Search Results (750)

Introduction: This study explores the impact of logistics information technology (LIT) on supply chain relationships and distribution performance in small and medium-sized enterprises (SMEs) using South Africa as a case study. Although digital supply chain solutions are increasingly important, there is limited evidence of SME efficiency in emerging markets using LIT. Methods: This study utilises a survey of 313 SMEs from four South African provinces. Bayesian structural equation modelling (Bayesian SEM) was then used to examine LIT’s effects on distribution performances in terms of timeliness, product availability, and condition. Results: The results show that the adoption of LIT strengthens buyer–seller networks (β = 0.524, CI = [0.434, 0.613]) and improves distribution by enhancing both timeliness performance (β = 0.237, CI = [0.098, 0.372]) and product condition performance (β = 0.175, CI = [0.042, 0.259], β = 0.222, p < 0.001). However, it does not directly enhance product availability performance (β = 0.085, CI = [−0.030, 0.199]), signifying that LIT adoption by itself fails to improve product availability. The results also demonstrate that SME network relationships mediate the connection between LIT adoption and distribution performance metrics. Discussion: This study’s findings contribute to the literature and offer valuable information and guidance to policymakers as they underscore the importance for SMEs to invest in LIT integration and compatibility, as well as inventory optimisation and improved supplier communication to minimise transit time variation. Policymakers should support SMEs’ digital transformation through interventions including funding and training for LIT adoption. This study confirms the essential role of LIT in SME supply chains and illustrates that technology-facilitated relationships enhance distribution performance, which enhances SME competitiveness. Full article

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that determines optimal customer order decoupling point (CODP)/product differentiation point (PDP) positions and SFI quantities (both generic and dedicated) for each production period, employing particle swarm optimization for solution derivation and validating findings through a comprehensive case study of a steel manufacturer with characteristic long-period production processes. The analysis yields two significant findings: (1) single-period operations demonstrate marked cost sensitivity to service level requirements and delay penalties, necessitating end-stage inventory buffers, and (2) multi-period optimization generates a distinctive cost-smoothing effect through strategic order deferrals and cross-period inventory reuse, resulting in remarkably stable total costs (≤2% variation observed). The study makes seminal theoretical contributions by revealing the convex cost sensitivity of short-term inventory decisions versus the near-flat cost trajectories achievable through multi-period planning, while establishing practical guidelines for process industries through its empirically validated two-period threshold for optimal order deferral and inventory positioning strategies. Full article

Manure is a valuable organic resource for sustainable agriculture, enhancing soil fertility and promoting nutrient cycling; however, it also contributes significantly to methane and nitrous oxide emissions. The European Green Deal and Latvia’s National Energy and Climate Plan have set targets for reducing agricultural greenhouse gas (GHG) emissions, including those related to improved manure management. Therefore, this research aims to estimate the future manure production in Latvia to determine the potential for reducing GHG emissions by 2050. Using the LASAM model developed in Latvia, the number of farm animals, the amount of manure, and the associated GHG emissions were projected for the period up to 2050. The calculations followed the Intergovernmental Panel on Climate Change (IPCC) methodology and were based on national indicators and current national GHG inventory data covering the period of 2021–2050. Significant changes in the structure of manure in Latvia are predicted by 2050, with the proportion of liquid manure expected to increase while the amounts of solid manure and manure deposited by grazing animals are expected to decrease. The GHG emission projection results indicate that by 2050, total emissions from manure management will decrease by approximately 5%, primarily due to a decline in the number of farm animals and, consequently, a reduction in the amount of manure. In contrast, methane emissions are expected to increase by approximately 5% due to production intensification. The research results emphasise the need to introduce more effective methane emission reduction technologies and improved projection approaches. Full article

Background: This technical note presents a methodological approach to agrichemical inventory documentation. It complements exposure assessments in field settings with time-restricted observational periods. Conducted in Michoacán, Mexico, this method leverages large language model (LLM) capabilities for categorizing agrichemicals from brief video footage. Method: Given time-limited access to a storage shed housing various agrichemicals, a short video was recorded and processed into 31 screenshots. Using OpenAI’s ChatGPT (model: GPT-4o^®), agrichemicals in each image were identified and categorized as fertilizers, herbicides, insecticides, fungicides, or other substances. Results: Human validation revealed that the LLM accurately identified 75% of agrichemicals, with human verification correcting entries. Conclusions: This rapid identification method builds upon behavioral methods of exposure assessment, facilitating initial data collection in contexts where researcher access to hazardous materials may be time limited and would benefit from the efficiency and cross-validation offered by this method. Further refinement of this LLM-assisted approach could optimize accuracy in the identification of agrichemical products and expand its application to complement exposure assessments in field-based research, particularly as LLM technologies rapidly evolve. Most importantly, this Technical Note illustrates how field researchers can strategically harness LLMs under real-world time constraints, opening new possibilities for rapid observational approaches to exposure assessment. Full article

