Search Results (8)

In recent years, as an important alternative to traditional gasoline-powered vehicles, new electric vehicles (NEVs) have gained widespread attention and rapid development globally. In the traditional automotive industry chain, downstream vehicle manufacturers need to master core technologies, such as engines, chassis, and transmissions. In contrast to the traditional automotive industry chain, where downstream vehicle manufacturers must master core technologies, like engines, chassis, and transmissions, the electric vehicle industry chain has evolved in a way that the development of core components is gradually separated from the vehicle manufacturers. Downstream vehicle manufacturers can now outsource key components, such as batteries, electric controls, and motors. Additionally, in terms of sales models, the electric vehicle industry chain can adopt either the traditional 4S dealership model or a direct-sales model. As the research and development of core components are increasingly separated from vehicle manufacturers, the downstream vehicle manufacturers can source components, like batteries, electric controls, and motors, externally. At the same time, they can choose to use either the traditional 4S dealership model or the direct-sales model. The underlying mechanisms and channel selection in this context require further exploration. Based on this, a mathematical model is established by incorporating terminal marketing input, product competitiveness, and after-sales service levels from the literature to solve for the optimal pricing under centralized and decentralized pricing strategies. Using numerical examples, the pricing and profit performance under different market structures are analyzed to systematically examine the impact of the electric vehicle supply chain on business operations, as well as the changes in various elements across different channels. We will focus on how after-sales services (including the spare part supply) influence the pricing strategy and profit distribution in the supply chain, aiming to provide insights into advanced manufacturing system management for manufacturing enterprises and improve the efficiency of intelligent logistics management. The research indicates that (1) The direct-sales model helps to improve the terminal marketing input, after-sales service quality, and product competitiveness for supply chain stakeholders; (2) It is noteworthy that the manufacturer’s direct-sales model also significantly contributes to lowering prices, highlighting that the direct-sales model has substantial impacts on both supply chain stakeholders and, importantly, consumers. Full article

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. Full article

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. Full article

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results. Full article

In recent decades, retail has been faced with a challenging scenario, resulting from the digital transformation driven by advances on the internet that has transformed retail business models, especially in commercial transactions, giving consumers a new shopping experience. However, it has been a challenge for retailers to maintain the same shopping experience in different marketing channel formats, mainly with regard to understanding the consumption habits of consumers and what can influence their purchase decisions. As far as is known, the buying process is not only about the act of buying. There is an entire buying journey that must be studied to ensure customer satisfaction from the first contact to the after-sales experience. In this context, this article identifies and analyzes the stages of the omnichannel retail purchase journey from the consumer’s perspective. To achieve the proposed objective, this study was conducted through a systematic literature review in accordance with the SPAR-4-SLR protocol. The results present several analyses that demonstrate the complexity involving the consumer’s perspective in the purchase decision process. The insights show how complex it can be to for companies to manage the purchase journey due to the individuality of each consumer. The main finding shows that most marketing studies do not address the omnichannel consumer journey and, when they do, they focus on specific parts to the detriment of a more holistic view of the buying process. The originality of this article lies in the fact that few studies on omnichannel retail have focused on the integration of all touchpoints using an empirical longitudinal evaluation. Full article

The purpose of this research is to identify the risks and deficiencies that affect the performance of companies that provide vehicle after-sales services. Thus, this paper highlights the results of a comparative study based on a questionnaire conducted at the level of six brands in the automotive industry. A model was developed to investigate the factors that affect the global performance of the after-sales sector and the authenticity of the information related to the issue studied. Moreover, based on the collected data, this study evaluates the strategies related to performance management used by the organizations studied. In the end, even if the results showed a score of 81% on the questionnaire, we found that companies that provide vehicle after-sales services have not implemented and do not maintain totally the strategies related to performance management. Consequently, the need for change can be emphasized. Based on the analyzed data in the second part of the paper, we identified deficiencies and risks in terms of the organization, operation and management of the service units. These results confirm that the vehicle repair service has a significant influence on employee and customer satisfaction, on the quality of the vehicles repaired and the repair completion time. Full article

In recent years, the application of traceability systems in the food and drug industry has developed rapidly, but it is rarely used for wind turbines. From the aspects of low information transparency and information islands in the supply chain process for wind turbines, a reliable traceability system is essential. However, the existing traceability systems are not suitable to be directly applied to wind turbines. Consequently, according to the characteristics of the wind power industry, a semi-centralized traceability architecture based on Internet of Things technology was proposed. Furthermore, a traceability platform was constructed by analyzing the information collected in each stage related to various user needs of wind turbines, and various applications, including manufacturing management and spare parts management, were developed. Compared with the existing systems, the proposed platform was wind-turbine-oriented, effectively improved traceability efficiency and enterprises’ information security, and extended the length of the traceability chain by integrating the after-sales information. The traceability of key components of wind turbines during their life cycle provides a useful reference for further improving the parts quality management system of the wind power industry. Full article

Quality improvement practice (QIP), as a competitive strategy, is increasingly vital for auto factories to improve the product quality and brand reputation. Quality activity on selected automotive parts among a variety of competing candidates is featured by prioritization calculation. It arouses our interest how to select the appropriate auto part to perform quality improvement action based on the collected data from the after-sale source. Managers usually select the QIP part by the rule of thumb that is based on the quantitative criterion or the subjective preference of individuals. The total quality management (TQM) philosophy requires multiple stakeholders’ involvement, regarded as a multi-criteria decision making (MCDM) issue. This paper proposes a novel hybrid MCDM framework to select the best quality improvement solution combining the subjective and objective information. The rough set-based attribute reduction (RSAR) technique was employed to establish the hierarchy structure of influential criteria, and the decision information was collected with triangular fuzzy numbers (TFNs) for its vagueness and ambiguity. In addition, the novel hybrid MCDM framework integrating fuzzy DEMATEL (decision making trial and evaluation laboratory) method, the anti-entropy weighting (AEW) technique and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) was developed to rank the alternatives with the combined weight of criteria. The results argue that the optimal solution keeps a high conformance with Shemshadi’s and Chaghooshi’s methods, which is better than the existing determination. Besides, the result analysis shows the robustness and flexibility of the proposed hybrid MCDM framework. Full article