Inventory Management Model Research Articles

Understanding the determinants of consumer grocery stockpiling behavior

AbstractGrocery stockpiling is a common behavioral response to the emergence of disasters or heightened uncertainty. Nonetheless, the phenomenon and methods for mitigating it are not well understood. Using a model of household shopping and inventory management, we conceptualize stockpiling as a result of an increase in the fixed cost of making grocery shopping trips, or the opportunity cost of time associated with shopping. In a laboratory experiment, we find that stockpiling increases (decreases) by 78 and 41% (22%) with an increase in fixed costs and price reductions (imposition of purchase limits), respectively. We also find that stockpiling leads to fewer (more) grocery trips by 33 and 22% (36%) under the same three conditions, respectively. Our experiment and subsequent cluster analysis suggest that loss aversion suppresses stockpiling. Our experiment shows that imposing purchase limits, a common retail response to stock‐outs, can trigger stockpiling during shopping trips without purchase limits. Although we do not claim external validity, our study suggests that store managers and policymakers should be careful about solutions during a stockpiling event, such that they do not exacerbate stockpiling, which may disproportionately affect vulnerable groups and disrupt supply chains.

Blockchain-Based Intelligent Inventory Management for Hospital Supply Chain (HSC) with Deep Learning Techniques

A medication shortage may have far-reaching consequences, making it an important inventory management concern for healthcare organizations. Intelligent demand forecasting is essential to make cold chain businesses more competitive and save transportation and storage costs. Hence, this paper proposes a blockchain-based intelligent inventory management model (BC-IIMM) with deep learning techniques for hospitals to prevent running out of supplies, run their systems sustainably, and optimize their profits in the hospital supply chain (HSC).Preventing the contamination of pharmaceuticals requires a pharmaceutical cold chain monitoring system that uses IoT devices, blockchain, and cloud computing to track items across their entire supply chain reliably. One other perk is that both doctors and patients may benefit from drug recommendations made by a deep belief network (DBN) model. Further, to construct deep learning (DL), the proposed cold chain monitoring system produces large-scale, high-quality data. Holding cold chain items would become less expensive and cold chain inventory management decisions would be easier with it.Next, they provide a numerical result proving the suggested method works better than the baselines regarding refilling cost and shortage rate.

Study on Optimizing Inventory to Minimize Waste in the Fresh Produce Supply Chain

Abstract: The increasing demand for fresh produce, particularly greens, presents unique challenges in inventory management due to their short shelf life and susceptibility to spoilage. Inefficient management often results in significant waste, leading to financial losses for retailers and increased environmental burdens from the disposal of spoiled goods. Despite various strategies being employed in the agri-food sector, gaps remain in understanding how to optimize inventory levels to minimize waste at the retails level while maintaining high service levels for customers. This research aims to develop an inventory management model tailored to fresh produce that can mitigate these issues by balancing stock availability and minimizing wastage, particularly in large retail shops. To address the research problem, a mixed-integer linear programming (MILP) model was developed, focusing on three service levels—90%, 92%, and 95%—to evaluate the trade-offs between customer satisfaction and total inventory costs. The model incorporates key constraints, including stock balance, shelf-life limitations, and demand satisfaction. The model was validated using actual data, including stock, sales, and wastage records for the month of August, 2024. The objective function aimed to minimize total costs, encompassing holding costs, wastage, and lost revenue. The findings indicate that higher service levels, while reducing stockouts, significantly increase costs due to excess inventory and waste. The most cost-effective solution was observed at the 90% service level, which balanced waste reduction and customer satisfaction. The study also emphasizes the importance of integrating total cost reduction and minimize wastage. Future work should focus on incorporating consumer behavior patterns and extending the model to multi-echelon inventory systems, which include both distribution centers and retail outlets, to capture a more holistic view of the supply chain.

