Inventory Dynamics Research Articles

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.

Supply Chain Shocks and Retail Resilience: The Dynamics of Global Value Chains and Inventories

Supply Chain Shocks and Retail Resilience: The Dynamics of Global Value Chains and Inventories

Optimum production lot size for a perishable product under exponential demand with partial backordering and rework

The management of inventory significantly impacts an organization's profitability. Numerous researchers have developed the production model tailored for decaying items to boost sales and minimize inventory, thereby increasing turnover ratios. This study focuses on the EPQ model for reworkable items under exponential combined with inventory dynamics. The primary objective is to optimize the production plan to manage maintenance costs effectively, ensuring optimal Leveraging human and technological resources. The research determines the optimal cycle length, backordered quantities, and production quantities to minimize overall costs. Specifically, penalties are incurred for excessive production times during rework and non-rework phases. This model finds applications in industries such as printed circuit board assembly in semiconductor manufacturing, metal parts, and plastic molding. The study concludes with numerical examples comparing total costs under the proposed model, supplemented by sensitivity analyses that explore the impacts of parameter variations on decision-making.

Sustainable Product Design Strategy: A Comparative Analysis on the M113

This empirical study investigates the relationship between the inventory trends of M113 armored vehicles and sustainable design strategies in the defense sectors of Türkiye and the USA with the perspective of industrial design. Utilizing ARIMA modeling and linear correlation analysis, data collected up to 2022 reveals distinct patterns in vehicle inventory dynamics. The ARIMA analysis produced reliable coefficients despite limited data availability, suggesting feasible numerical assessments. Results indicate that in the USA, the introduction of alternative vehicles such as the Bradley, designed with sustainable principles, led to the phased reduction of M113s. Conversely, Türkiye's inventory trends reflect a linear relationship between the absence of vehicles with design integrity and sustained M113 usage, with modernization efforts prolonging its presence. The study underscores the economic advantages of implementing sustainable product design strategies in armored vehicle development, particularly evident in the USA's production of the Abrams tank and Bradley tracked armored carrier. Despite Türkiye's prolonged reliance on M113s, it highlights the necessity of comprehensive feedback mechanisms and professional units to perform sustainable design strategies. Moreover, it reveals the inefficiency of solely relying on modernization for operational vehicle counts in developing countries, emphasizing the importance of balancing technological advancements with vehicle design integrity. This study sheds light on the evolving nature of sustainable design strategies in defense sectors, urging countries to integrate comprehensive feedback mechanisms and design integrity principles into their armored vehicle development processes for long-term effectiveness and economic viability.

Three warehouse inventory model for non-instantaneous deteriorating items with quadratic demand, time-varying holding costs and backlogging over finite time horizon

This paper presents a novel sustainable inventory model tailored for non-instantaneous deteriorating items, incorporating quadratic demand and time-dependent holding costs. Notably, the model accounts for shortages over a finite time horizon, simulating real-world scenarios. The inventory management the strategy involves distributing products across three warehouses: the Own Warehouse (OW) and two Rental Warehouses (RW), with applications spanning supermarkets, bakeries, malls, and floral businesses. The primary objective is minimizing total costs to enhance consumer satisfaction and bolster profits. The theoretical framework is substantiated through numerical examples, focusing on the critical finding regarding optimal total cost. A special methodology is employed to balance inventory dynamics amidst various warehouses, optimizing resource allocation and mitigating shortages. Furthermore, a comprehensive sensitivity analysis using MATLAB Software unveils the repercussions of parameter modifications, providing valuable insights for decision-makers in diverse industrial settings.

Optimizing inventory dynamics: a smart approach for non-instantaneous deteriorating items with linear time function dependent variable demands and holding costs, shortages with backlogging

Optimizing inventory dynamics: a smart approach for non-instantaneous deteriorating items with linear time function dependent variable demands and holding costs, shortages with backlogging

Optimal operation of a hydropower plant in a stochastic environment

Given the currently changing climate conditions it is of primary importance to optimise the management of hydropower resources. This paper proposes a framework in a dynamic setting to determine the water outflow that maximises the value of a water resource for a given reservoir. The model includes two sources of uncertainty, the water inventory determined mainly by the water inflow and the electricity prices. It is implemented under the stochastic optimal control approach and calibrated using monthly data of reservoir characteristics from ResOpsUs. The results indicate that the inventory dynamics are specially important in valuing reservoir resources. The application of optimal management policies guarantees the long-term sustainability of the reservoir. The possible effects of climate change are considered in a sensitivity analysis to changes in the price and water inventory dynamics.

