Optimal Inventory Level Research Articles

Revolutionizing inventory management: A comprehensive automated data-driven model using power BI incorporating industry 4.0

This is a study on advanced inventory management approaches in the mechanical industry using data-driven models, accompanied by Power BI analytics. Drawing insights from broad collaboration with relevant industrial expertise at different organizational scales, our research work addresses the intricacies and challenges in maintaining optimal inventory levels concerning growing industries and operational risks. In this global mechanical firm, we analyzed historical data using descriptive and predictive analytics to understand the trends of the past and to forecast future inventory needs. Descriptive analytics provided base insights about the present status of inventory, while predictive analytics helped proactive management of stock levels classified into on-hand and critical level categories for greater efficiency in strategies related to planning at the coordination end with its suppliers and mitigating risks. Core to our methodology will be the implementation of an automated data-driven machine-learning approach that brings minimal intervention from humans to solve some of the most critical problems in inventory management. One major contribution this study adds is a major product: a Power BI dashboard that visualizes critical part numbers falling below threshold values and recognizes suppliers with historical shipment shortages. Armed with this intuitive dashboard, supplier-coordinating engineers are better placed to take proactive action on inventory shortages, greatly improving operational efficiency in supply chain resilience.

A Hybrid Fuzzy Mathematical Programming Approach for Manufacturing Inventory Models with Partial Trade Credit Policy and Reliability

This study introduces an inventory model for manufacturing that prioritizes product quality and cost efficiency. Utilizing fuzzy logic and mathematical programming, the model integrates fuzzy numbers to describe uncertainties associated with manufacturing costs and quality control parameters. The model extends beyond conventional inventory systems by incorporating a dynamic mechanism to halt production, employing fuzzy decision variables to optimize the economic order quantity and minimize total costs. Key innovations include the application of approaches related to graded mean integration for defuzzification and the use of Kuhn–Tucker conditions to ensure optimal solutions under complex constraints. These approaches facilitate the precise management of production rates, inventory levels, and cost factors, which are essential in achieving a balance between supply and demand. A computational analysis validates the model’s effectiveness, demonstrating cost reductions while maintaining optimal inventory levels. This underscores the potential of integrating fuzzy arithmetic with traditional optimization techniques to enhance decision making in inventory management. The model’s adaptability and accuracy indicate its broad applicability across various sectors facing similar challenges, offering a valuable tool for operational managers and decision makers to improve efficiency and reduce waste in production cycles.

Optimizing pharmaceutical inventory management: A global framework for efficiency and cost reduction

Pharmaceutical inventory management is critical in ensuring continuous drug availability and cost efficiency in healthcare institutions globally. However, maintaining optimal inventory levels is challenging due to factors such as overstocking, stockouts, and global supply chain disruptions. This paper reviews best practices for efficient pharmaceutical inventory management, emphasizing the integration of data analytics and predictive models to forecast demand, reduce waste, and streamline procurement. It explores strategies for implementing unified inventory systems across diverse healthcare settings, balancing cost efficiency with uninterrupted drug availability. Additionally, the paper examines the impact of global supply chain disruptions on pharmaceutical supply and recommends solutions for building resilience. Through real-world examples and recommendations for future improvements, this review highlights the potential for enhanced global inventory management practices to reduce costs while ensuring the continuous availability of essential medications. Keywords: Pharmaceutical Inventory Management, Data Analytics, Cost Efficiency, Predictive Models, Supply Chain Resilience, Drug Availability

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

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

Optimal procurement decisions based on smoothing strategy for dual sourcing with different lead times

This study establishes a discrete-time inventory model with a smoothing procurement strategy in dual sourcing. Specifically, an assembly manufacturer purchases components from two suppliers: one overseas supplier with a long lead time but a low price, and one local supplier with a short lead time but a high price. A smoothing procurement approach taking into account previous ordering record and current demand is adopted by the assembly manufacturer. We start by exploring the scenario where storage space is not a constraint. The cost-minimization conditions for single sourcing and dual sourcing are studied. Assuming demand follows a normal distribution, the optimal order quantities and inventory levels are subsequently derived and shown to follow the same distribution. By comparing the inventory costs, it is found that dual sourcing is superior to single sourcing. Then, we examine the scenario where an assembly manufacturer with limited storage space collaborates with a third-party logistics company. The optimal order quantities, inventory levels, smoothing procurement coefficient and storage space are derived for the assembly manufacturer. Finally, numerical experiments with practical data are constructed to verify our findings.