OPTIMAL DECISION-MAKING FOR NEWSPAPER INVENTORY: BALANCING DEMAND UNCERTAINTY AND PROFITABILITY

Estimating cost-benefit analysis, calculating the real value of anything before buying it is essential in order to know if this purchase is fetching or not. In the industry of selling newspapers, since demand is determined by various unpredictable features like news quality and types of reader interest, it may be difficult to estimate how many copies you should buy daily. On the other hand, this study addresses the classic Newsvendor Problem by analyzing a single-period inventory management model to find the optimal order quantity that maximizes profit. Simulating different demand scenarios up to 20 days into the future, using financial-based assumptions (e.g.: selling price, buying cost, and salvage value) on order quantities in order to investigate profits. The results show that we can identify the optimal level of order quantity in the cost-benefit analysis and profit analysis under different demand situations. This method reduces both excess waste and missed sales opportunities, making an optimal answer for inventory alignment in sought-after uncertainty-based industries. The insights of this analysis are not just relevant to newspaper sales but also to all comparable industries where accurate inventory management becomes challenging.

MODEL OF SPARE PARTS INVENTORY MANAGEMENT IN THE SUPPLY SYSTEM: THE CASE OF THE ‘OLL TRAK PARTS’ COMPANY

The article identifies the relevance of the problem of optimisation of material reserves of a company and their effective management, as the state of existing reserves has a decisive impact on the competitiveness of the company, its financial condition, and the corresponding performance results. It allows the formulation of the research objective in this development. It is a given that inventory management technology is an integral part of the management system of any manufacturing and trading company. The efficiency of these companies depends on such critical criteria as the number of costs incurred for inventory management. Traditional indicators, such as inventory volume, resource turnover, and continuity of supply, cannot be used in isolation to accurately measure the extent to which an inventory management system is improving since they are part of the overall cost criterion. The article examines scientific developments of theoretical foundations for implementing rational solutions in the supply chain, where the authors use modern research methods and approaches. After analysing these studies, we identify the main problematic issues: determining a rational way of servicing orders by the core production warehouse systems, organising an efficient product supply form, and developing rational inventory management technologies throughout the entire product supply chain. We create a structural model of interaction between the warehouse system of the ‘OLL TRAK PARTS’ company and suppliers and customers, formed with the participation of certain parties in all relevant operations. The structure of stocks accounts for the customer’s needs in a multi-nomenclature offer of spare parts for trucks, cars, and agricultural machinery supplied by ‘OLL TRAK PARTS’. The authors build a mathematical model of spare parts inventory management under limited demand conditions, which considers the following parameters of influence: the period for ordering spare parts, the cost of supplying ordered spare parts, and the cost of storing one unit of spare parts inventory. To achieve maximum measurement accuracy with a minimum number of trials and to maintain the statistical reliability of the results, we develop a full-factor experiment plan for three input parameters with three levels of values, consisting of 27 series of trials. The experiment allowed us to model the change in minimum costs with different order volumes of spare parts. Based on the results of the regression analysis of the obtained values of the estimated indicator, we determined the type of regression model – a linear form with a non-zero coefficient. Keywords: model, warehouse, system, logistics, interaction.

The principle of optimality in dynamic programming: A pedagogical note

The principle of optimality is a fundamental aspect of dynamic programming, which states that the optimal solution to a dynamic optimization problem can be found by combining the optimal solutions to its sub-problems. While this principle is generally applicable, it is often only taught for problems with finite or countable state spaces in order to sidestep measure-theoretic complexities. Therefore, it cannot be applied to classic models such as inventory management and dynamic pricing models that have continuous state spaces, and students may not be aware of the possible challenges involved in studying dynamic programming models with general state spaces. To address this, we provide conditions and a self-contained simple proof that establish when the principle of optimality for discounted dynamic programming is valid. These conditions shed light on the difficulties that may arise in the general state space case. We provide examples from the literature that include the relatively involved case of universally measurable dynamic programming and the simple case of finite dynamic programming where our main result can be applied to show that the principle of optimality holds.

Inventory Management and Strategy Optimization from The Perspective of Green Supply Chain Management

With the increasing severity of global environmental problems, the business community has begun to seek more sustainable business models. Green Supply chain Management (GSCM), as an important strategy to achieve this goal, emphasizes the integration of environmental protection and resource conservation concepts throughout the supply chain. This paper analyzes the impact of traditional inventory management on the environment, discusses the new model of inventory management under the perspective of GSCM, and puts forward the corresponding optimization strategy. This paper finds that green inventory management not only helps to reduce resource consumption and pollution emissions, but also improves the competitiveness and social image of enterprises. Therefore, the promotion of green inventory management is an important way to achieve sustainable development of enterprises and environmental protection.