Inventory Optimization Using an AI-Powered Holistic Model for Decision-Support System

Abstract: This study presents a groundbreaking approach to inventory optimization through the implementation of an AIpowered holistic model within a decision-support system. Leveraging machine learning algorithms, specifically Logistic Regression (LR) and Decision Trees, the methodology explores historical sales data, supplier metrics, and market trends during an extensive Exploratory Data Analysis phase. The implementation of an AI-powered holistic model for inventory optimization in a decision-support system presents a transformative approach to managing and enhancing supply chain efficiency. The proposed methodology, integrating machine learning algorithms such as Logistic Regression and Decision Trees, has demonstrated its efficacy in achieving superior results compared to traditional models, as evidenced by higher accuracy, precision, recall, and F1 scores. The comprehensive exploration of historical sales data, supplier metrics, and market trends during the exploratory data analysis phase has facilitated a nuanced understanding of inventory dynamics. The seamless integration of the AI-powered model into the decision-support system has empowered organizations with timely and data-driven insights, fostering more agile and informed decision-making. The seamless integration of the AI-powered model into the decision-support system provides organizations with timely and data-driven insights, fostering agile and informed decisionmaking. Performance evaluation, including accuracy, precision, recall, and F1 scores, reveals that the proposed LR and Decision Tree models outperform the existing Support Vector Machine (SVM) model across all metrics. The LR model exhibits commendable precision, recall, and F1 score values of 0.95, 0.97, and 0.98, respectively, while the Decision Tree model demonstrates even higher values, with precision and recall at 0.96 and an exceptionally high F1 score of 0.99. These outcomes underscore the practical utility and robustness of the AI-powered holistic model in revolutionizing inventory management practices. While acknowledging challenges and limitations, this research signifies a crucial advancement in establishing responsive and intelligent decision-support systems for inventory optimization, paving the way for future innovations in supply chain management

Investment under Uncertainty: The Role of Inventory Dynamics

Finished-good inventory is very common under market uncertainty. We build a continuous-time model to study how the inventory will impact firm value and investment decisions. Our model shows that the value of a company following the optimal inventory policy can be significantly higher than the traditional non-inventory company, particularly if the inventory-holding cost is not large. This premium becomes small as holding cost is increased, and large when demand is volatile, and when price elasticity is large. We also show that the optimal investment size can be significantly larger than the traditional no-inventory firm, particularly when the inventory-holding cost is low, demand volatility is high, and price elasticity is low. This paper develops a simulation algorithm to solve iterative optimization problem in a path-dependent economy.

Production and Inventory Dynamics in the Mining Supply Chain: The Impact of Extraction Routings

This paper deals with integrated production-inventory-routing decisions in mining industries. It treats the impact of phosphate extraction routings adopted in open pit mines in providing stocks with diversified characteristics, to satisfy customer requirements at a minimum production cost. Those routings aim to control minerals’ characteristics in terms of quality, volume, and delivery time. For planning production activities, there is a need to select the right extraction routings in mines, to enrich (or not) the raw material and then blend ingredients according to their availability and customer requirements. Less attention was given by literature to the impact of extraction routings in production and stock dynamics. The primary focus revolves around filling the gap and recognizing their pivotal impact in open pit mines. While washing and blending ingredients play crucial roles, they alone may not always suffice to meet customer requirements. This paper undertakes an exhaustive study and modeling, using the case study of a Moroccan company specialized in phosphate rock production and its derivatives, aiming to underscore the significance of extraction routings through tangible and real examples. The findings emphasize a crucial need for variating the extraction routings in providing stocks of raw materials with the optimal characteristics regarding customer requirements.