Penentuan Persediaan Produk Perishable pada Rumah Tangga: Analisis Faktor dan Strategi dalam Mencapai Efisiensi

The low level of public knowledge regarding determining the inventory of perishable products is the knowledge-sharing of community service activities to increase community knowledge and understanding in determining optimal inventory levels in households regarding perishable products. In community service activities using socialization and knowledge-sharing methods, it is hoped that using this method will increase community understanding. Apart from that, the results of the service increase public knowledge and understanding in determining perishable product inventory management and applying it in everyday life to reduce waste.

Distributionally Robust Newsvendor Under Stochastic Dominance with a Feature-Based Application

Problem definition: In this paper, we study the newsvendor problem under some distributional ambiguity sets and explore their relations. Additionally, we explore the benefits of implementing this robust solution in the feature-based newsvendor problem. Methodology and results: We propose a new type of discrepancy-based ambiguity set, the JW ambiguity set, and analyze it within the framework of first-order stochastic dominance. We show that the distributionally robust optimization (DRO) problem with this ambiguity set admits a closed-form solution for the newsvendor loss. This result also implies that the newsvendor problem under the well-known infinity-Wasserstein ambiguity set and Lévy ball ambiguity set admit closed-form inventory levels as a by-product. In the application of feature-based newsvendor, we adopt general kernel methods to estimate the conditional demand distribution and apply our proposed DRO solutions to account for the estimation error. Managerial implications: The closed-form solutions enable an efficient computation of optimal inventory levels. In addition, we explore the property of optimal robust inventory levels with respect to the nonrobust version via concepts of perceived critical ratio and mean repulsion. The results of numerical experiments and the case study indicate that the proposed model outperforms other state-of-the-art approaches, particularly in environments where demand is influenced by covariates and difficult to estimate. Funding: X. Li is supported by the Singapore Ministry of Education [Tier 1 Grant 23-0619-P0001, 24-0500-A0001] and National Natural Science Foundation of China [Grant 72331004]. L. Zhang is partially supported by the National Natural Science Foundation of China [Grants 72171156 and 72231002] and the Hong Kong Research Grants Council [Grant 16212419]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0159 .

Analysis of Inventory Management System at Pharmacy of XYZ Hospital

This research examines inventory management at the XYZ Hospital Pharmacy Installation in Bandung, with a special focus on medicines and medical devices. The background to this research is the significant increase in health costs and the importance of effective supply chain management to reduce unnecessary costs. Based on monthly stock and daily sales data from October 2023 to February 2024, this research uses quantitative methods to calculate optimal inventory levels, including Economic Order Quantity (EOQ), safety stock, and reorder point (ROP). This research also applies ABC analysis and cycle counting to prioritize inventory control. The research results show that the proposed inventory policy, especially the continuous review strategy, has the potential for significant cost savings for the XYZ Hospital Pharmacy Installation. For pharmaceutical products, achieving a 99% service level can result in savings of IDR 302,697,429, which is 48.17% of the average inventory level. For medical devices, potential savings reach IDR 70,602,064, which is 48.77% of the average inventory level. The total potential savings for all products is IDR 373,299,493. These findings highlight that hospitals currently do not have effective controls in managing inventory of single-use medical devices. Implementing strong inventory policies and procedures is critical to improving cost efficiency and optimizing inventory levels within an organization.

