock waste disposal. The value of this study lies in the fact that classifying inventory to detect the presence of potential dead stocks is an essential first step in solving the dead stock problem not just for business profitability but also for sustainable resource consumption and a sustainable environment.

Using blockchain technology to extend the vendor managed inventory for sustainability

Nowadays, Information Technology (IT) is changing the way traditional enterprise management concepts work. One of the most dominant IT achievements is the Blockchain Technology. This technology enables the distributed collaboration of stakeholders for their interactions while fulfilling the security and consensus rules among them. This paper has focused on the application of Blockchain technology to enhance one of traditional inventory management models. The Vendor Managed Inventory (VMI) has been considered one of the most efficient mechanisms for vendor inventory planning by the suppliers. While VMI has brought competitive advantages for many industries, however its centralized mechanism limits the collaboration of a pool of suppliers and vendors simultaneously. This paper has studied the recent research for VMI application in industries and also has investigated the applications of Blockchain technology for decentralized collaboration of stakeholders. Focusing on sustainability issue for total supply chain consisting suppliers and vendors, it has proposed a Blockchain based VMI conceptual model. The different capabilities of this model for enabling the collaboration of stakeholders while maintaining the competitive advantages and sustainability issues have been discussed.

ANALYSIS OF THE MANAGEMENT OF MATERIAL AND TECHNICAL ASSETS OF ENTERPRISES

Variability of demand for goods, depending on their availability and cost is a frequently observed phenomenon in the sphere of production and consumption. This paper discusses the possibility of using the ABC analysis for the inventory management system. This type of analysis makes it possible to apply various methods of inventory management for increasing the income and reducing the costs of particular products for manufacturers.&#x0D; As is known, the management process approach emphasizes the systematic study of management by defining management functions in an organization and then examining each in detail. There is a general agreement on the planning, organizing, and controlling functions.&#x0D; Traditional inventory management systems, such as the Lean Order Quantity (EOQ) model, are based on the assumptions of constant demand rate, constant inventory cost, and instant order. The EOQ assumption of instant order receipt means that the entire order quantity, that is, all units of the purchased lot is immediately received from the supplier. The Economic Production Quantity (EPQ) model weakens this assumption by including incremental order receipt, i.e. debit.&#x0D; In recent years, major advances in the sphere of management include the following elements:&#x0D; process approach&#x0D; management science&#x0D; decision support approach,&#x0D; scientific approach to human resources development, and sustainable competitive advantage.&#x0D; These four approaches complement one another in current practice and provide a useful framework for project management.&#x0D; This paper presents a realistic model of the goods production and their inventory, since ABC analysis or ABC classification is an integral part of materials management. According to this approach, the inventory is divided into three categories depending on income generation. The ABC analysis helps entrepreneurs identify the main types of products in the warehouse, prioritize the goods management based on their cost, and analyze customer demand for a specific product.

Simulation of inventory management systems in retail stores: A case study

Inventory management has become a key factor in today's world of uncertainty, particularly in the retail sector. Accordingly, there is a high requirement of managing and controlling the inventory with appropriate policies to elevate the organization's performance. In fact, a proper system has to be implemented for monitoring customer demand. This system will, in turn, assist in maintaining the right level of inventory. In this direction, the present research focuses on a retail store and explores a solution for an inventory-related problem experienced by the firm. A simulation model is developed and run for particular merchandise using Arena simulation software. Rigorous experimentation is conducted with the model by altering the inputs/model characteristics, and a more effective system is proposed. Compared with the existing traditional inventory management system, the proposed system will reduce the inventory level by 40% and lost sales by 87%. Furthermore, the proposed system is optimized using the OptQuest module in Arena simulation software. As a result, the inventory level is further reduced by 73% compared to the existing system. Store managers in various organizations may utilize the proposed methodology for improving their inventory management system.

Multi-Period Newsvendor Problem for the Management of Battery Energy Storage Systems in Support of Distributed Generation

Stakeholders’ interests on renewable and clean energy sources experienced a huge increase in the last decades, thanks to the remarkable benefits on climate, economic, and social issues. The integration of flexible energy storage systems represents a great chance for a further increased penetration as they support the mitigation of renewables’ main drawbacks (i.e., stochastic behavior) and guarantee the balance between energy supply and demand enabling non-simultaneous production and consumption. The increased focus on distributed generation and storage was also of interest to the research community which investigated both the economic and technical performances of the integrated systems. The operations management branch addressed this topic, since storage devices present many similarities with traditional inventory management applied to regular commodities. At a user level, the relation between energy production and storage can be studied by analogy with inventory models. Specifically, this study presents a multi-period newsvendor model for the management and optimal sizing of a battery energy storage system installed to increase the self-consumption rate by allowing loads shifting. This work aims to extend the traditional inventory management applying its concepts to energy systems operations in order to minimize the total energy cost. A numerical study is provided to show the behavior of the model.

