Inventory Allocation Research Articles

Leveraging Predictive Analytics for Resource Optimization in Non-Profit Organizations

Aim: This study explores the use of predictive analytics for optimizing resource management and operational efficiency within non-profit organizations (NPOs) with a focus on recent trends in technology developments. Study Design: A comprehensive review of literature in relation to the use of predictive analytics within the non-profit organization sector, especially between 2020 and 2025, with a focus on data-driven decision-making and improvement frameworks. Methodology: The review adopted a systematic literature review approach, gathering articles from peer-reviewed journals like Google Scholar, Scopus, SSRN, and Business Source Complete. Results: The study integrated knowledge from 15 recent papers to show that predictive analytics improves the efficiency of fundraising, volunteer management, beneficiary targeting, and allocation of inventory. Technologies like machine learning algorithms, regression models, and time-series forecasting significantly contribute to forecasting donor behavior, demand cycles, and operational constraints. Implementation challenges including data privacy concerns, algorithmic bias risks, and organizational capacity limitations were consistently identified across studies. Conclusions: Predictive analytics presents a transformative opportunity for non-profits to maximize the use of limited resources. However, challenges such as data quality, organizational capacity, ethical considerations around data use, and appropriate governance frameworks require tailored approaches to maximize the potential of analytics in the non-profit environment.

Inventory Allocation: Omnichannel Demand Fulfillment with Admission Control

Ensuring the profitability of retailers utilizing in-store inventory for online fulfillment is a pivotal issue in omnichannel retailing. This study examines the inventory allocation challenges faced by retailers when managing interactions between online and offline channels to identify strategies that maximize revenue. The findings enable retailers to address key operational conflicts while implementing omnichannel strategies. We develop an omnichannel newsvendor model, deriving an optimal strategy for retailer inventory level and online acceptance thresholds, demonstrating the economic superiority of this approach over traditional policy. Furthermore, this paper further explores how carry-over inventory influences strategic decisions, particularly in quantifying the trade-off between the cancellation cost and the inventory holding cost. The results reveal that cancellation costs incentivize retailers to increase safety stock and reduce online acceptance thresholds, with strategy sensitivity intensifying as offline demand dispersion grows. Compared to the traditional policy, our policy demonstrates superior performance when the cancellation cost remains below a critical value, though its effectiveness decreases under high offline demand dispersion. Moreover, dynamic strategy adjustments must balance the cancellation cost against the holding cost in the carry-over scenario. The proposed framework systematically integrates inventory allocation with demand admission control, addressing a critical gap in existing literature that has failed to comprehensively link these two operational levers. This dual-focused perspective significantly advances omnichannel inventory management theory.

Gridemis V2.0: A highly integrated algorithm scheme for high-resolution and multi-component allocation of emission inventories used in air quality models

Gridemis V2.0: A highly integrated algorithm scheme for high-resolution and multi-component allocation of emission inventories used in air quality models

The Role of Artificial Intelligence in Advancing Supply Chain Management

All over the globe, AI is changing industry after industry, and supply chain management is no different. Known AI technologies like machine learning, natural language processing, robotics, and predictive analytics deeply influence the way supply chains perform and can enable businesses to improve their productivity, lower their costs, and increase customer satisfaction. Moreover, AI is transforming SCM with accurate demand predictions, effective inventory control, advanced logistic solutions, multi-level transportation, and comprehensive risk management. With the use of advanced machine learning algorithms and big data, AI can enhance operations, improve decision making, and build agile supply chains. The above listed goals introduce the importance of AI adoption in SCM, and to achieve the goals, this research outlines AI’s most significant implementations in SCM, including but not limited to: demand forecasting, inventory allocation, transportation activities, and risk forecasting and management. This paper adequately captures the pros that any organization will enjoy after an AI adoption like better operational performance, reduced spending, and higher service quality. In addition, the paper analyses how AI-powered systems pose challenges in supply chains such as data integrity, integration with older systems, cost issues, and insufficient workforce skills. Using case studies from leaders such as Amazon, Walmart, and UPS, this paper illustrates the impact of AI on SCM and provides a guide to organizations that want to adopt AI technologies. In the last part, the paper looks at new directions like autonomous supply chains, sustainability driven by AI, and the fusion of AI and blockchain which will further enhance the future of SCM. Emerging AI technologies will continue to provide greater opportunities to build complex, efficient, and flexible supply chains which makes the adoption of AI a necessity for companies wanting to maintain relevance in today's fast changing environment. To understand why AI and machine learning technology is evolving so rapidly in supply chains, this paper aims to inform practitioners and academics on the issues and possibilities in supply chain management.