Accurate stem-volume estimation supports inventory, valuation and carbon accounting, but Pressler’s single-section formula has never been tested in the highly productive European-beech forests of the Central Rhodope Mountains, Greece. We quantified the bias of Pressler estimates and developed size-specific correction factors. Sixty Fagus sylvatica L. trees felled in 2023–2024 were measured destructively at 1-m intervals. Pressler standing volumes were compared with Smalian-plus-cone reference volumes (hereafter referred to as true volumes) and analysed with generalized additive models. Pressler underestimated true volume (mean bias = −0.088 m³; RMSE = 0.204 m³; MAPE = 21%). Under-estimation increased with diameter. A GAM with DBH and height explained 96.7% of the variance in true volume. We also fit a Random Forest as a complementary check. Multipliers of 1.30 (<25 cm DBH), 1.20 (25–45 cm), 1.30 (45–55 cm) and ≥1.35 (≥55 cm) cut residual error to ≤20% overall and <10% inside the well-sampled 35–45 cm class. A simple DBH-class correction table restores Pressler’s speed while meeting modern accuracy standards for inventory and carbon reporting. Full article

Teak (Tectona grandis L.f.) is a leading tropical plantation species valued for high-quality timber and carbon (C) storage. This study assessed stand growth across ages and sites, quantified biomass and C by tree component and stand, and developed DBH-based allometric equations for biomass and C estimation. Six stand ages (5, 6, 9, 11, 14, and 17 years) were assessed in three municipalities of Nayarit, Mexico. Dendrometric inventories in permanent plots and destructive sampling of 35 trees provided calibration data for leaves, branches, stem, and roots. C concentration was determined with an elemental analyzer, and nonlinear regression models were adjusted and validated. Stand biomass and C increased with age, peaking at ages 11–14 (>130 Mg ha⁻¹; >60 Mg C ha⁻¹), with lower values at age 17. San Blas and Rosamorada accumulated significantly more than Tuxpan, reflecting site quality. C concentration was stable across sites and ages, with stem and roots consistently ranging between 48% and 50%, and leaves and branches averaging 45%–46%. Allometric equations were most accurate for stem and total biomass/C (R² = 0.73–0.79), while foliage showed higher variability. On average, 60%–70% of biomass was allocated to the stem and 15%–20% to roots. Indicators were stable, with an aboveground-to-belowground ratio (A:B) ≈ 4.9 and a biomass expansion factor (BEF) ≈ 1.5. The current annual increment (CAI) presented two main peaks: ~20 Mg ha⁻¹ yr⁻¹ at ages 5–6 and ~11 Mg ha⁻¹ yr⁻¹ at ages 9–11, followed by a decline after age 14. Teak in western Mexico reaches peak productivity at ages 6–11, with belowground biomass essential for accurate C accounting. Full article

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses on the cross-border cold chain storage scenario for Malaysia’s Musang King durians. Influenced by the fruit’s extremely short 3–5-day shelf life and the concentrated harvesting period in primary production areas, the issue of delayed dynamic demand response is particularly acute. Utilizing actual sales order data for Mao Shan Wang durians from Beigang Logistics in Guangxi, this study constructs a demand forecasting model integrating Bayesian optimization with bidirectional long short-term memory networks (BO-BiLSTM). This aims to achieve precise forecasting and optimization of cold chain storage inventory. Experimental results demonstrate that the BO-BiLSTM model achieved an R² of 0.6937 on the test set, with the RMSE reduced to 19.1841. This represents significant improvement over the baseline LSTM model (R² = 0.5630, RMSE = 22.9127). The bidirectional Bayesian optimization mechanism effectively enhances model stability. This study provides a solution for forecasting inventory demand of fresh durians in cold chain storage, offering technical support for optimizing the operation of backbone hub cold storage facilities along the New Western Land–Sea Trade Corridor. Full article

This article shows that there is a methodological problem in the traditional IS-LM model. If production cannot be sufficiently adjusted downwards, there is no uniform interest rate that simultaneously clears the money and goods markets. An extension of the credit market in the tradition of the loanable funds theory resolves this contradiction and yields a coherent mechanism for crisis dynamics and policy transmission. In this expanded model, the interest rate is determined by the credit market whereby the total supply of credit results from household savings and the credit supply of banks and the demand for credit is due to investment demand and the demand for liquidity. This methodological approach facilitates the explanation of crisis dynamics, as involuntary inventory investment generates liquidity problems and disequilibria in the goods market lead to imbalances in the financial markets. Full article