Economic production quantity for a decaying item with stochastic demand and positive lead time

This paper considers a single-item, single-location production-inventory system subject to decay and random demand. Production occurs on a single machine at a constant and finite production rate. Shortages are allowed and partially backordered, while the other fraction of unmet demand is lost. A continuous review, lot size-reorder point policy is implemented to determine the size and the timing of production orders. The typical zero-lead time assumption made in existing economic production quantity models for a decaying inventory with random demand is relaxed here: when a production order is issued, a positive lead time is required before production starts. The long-run expected total cost per time unit is derived characterizing the stock dynamics as an Itō diffusion. Several properties concerning the inventory dynamics and the cost function are demonstrated, and an optimization procedure that finds the optimal solution for the developed cost function is presented. Numerical experiments are carried out with a twofold objective: (i) to validate the inventory model through simulation, and (ii) to evaluate the model sensitivity with respect to variations in parameter values.

Control of Dual-Sourcing Inventory Systems Using Recurrent Neural Networks

A key challenge in inventory management is to identify policies that optimally replenish inventory from multiple suppliers. To solve such optimization problems, inventory managers need to decide what quantities to order from each supplier given the net inventory and outstanding orders so that the expected backlogging, holding, and sourcing costs are jointly minimized. Inventory management problems have been studied extensively for more than 60 years, and yet even basic dual-sourcing problems, in which orders from an expensive supplier arrive faster than orders from a regular supplier, remain intractable in their general form. In addition, there is an emerging need to develop proactive, scalable optimization algorithms that can adjust their recommendations to dynamic demand shifts in a timely fashion. In this work, we approach dual sourcing from a neural network–based optimization lens and incorporate information on inventory dynamics and its replenishment (i.e., control) policies into the design of recurrent neural networks. We show that the proposed neural network controllers (NNCs) are able to learn near-optimal policies of commonly used instances within a few minutes of CPU time on a regular personal computer. To demonstrate the versatility of NNCs, we also show that they can control inventory dynamics with empirical, nonstationary demand distributions that are challenging to tackle effectively using alternative, state-of-the-art approaches. Our work shows that high-quality solutions of complex inventory management problems with nonstationary demand can be obtained with deep neural network optimization approaches that directly account for inventory dynamics in their optimization process. As such, our research opens up new ways of efficiently managing complex, high-dimensional inventory dynamics. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (NCCR Automation) [Grant P2EZP2 191888] and the Army Research Office [Grant W911NF-23-1-0129]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0136 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0136 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

On the dynamics of order pipeline inventory in a nonlinear order-up-to system

Managing order pipeline inventory is important for controlling unwanted system dynamics, especially the bullwhip effect. We analytically explore the impact of desired target order pipeline inventory, advocated as a key decision in managing pipeline inventory, on system dynamics performance. Using control theory and system dynamics simulation, we evaluate two control mechanisms, termed as Reactive Pipeline Control (RPC) and Proactive Pipeline Control (PPC) approaches, in a nonlinear forbidden returns supply chain. We derive the analytical expressions of bullwhip under shock and seasonal demands and propose bullwhip avoidance strategies. The results indicate that an RPC based system always shows slower inventory convergence speed than that in the PPC based system, although the system with PPC policy may produce more unwanted oscillatory behaviour. Also, PPC always generates more bullwhip than that in an RPC-controlled system regardless of physical delays and system control parameters e.g. forecasting and inventory adjustment. Furthermore, compared with the linear system, the nonlinear forbidden returns system always generates less bullwhip and less oscillation at the expense of slow inventory recovery speed regardless of order pipeline control policies. Managers may consider different order pipeline control policies by jointly assessing their inherent system structure, control policies and customer demand characteristics, such as frequency and variance.

A stock-flow consistent model of inventories, debt financing and investment decisions

The paper develops a Kalecki–Steindl–Minsky stock-flow consistent (SFC) model by including inventories, as well as firm’s deposits and debt financing into the investment function. The role of investment decisions in shaping aggregate fluctuations over the business cycle is assessed by considering a model populated by five types of economic actors: workers, rentiers, firms, commercial banks and the central bank. Business cycle fluctuations are investigated assuming a deterministic steady growth path in the long period, in line with recent developments in heterodox growth theory. Next, we simulate the model, calibrating it for the US economy. Our main results confirm the relevance of inventory dynamics in economic cycles, similar to Metzler’s (1941, 1947) analysis. First, inventory investment propagates shocks and affects the length of waves, deviation size and time to convergence. Second, expectations may determine whether there are oscillations or a steady convergence to the long-term trend. Third, we suggest that inventory investment accommodates rapid aggregate demand variations, although not presenting a multiplier effect.

