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Based on the monthly panel data of China's imports of agricultural machinery products from 2016–2022, this paper uses a multi-period double-difference model to assess the impact of China's imposition of counter-tariffs on China's imports of agricultural machinery in the context of U.S.–China trade friction. It is found that China's implementation of counter-tariffs significantly reduces China's imports of agricultural machinery products from the U.S. and significantly increases imports from 16 other countries, but the trade diversion effect is lower than the trade suppression effect. Mechanism analysis finds that China–U.S. trade friction affects the technological innovation capacity of agricultural machinery enterprises and the degree of uncertainty in the Chinese economy, which in turn affects China's agricultural machinery imports. Heterogeneity analysis finds that China–U.S. trade friction has a more significant inhibitory effect on seeding and planting and fertilizing machinery, drainage, irrigation, and water lifting machinery, and other machinery imported from the U.S., and a more significant diversionary effect on agricultural primary processing machinery and harvesting machinery imported from 16 other countries. The imposition of countervailing tariffs mainly affected imports of complete machinery products rather than machinery spare parts. [ABSTRACT FROM AUTHOR]

The quick development of machine learning techniques provides a superior capability for manufacturing enterprises to make effective decisions about inventory management based on spare parts demand (SPD) data. Since SPD sequences in practical maintenance applications usually show an intermittent distribution, it is not easy to represent the demand pattern of such sequences. Meanwhile, there are some aspects like manual report errors, environmental interference, sudden project changes, etc., that bring large and unexpected fluctuations to SPD sequences, i.e., anomalous demands. The inventory decision made based on the SPD sequences with anomalous demands is not trusted by enterprise engineers. For such SPD data, there are two great concerns, i.e., false alarms in which sparse demands are recognized to be anomalous and missing alarms in which the anomalous demands are categorized as normal due to their adjacent demands having extreme values. To address these concerns, a new unsupervised anomaly-detection method for intermittent time series is proposed based on a dual-tailed probability. First, the multi-way delay embedding transform (MDT) was applied on the raw SPD sequences to obtain higher-order tensors. Through Tucker tensor decomposition, the disturbance of extreme demands can be effectively reduced. For the reconstructed SPD sequences, then, the tail probability at each time point, as well as the empirical cumulative distribution function were calculated based on the probability of the demand occurrence. Second, to lessen the disturbance of sparse demand, the non-zero demand sequence was distilled from the raw SPD sequence, with the tail probability at each time point being calculated. Finally, the obtained dual-tailed probabilities were fused to determine the anomalous degree of each demand. The proposed method was validated on the two actual SPD datasets, which were collected from a large engineering manufacturing enterprise and a large vehicle manufacturing enterprise in China, respectively. The results demonstrated that the proposed method can effectively lower the false alarm rate and missing alarm rate with no supervised information provided. The detection results were trustworthy enough and, more importantly, computationally inexpensive, showing significant applicability to large-scale after-sales parts management. [ABSTRACT FROM AUTHOR]

The maintenance service network is always designed as a multi-level service network to provide timely maintenance service for failed machinery, and is rarely studied in agriculture. Thus, this paper focuses on a three-level maintenance service network location–allocation problem in agriculture, which contains several spare part centres, service stations, and service units. This research aims to obtain the optimal location of spare part centres and service stations while determining service vehicle allocation results for service stations, and the problem can be called a multi-level facility location and allocation problem (MLFLAP). Considering contiguity constraints and hierarchical relationships, the proposed MLFLAP is formulated as a mixed-integer linear programming (MILP) model integrating with P-region and set covering location problems to minimize total service costs, including spare part centre construction costs, service vehicle usage costs, and service mileage costs of service stations. The Benders decomposition-based solution method with several improvements is then applied to decompose the original MLFLAP into master problem and subproblems to find the optimal solutions effectively. Finally, a real-world case in China is proposed to evaluate the performance of the model and algorithm in agriculture, and sensitivity analysis is also conducted to demonstrate the impact of several parameters. [ABSTRACT FROM AUTHOR]

Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises. Prediction of the demand for spare parts plays a crucial role in inventory management and lifecycle quality management for the aftermarket service of large-scale manufacturing enterprises. In real-life applications, however, demand for spare parts occurs randomly and fluctuates greatly, and the demand sequence shows obvious intermittent distribution characteristics. Additionally, due to factors such as reporting mistakes made by personnel or environmental changes, the actual data of the demand for spare parts are prone to abnormal variations. It is thus hard to capture the evolutional pattern of the demand for spare parts by traditional time series forecasting methods. The reliability of prediction results is also reduced. To address these concerns, this paper proposes a tensor optimization-based robust interval prediction method of intermittent time series for the aftersales demand for spare parts. First, using the advantages of tensor decomposition to effectively mine intrinsic information from raw data, a sequence-smoothing network based on tensor decomposition and a stacked autoencoder is proposed. Tucker decomposition is applied to the hidden features of the encoder, and the obtained core tensor is reconstructed through the decoder, thus allowing us to smooth outliers in the original demand sequence. An alternating optimization algorithm is further designed to find the optimal sequence feature representation and tensor decomposition factors for the extraction of the evolutionary trend of the intermittent series. Second, an adaptive interval prediction algorithm with a dynamic update mechanism is designed to obtain point prediction values and prediction intervals for the demand sequence, thereby improving the reliability of the forecast. The proposed method is validated using the actual aftersales data from a large engineering manufacturing enterprise in China. The experimental results demonstrate that, compared with typical time series prediction methods, the proposed method can effectively grab the evolutionary trend of various intermittent series and improve the accuracy of predictions made with small-sample intermittent series. Moreover, the proposed method provides a reliable elastic prediction interval when distortion occurs in the prediction results, offering a new solution for intelligent planning decisions related to spare parts in practical maintenance. [ABSTRACT FROM AUTHOR]

This paper studies a periodic-review, infinite-horizon, backlogging inventory model with two demand classes and a constant lead time, where inventory replenishment follows a base-stock policy. We consider an anticipated rationing policy which reserves inventory for future high-priority demands with higher backlogging costs by taking the coming delivery of the next period into consideration. Due to the lack of nice properties such as convexity, both the optimal base-stock level and the optimal critical level when minimising inventory costs have to be found by an exhaustive search. Instead, we study a single-period problem truncated from the original infinite-horizon problem and derive its optimal reservation level with a closed-form expression. Surprisingly, the solution form of the single-period problem coincides exactly with the anticipated rationing policy and hence this solution serves as a good approximation for the optimal critical level of the infinite-horizon problem. An empirical study further demonstrates that our closed-form approximation is quite attractive in both solution accuracy and computation efficiency based on spare parts inventory data from a petrochemical plant in China. [ABSTRACT FROM AUTHOR]

Optimizing the operation and maintenance (O&M) schedule of offshore wind farms is an effective approach to controlling O&M costs. This paper takes the perspective of appropriately increasing O&M costs to enhance environmental protection around the wind farm and improve the safety of the O&M process. Firstly, the minimization of total O&M cost, carbon emissions from O&M vessels, and the standard deviation of vessel sailing duration are set as optimization objectives. Constraints are determined, including vessel flow conservation, O&M personnel and spare parts, O&M time, external factors, and decision variable types. A new O&M scheduling model for offshore wind farms, namely the CCB-O&MS model, is constructed. A new algorithm, called the chaotic quantum Harris hawks optimization (CQMHHO), is also proposed. Additionally, the CCB-O&MS model's encoding rules (CRD) are designed, a feasible integerization algorithm (FIA) is developed, and a method for determining the number of wind turbines to be accessed (M-DNTA) is formulated. Consequently, a new optimization method for offshore wind farm O&M scheduling, namely CQMHHO-CCB-O&MS, has been established. Subsequently, the feasibility and superiority of the proposed scheduling model is tested using data from offshore wind farms in southern China. Experiment results demonstrate that, compared to other alternative models, the proposed model can achieve a comprehensive optimal scheduling plan. The improved algorithm is more effective than other benchmark algorithms in solving the CCB-O&MS model. • A new chaotic quantum Harris hawk optimization named CQMHHO is proposed to address the issues of premature convergence. • The proposed algorithm introduces a fast search strategy based on quantum computing. • Experimental results demonstrate that CQMHHO outperforms other benchmarked algorithms when applied to solve CCB-O&MS. • A novel offshore wind farm operation and maintenance scheduling method, namely CQMHHO-CCB-O&MS, is established. • The proposed CQMHHO-CCB-O&MS model can reduce the carbon emissions of vessels and balance the duration of vessel sailing. [ABSTRACT FROM AUTHOR]