This paper addresses the underrepresentation of traffic activity in Life Cycle Assessment (LCA) practice despite its critical influence on the energy and environmental footprint of both electrified and conventional vehicles. To bridge this gap, the paper proposes a new framework that enhances the integration of traffic dynamics into fleet LCAs while maintaining computational simplicity. The approach combines Macroscopic Fundamental Diagrams (MFDs), which estimate network-level traffic performance, with an average-speed-based emissions model to evaluate on-road energy use and emissions performance of traffic. This quantification is further extended by applying life cycle inventory emission factors to account for upstream and downstream impacts, including energy production, vehicle manufacturing, and end-of-life treatment. The framework is demonstrated through a case study involving urban traffic networks in Zurich and Thessaloniki. Results illustrate the method’s capacity to evaluate multiple vehicles within realistic flow scenarios and adaptability to variable traffic conditions, offering a practical and scalable tool for improved energy and environmental assessment of road transport fleets. Full article

Background: Price is the most authoritative constituent among the factors shaping consumer demand. Growing consciousness among global communities regarding environmental issues makes greenness one of the key factors controlling demand, along with time, which drives demand in markets. This paper addresses such issues associated with a retail purchase scenario. Methods: Consumer’s demand for products is hypothesized to be influenced by pricing, time and the green level of the product in the proposed model. Time-dependent inventory carrying cost and green level-induced purchasing cost are considered. The average cost during the decision cycle is the objective function that is analyzed in trade credit phenomena, involving delayed payment by the manufacturer to the supplier. The Convex optimization technique is used to find an optimal solution for the model. Results: Once a local optimal solution is found, sensitivity analysis is conducted to determine the optimal value of the objective function and decision variables for other impacting parameters. Results reveal that demand-boosting parameters, for instance, discounts on price and green activity, result in additional average costs. Conclusions: Discounts on price and green activity advocate a large supply capacity by boosting demand, creating opportunities for the retailer to earn more revenue. Full article

This paper examines the transformative role of the Digital Twin-Computer Vision combination (DT-CV combo) in industrial operations, focusing on its applications, challenges, and future directions. It aims to synthesize the existing literature and explore the practical use cases in operations management (OM). A comprehensive systematic literature review is conducted using PRISMA guidelines to analyze the DT-CV combo across the classification of industrial OM. However, given the breadth and importance of manufacturing and the OM field, the study excludes the literature on the DT-CV combo applied to other domains such as healthcare, smart buildings and cities, and transportation. We found that the DT-CV combo in OM is a relatively young but growing field of research. To date, only 29 articles have examined DT-CV combo solutions from various OM perspectives. Case studies are rare, with most studies relying on experimentation and laboratory testing to investigate DT-CV applications in the OM context. According to the cases and methods reviewed in the literature, the DT-CV combo has applications in different OM areas such as design, prototyping, simulation, real-time production monitoring, defect detection, process optimization, hazard detection and mitigation, safety training, emergency response simulation, optimal resource allocation, condition monitoring, inventory management, and scheduling maintenance. We also identified several benefits of DT-CV combo solutions in OM, including reducing human error, ensuring compliance with quality standards, lowering maintenance costs, mitigating production downtime, eliminating operational bottlenecks, and decreasing workplace accidents, while simultaneously improving the effectiveness of training. In this paper, we classify current applications of the DT-CV combo in OM, highlight gaps in the existing literature, and propose research questions to guide future studies in this domain. By considering the rapid phase of AI technology development and combining it with the current state of the art applications of the DT-CV combo in OM, we suggest novel concepts and future directions. The digital twin-vision language model (DT-VLM) combo as a future direction, emphasizing its potential to bridge physical–digital interfaces in industrial workflows, is one of the future development directions. Full article

In recent years, the synergistic effect among production, maintenance, and quality control within manufacturing systems has garnered increasing attention in academic and industrial circles. In high-quality production settings, the real-time identification of minute process deviations holds significant importance for ensuring product quality. Traditional approaches, such as routine quality inspections or Shewhart control charts, exhibit limitations in sensitivity and response speed, rendering them inadequate for meeting the stringent requirements of high-precision quality control. To address this issue, this paper presents an integrated framework that seamlessly integrates stochastic process modeling, dynamic optimization, and quality monitoring. In the realm of quality monitoring, an exponentially weighted moving average (EWMA) control chart is employed to monitor the production process. The statistic derived from this chart forms a Markov process, enabling it to more acutely detect minor shifts in the process mean. Regarding maintenance strategies, a state-dependent preventive maintenance (PM) and corrective maintenance (CM) mechanism is introduced. Specifically, preventive maintenance is initiated when the system is in a statistically controlled state and the inventory level falls below a predefined threshold. Conversely, corrective maintenance is triggered when the EWMA control chart generates an out-of-control (OOC) signal. To facilitate continuous production during maintenance activities, an inventory buffer mechanism is incorporated into the model. Building upon this foundation, a joint optimization model is formulated, with system states, including equipment degradation state, inventory level, and quality state, serving as decision variables and the minimization of the expected total cost (ETC) per unit time as the objective. This problem is formalized as a constrained dynamic optimization problem and is solved using the genetic algorithm (GA). Finally, through a case study of the production process of vibroseis equipment, the superiority of the proposed model in terms of cost savings and system performance enhancement is empirically verified. Full article

