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Article
Publication date: 11 November 2013

Jamal Shahrabi, Esmaeil Hadavandi and Maryam Salehi Esfandarani

In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated anthropometric data…

509

Abstract

Purpose

In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated anthropometric data and the classification system that introduces the appropriate size from the sizing chart to each person. To solve this problem, as a first study in the literature, a hybrid intelligent classification model as a size recommendation expert system is proposed. The paper aims to discuss these issues.

Design/methodology/approach

Three stages for developing a hybrid intelligent classification system based on data clustering and probabilistic neural network (PNN) are proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference for developing a new intelligent classification system by using a PNN. At the last stage, the accuracy of the proposed model is evaluated by using the Iranian male's body type data set.

Findings

Experimental results show that the proposed model has a good accuracy and can be used as a size recommendation expert system to specify the right size for the customers. By using the proposed model and designing an interface for it, a decision support system was developed as a size recommendation expert system that was used by an apparel sales store. The results were time saving and more satisfying for the customers by selecting the appropriate apparel size for them.

Originality/value

In this paper, as a first study in literature, a hybrid intelligent model for developing a size recommendation expert system based on data clustering and a PNN to enable the salesperson to help the consumer in choosing the right size is proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference to develop a new intelligent classification system by using a PNN. In the last stage, the accuracy of the proposed model is evaluated by using testing data. The proposed model achieved an 87.2 percent accuracy rate that is very promising.

Details

International Journal of Clothing Science and Technology, vol. 25 no. 5
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 1 November 2012

Maryam Salehi and J. Shahrabi

This article uses a series of data mining techniques to analyze body types and introduce a new sizing chart in order to produce garments for males. A principle component analysis…

59

Abstract

This article uses a series of data mining techniques to analyze body types and introduce a new sizing chart in order to produce garments for males. A principle component analysis and hierarchical and non-hierarchical clustering approaches are used to form a new sizing chart. All variables are grouped into two main components with a principle component analysis. Agglomerative hierarchical clustering is used to determine the number of clusters, and then a k-means algorithm is applied to segment the heterogonous population to actually form the clusters. The resultant innovations in designing garments have improved both non-price and price factors, the fittings of garments on all bodies have effectively improved and fabric waste has decreased, so the main goals which include improvement in quality with more comfort and a lower price have been met.

Details

Research Journal of Textile and Apparel, vol. 16 no. 4
Type: Research Article
ISSN: 1560-6074

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Article
Publication date: 6 March 2017

Morteza Hoseinieh, Taghi Shahrabi, Morteza Farrokhi Rad and Bahram Ramezanzadeh

The aim of this paper is to investigate the influence of sour crude oil contaminant on the calcareous scale deposition under cathodic protection of carbon steel in artificial…

144

Abstract

Purpose

The aim of this paper is to investigate the influence of sour crude oil contaminant on the calcareous scale deposition under cathodic protection of carbon steel in artificial seawater.

Design/methodology/approach

Electrochemical and surface characterizations are carried out using chronoamperometry, electrochemical impedance spectroscopy, scanning electron microscope/energy dispersive spectroscopy, X-Ray diffraction and Raman spectroscopy techniques.

Findings

Results showed that sour oil limited the deposit nucleation regarding its volume concentrations. The inhibition mechanism was examined to be simultaneous acts of pH reduction and mackinawite formation beside minor physical adsorption of oil molecules on steel electrode.

Originality/value

There is no paper concerning the proposed subject, and the idea of this work is fully novel which is of great significance because of the consequences of disastrous oil spill phenomena on the integrity of exposed offshore facilities in terms of optimum protection against probable corrosion mechanisms.

Details

Anti-Corrosion Methods and Materials, vol. 64 no. 2
Type: Research Article
ISSN: 0003-5599

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Article
Publication date: 14 August 2018

Waqas Khalid and Zaza Nadja Lee Herbert-Hansen

This paper aims to investigate the application of unsupervised machine learning in the international location decision (ILD). This paper addresses the need for a fast…

693

Abstract

Purpose

This paper aims to investigate the application of unsupervised machine learning in the international location decision (ILD). This paper addresses the need for a fast, quantitative and dynamic location decision framework.

Design/methodology/approach

Unsupervised machine learning technique, i.e. k-means clustering, is used to carry out the analysis. In total, 24 different indicators of 94 countries, categorized into five groups, have been used in the analysis. After the clustering, the clusters have been compared and scored to select the feasible countries.

Findings

A new framework is developed based on k-means clustering that can be used in ILD. This method provides a quantitative output without personal subjectivity. The indicators can be easily added or extracted based on the preferences of the decision-makers. Hence, it was found out that the unsupervised machine learning, i.e. k-means clustering, is a fast and flexible decision support framework that can be used in ILD.

Research limitations/implications

Limitations include the generality of selected indicators and clustering algorithm used. The use of other methods and parameters may lead to alternate results.

Originality/value

The framework developed through the research intends to assist the decision-makers in deciding on the facility locations. The framework can be used in international and national domains. It provides a quantitative, fast and flexible way to shortlist the potential locations. Other methods can also be used to further decide on the specific location.

