Search Results (35)

Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) and operational metrics, such as fill rate and stockouts. Methods: The workflow integrates daily, store-level transactions with external covariates, constructs temporal and lag features, and trains a Random Forest (RF) model using chronological splitting and time-series validation. Daily forecasts are then aggregated to the monthly level and used as inputs to an inventory simulation and an ROI-based economic model. Building on this simulation, a Genetic Algorithm (GA) optimizes the parameters of a monthly replenishment policy, incorporating minimum-coverage constraints. Results: In testing, the forecasting model achieved a mean absolute percentage error (MAPE) below 13%, and the RF+GA scheme outperformed the 28-day moving average baseline (MA28) in ROI across all five stores, with an average improvement of 4.52 percentage points; statistical significance was confirmed using the Wilcoxon test. Conclusions: Overall, the RF+GA approach serves as a decision-support tool that generates monthly order quantities consistent with demand and operational constraints, delivering verifiable improvements in both economic and service metrics. Full article

The purpose of this research is to reduce the environmental burden of textiles, specifically focusing on the production of Merino woollen jumpers. The study addresses two techniques to lessen the environmental burden: (1) recycling of wool garments by shredding or unravelling and (2) preventing the overstocking of products through on-demand knitting. The environmental burden is measured via LCA using Idemat. The results are reported in terms of eco-costs (EUR) and carbon footprint (kg CO₂-e). A cradle-to-gate analysis of recycling by either shredding or unravelling is compared with the use of virgin wool. The results are: EUR 3.53 in eco-costs and 21.93 kg CO₂-e as the carbon footprint for a virgin wool jumper to EUR 0.31 eco-costs and 1.56 kg CO₂-e for a recycled wool jumper and EUR 0.19 eco-costs and 0.89 kg CO₂-e for an unravelled wool jumper. Additionally, a cradle-to-grave calculation per wear was made, resulting in: EUR 0.045 and 0.278 kg CO₂-e, EUR 0.004 and 0.020 kg CO₂-e, and EUR 0.002 and 0.011 kg CO₂-e, respectively. A revenue-normalized comparison between on-demand knitting and mass production based on the eco-costs/value ratio (EVR) shows a 44% higher environmental impact for a mass production system. Full article

It is crucial to clarify the grassland carrying capacity (CC) and the balance between grass and livestock under different scenarios for ecological protection and sustainable development in the Three River Headwaters Region (TRHR). This study focused on the TRHR and used livestock data, MODIS Net Primary Productivity (NPP) data, and artificial supplementary feeding data to analyze grassland CC and explore changes in the grass–livestock balance across various scenarios. The results showed that the theoretical CC of edible forage under complete grazing conditions was much lower than that of crude protein under nutritional carrying conditions. Furthermore, without increasing the grazing intensity of natural grasslands, artificial supplementary feeding reduced overstocking areas by 21%. These results suggest that supplementary feeding effectively addresses the imbalance between forage supply and demand, serving as a key measure for achieving sustainable grassland livestock husbandry. Despite the effective mitigation of grassland degradation in the TRHR due to strict grass–livestock balance policies and ecological restoration projects, the actual livestock CC exceeded the theoretical capacity, leading to overgrazing in some areas. To achieve desired objectives, more effective grassland management strategies must be implemented in the future to minimize spatiotemporal conflicts between grasses and livestock and ensure the health and stability of grassland ecosystems. Full article

Reliable demand forecasting is crucial for effective supply chain management, where inaccurate forecasts can lead to frequent out-of-stock or overstock situations. While numerous statistical and machine learning methods have been explored for demand forecasting, reinforcement learning approaches, despite their significant potential, remain little known in this domain. In this paper, we propose a multi-agent deep reinforcement learning solution designed to accurately predict demand across multiple stores. We present empirical evidence that demonstrates the effectiveness of our model using a real-world dataset. The results confirm the practicality of our proposed approach and highlight its potential to improve demand forecasting in retail and potentially other forecasting scenarios. Full article