Utilizing industrial engineering methods and strategies to determine optimal inventory levels of spare parts: A review

Utilizing industrial engineering methods and strategies to determine optimal inventory levels of spare parts: A review

Potato Consumption Forecasting Based on a Hybrid Stacked Deep Learning Model

AbstractPotato consumption forecasting is crucial for several stakeholders in the food market. Due to the market flexibility, the farmers can manipulate the volumes planted for a given type of produce to reduce costs and improve revenue. Consequently, it means that establishing optimal inventories or inventory levels is possible and critical in that sense for the sellers to avoid either inadequate inventory or excessive inventories that may lead to wastage. In addition, governments can predict future food deficits and put measures in place to guarantee that they have a steady supply of food some of the time, especially in regions that involve the use of potatoes. Increased potato-eating anticipation has advantages for the sellers and buyers of the potatoes. The experiments of this study employed various machine learning and deep learning (DL) models that comprise stacked long short-term memory (Stacked LSTM), convolutional neural network (CNN), random forest (RF), support vector regressor (SVR), K-nearest neighbour regressor (KNN), bagging regressor (BR), and dummy regressor (DR). During the study, it was discovered that the Stacked LSTM model had superior performance compared to the other models. The Stacked LSTM model achieved a mean squared error (MSE) of 0.0081, a mean absolute error (MAE) of 0.0801, a median absolute error (MedAE) of 0.0755, and a coefficient of determination (R2) value of 98.90%. These results demonstrate that our algorithms can reliably forecast global potato consumption until the year 2030.

Relief item inventory planning under centralized and decentralized bilateral cooperation and uncertain transshipment quantities

Pre-positioning relief inventory ensures timely delivery of in-kind aid after a catastrophe. Tragic disasters like major earthquakes are rare and unpredictable; therefore, stockpiled items may not be used. To avoid over-stocking and reduce shortage risk, the cooperation of two humanitarian agencies in supporting each other in case of shortages is suggested in the literature. In this study, we utilize newsvendor-based quantitative models to optimize the pre-disaster stocking decisions of agencies under centralized and decentralized cooperation mechanisms. In the former, both agencies jointly determine their inventory levels to maximize their combined benefits of relief operations, whereas, in the latter, each agency establishes its stocking level in isolation via a game theoretic approach. In both systems, the two agencies agree to transship their excessive items to the other party if needed. In this regard, we investigate the situation where only a portion of the transshipped items, denoted as the reliability factor, can be received and effectively utilized at the destination due to the chaotic nature of the disaster. Considering a deterministic reliability factor, we obtain the singular optimal inventory levels in the centralized system and identify the unique Nash Equilibrium in the decentralized system. Subsequently, we formulate a two-stage stochastic program, considering a random reliability factor for both cooperation systems. The study concludes by offering a range of managerial insights. Our analyses quantify the sub-optimality resulting from decentralized decision-making across diverse parameter settings using the concept of the price of anarchy. The findings highlight that centralized cooperation becomes particularly advisable when the average demand within either agency is high, the transshipment process is secure (i.e., the reliability factor is high), and transshipment costs remain low.

Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit

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.

On the Number of Customer Classes in a Single-Period Inventory System

A common practice in inventory systems with several customers requiring differentiated service levels is to group them into two or three classes, where a customer class is a group of customers with the same preset service level in terms of product availability. However, there is no evidence that grouping customers into two or three classes is optimal in terms of the ordering policy parameters. This paper studies the effect of the number of customer classes on the inventory level of a single-period inventory system with stochastic demand and individual service-level requirements from multiple customer classes. Using a Sample Average Approximation approach, we formulate computationally tractable multi-class service level models, under responsive and anticipative priority policies in cases of shortage, as mixed integer linear problems (MIPs). The effect of the number of classes on the inventory level is determined using a round-up aggregation scheme; i.e., given a sufficiently large initial number of classes, it is reduced by adding the lower service level classes to the next higher class. We analytically characterize the optimal inventory level under responsive and anticipative priority policies as a function of the initial number of classes and the number of classes grouped based on the round-up aggregation scheme. Under a responsive priority policy, we show that there is an optimal number of classes, while under an anticipative priority policy, the optimal number of classes is equal to the initial number of classes. The effect of free-riders resulting from the round-up aggregation scheme on the optimal inventory level is studied through numerical experiments.