Copyright of Maintenance & Reliability / Eksploatacja i Niezawodność is the property of Polish Scientific & Technical Society Consumables, Polish Maintenance Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The functional breakdown of CRAFT cryogenic system was performed. • The failure mode.effect and criticality analysis of 200 W/4.5 K helium refrigerator system was performed. • The reliability block diagram analysis of 200 W/4.5 K helium refrigerator system was carried out. • Risk mitigation measures were proposed based on RAMI analysis results of 200 W helium refrigerator. The Comprehensive Research Facility for Fusion Technology (CRAFT) is a large scientific device in China's 13th Five-Year Plan. The RAMI (reliability, availability, maintainability, and inspectability) analysis method is used to control the technical risk of the CRAFT cryogenic system, which can better guarantee the stable operation of the CRAFT system. Functional breakdown is performed first to understand the functions of the CRAFT cryogenic system, and then IDEFØ diagram is used to describe the hierarchical structure and relationship between each function in the cryogenic system. A preliminary RAMI analysis of 200W@4.5 K helium refrigerator system with the main function of DF1 was carried out. Failure mode, effect and criticality analysis was used to identify and evaluate the failure mode of 200 W helium refrigerator. A criticality chart of the failure mode was drawn based on the evaluation results. For the failure modes with medium and high risks in the initial criticality chart, there was no high risk after risk mitigation measures were taken.The logical basis of the reliability block diagram was obtained from the structural relationship between each function described in the IDEF0 diagram. Based on these logical structures, a reliability block diagram composed of components that implement basic functions was drawn. Input reliability data to calculate the reliability and inherent availability of the main function DF1.The results of Reliability block diagram analysis showed that the inherent availability can be greatly improved after taking improvement measures such as preparing spare parts to reduce the mean time to repair. Reliability was also greatly improved when measures such as regular inspection and maintenance were taken to reduce the failure rate. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine how the spare auto parts market in Mexico is being affected by the introduction of Chinese auto parts. The trade deficit between Mexico and China in the auto parts subsector has increased in recent years and Chinese car parts belong to a supply strategy that has seen the set-up of companies to lower manufacturing costs, open new lines and avoid product shortages. This is necessary in a spare parts market growing at an annual rate of 10% where there has been an important increase in makes and models, creating a need for companies to specialize. However, quality and performance standards of the auto parts introduced to the country do not exist; there are piracy issues and the auto parts on the market are perceived as being of bad quality. [ABSTRACT FROM AUTHOR]

Purpose – Despite the proven benefits of high-performing spare parts logistics, recommendations on how to organize spare parts logistics in China are rather rare. The absence of spare parts logistics concepts for China is surprising, since the spare part business is the profit pool of the capital goods industry: spare parts create about 17 percent of the industry's total revenue. The margins involved in this spare parts revenue are, on average, 25 percent compared to 2-3 percent of the capital goods. This paper aims to offer recommendations to increase spare parts logistics performance. Design/methodology/approach – The authors conducted an extensive benchmarking project with a variety of firms (focus group and single case study) in order to gain a better understanding of spare parts logistics in China. By reviewing the first benchmarking findings with a single company that struggled to achieve sufficient spare parts logistics performance, additional insight was gained into how spare parts logistics should be organized in China. Findings – The paper attempts to provide a better understanding of the necessary changes for improving logistics performance in the Chinese market. It analyzes the necessary changes to achieve a cutting-edge logistics solution, and shows how companies can implement the solution. Research limitations/implications – Research limitations come from the qualitative nature of the research. Practical implications – Managers can use the results obtained in this study to challenge their current logistics practices and develop a project procedure on how to initiate logistics projects that lead to cutting-edge logistics performance. Originality/value – Rather than concentrating on performance benchmarks of the supply chain of spare parts or specific aspects of spare parts management, the paper develops the setting up of a cutting-edge logistics solution for China and Asia. The cutting-edge solution is based on two main pillars: companies should try to develop logistics solutions for Asia that consider existing Asian and Chinese constraints instead of taking the logistics practices used in mature markets and trying to adapt them to the Chinese market, and the development of the logistics solution should be in intensive collaboration with the logistics providers. [ABSTRACT FROM AUTHOR]

The article focuses on Ningbo Poli Industrial and Trading Co. Ltd., a Hangzhou, China-based company that develop and export spare parts for weaving machines such as grippers, rapier tapes, wheels, guide hooks, reeds, frames, healds, drop wires, tape weaving looms, Velcro looms and jackard machines.

This article explains that the Chinese company Wuxi Hongfei Industrial Trading Co. Ltd. is specialized in manufacturing spare parts such as high speed bearings for open-end spinning machines. It will present the following products at the ITMA Asia trade show: double-row ball bearings for rotors; double-row ceramic-steel mixed ball bearings; other double-row ball bearings for rotors; and false twist bearings.