Online helium inventory monitoring of JLab cryogenic systems

There are five cryogenic plants at Jefferson Lab which support the LINAC, experiment hall end-stations and test facility. The majority of JLab’s helium inventory, which is around 15 tons, is allocated in the LINAC cryo-modules, with the majority of the balance of helium distributed at the cryogenic-plant level mainly as stored gas and liquid for stable operation. Due to the organic evolution of the five plants and independent actions within the experiment halls, the traditional inventory management strategy suffers from rapid identification of potential leaks. This can easily result in losses many times higher than the normally accepted (average) loss rate. A real-time program to quickly identify potential excessive leakage was developed and tested. This program was written in MATLAB© for portability, easy diagnostics and modification. It interfaces directly with EPICS to access the cryogenic system state, and with and NIST REFPROP© for real fluid properties. This program was validated against the actual helium offloaded into the system. The present paper outlines the details of the inventory monitoring program, its validation and a sample of the achieved results.

Inventory transparency for agricultural produce through IOT

Re-structuring the practices of traditional inventory management is becoming more essential to optimize the supply chain transparency and accuracy of agricultural produce. A flexible and transparent inventory management system is becoming the need of any agricultural commodity. It was noticed that the major setback for the farmers who are the suppliers of the farm produce is due to poor supply chain integration. The recent advent technologies and IT explosion can bring up a greater impact in the process of storing, tracking, distributing and monitoring perishable agriculture produce of day to day life. The primary focus of this paper is to integrate IoT into inventory management and other inbound logistics management of agriculture produce. The unique features of agricultural produce like a prediction of supply, demand, the location of warehouses, distribution and tracking of inventory can be integrated through IoT. This paper proposes a conceptual framework for inventory management transparency involved in the supply chain of agriculture produce.

Implementation of Vendor Managed Inventory (VMI) in Managing Inventory of Intra Ocular Lenses (IOL) in an Eye Care Organization

The Indian healthcare industry is a fast growing industry and needs attention for reengineering of many processes. The supply chain process is one of the important processes. But it lacks the attention of stakeholders due to which lack of efficiency of the process leads to direct or indirect impact on the businesses. Vendor managed inventory (VMI) is such a programe or tool that is still less used in the healthcare industry. VMI can help in increasing the efficiency of the supply chain process by delegating the responsibility of inventory management to the suppliers. The purpose of this paper was to measure the effectiveness of VMI over traditional inventory management system of IOLs (lenses that are used in surgeries of eye hospitals). A matrix was also designed to understand the key performance indicators of supply chain to assess the results of implementation of VMI in place of traditional managed inventory. True experimental design of quantitative research with pretest posttest was used to measure before and after impact of VMI on management of inventory of IOLs. A sample size of 14 types of lenses was taken to assess the impact of the VMI technique. Literature review of the traditional inventory management system and VMI was done and data was obtained from an eye care organization of inventory of IOLs to test the hypothesis to measure the effectiveness of VMI over traditional method of inventory management. The results concluded the success of VMI over traditional inventory management based on the performance indicators (OFR/TAT) of the supply chain.

Collaborative predictive business intelligence model for spare parts inventory replenishment

In today?s volatile and turbulent business environment, supply chains face great challenges when making supply and demand decisions. Making optimal inventory replenishment decision became critical for successful supply chain management. Existing traditional inventory management approaches and technologies showed as inadequate for these tasks. Current business environment requires new methods that incorporate more intelligent technologies and tools capable to make fast, accurate and reliable predictions. This paper deals with data mining applications for the supply chain inventory management. It describes the unified business intelligence semantic model, coupled with a data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions and to deliver this information to relevant decision makers in a user-friendly manner. Experiments carried out with the real data set, from the automotive industry, showed very good accuracy and performance of the model which makes it suitable for collaborative and more informed inventory decision making.

Inventory Control of Supply Chain Environment

Supply chain management , so that enterprises not only to face the fierce competition in the international market , and can withstand demand uncertainty and rapid technological innovation and other factors that impact . Production and inventory management is always an important part of business process indispensable , is the added value chain to achieve the important part. In supply chain management, inventory level not only affects the overall cost of a single enterprise , but also restricts the performance of the entire supply chain. Based on the analysis of the traditional inventory management , supply chain theory combined with the latest developments , the use of vendor-managed inventory , joint management of inventory control, inventory control, multi-level strategy, inventory control, supply chain environment for further improvements , making inventory control of the supply chain environment more perfect .