The Stochastic Dynamic Postdisaster Inventory Allocation Problem with Trucks and UAVs

Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas. This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time. It introduces a novel stochastic dynamic postdisaster inventory allocation problem (SDPDIAP) with trucks and unmanned aerial vehicles (UAVs) delivering relief goods under uncertain supply and demand. The relevance of this humanitarian logistics problem lies in the importance of considering the intertemporal social impact of deliveries. We achieve this by considering social costs (transportation and deprivation costs) when allocating scarce supplies. Furthermore, we consider the inherent uncertainties of disaster areas and the potential use of cargo UAVs to enhance operational efficiency. This study proposes two anticipatory solution methods based on approximate dynamic programming, specifically decomposed linear value function approximation (DL-VFA) and neural network value function approximation (NN-VFA) to effectively manage uncertainties in the dynamic allocation process. We compare DL-VFA and NN-VFA with various state-of-the-art methods (e.g., exact reoptimization and proximal policy optimization) and results show a 6%–8% improvement compared with the best benchmarks. NN-VFA provides the best performance and captures nonlinearities in the problem, whereas DL-VFA shows excellent scalability against a minor performance loss. From a practical standpoint, the experiments reveal that consideration of social costs results in improved allocation of scarce supplies both across affected districts and over time. Finally, results show that deploying UAVs can play a crucial role in the allocation of relief goods, especially in the first stages after a disaster. The use of UAVs reduces transportation and deprivation costs together by 16%–20% and reduces maximum deprivation times by 19%–40% while maintaining similar levels of demand coverage, showcasing efficient and effective operations. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.

Equivalent combined cycle modeling and performance optimization for a three-heat-reservoir thermal Brownian heat transformer with external heat-transfer

Equivalent combined cycle modeling and performance optimization for a three-heat-reservoir thermal Brownian heat transformer with external heat-transfer

An inventory allocation model for cost reduction in the supply chain of a payment means company

The Supply Chain Management (SCM) has been gaining greater relevance in the sector, leading companies to improve the level of customer service and keep costs under control. However, it was identified that SCM is a less explored field in the acquirer market, so this work is an opportunity to contribute to the academic research. It was observed the opportunity to build an inventory model considering transportation as a variable cost, unlike most models found in literature. The objective of the study was to develop an inventory allocation model for the SIMcard chain in a Brazilian company in the acquiring sector, keeping the service level agreed with the customer. Inventory and transport costs were calculated for different supply cycles for all SIMcards operators and transportation hubs, to obtain the lowest total cost configuration in the supply chain. The results show that the costs cannot be considered individually and considering both costs, transport and inventory, there is an opportunity to increase efficiency in the studied operation of this work.

Exploring Inventory Management System with Priority Sequencing

Effective inventory management is crucial for businesses to optimise their resources and meet customer demand. This abstract introduces an Inventory Management System (IMS) that utilises priority sequencing to enhance the control and organisation of inventory items. The system employs various prioritisation criteria, such as item demand, shelf life, or profitability, to determine the sequence in which items are received, stored, and dispatched. Prioritisation Criteria: The IMS allows businesses to set specific rules and criteria for prioritising inventory items. This includes factors like customer demand, product shelf life, or item profitability. Real-time Monitoring: The system provides real-time visibility into inventory levels, allowing businesses to make informed decisions on restocking, order processing, and inventory allocation. Automated Reordering: IMS can automatically generate reordering recommendations based on preset priorities, ensuring that essential items are always in stock. Cost Reduction: By efficiently managing inventory based on priority sequencing, businesses can minimise holding costs, reduce wastage, and optimise working capital. Enhanced Customer Satisfaction: Prioritising items based on customer demand ensures that popular products are readily available, leading to improved customer satisfaction and retention. Forecasting Capabilities: The system can use historical data to predict future demand trends and adjust prioritisation accordingly. Reporting and Analytics: IMS provides detailed reports and analytics to help businesses fine-tune their inventory management strategies.

Implementation of the Agglomerative Hierarchical Clustering Method in Ordering Hijab Products

The ever-evolving internet technology has an impact on various sectors, including the hijab business, where the demand for hijab products is increasing through online transactions. This research was conducted at the Kinan Hijab Store in Kota Pinang, North Sumatra, with the aim of optimizing the management of hijab product stock. The problem faced is the imbalance in the stock of hijab products, where some hijab products have excess stock that are less in demand while popular hijab products often experience a shortage of stock. To solve this problem, the Agglomerative Hierarchical Clustering method is used to group hijab products based on sales data, product type, and price. This study uses hijab sales data from May to July 2024. After the clustering process, hijab products are grouped into two categories: "Popular" and "Less Desirable". The "Popular" category includes 190 products, while the "Less Desirable" category includes 983 products. Product stock in the "Popular" category will be increased by 50% of the average sales, while stock in the "Less Desirable" category will be reduced by 25%. the effectiveness of the Agglomerative Hierarchical Clustering (AHC) method in stock planning and management by showing that it improved the inventory allocation based on customer demand patterns. The clustering method categorized hijabs into two main groups: "Popular" and "Less Preferred", based on key sales metrics such as quantity sold, price, and total sales. The implementation of the stock plan is carried out based on the sales pattern of each hijab category. Overall, the application of this method not only helps stores in understanding customer purchasing patterns but also optimizes product availability, which can ultimately increase customer satisfaction.