In recent years, Out-of-Stock (OOS) occurrences have posed a persistent challenge for both retailers and manufacturers. In the context of grocery retail, an OOS event represents a situation where customers are unable to locate a specific product when attempting to make a purchase. This study analyzes the issue from the manufacturer’s perspective. The proposed system, named the “Multi-modal yield eSTimation System of in-prOmotion Commercial Key-ProductS” (MySTOCKS) platform, is a sophisticated multi-modal yield estimation system designed to optimize inventory forecasting for the agrifood and large-scale retail sectors, particularly during promotional periods. MySTOCKS addresses the complexities of inventory management in settings where Out-of-Stock (OOS) and Surplus-of-Stock (SOS) situations frequently arise, offering predictive insights into final stock levels across defined forecasting intervals to support sustainable resource management. Unlike traditional approaches, MySTOCKS leverages an advanced deep learning framework that incorporates transformer models with self-attention mechanisms and domain adaptation capabilities, enabling accurate temporal and spatial modeling tailored to the dynamic requirements of the agrifood supply chain. The system includes two distinct forecasting modules: TR1, designed for standard stock-level estimation, and TR2, which focuses on elevated demand periods during promotions. Additionally, MySTOCKS integrates Elastic Weight Consolidation (EWC) to mitigate the effects of catastrophic forgetting, thus enhancing predictive accuracy amidst changing data patterns. Preliminary results indicate high system performance, with test accuracy, sensitivity, and specificity rates approximating 93.8%. This paper provides an in-depth examination of the MySTOCKS platform’s modular structure, data-processing workflow, and its broader implications for sustainable and economically efficient inventory management within agrifood and large-scale retail environments. Full article

This study explores how digital technologies and data analytics can transform urban waste management in smart cities by addressing systemic inefficiencies. Integrating perspectives from the Resource-Based View, Socio-Technical Systems Theory, Circular Economy Theory, and Institutional Theory, the research examines sustainability, operational efficiency, and resilience in extended supply chains. A case study of Company A and its demand-side supply chain with Retailer B highlights key drivers of waste, including overstocking, inventory mismanagement, and inefficiencies in transportation and promotional activities. Using a mixed-methods approach, the study combines quantitative analysis of operational data with advanced statistical techniques and machine learning models. Key data sources include inventory records, sales forecasts, promotional activities, waste logs, and IoT sensor data collected over a two-year period. Machine learning techniques were employed to uncover complex, non-linear relationships between waste drivers and waste generation. A waste-type-specific emissions framework was used to assess environmental impacts, while IoT-enabled optimization algorithms helped improve logistics efficiency and reduce waste collection costs. Our findings indicate that the adoption of IoT and AI technologies significantly reduced waste by enhancing inventory control, optimizing transportation, and improving supply chain coordination. These digital innovations also align with circular economy principles by minimizing resource consumption and emissions, contributing to broader sustainability and resilience goals in urban environments. The study underscores the importance of integrating digital solutions into waste management strategies to foster more sustainable and efficient urban supply chains. While the research is particularly relevant to the food production and retail sectors, it also provides valuable insights for policymakers, urban planners, and supply chain stakeholders. By bridging theoretical frameworks with practical applications, this study demonstrates the potential of digital technologies to drive sustainability and resilience in smart cities. Full article

We develop and employ a novel dual-order inventory planning model tailored to the inventory planning policy of a fashion retailer in the city of Thessaloniki, Greece. In our approach, the first order is placed at the beginning of the season, while the second order is placed if the stock level of a stock-keeping unit (SKU) falls below a threshold inventory level during the optimal review period. With this dual-order model, the retailer can capture random changes in consumer preferences during the season. The insights derived from implementing the developed methodology in a real-world case of a fashion retailer reveal that the dual-order model significantly mitigates the risks of overstock and stockouts by allowing dynamic adjustments to stock levels in response to actual sales trends and market changes. Moreover, a late ordering policy in week 19 of the season will result, on average, in a 7.9% reduction in total inventory planning costs compared to the costs associated with the different review periods examined. Full article

Food loss and waste pose significant social, economic, and environmental challenges worldwide, threatening food security and hindering sustainable development. While developing countries primarily face losses during production and storage, developed nations struggle with waste driven by consumer habits, spoilage, and overstocking, particularly in markets, restaurants, and homes. This study was aimed to analyze the complex relationships between food loss, waste, and various economic and environmental variables. The study examined the effects of variables such as education expenditures, food security, food prices, greenhouse gas emissions, and carbon emissions per capita on food losses and waste. These analyses shed light on the development of sustainable food policies at both national and global levels. Interventions to reduce food loss and waste will not only optimize food production and consumption processes but will also support a sustainable management of resources. As a result, this study aimed to understand the long-term effects of food loss and waste on economic growth, environmental sustainability, and social welfare. The findings of the study are of great importance in terms of directing future policies and aim to be an important guide in the transition to sustainable food systems. Full article