Heuristic Rules for the Dynamic Pricing Problem

This paper is devoted to the development of heuristics for the dynamic pricing problem. A discrete time model of dynamic pricing on the fixed time horizon is proposed. It is applicable to products that satisfy two properties: 1) product value expires at a certain predetermined date, and 2) consumers demand at most a single unit of the product. This type of demand structure allows deriving a simple system of recursive equations for optimal prices using dynamic programming techniques. Optimal pricing policy is expressed as a function of time to expiration and inventory levels of unsold products. An analytical solution to this problem was obtained for special cases, while for the general case, a numerical algorithm has been developed. Qualitative characteristics of the optimal pricing policy are established, and their implications for dynamics of inventories and prices are discussed. Based on these observations, a simple heuristic rule for dynamic price adjustments is proposed. Performance of this heuristic is evaluated against the optimal dynamic and fixed-price policies using Monte-Carlo experiments. Results demonstrate high efficiency of the proposed heuristic strategy and its even simpler derivatives. Heuristics’ adaptability and ease of implementation should make it suitable and attractive for small and medium businesses.

Dynamic discount pricing in online retail systems: Effects of post-discount dynamic forces

Discount offer is one of the widely used strategies of e-commerce retailers to promote sales. However, the success of a discount offer depends on how well inventory levels and discount offers are dynamically managed in relation to each other. In addition, empirical researches have proven that discount offers alter customers' reference prices. However, such alterations are ignored in determining the percentages of discounts despite their importance. This paper proposes a dynamic model determining the optimal discount rates based on the states of inventory and customers’ reference prices in online retailing systems. The model also determines when is the optimal time to stop discounts to have inventory under control, by considering the time lag in which the impacts of traces left of discounts in the post-discount period will fade. The results show that choosing the time to stop the discount, by considering inventory dynamics, demand dynamics, and traces left of discounts, helps in maintaining the inventory availability and is more profitable for the retailer compared with another arbitrarily chosen timepoint. Besides, the model identifies the last-instant discount rate as an effective factor in controlling the rate of decrease in reference prices in the post-discount period. The model can help the online retailers to better optimize their discount offers by dynamically adjusting the discounts.

Examining demand and supply-chain antecedents of inventory dynamics: Evidence from automotive industry

This study examines inventory dynamics on durable finished goods in an automotive supply chain with respect to manufacturers, distributors, and retailers. We apply a regression method to product-level industry data covering a period from 2010 to 2019. We test the demand-oriented and supply chain-associated influences on the development of inventory dynamics to reveal the practical implications that are important to managing a business. Our results show that essential factors that directly affect inventory dynamics include information distortion, product variety, and product attractiveness. In addition, our study also shows that the effects of product variety and product attractiveness are mediated by the demand variation to the development of inventory dynamics. Further, we demonstrate that the studied factors may influence inventory dynamics in various degree when the test is made based on individual product type.

Москвіченко І. М., Стаднік В. Г., Коросан А. П. ПІДВИЩЕННЯ ЯКОСТІ ПОСЛУГ ОРГАНІЗАЦІЇ ДОСТАВКИ КОНТЕЙНЕРНИХ ВАНТАЖІВ КОМПАНІЯМИ ЛОГІСТИЧНОГО АУТСОРСИНГУ З УКРАЇНИ В УМОВАХ МОРСЬКОЇ БЛОКАДИ ПОРТІВ