Background: Inventory planning in retail supply chains requires balancing cost efficiency and service reliability under demand uncertainty and financial limitations. The literature has seldom addressed the joint integration of stochastic demand, deterministic lead times, and supplier-specific constraints in multi-product and multi-warehouse settings, particularly in the context of small- and medium-sized enterprises. Methods: This study develops a Stochastic Pure Integer Linear Programming (SPILP) model that incorporates stochastic demand, deterministic lead times, budget ceilings, and trade credit conditions across multiple suppliers and warehouses. A two-step solution procedure is proposed, combining a chance-constrained approach to manage uncertainty with warm-start heuristics and relaxation-based preprocessing to improve computational efficiency. Results: Model validation using data from a Colombian retail distributor showed cost reductions of up to 17% (average 15%) while maintaining or improving service levels. Computational experiments confirmed scalability, solving instances with more than 574,000 variables in less than 8800 s. Sensitivity analyses revealed nonlinear trade-offs between service levels and planning horizons, showing that very high service levels or short planning periods substantially increase costs. Conclusions: The findings demonstrate that the proposed model provides an effective decision support system for inventory planning under uncertainty, offering robust, scalable, and practical solutions that integrate operational and financial constraints for medium-sized retailers. Full article

Quantifying air emissions from livestock pastures remains challenging due to spatial variability and temporal fluctuations in emissions due to weather conditions. In this study we used a small unmanned aerial vehicle (sUAV) equipped with real-time sensors and an air sample collection system to directly measure carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), nitrous oxide (N₂O), nitrogen dioxide (NO₂), hydrogen sulfide (H₂S), total volatile organic compound (VOC), and particulate matter (PM₁, PM_2.5, PM₁₀) emissions across two dairy pastures, two beef pastures, and one sheep pasture in Wisconsin. Emission rates were calculated using the Lagrangian mass balance model and validated against ground-level dynamic flux chamber (DFC) measurements. UAV-based CO₂ concentrations showed a strong correlation with DFC measurements (R² = 0.86, RMSE = 21.5 ppm, MBE = +9.7 ppm). Dairy 1 yielded the highest emissions for most compounds, with average emission rates of 0.50 ± 0.28 g m⁻² day⁻¹ head⁻¹ for CO₂, 8.48 ± 2.75 mg m⁻² day⁻¹ head⁻¹ for CH₄, and 0.20 ± 0.60 mg m⁻² day⁻¹ head⁻¹ for NH₃. The sheep pasture, on the other hand, had the lowest CH₄ and NH₃ emission rates, averaging 0.35 ± 0.22 mg m⁻² day⁻¹ head⁻¹ and 0.02 ± 0.05 mg m⁻² day⁻¹ head⁻¹, respectively. Rainfall events (≥ 5 mm within five days of sampling) significantly elevated N₂O emissions (0.56 ± 0.40 vs. 0.13 ± 0.17 mg m⁻² day⁻¹ head⁻¹). Particulate matter emissions were significantly affected by forage density. PM_2.5 emission rates reached 1.25 × 10⁻⁴ g m⁻² day⁻¹ head⁻¹ under low vegetative cover. It was concluded that emissions were affected by both animal species and the environmental conditions. The findings of this study provide a foundation for further development of emission inventories for pasture-based livestock production systems. Full article

Background: This study explores how consumer behaviour influences household food waste and its ripple effects on the efficiency of the agri-food supply chain. Methods: Using survey data, we applied regression analysis to analyse the links between shopping habits, household demographics, waste reduction goals, and disposal practices. Results: Results show that purchasing driven by promotions significantly boosts household waste, while waste reduction goals strongly reduce disposal behaviours. These results illustrate how irregular consumer purchasing patterns create upstream demand fluctuations, making inventory management and production planning more complex. The findings highlight opportunities for logistics improvements, such as demand-based inventory systems, optimised purchasing routines, adjusted promotional strategies, and consumer-involved forecasting models to cut waste and promote resource sustainability. Conclusions: This research connects consumer behaviour with supply chain management, offering practical insights for building more sustainable and efficient food supply chains through targeted logistics actions. Full article