Benefits of using ad-hoc inventory management models in the presence of return flows

In this work we analyze the impact of reverse logistics in inventory management models through the analysis of implications and extensions of some literature models. Through experimentation with deterministic inventory models we found that in most cases, the traditional inventory management costs are higher than the models considering return flows. Only in few cases, mainly when the recoverable inventory holding cost was very high in relation to the serviceable inventory holding cost, the benefits of ad-hoc models was negligible. We also observe that there is no quantitative comparison in the literature between a traditional inventory management and one which considers return flows.

Knowledge-based inventory management in production logistics: A multi-agent approach

With the rapidly changing market conditions, an accurate and highly dynamic inventory management model is much needed for making an enterprise more predictable to the global competition. However, traditional inventory management in production logistics is inadequate in managing inventories with high fluctuation in demand and value. To establish effective inventory management strategy, this paper presents a knowledge-based inventory management system for active inventory replenishment based on multi-agent dynamic forecasting and knowledge-based system technologies. The system dynamically forecasts the fluctuation of the demand and updates price of its raw materials. Hence, appropriate inventory management strategies are derived to respond and adapt to the rapid market changes and the material requirement. A prototype system has been built and successfully trial implemented in a manufacturing enterprise.

Integrating the Promotional and Service Roles of Retail Inventories

In some retail contexts, higher inventories not only improve service levels, but also stimulate demand by serving as a promotional tool (e.g., by increasing product visibility). Motivated by a building-products retailer's practice of stocking large quantities of products to stimulate demand, we study inventory management policies when demand is uncertain but increases with stocking quantity. For general inventory-dependent demand distributions, we characterize the profit-maximizing policy for a stochastic inventory model that extends the classical newsvendor setting. We contrast this integrated policy with two functionally-oriented approaches - a demand-driven policy that might represent marketing managers' view of inventories as a means for promoting products, and a critical fractile policy that reflects the traditional inventory management tradeoff between the costs of overage and lost sales. Our analysis provides insights regarding the structure of the optimal policy, and its relationship to the functional viewpoints. We prove that the optimal stocking quantity always exceeds the critical fractile solution, and can even exceed the demand-driven stocking quantity. Our analysis further shows that the functional orientations are complementary, with one policy performing well when the other fares poorly. By synthesizing the two functional approaches, the optimal stocking policy can achieve considerably higher profits than either functional policy. We also address the problem of jointly optimizing price and stocking quantity for demand-stimulating inventories by considering two alternative models for incorporating the dependence of demand on price - a distribution-lifting model and a multiplicative model. We show that the models permit sequential optimization of the price and stocking quantity decisions, and that ignoring demand stimulation results in a less-than-optimal stocking quantity.

A study on time series pattern extraction and processing for competitive intelligence support

In the era of rapid growth and high competition, a company must possess an information/knowledge advantage in order to hold the upper hand in the industry. Therefore, the company has to continuously monitor its competitors in order to get enough information and convert the information into competitive knowledge. Although information technology has been used in many areas and has many successful examples, it is rarely the case that information technology was used for the task of competitor intelligence. Accordingly, this study devises a method of time series pattern extraction and processing for the task of obtaining place-prospect competitor intelligence in order to advance an enterprise with a competitive knowledge advantage. The purposes of the method are two-fold: (1) For a product manufactured by the company, the gathered data are mined into a knowledge advantage—an appropriate amount of the stock to be allocated at a timely fashion at a retailer in face of competition. (2) This knowledge advantage alerts the company's decision makers to what is unknown and forces them to make good decisions on the stock allocation problem, freeing them from the dilemma of over-stock or under-stock with respect to competitors’ stock. Our approach differs from traditional inventory management in the grounds they are based: traditional inventory management is based on the perspective of cash flow while our approach is based on the perspective of competition encountered. The results show our method is quite promising to this end, obtaining the intelligence to gain competitive advantage.

Networked inventory management information systems: materializing supply chain management

Aims to explain the driving forces for networked inventory management. Discusses major developments with respect to customer requirements, networked organizations and networked inventory management. Presents high level specifications of networked inventory management information systems (NIMISs). Reviews some decision systems for inventory management, and compares traditional inventory management to networked inventory management. Uses these insights to outline NIMISs for several types of inventory management decision systems. Summarizes the results of the study, and provides an outlook on further research.