Annotation. The purpose of the article is to develop theoretical and applied approaches to selecting effective supply chains for container cargo by logistics outsourcing companies from Ukraine in the context of the sea blockade of ports. Research methodology. The goal set in the article is achieved using the following research methods: logical generalization and scientific abstraction, structural analysis, and the analytical method. Findings. The cost of various container export delivery schemes through European seaports with cargo delivery to the port of departure by various modes of transport was calculated. As a result, the most efficient delivery scheme was chosen - the export of containers with Ukrainian grain through the Port of Gdynia to the port of Xingang by rail, which is offered by Western Container Terminal LLC, using the sea freight tariff of the Hapag-Lloyd container line. At the current stage, the existence of Ukrainian logistics companies depends on the development of the most variable transport delivery schemes, so the prospects for further research are in the use of dynamic simulation modeling of supply chains, for example, using the AnyLogistix platform, which combines analytical optimization and dynamic modeling in one platform. Dynamic modeling makes it easy to experiment with your supply chain and choose the best solutions to implement. A dynamic simulation model can serve as a digital double of the supply chain, which allows you to experiment with improvements and track all important indicators: costs, revenue, inventory dynamics, service quality, bullwhip effect, capacity, inbound/outbound orders. Logistics outsourcing companies will be able to test multiple supply chain transformation scenarios, vary parameters, and make business decisions based on testing and evaluating results in a risk-free virtual environment. Practical value. The practical use of the proposed theoretical and applied approaches will improve the effectiveness and quality of services of Ukrainian logistics outsourcing companies. Logistics companies can test multiple supply chain transformation scenarios, vary parameters, and make business decisions based on testing and evaluating results in a risk-free virtual environment.

Inventory dynamics at the retailer—An economic and environmental analysis of packaging fresh produce

The use of packaging materials for food products and the associated environmental impact has received increasing attention in public discourse over recent years. At the same time, packaging solutions provide functional properties that help to preserve fresh produce and protect food from external damage. Given these trade-offs, it can be very challenging for retailers to make the right decisions, lowering their environmental impact. This is further complicated by the complex nature of inventory dynamics in the case of perishable products, as multiple factors can impact food waste and the environmental impact at the retailer. This research proposes a simulation model to investigate the effect of packaging as well as a variety of other factors on profit, waste and the environmental impact of products. For this purpose, the environmental impacts of three different products and their corresponding packaging solutions were quantified using a life cycle approach, and a number of experiments considering different demand patterns, service level requirements, batch sizes and order policies were carried out. The findings of this research are manifold, highlighting the importance of considering inventory and packaging decisions within an integrated framework. Moreover, while the shelf life extension obtained from the use of packaging has clear economic benefits due to the resulting lower waste levels, the environmental impact varies depending on the considered products, packaging material and environmental indicator.

Dynamics, Risk and Management Performance of Urban Real Estate Inventory in Yangtze River Delta

(1) Background: Inventory management is a key point in the achievement of the virtuous cycle and sustainable development of the real estate industry. In response to the practical needs of city-based policies, this paper constructs a new research approach of “evolution dynamics—risk analysis—performance evaluation—policy design” of real estate inventory, and conducts a case study on the Yangtze River Delta. (2) Methods: This paper studies the change characteristics, trends and spatial patterns of real estate inventory changes in the Yangtze River Delta based on Geographic Information System software, and quantitatively evaluates the risk level and management performance of real estate inventory by introducing the Boston Consulting Group Matrix for corporate management and the Super- Data Envelopment Analysis Model for operations research, providing a basis for policy design. (3) Results: First, the “destocking” policy has gained results to some extent and diversified the inventory evolution, thus alleviating or curbing the negative trend in most cities. Second, the real estate inventory in the Yangtze River Delta is divided into high, low, potential and zero pressure zones by risk levels, and the proportion of cities with increased, decreased and unchanged risk levels is essentially the same. Third, the average real estate inventory management performance index has been steadily improving, but overall, it is still unsatisfactory, with cities in an effective state accounting for 40% and below for a long time. Fourth, real estate inventory and its management performance both show significant spatial effects, with cold and hot spot cities characterized by a “center-periphery” spatial pattern in geographical distribution, and the cities in the study area are classified into four types: super-efficiency, efficiency, inefficiency, and super-inefficiency. Fifth, the real estate inventory in most cities is continuing to grow positively, and a small number of cities have been in the high-risk zone for a long time or become new members of the high-risk zone, making the government and enterprises still faced up with great pressure and challenges in inventory management with the risk level further increased but management performance growing slowly. (4) Conclusions: The study area is divided into four types of policy areas, that is, red key area, yellow important area, green auxiliary area, and path-dependent area, and suggestions for optimization are made from the perspectives of risk control, performance improvement, benchmarking recommendation, and redundancy governance, providing a basis for the government’s real estate inventory management policy design and the enterprise’s high-quality development decision.