Strategic Behavior and Optimal Inventory Level in a Make-to-Stock Queueing System with Retrial Customers

In this article, we consider a make-to-stock queueing system with retrial customers. Upon their arrival, customers make a decision to either join the system or not based on a reward–cost function. If customers join the retrial queue, they become repeat customers. Each repeat customer repeats their demand after an exponential amount of time until they have been successfully served. We explore the equilibrium strategies of customers in both the almost observable and unobservable cases. Furthermore, we also analyze the expected costs of the entire system based on the customers’ behavior in these two cases. Additionally, we determine the optimal inventory levels in both cases through numerical experiments.

Unlocking Inventory Efficiency: Harnessing Machine Learning for Sales Surge Prediction

Purpose: Sales forecasting plays a crucial role in inventory optimization for retail stores, especially during special events such as promotions, advertisements, holiday season, weather, social and economic situations etc. These events drive significant changes in customer buying patterns. Some of these events are captured in the current forecasting models as part of trend, seasonality, and cyclicality. But many times, unexpected local events such as extreme weather conditions, riots, and regional events such as marathons, concerts have a significant impact on sales surges which are usually not captured in the sales forecast. This leads to inventory being out of stock and store managers placing last-minute manual orders. By accurately predicting these sales pattern changes, retailers can make informed decisions, ensuring optimal inventory levels and maximizing profits. Methodology: In this paper, a data-driven solution was discussed that leverages machine learning models to predict sales pattern changes and surges during local events in a specific geographical location. Web scraping can be used to gather data on local events from a range of online sources, including Google News, local news channels, and social media platforms. By extracting pertinent details about upcoming events, it is possible to compile a thorough database of local happenings. Findings: Data Analysis of historical sales data mapped to its local events can provide insights on key department-categories where there is a surge in sales. Machine learning models can be used to analyze, experiment and train historical sales data in conjunction with the events data. Unique contribution to theory, practice and policy: Machine learning models would be trained to capture the complex relationships between different local events and their impact on sales. Therefore the study recommends using the machine learning approaches specified to consider various factors, such as event type, location, duration, and historical sales patterns, our models can effectively predict sales fluctuations during specific events.

Inventory Management in Pharmaceutical Industry

This research paper explores the intricacies of managing inventories of medicines in the pharmaceutical industry, focusing on meeting the healthcare needs of both humans and animals. Through a comprehensive review of existing literature and analysis of real-world case studies, this paper identifies key challenges, strategies, and best practices associated with pharmaceutical inventory management. The literature review reveals the complexities of inventory management in the pharmaceutical sector, including demand variability, regulatory compliance, and the need for precise temperature control for perishable medicines. Strategies such as economic order quantity (EOQ) and just-in-time (JIT) inventory management are discussed for managing normal inventories, while specialized approaches like cold chain management are highlighted for managing perishable inventories. Technological innovations, such as RFID technology and predictive analytics, are recognized for their potential to enhance inventory visibility and mitigate supply chain risks. Case studies provide valuable insights into successful inventory management practices within the pharmaceutical industry. Examples demonstrate the importance of collaboration among stakeholders, investment in advanced inventory management systems, and adherence to regulatory compliance measures. Furthermore, case studies underscore the significance of maintaining optimal inventory levels, minimizing stockouts, and ensuring medication safety and efficacy. This research paper contributes to the ongoing discourse on pharmaceutical supply chain optimization, patient-centric care delivery, and the pursuit of sustainable healthcare solutions. By synthesizing insights from literature and real-world examples, stakeholders can develop strategies to address challenges, enhance inventory management practices, and improve patient outcomes in the pharmaceutical industry. KEYWORDS Pharmaceutical Inventory Management, Medicine Inventory Control, Perishable Inventory, Normal Inventory, Supply Chain Optimization, Regulatory Compliance, Demand Variability, Cold Chain Management, RFID Technology, Patient Safety, Supply Chain Efficiency, Case Study Analysis, Healthcare Logistics, Sustainable Healthcare Solutions.