Inventory pricing model under different carbon emission policies

Considering carbon emission in inventory management model is a hot topic in current academic circles. Studying inventory pricing can provide managers with better managerial insights. Economic order quantity model mainly solves the inventory management problem of demand determination. However, due to the complex market environment in real life, it is difficult to meet the conditions of constant demand. In addition, with the enhancement of low-carbon awareness of enterprises, the study of pricing model based on low-carbon policies has also attracted the attention of a large number of scholars. The robust optimization theory is applied, the inventory pricing model of demand fluctuation under carbon cap, carbon tax and carbon cap-and-trade policies is considered, the robust equivalence form of the problem is studied. The profit comparison under different policies is given. Numerical experiments show that the carbon cap-and-trade policy can better control the carbon emission of enterprises.

Collaborative system model construction and simulation optimization of e-commerce enterprise supply chain

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

Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting

Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting

An Integrated Model of Digital Adoption, Strategic Sourcing, Inventory Management and Procurement Excellence Driving Firm Performance

An Integrated Model of Digital Adoption, Strategic Sourcing, Inventory Management and Procurement Excellence Driving Firm Performance

A comprehensive inventory management model with weibull distribution deterioration, ramp-type demand, carbon emission reduction, and shortages

This study examines the challenges of two warehouses operating under Last-In-First-Out (LIFO) order policies in inventory management, including unpredictable demand patterns, the decay of Weibull distribution, and the need to reduce carbon emissions by adopting green technology. The research addresses various shortage circumstances using advanced inventory modeling techniques to manage ramp-type demand and Weibull distribution deterioration. Additionally, it aims to reduce carbon emissions by incorporating environmentally friendly technologies. By combining advanced inventory modeling with green technology, businesses can effectively manage unpredictable market situations while actively contributing to the global initiative of reducing carbon emissions. It also takes into account various potential backlog scenarios. The proposed model also considers no, partial, and complete shortages and their combinations. This research aims to determine the optimal cycle duration for retailers to increase their total profit while simultaneously investing in green technology. A numerical illustration is provided, and a sensitivity analysis is performed in MATLAB on the optimal solutions concerning parameters to demonstrate applicability.

Prospects and challenges in logistics management of international companies

Inventory management models are aimed at establishing optimal batch sizes of resources that will be subsequently used in the production process. The author believes that the use of this model makes it possible to set order sizes not «manually» by employees of the supply department, but rely on an optimization economic and mathematical model. Considering that these calculations are given only for one item of purchased inventory items, we can conclude that expanding the use of this approach to other types of purchased parts and raw materials will contribute to greater savings in financial resources. The option proposed in the article helps to save the Company's expenses on the type of inventory under consideration by $244,4 per year compared to the original option (or by 1,3 %). Minimizing overall transportation costs will depend on optimal vehicle loading. Optimal loading of vehicles will minimize the number of vehicles involved in transportation, and therefore reduce the overall costs of delivering products to customers. The results of the work can be used to improve the Company's inventory management, as well as to solve problems of transport support for entering a new market (transport routing). The combination of ABC and XYZ analyzes will make it possible to have a visual representation of all inventories in the Company’s warehouse through the formation of standard groups of inventories, in relation to each of which the inventory management policy will be different. Considering the steady growth rate of the company's sales, the author recommends expanding the sales network by entering new markets, which could be, for example, regions of the Russian Federation.

Proposal of Set of Optimization Criteria and Their Specific Calculation for Effective Inventory Management in an Industrial Enterprise

Currently, there are many mathematical models and tools for inventory management and optimization. Based on the literature review, the authors conclude that inventory management tools and models are mostly one-sided and focused on the cost aspect of the inventory management process. The research goal was to propose a set of criteria for inventory optimization as a basis for the development of an inventory management model whose structure would consider both the economic aspect and the stochasticity of demand and the reliability of delivery. Optimization criteria are based on extensive analyses of influences and correlations between logistic chain subsystems in relation to inventory. The interactions of the subsystems are then mathematically expressed, which represents the main output of the created set of optimization criteria. The defined criteria can be used as a basis for creating a new optimization model of inventory management.