In the fast-moving consumer goods (FMCG) industry, inventory management is a critical component of supply chain management because it directly impacts cost efficiency and customer satisfaction. For instance, effective inventory management can minimize overstocking and reduce replenishment delays, which are particularly important in multi-echelon supply chain systems characterized by high complexity and dynamic demand. This study proposes a method based on deep reinforcement learning (DRL) aimed at optimizing replenishment decisions in multi-echelon inventory systems for FMCG industries. We designed a Dynamic Replenishment FMCG Multi-Echelon Optimization (ME-DRFO) model and incorporated a Markov Decision Process (MDP) to model the multi-echelon inventory system. By applying an improved Soft Actor–Critic with an adaptive alpha and learning rate (SAC-AlphaLR) algorithm, which introduces adaptive temperature parameters and adaptive learning rate mechanisms, our approach not only dynamically adapts to environmental changes but also effectively balances exploration and exploitation, ultimately achieving global replenishment cost minimization while ensuring supply chain stability. Through numerical experiments, our method demonstrates excellent performance by reducing replenishment costs by 12.31% and decreasing inventory shortages to 2.21%, significantly outperforming traditional methods such as overstocking, Particle Swarm Optimization (PSO), and the standard Soft Actor–Critic (SAC). This research provides new theoretical insights into multi-echelon inventory optimization and practical solutions for effectively managing complex supply chains under uncertain and dynamic conditions. Full article

Background: In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis. Results: The application of these ML models resulted in significant improvements across key operational areas. We achieved a 15% increase in demand forecasting accuracy, a 10% reduction in overstock and stockouts, and a 95% accuracy in predicting order fulfillment timelines. Additionally, the approach identified at-risk shipments and enabled customer segmentation based on delivery preferences, leading to more personalized service offerings. Conclusions: Our evaluation demonstrates the transforma-tive potential of ML in making supply chain operations more responsive and data-driven. The study underscores the importance of adopting advanced technologies to enhance deci-sion-making, evidenced by a 12% improvement in lead time efficiency, a silhouette coefficient of 0.75 for clustering, and an 8% reduction in replenishment errors. Full article

Inventory management is crucial for companies to minimize unnecessary costs associated with overstocking or understocking items. Utilizing the economic order quantity (EOQ) to minimize total costs is a key decision in inventory management, particularly in achieving a sustainable supply chain. The classical EOQ formula is rarely applicable in practice. For example, suppliers may enforce a minimum order quantity (MOQ) that is much larger than the EOQ. Some conditions such as imperfect quality and growing items represent variants of EOQ. Moreover, some requirements, such as the reduction of CO₂ emissions, can alter the formula. Moreover, disruptions in the supply chain, such as COVID-19, can affect the formula. This study investigates which requirements must be considered during the calculation of the EOQ. Based on a literature review, 18 requirements that could alter the EOQ formula were identified. The level of coverage for these requirements has been tracked in the literature. Research gaps were presented to be investigated in future research. The analysis revealed that, despite their importance, at least 11 requirements have seldom been explored in the literature. Among these, topics such as EOQ in Industry 4.0, practical EOQ, and resilient EOQ have been identified as promising areas for future research. Full article

Rice is one of the most significant food crops for human sustenance. Every year, many countries around the world hoard enormous amounts of rice to avert emergencies and guarantee food security and sufficiency. As a result, the inventory of aged rice is growing as the number of inventory years rises. Aged rice stored over three years loses its nutritional value and is no longer suitable for human consumption. There is a pressing need to find a solution to effectively utilize aged brown rice produced from aged rice after dehulling. Developing and utilizing aged brown rice as feed is economical and efficient due to its massive resources and rich nutritional content, which will also lessen food waste while resolving the problem of excessive hoarding of aged rice. This review mainly summarizes the nutritional value, application in feed, and nutritional limiting factors of aged brown rice. It provides a theoretical basis for solving the overstock of aged brown rice and the feasibility of using aged brown rice as feed in a cost-effective way. Full article