Prediction of customer demand for perishable products in retail inventory management, using the hybrid prophet-XGBoost model during the post-COVID-19 period

ABSTRACT Retail inventory management (IM) presents challenges in decision-making, especially in determining optimal inventory levels for future customer demand alignment. This study focuses on predicting dairy product demand by a significant European retail company using historical order data. As dairy products are perishable and constitute a relevant portion of retail trade volume, accurate IM is crucial to prevent wastage and meet customer needs. However, external factors like COVID-19 may impact demand volatility and seasonal patterns. Given the absence of scientific studies on predicting post-COVID-19 demand, our research aims to fill this gap by proposing alternatives, such as excluding the pandemic period and developing a historical imputation. The study employs two predictive models, Prophet and XGBoost, known for their superior performance in predicting demand. Moreover, a hybrid approach combining both models is proposed to enhance prediction accuracy by leveraging the capacity of the Prophet model to handle seasonal and holiday-period effects and XGBoost’s regularization to prevent overfitting. The results demonstrate the feasibility of historical imputation and the hybrid model approach, improving significantly individual model performance. The principal application of the study is to propose an approach to predict the shipment of other products in a post-COVID-19 context.

The Optimization of Production and Inventory Management Processes in Tissue Paper Production: The Goal Programming Approach

This paper aimed to apply the Goal Programming approach to optimize important parameters in the production and inventory management processes of tissue papers and paper towel manufacturing. A real-life scenario problem was formulated and solved, and a sensitivity analysis was conducted to examine the effects of changes in goal priorities and target values. The goal programming solution optimized the company’s profit while meeting the demand and also minimizing raw material and labor usage. The results show the optimal inventory levels for the four products as determined by market demand in the target month to be: I1* = 215 tons (SSTP), I2* = 380 tons (MSTP), I3* = 550 tons (JSTP), and I4* = 275 tons (PT). The sensitivity analysis reveals that changes in goal priorities and target values have significant effects on optimal production and inventory levels, thereby highlighting the importance of careful goal setting and prioritization. The findings from the research revealed that a 50-tonne increase in production will increase the profit margin by ten percent for jumbo-size toilet paper. The proper understanding of the production implications will enable the mill to optimize its production, manage resources, and also maximize profit.

Equilibrium strategies of customers and optimal inventory levels in a make-to-stock retrial queueing system

<abstract><p>We examined a retrial make-to-stock system based on a double-ended queue. When the queue length was negative, the inventory system contained only products, and customers were waiting in the retrial queue when the queue length was positive. We developed a model to study the expected cost of the entire system with strategic customers in the observable case and the fully observable case. We also obtained the optimal inventory levels under these two levels of information based on numerical experiments.</p></abstract>

Grocery Store Management System

A Grocery store management system helps the Grocery shops to streamline all the store operations well and run the business without any hassle. With a POS software for a Grocery store, you can offer the best customer experience, increase your customer retention rates and attract more customers. You may use it to keep expenses in check, boost sales, and make customers happier. Optimal inventory levels, reduced costs, and continued competitiveness in the dynamic grocery retail market are all possible outcomes of an investment in an inventory management system. A Grocery software is a fast, reliable, and one-stop solution that facilitates easy and efficient operations by automating with the help of technology. The Grocery stores can manage all their store operations in a single software which includes Sales, Purchase, Inventory, Accounting, CRM, Loyalty, and many more. Thus, the basic functions, to manage a store, carried out are:1. Receiving of incoming consignments (goods) 2. Safe keeping of goods (Custody)3. Disposal of undesirable goods.4. Inventory Management.5. Housekeeping and record maintenance.