Investigating the impact of postponement and stock‐sharing strategies on prepositioned relief stocks

Abstract Prepositioning is a critical disaster preparedness mechanism for humanitarian organizations (HOs) but requires significant investment. Improving the efficiency of prepositioned stocks is a primary concern within the humanitarian community. This study, conducted in collaboration with the Emergency Supply Prepositioning Strategy (ESUPS) Working Group, examines the impact of implementing postponement and stock‐sharing strategies in a regional humanitarian warehouse. We focus on a setting where multiple HOs preposition supplies within the same warehouse to serve disaster‐affected countries. Traditionally, these prepositioned supplies are branded with the respective HOs' logos, hindering the sharing of surplus stock during disaster response. Our system defers the branding process for a portion of the stockpile until after a disaster, facilitating the sharing of unbranded stock among HOs. To evaluate the benefits of postponement in this humanitarian context, we develop a two‐phase inventory allocation framework for distributing branded and unbranded stocks to disaster‐affected countries and for sharing unbranded stocks among HOs. We then incorporate our inventory allocation models into a Monte Carlo simulation algorithm that accounts for uncertainties regarding the occurrence and impact of disasters. Using a case study based on data from ESUPS members in the Caribbean region, we demonstrate that the proposed strategy can significantly enhance the efficiency and effectiveness of prepositioning. Remarkably, we observe a U‐shaped relationship in response time as the postponement rate increases, while the fill rate and inventory utilization consistently improve. Our numerical study provides valuable insights for decision making in humanitarian logistics.

Data-driven approach for rational allocation of inventory in a FMCG supply chain

Data-driven approach for rational allocation of inventory in a FMCG supply chain

MEIAT-CMAQ: A modular emission inventory allocation tool for Community Multiscale Air Quality Model

MEIAT-CMAQ: A modular emission inventory allocation tool for Community Multiscale Air Quality Model

Inventory reallocation in a fashion retail network: A matheuristic approach

We consider a fashion retail network consisting of a central warehouse, owned by a fashion firm, and a fairly large number of retail stores. Some stores are owned by the firm itself, whereas others are owned by franchisees. An initial inventory allocation decision is made at the beginning of the selling season and is periodically revised. Inventory reallocation comprises both direct shipments from the warehouse to stores and lateral shipments among the stores. Besides stock availability and shipping costs, a suitable reallocation policy must take into account the probability of selling each item, some operational constraints, as well as other preference factors that define the utility of shipping an item from a node of the network to another one. Since the problem does not lend itself to the application of typical tools from inventory theory, we propose an optimization model that complements such tools. The model, given the number of nodes and SKUs, may involve about one million binary variables, and just solving the LP relaxation may take hours using state-of-the-art software. Since typical metaheuristics for combinatorial optimization do not seem a viable alternative, we propose a matheuristic approach, in which a sequence of maximum-weight matching problems is solved in order to reduce the problem and restrict the set of potential shipping pairs, with a corresponding drop in the number of decision variables. Computational results obtained on a set of real-life problem instances are discussed, showing the viability of the proposed algorithm.

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

In response to the growing complexities in supply chain management, there is an imperative need for a data-driven methodology aimed at optimizing inventory allocation strategies. The purpose of this research is to enhance the efficiency of allocation and operational scheduling, particularly concerning the stock keeping units (SKUs). To achieve this, one year's operational data from a specific organization's SKUs is taken and machine learning tools are employed on the data collected. These tools are instrumental in identifying clusters of SKUs that exhibit similar behaviour. Consequently, this research offers recommendations for rational inventory allocation strategies that are finely attuned to the unique characteristics of each SKU cluster. Results obtained reveals substantial disparities between the recommended strategies for the organization's SKUs and those typically found in the literature such as same strategy cannot be used for all different types for products. This underscores the critical importance of adopting a tailored approach to supply chain management. Furthermore, the research demonstrates the remarkable efficiency of unsupervised machine learning algorithms in determining the optimal number of segments within the SKUs. The current research differentiates from others in a way that in most of the research, the holistic data-driven approach is underutilized, right from the selection of the clustering algorithm to the validation of segments.

Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach

This study systematically investigates the pivotal role of inventory management within the framework of “cloud supply chain” operations, emphasizing the efficacy of leveraging machine learning methodologies for inventory allocation with the dual objectives of cost reduction and heightened customer satisfaction. Employing a rigorous data-driven approach, the research endeavors to address inventory allocation challenges inherent in the complex dynamics of a “cloud supply chain” through the implementation of a two-stage model. Initially, machine learning is harnessed for demand forecasting, subsequently refined through the empirical distribution of forecast errors, culminating in the optimization of inventory allocation across various service levels.The empirical evaluation draws upon data derived from a reputable home appliance logistics company in China, revealing that, under conditions of ample data, the application of data-driven methods for inventory allocation surpasses the performance of traditional methods across diverse supply chain structures. Specifically, there is an improvement in accuracy by approximately 13% in an independent structure and about 16% in a dependent structure. This study transcends the constraints associated with examining a singular node, adopting an innovative research perspective that intricately explores the interplay among multiple nodes while elucidating the nuanced considerations germane to supply chain structure. Furthermore, it underscores the methodological significance of relying on extensive, large-scale data. The investigation brings to light the substantial impact of supply chain structure on safety stock allocation. In the context of a market characterized by highly uncertain demand, the strategic adaptation of the supply chain structure emerges as a proactive measure to avert potential disruptions in the supply chain.

Inventory Allocation under the Greedy Fulfillment Policy: The (Potential) Perils of the Hindsight Approach

Inventory Allocation under the Greedy Fulfillment Policy: The (Potential) Perils of the Hindsight Approach

An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain management

An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain management

Enhancing Autonomous Operations in Smart Objects and Devices through the Internet of Robotic Things

This study investigates the field of the Internet of Robotic Things (IoRT) and its capacity to transform the functioning of mobile context and robots’ awareness systems. IoRT facilitates autonomous operations in smart objects and devices via the use of data analytics technologies, intelligent data processing tools, deep reinforcement learning, and edge computing techniques. This article examines the use of sensor networks, cloud robotics, machine learning algorithms, and collaborative context-aware robotic networks for the purpose of enhancing job performance, decision-making skills, and operational efficiency in diverse industrial and collaborative settings. The research also investigates the incorporation of route planning tools and motion, cognitive decision-making processes, and sensor data to improve the efficiency of robotic systems in tasks involving object handling. Furthermore, this study investigates the impact of cloud computing, wireless sensor networks, and cognitive approaches on enhancing inventory allocation procedures and company performance. The main purpose of this article is to provide a scholarly contribution to the field of IoRT by exploring its technological advancements and examining its potential applications across many sectors.

Heat transfer effect on the performance of three-heat-reservoir thermal Brownian refrigerator

Abstract A finite-time thermodynamic (FTT) model of three-heat-reservoir thermal Brownian refrigerator is established in this paper. This model can be equivalent to the coupling of a thermal Brownian engine and a thermal Brownian refrigerator with heat transfer effects. Expressions for cooling load and coefficient of performance (COP) are derived by combining FTT and non-equilibrium thermodynamics (NET). The system performance is studied and compared with those of previous models. For fixed internal parameters, the thermal conductance distributions among three heat exchangers are optimized for maximal cooling load. For fixed inventory allocations, the internal parameters are also optimized for maximal cooling load. Finally, the double-maximum cooling load is obtained by optimizing internal parameters and external thermal conductance distributions simultaneously, and the optimal operating temperatures are also derived. Results show that half of total thermal conductance should be placed in condenser to reject heat to ambient under maximal cooling load regime. The heat transfer determines system performance by controlling the working temperatures and the coupling of two external loads. The system works in reversible state when COP reaches its maximum value. The new performance limits can predict that of three-heat-reservoir thermal Brownian refrigerator more accurately, and also include those of NET model.

Streamlining multi-commodity inventory allocation and redistribution

To prevent excess unsold goods caused by market fluctuations, retailers can redistribute surplus commodities among their stores to maximise profits. This paper introduces a novel approach to model the common real-world problem of multi-commodity inventory allocation and redistribution. Our unified approach integrates the two problems and seeks to optimise maximum profit. The study encompasses various factors such as inventory capacity, reallocation constraints, vehicle capacity, time windows for pickup and delivery, and a homogeneous fleet of vehicles. We propose two mixed-integer programming paradigms, the integrated and sequential formulations, along with an improved variable neighborhood search (IVNS) algorithm to solve the problem. Computational results demonstrate the effectiveness of the IVNS algorithm, while further analysis highlights the pros and cons of the two formulation paradigms. Notably, the integrated formulation yields superior solutions at the expense of increased computational time.