Background: The US retail sector grapples with persistent challenges related to supply chain waste, including inefficiencies, overstocking, and logistical barriers, necessitating targeted reduction strategies to mitigate escalating costs, environmental impacts, and diminished profitability. Methods: This study adopts a qualitative research method that draws on secondary data sources such as books, journals, articles, and websites to explore supply chain waste reduction strategies within the US retail industry. The study delineates various supply chain waste types, examines associated challenges and drivers, and proposes a simplified model tailored to the US retail landscape to enhance efficiency and sustainability through waste reduction and control. Results: The findings of this paper underscore the necessity for proactive measures within the US retail sector to minimize supply chain waste, optimize operations, and bolster environmental stewardship. Conclusions: By offering a comprehensive overview actionable insights and proposed reverse logistics model, this study aims to equip US retailers with strategies conducive to sustainable growth and heightened competitiveness while advancing the broader discourse on supply chain efficiency and waste reduction. Full article

Situated within China’s Liaoning Province, Tanghe Reservoir stands as an exemplar in the realm of reservoirs dedicated to eco-friendly fisheries development. Regrettably, frequent incidents compromising water quality and substantial reductions in reservoir fishery profits have plagued the area due to the absence of effective stocking theory guidance. However, the internal ecosystem drivers responsible for these outcomes have remained elusive. This study, leveraging an Ecopath model, delves into an exploration of the food web structure and ecosystem characteristics inherent to Tanghe Reservoir. The findings gleaned from this research demonstrate that the Tanghe Reservoir ecosystem boasts a considerable capacity for material cycling, yet it has not reached full maturity. A multitude of fish species, zoobenthos, and even zooplankton entities exhibit eco-trophic efficiencies exceeding 0.9, indicative of their rampant overexploitation. Notably, the primary cultured species, Aristichthys nobilis and Hypophthalmichthys molitrix, command significant biomass levels but register lower nutritional conversion efficiencies, signifying their overstocked status. Drawing from the tenets of maximum sustainable yield (MSY) theory, we advocate for a heightened emphasis on the harvest of Aristichthys nobilis and Hypophthalmichthys molitrix. Full article

An essential component required for calculating stocking rates for livestock grazing extensive rangeland is dry matter intake (DMI). Animal unit months are used to simplify this calculation for rangeland systems to determine the rate of forage consumption and the cattle grazing duration. However, there is an opportunity to leverage precision technology deployed on rangeland systems to account for the individual animal variation of DMI and subsequent impacts on herd-level decisions regarding stocking rate. Therefore, the objectives of this study were, first, to build a precision system model (PSM) to predict total DMI (kg) and required pasture area (ha) using precision body weight (BW), and second, to evaluate differences in PSM-predicted stocking rates compared to the traditional herd-level method using initial or estimated mid-season BW. A deterministic model was constructed in both Vensim (version 10.1.2) and Program R (version 4.2.3) to incorporate individual precision BW data into a commonly used rangeland equation using %BW to estimate individual DMI, daily herd DMI, and area (ha) required to meet animal DMI requirements throughout specific grazing periods. Using the PSM, differences in outputs were evaluated using three scenarios: (1) initial BW (business as usual); (2) average mid-season BW; and (3) individual precision BW using data from two precision rangeland experiments conducted at the South Dakota State University Cottonwood Field Station. The data from the two experiments were used to develop PSM case studies. The trial data were collected using precision weight data (SmartScale™) collected from replacement heifers (Case study 1, n = 60) and steers (Case study 2, n = 254) grazing native rangeland. In Case study 1 (heifers), Scenario 1 versus Scenario 3 resulted in an additional 73.41 ha required. Results from Case study 2 indicated an average additional 4.4 ha required per pasture when comparing Scenario 3 versus Scenario 1. Sensitivity analyses resulted in a difference between maximum and minimum simulated values of 27,995 and 4265 kg forage consumed, and 122 and 8.9 pasture ha required for Case studies 1 and 2, respectively. Thus, results from the scenarios indicate an opportunity to identify both under- and over-stocking situations using precision DMI estimates, which helps to identify high-leverage precision tools that have practical applications for enhancing animal and plant productivity and environmental sustainability on extensive rangelands. Full article