HARMONIC ANALYSISIN INVENTORY MANAGEMENT IN LOGISTICSAND SUPPLY CHAINS

The article examines the possibilities of using harmonic analysis apparatus while planning the need in stocks of various categories. The issues of improving material planning (forecasting) are currently quite relevant applied tasks in business, and are also of interest in terms of developing the theory of logistics and supply chain management. Among the approaches to planning the need for inventory, a wide range of statistical theory methods are used in practice, among which two large groups can be distinguished, such as one-parameter time series analysis models for goods of stable and relatively stable demand, as well as correlation and regression models for chaotic consumption stocks. Most of the goods sold to end consumers have a seasonal component, which may not always be explicitly expressed in statistics. However, due to change of seasons, regular fluctuations in the values of consumption from the warehouse, around the average chronological or trend, may necessitate regular recalculation of the main parameters of inventory management models, such as maximum, insurance, threshold inventory levels, etc. In addition, sales statistics may contain fluctuations of a higher order if the stocks sold have life cycles of interest in goods on the market, which can also be attributed to periodic or quasi-periodic processes. One of the methods to formalize such cyclic processes can be trigonometric approximation or harmonic analysis, when the values of a time series are represented as terms of a Fourier series (harmonics) having corresponding amplitudes and phases. The article compares the effectiveness (accuracy) of forecasts of inventory requirements made using harmonic analysis and traditional time series extrapolation methods.

Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts

Spare parts redundancy is an effective way to improve system reliability and prolong system lifetime. Generally, preventive maintenance and inventory management of a standby system can significantly reduce operating and maintenance costs, particularly crucial in industrial systems. Additionally, during storage, spare parts may experience degradation failure due to internal mechanisms and sudden failure due to external shocks, complicating the health management of the standby system. Unfortunately, few existing studies consider the impact of spare parts’ competing failures in standby systems. To address this gap, this paper proposes a novel method for predicting the lifetime and optimizing the maintenance policy of nonlinear degradation standby systems, considering both degradation and sudden failures of spare parts. Firstly, we established the degradation model under the competing failure of spare parts and derived the analytical formula for life prediction of the standby system in the presence of competing failures, assuming that sudden failure follows the Weibull distribution. Furthermore, we developed a joint optimization model for replacement maintenance and inventory management based on predictive information, aiming to minimize the expected cost, in which the block replacement interval and the number of spare parts are treated as decision variables. Finally, the effectiveness and potential application value of the proposed method are verified through numerical simulation and a case study.

Data-driven Wasserstein distributionally robust dual-sourcing inventory model under uncertain demand

Dual-sourcing inventory management, which is aimed at replenishing inventory through two supply sources, has been extensively incorporated across various industries as it can mitigate supply chain related operational risks. Given the practical relevance of this framework, many dual-sourcing inventory models based on stochastic and robust optimization approaches have been developed. However, these approaches encounter challenges such as the curse of dimensionality or solution conservativeness. In this study, we developed a data-driven distributionally robust optimization model for dual-sourcing inventory management under uncertain demand conditions, in which partial information regarding the distribution of the uncertain demand is available. A tractable model was constructed to solve the problem, and an optimal solution was derived in a closed-form expression. Numerical experiments were conducted to evaluate the performance of the proposed model in comparison with benchmark models in terms of the order-, stock-, and rolling-horizon-related parameters and demand distributions. The results demonstrated the benefit of adopting the dual-sourcing strategy in inventory management based on the distributionally robust optimization approach. In addition, the proposed model outperformed the benchmark models in terms of mitigating the bullwhip effect.

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.

Mathematical models for improving the quality of services in hospitality industry

The article discusses the importance of developing a high-quality hotel product in the context of modern requirements for technical, technological, and environmental standards. The paper identifies key parameters that influence the assessment of the quality of a hotel offer, such as the location of the hotel, range of services, infrastructure, service technologies, pricing, and others. In addition, various mathematical models are considered, including a customer satisfaction assessment model, a demand forecasting model, a pricing optimization model, an inventory management model, and others, which can be applied to improve the quality of service in the hotel industry. This article aims to better comprehend the dynamics of changing customer needs and effectively adapt the hotel offer to market requirements. The authors draw attention to the importance of high-quality service for creating customer satisfaction and brand loyalty. The article also discusses in detail the aspects of checking the quality of hotel services, the problem of conflict of interests between customer expectations and reality, as well as mathematical methods for studying customer preferences for hotel services.

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.