Optimal Inventory Strategy Research Articles

Enhancing Inventory Management through Safety-Stock Strategies—A Case Study

Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.

Advance Selling Strategy with Return Service: The Impacts of Information Disclosure and Disclosure Methods

The return cost burdens both consumers and merchants, leading merchants to emphasize information disclosure during pre-sales of new products. This study analyzes the effects of information disclosure methods on consumer valuation and merchants’ pre-sale strategies across four scenarios: no advance selling, no information disclosure, traditional information disclosure, and blockchain-enabled information disclosure. We examine optimal pricing and inventory strategies under each scenario, assessing the value of information disclosure and the merchant’s optimal advance selling strategy. We find that traditional information disclosure could lead to various pre-sale pricing outcomes based on consumer valuation distribution bounds, trust levels, and merchant return losses. Blockchain-based disclosure influences merchants to implement premium pricing, which may render blockchain unnecessary. Offering pre-orders is beneficial with any information disclosure method. If consumers have varied product understandings, merchants benefit more from using an information disclosure strategy rather than just offering returns; otherwise, the reverse is true. The paper provides theoretical insights into optimal pre-sale information disclosure practices.

Supply chain risk in grain trading: Inventories as real options for shipping grain

AbstractIntegrating trading and logistics is an important challenge in commodity trading. Trading and logistics are strategic decisions and are integral to most commodities including grain shipping by rail, in addition to other modes (barges, ocean shipping). There are substantial risks, such as the ordering and placement of rail cars. The other risk is having sufficient grain stocks to load rail cars. Alternatives for managing these risks include holding grain inventories and the strategic use of shipping options. The purpose of this study is to develop a model for determining an optimal grain inventory strategy for shippers. The real option methodology is used to value uncertainty in rail car velocity and determine optimal purchases and inventories. The results show the importance of integrating trading and logistics decisions and illustrate that inventories can be interpreted and valued as real options that are affected by uncertainties. Taken together, the real options indicate how much extra inventory shippers should maintain to mitigate risks and maximize profits.

Optimal inventory management strategies for sequentially convertible items with different distribution patterns: A comprehensive stochastic approach

Optimal inventory management strategies for sequentially convertible items with different distribution patterns: A comprehensive stochastic approach

Dynamic supply chain risk management plans for mitigating the impacts of the COVID-19 pandemic

COVID-19 pandemic prompted supply chain (SC) disruptions and heightened demand for crucial items like facemasks and ventilators. Lockdowns and border closures hindered raw material supply and manufacturing capacity expansion. Consequently, manufacturers faced challenges in inventory, transport, and delivery, resulting in higher shortage costs, elevated SC expenses, and reduced SC efficacy. Using an integrated agent-based model (ABM) and optimisation, this paper examines COVID-19's multifaceted impacts on facemask SCs. It assesses four primary resilience strategies: enhancing manufacturing capacity, improving raw material supply, increasing transportation and distribution facilities, and maintaining dynamic inventory policy. Moreover, the model tested the proposed strategies under different scenarios by optimising the inventory policy and transportation strategies, leading to improved facemask production and delivery during extreme events. Our study found that increased production capacity through an optimal inventory and transportation strategy for a long period reduced the multiple impacts of the pandemic on facemask SCs, resulting in diminished total SC costs and increased consumer access to finished products. Based on demand forecasts, maintaining dynamically optimal reordering points and order up to levels can help maximise raw material supply and inventory levels, thereby minimising risks. Using these findings, future risks related to outbreaks and pandemics can be more effectively planned.

Inventory control strategy: based on demand forecast error

PurposeThis study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.Design/methodology/approachThis study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.FindingsThe study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.Practical implicationsThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.Originality/valueThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Optimal Greening Efforts, Pricing and Inventory Strategies for Non Instantaneous Deteriorating Perishable Products under Price, Freshness and Green Efforts Dependent Demand with Price Discount

Optimal Greening Efforts, Pricing and Inventory Strategies for Non Instantaneous Deteriorating Perishable Products under Price, Freshness and Green Efforts Dependent Demand with Price Discount

Optimisation of the EOQ model when renting storage locations taking into account the factor of vehicle capacity and losses due to the immobilization of funds in stocks during the reconfiguration of supply chains

The article presents a modification of the EOQ formula for a model with rental of storage facilities, which will allow managers operating in the conditions of crisis reconfiguration of supply chains to find the optimal inventory management strategy, taking into account the combined impact of a number of factors: the capacity of the vehicle, the time value of money when optimizing supplies and financial losses associated with funds frozen in stocks. The procedures for modifying the EOQ formula for such situations are carried out in relation to models important for practice, when payment for the order and the corresponding transaction costs of the supply chain are realized from the revenue received at the re-order interval. At the same time, necessary and sufficient conditions have been established based on which managers will be able to identify models of the specified type. The purpose of the article is to draw the attention of managers to additional opportunities to improve the efficiency of inventory management systems in modern business development conditions by considering these factors when optimizing supplies.

