Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs

Xia, Min; Zhang, Yingchao; Weng, Liguo; Ye, Xiaoling

doi:10.1016/j.knosys.2012.07.002

Cited by 89 publications

(45 citation statements)

References 26 publications

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“…Furthermore, a firm's big data capabilities unify today's business with innovation to achieve a competitive advantage. Our results are consistent with Xia et al [43] who employed a versatile sales forecasting system in Hong Kong's fashion retail industry there by averting inventory stock outs. A knowledge-based perspective can also be discerned in the work of Womack et al [93], Wheelwright and Clark [94].…”

Section: Discussionsupporting

confidence: 92%

“…Others point out how big data helps in efficiency of design, production, intelligence and service processes employed in product life cycle management (PLM) [42]. For example, Xia et al [43] apply a robust big data system to avoid stock-outs and maintain high fill rates that enable more accurate sales forecasting. Shen and Chan [44] address demand forecasting and supply forecasting using BDA.…”

Section: Introductionmentioning

confidence: 99%

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Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

et al. 2017

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The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk.In reviewing literature on big data analytics (BDA) and sustainability, Keeso [12] argues that the application of big data in sustainable operations is often slow to achieve desired outcomes. Although sustainable supply chain management has little history in academic literature, it has become a mainstream area for practitioners [13][14][15]. On the other hand, building on the knowledge-based perspective some suggest the need for BDA as a knowledge resource that can be harvested and retained. The knowledge-based view can be traced from Simon's [16] seminal work with significant extensions by Grant [17,18], Huber [19] and Levitt and March [20]. Further, the knowledge-based perspective suggests how big data can fuel the purposive search for market and resource innovation opportunities [21]; such resources can provide corporations a competitive advantage [22].While building knowledge-based resources, it is also imperative to predict and avoid risks causing potential disruptions [23]. Recently, supply chain risk management (SCRM) has become a priority because of its capability to avert potential disruption and recover more quickly from disruptions that cannot be averted [24]. There are various risks including material flow risk, information flow risk and financial flow risk that cause supply chain disruptions [25]. Among these risks, supply chain social issues can impose significant operational risks on the supply chain [26]. In the literature the linkage between supply chain social issues, and risk management has been established [26]. Further, Klassen and Vreecke [26] show that social issues can be managed through effective monitoring, innovation cap...

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

et al. 2017

View full text Add to dashboard Cite

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“…The approach needs to manually set up the number of nearest neighbors, and by adjusting the free parameter (K = 2, 4, 6), the diversity of the ensemble mechanism will increase. • Kernel diversity The extreme learning machine (ELM) of Huang et al [23] is a simple and efficient learning method for single-hidden layer feed-forward neural networks (SLFN) and has been successfully utilized on a number of real-world applications, such as face recognition tasks, UCI datasets, imbalanced datasets, bankruptcy prediction, and voice recognition [31,33,49,50,54,55,58,59]. The method offers a superior generalization performance at an extremely fast learning speed and provides a highly accurate learning solution for both classification and regression tasks.…”

Section: Hybrid Ensemble Learning Forecasting Mechanism (Helm)mentioning

confidence: 99%

An emerging hybrid mechanism for information disclosure forecasting

Hsu

Lin

2014

Int. J. Mach. Learn. & Cyber.

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Corporate governance mechanisms ensure that investors get a fair return on their investment. A wellestablished governance mechanism reduces the information asymmetry and agency cost between a firm's management and stakeholders, but decision makers find it difficult to assess the corporate governance status of publicly-listed firms before the annual official announcement the following year. This study proposes a hybrid ensemble learning forecasting mechanism (HELM), whose single-component candidates from the extreme learning machine (ELM) algorithm with dissimilar ensemble strategies (that is, data diversity, parameter diversity, kernel diversity, and preprocessing diversity) form one initial dataset. We implement locally linear embedding into the proposed mechanism to handle the dimensionality task and then utilize the weighted voting taken from the base components' crossvalidation performance on a training dataset as the integration mechanism. Experimental results show that the proposed HELM significantly outperforms the other classifiers, but its superior performance under many real-life application domains comes with a critical drawback: it is incapable of providing an explanation for the underlying reasoning mechanisms. Thus, this study advances the utilized rough set theory with its explanation capability to extract the inherent knowledge from the ensemble mechanism (HELM). The informative rules can be used as a guideline for decision makers to make a reliable judgment under turbulent financial markets.

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“…They claim that the performance of their proposed model is superior to the traditional ARIMA models and two recently developed neural network models for fashion sales forecasting. Xia et al [46] examine a forecasting model based on extreme learning machine model with the adaptive metrics. In their model, the inputs can solve the problems of amplitude changing and trend determination, which in turn helps to reduce the effect of the over fitting of networks.…”

Section: Elm Based Hybridmentioning

confidence: 99%

Sales Forecasting for Fashion Retailing Service Industry: A Review

Liu

Ren

Choi

et al. 2013

Mathematical Problems in Engineering

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Sales forecasting is crucial for many retail operations. It is especially critical for the fashion retailing service industry in which product demand is very volatile and product's life cycle is short. This paper conducts a comprehensive literature review and selects a set of papers in the literature on fashion retail sales forecasting. The advantages and the drawbacks of different kinds of analytical methods for fashion retail sales forecasting are examined. The evolution of the respective forecasting methods over the past 15 years is revealed. Issues related to real-world applications of the fashion retail sales forecasting models and important future research directions are discussed.

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Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs

Cited by 89 publications

References 26 publications

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

An emerging hybrid mechanism for information disclosure forecasting

Sales Forecasting for Fashion Retailing Service Industry: A Review

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