Sustainable Supply Chain System for Defective Products with Different Carbon Emission Strategies

Many nations have created ecological policies and regulations to prevent industries from emitting excessive amounts of carbon emissions into the environment. While significant progress has been achieved in the direction of sustainable growth, many nations still rely on nonrenewable energy sources. This study explores the viability of investing in green technology to achieve the optimal decisions (lot sizes, lead time, and green investment amount) in a two-echelon supply chain system by considering human error with two carbon emission strategies: carbon taxes and limited carbon emissions. It entails the inspection of every shipped lot by the buyer to identify defective products that could have resulted from the vendor’s production process. We show a constrained non-linear program and design a calculus-optimization technique to solve it. The methodology used in this research is the quantitative method, which is based on the principles of operations research, and the models are built on mathematically oriented inventory theory. The results imply that an outsized ecological carbon footprint can be reduced without compromising customer service by designing optimal inventory strategies. The findings also confirm that green investment is the greatest economical method for reducing carbon emissions and system costs.

A cleaner EPQ inventory model involving synchronous and asynchronous rework process with green technology investment

The amount of carbon emission in the atmosphere continues to grow, and production, storage and transportation are major contributors that are repeatedly unnoticed when it comes to solving the problem. A carbon levy and cap EPQ model has been explored in this study for a controlled carbon emission in view of cleaner production under imperfect production processes where a fraction of the products is faulty. The model incorporates emissions from transportation, production, and storage activities, which are meant to be decreased by green investment. The manufacturer employs a rework approach to rectify the faulty products. Flexible rework allows the manufacturer to choose the rework rate, which can differ from the manufacturing rate, as well as the process of rework, which can be asynchronous or synchronous. A linear rework cost function of the rework rate is assumed. We sought to obtain the lowest cost with respect to optimal lot size, and backorder under the effect of controlling carbon emissions by applying the exponential form of green investment. Keeping these in mind, several mathematical models derived for various rework rate and rework process assumptions with the objective emphases on gaining a trade-off between cost and emissions, thereby finding the optimal production-distribution inventory strategy and an appropriate sustainable plan for handling both synchronous and asynchronous rework process. We show a constrained non-linear programme (NLP) and derive a calculus optimization technique to solve it. An iterative solution technique is used to arrive at the optimal decisions, which are shown analytically and numerically. The results validate the impacts of controlled carbon emissions on the production strategies. The findings mainly suggest that increasing the carbon levy reduces emissions. The firm has more options to decrease emissions caused by industrial activity with the help of green technologies. Although green technology has a higher upfront cost, the producer will reap the advantages of lower emissions.

Optimal pricing and inventory control strategy for a continuous-review system with product return

We consider joint pricing and inventory control strategies for a stochastic inventory system with product return. Customer's demand and product return follow independent point processes with demand rate being price-dependent. Using a general verification theorem, we prove that an (s⁎,S⁎,p⁎) policy is optimal, where the optimal price p⁎(x) is a quasi-concave function of inventory level with a nonpositive changeover point. Furthermore, we propose an algorithm to search for the optimal policy parameters and numerically study the effect of product return.

Optimal inventory strategies for deteriorating items with price-sensitive investment in preservation technology

With recent developments in the technological world, the issue of deterioration has been addressed efficiently through preservation technology by numerous researchers. The fundamental concept of preservation technology involves an investment that is majorly dependent on the initial deterioration rate, irrespective of the selling price of the product. Owing to this, two different products having the same deterioration rate but different selling price requires equal investment, which is irrational. Further, the investment in preservation technology was considered to be per unit time irrespective of the number of units being held, which is again a major drawback of the existing literature. Thus, this paper attempts to bridge the gap by proposing a realistic way of implementing preservation technology, where not only the investment is per unit, but also depends on the selling price of that unit. The model jointly optimizes the selling price, the fraction of per unit profit to be invested in preservation technology, and cycle length to maximize the overall profit. The proposed model proves out to be economically more viable, through superior cost management, when compared with the existing model. The model is further illustrated with comparative numerical examples and graphical representations. Moreover, sensitivity analysis is performed to convey model characteristics.

Метод пошуку рішень у динамічній моделі управління запасами за невизначеності

The article considers the method of finding solutions in the dynamic model of inventory management under uncertainty. An algorithm for formulating an optimal inventory management strategy is proposed using the developed method of determining the size of the optimal order lot and forming an optimal supply plan under conditions of stochastic demand. The developed solution is aimed at ensuring profit maximization in goods supply operations due to the optimization of inventory maintenance costs, an increase in inventory turnover ratio, a reduction of working capital maintenance costs, etc. The modelling was performed considering the mono-product strategy, the even distribution of sales in the cycle, and the instant supply of goods at the beginning of the cycle. The most common variables were listed as limitations of the model, which allows for a demonstration of the operation of the algorithm. The paper compares the simulation results of alternative methods of planning the inventory management process. The given results indicate the superiority of the proposed model.

Inventory management of vertically differentiated perishable products with stock-out based substitution

The need for optimal inventory control strategies for perishable items is of the utmost importance to reduce the large share of food products that expire before consumption and to achieve responsible food stocking policies. Our study allows for a multi-item setting with substitution between similar goods, deterministic deterioration, delivery lead times and seasonality. Namely, we model demand by a linear discrete choice model to represent a vertical differentiation between products. The verticality assumption is further applied in a novel way within product categories. Specifically, the same product typology is vertically decomposed according to the age of the single stock-keeping unit in a quality-based manner. We compare two different policies to select the daily size of the orders for each product. On the one hand, we apply one of the most classical approaches in inventory management, relying on the Order-Up-To policy, modified to deal with the seasonality. On the other hand, we operate a state-of-the-art actor-critic technique: Soft Actor-Critic (SAC). Although similar in terms of performance, the two policies show diverse replenishment patterns, handling products differently.

Optimal inventory strategies for an imperfect production system with advertisement and price reliant demand under rework option for defectives

Every industrial sector emits carbon emissions during production, which is always a major concern for environmental practitioners. To resolve this, innovative and smart ways are required that are not only cost-effective but also environmentally savvy. Moreover, the production processes are also prone to various imperfections, and thus the production of defectives is quite pragmatic. The handling of such defective products varies from industry to industry and type of products. The present study constructs an inventory model to handle the defective products via a proficient rework strategy that makes the product fit to be sold at the primary price. Here, the demand for the product is assumed to be dependent on the selling price and advertisements. The model also considers the energy usage during the production and the cost of carbon emissions while optimizing the production batch size and selling price. Optimality is proved graphically by using the software Mathematica 11.3.0. A comprehensive sensitivity analysis for all the parameters is performed to impart managerial insights and robustness to the model.

Cross-Border Internet of Things E-Commerce Warehouse Control System Based on TRIZ Theory

Cross-border e-commerce of the Internet of Things is affected by international situations and political factors. Supply chain interruption and returns will cause violent fluctuations in commodity inventory, making the inventory control of cross-border e-commerce very difficult. The TRIZ principle is utilized to solve the problem of the difficulty to evaluate the suppliers comprehensively in e-commerce warehouse management. The Markov algorithm is used to describe the change of inventory level. The cyclic expected time and cost function are constructed by the horizontal crossing method, updating the process and Martingale theory. The effect of the correlation between the demand and supply interruption on the optimal inventory control strategy is studied by simulation. The change of the optimal control strategy under the different interrupt and return types is analyzed, and the validity of the management system is verified.

Inventory strategy development under supply disruption risk

This paper proposes a multi-period decision-making framework that enables procurement managers to develop an optimal supply inventory strategy under uncertain supply conditions. The framework draws on financial options valuation techniques by adopting the view of a procurement manager facing several uncertainties, while seeking to maximise its profit by exercising its flexibility to procure supplies and use inventories. Given the multitude of uncertain variables influencing inventory management decision in practice, the model is developed to remain robust to the inclusion of several underlying stochastic variables; it uses an American options valuation method and a least squares Monte Carlo simulation technique to solve the underlying dynamic programming problem. To demonstrate the application of the developed framework, a case study is presented based on data from a dairy supply chain. The case study explores the decision problem in several scenarios, showing how a decision-maker’s perception of product demand and supply price uncertainties, expectations over the timing of a supply disruption, discount rate, price shocks and disruption duration can be suitably incorporated into the decision-making framework.

Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method

Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.

Research on Mathematical Genetic Algorithms Based on Evolutionary Computer Neural Network of Distribution Function Parameters

Genetic algorithm and neural network are both ideas and algorithm skills. The flexibility of this method provides a stage for many researchers and applications. This paper discusses the neural network model based on the improved genetic algorithm under the parameters of the distribution function and its application in multi-variable, multi-step nonlinear economic forecasting. First of all, this article discusses the method of using improved genetic algorithm to learn neural network connection weights, and proposes a secondary optimization strategy. The main innovations or improvement techniques include: initial population generation strategy, crossover operation secondary optimization strategy, mutation operation Secondary optimization strategy, optimal chromosome inventory strategy, etc. This article uses VB for programming. The developed software has a certain versatility and can be used for prediction problems similar to nonlinear economic data.

The Coordinator Role of Trade Credit Contract for Coordinating Integrated Pricing and Periodic Review Inventory Decisions With Stochastic Demand

The purpose of this study is to analytically coordinate joint pricing and periodic review ordering choices for a supply chain (SC) facing stochastic price-dependent demand. In this paper, first, the optimal inventory and pricing strategies made by SC members are obtained under decentralized and centralized models. Thereafter, employing a trade credit contract, it proposes to provide a win–win coordination for the investigated SC. Sensitivity analyses are provided both analytically and numerically which indicate the significance of trade credit contracts in improving both customer service level and customer satisfaction. The results indicate that using trade credit contracts, the coordination of joint pricing and periodic review ordering choices is achievable. Moreover, the proposed trade credit contract has a unique feature in achieving more economic benefit compared to the centralized model under some circumstances. In addition, sensitivity analyses reveal that the coordination of integrated periodic review ordering and pricing choices is beneficial, especially for items with high price sensitivity and high demand uncertainty. Our findings also show the applicability of the trade credit contract for an SC facing highly uncertain demand.