Details

Journal of Global Operations and Strategic Sourcing, vol. 11 no. 3
Type: Research Article
ISSN: 2398-5364

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Article
Publication date: 9 January 2017

Doris Chenguang Wu, Haiyan Song and Shujie Shen

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging…

5735

Abstract

Purpose

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Design/methodology/approach

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

Findings

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

Research limitations/implications

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

Practical implications

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

Originality/value

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

Details

International Journal of Contemporary Hospitality Management, vol. 29 no. 1
Type: Research Article
ISSN: 0959-6119

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Article
Publication date: 28 February 2023

Gautam Srivastava and Surajit Bag

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from…

3045

Abstract

Purpose

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

Design/methodology/approach

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

Findings

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Practical implications

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

Originality/value

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

Details

Benchmarking: An International Journal, vol. 31 no. 2
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 12 June 2020

Ming-Huan Shou, Zheng-Xin Wang, Dan-Dan Li and Yi-Tong Zhou

Since the issuance in 2009, the digital currency has enjoyed an increasing popularity and has become one of the most important options for global investors. The purpose of this…

266

Abstract

Purpose

Since the issuance in 2009, the digital currency has enjoyed an increasing popularity and has become one of the most important options for global investors. The purpose of this paper is to propose a hybrid model ( KDJ–Markov chain) which integrates the advantages of the stochastic index (KDJ) and grey Markov chain methods and provide a useful decision support tool for investors participating in the digital currency market.

Design/methodology/approach

Taking Litecoin's closing price prediction as an example, the closing prices from May 2 to June 20, 2017, are used as the training set, while those from June 21 to August 9, 2017, are used as the test set. In addition, an adaptive KDJ–Markov chain is proposed to enhance the adaptability for dynamic transaction information. And the paper verifies the effectiveness of the KDJ–Markov chain method and adaptive KDJ–Markov chain method.

Findings

The results show that the proposed methods can provide a reliable foundation for market analysis and investment decisions. Under the circumstances the accuracy of the training set and the accuracy of the test set are 76% and 78%, respectively.

Practical implications

This study not only solves the problems that KDJ method cannot accurately predict the next day's state and the grey Markov chain method cannot divide the states very well, but it also provides two useful decision support tools for investors to make more scientific and reasonable decisions for digital currency where there are no existing methods to analyze the fluctuation.

Originality/value

A new approach to analyze the fluctuation of digital currency, in which there are no existing methods, is proposed based on the stochastic index (KDJ) and grey Markov chain methods. And both of these two models have high accuracy.

Details

Grey Systems: Theory and Application, vol. 11 no. 1
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 4 June 2020

Dimitra Samara, Ioannis Magnisalis and Vassilios Peristeras

This paper aims to research, identify and discuss the benefits and overall role of big data and artificial intelligence (BDAI) in the tourism sector, as this is depicted in recent…

5392

Abstract

Purpose

This paper aims to research, identify and discuss the benefits and overall role of big data and artificial intelligence (BDAI) in the tourism sector, as this is depicted in recent literature.

Design/methodology/approach

A systematic literature review was conducted under the McKinsey’s Global Institute (Talwar and Koury, 2017) methodological perspective that identifies the four ways (i.e. project, produce, promote and provide) in which BDAI creates value. The authors enhanced this analysis methodology by depicting relevant challenges as well.

Findings

The findings imply that BDAI create value for the tourism sector through appropriately identified disseminations. The benefits of adopting BDAI strategies include increased efficiency, productivity and profitability for tourism suppliers combined with an extremely rich and personalized experience for travellers. The authors conclude that challenges can be bypassed by adopting a BDAI strategy. Such an adoption will stand critical for the competitiveness and resilience of existing established and new players in the tourism sector.

Originality/value

Besides identifying the benefits that BDAI brings in the tourism sector, the research proposes a guidebook to overcome challenges when introducing such new technologies. The exploration of the BDAI literature brings important implication for managers, academicians and consumers. This is the first systematic review in an area and contributes to the broader e-commerce marketing, retailing and e-tourism research.

研究目的

本论文旨在研究、指出、和讨论大数据和人工智能(BDAI)在旅游业中的优势和整体作用。这些方面也在近文献中有所提到。

研究设计/方法/途径

本论文采用系统综述方式, 在McKinsey’s Global Institute方法论的指导下, 确认BDAI可以在四种方面(预测、产出、提高、以及提供)创造价值。我们也通过阐述相关挑战来增强这个分析方法。

研究结果

本论文结果显示BDAI通过适当的传播方式来为旅游业中创造价值。采用BDAI战略的好处包括:对旅游提供商带来高效、多产、盈利, 以及对旅游者们带来极度丰富和个性化的旅游体验。我们还总结了采取BDAI战略带来的诸多挑战。采用BDAI战略对旅游业中现有和新参与者的竞争力和弹性起到至关重要的作用。

研究原创性/价值

除了指出了旅游业中BDAI带来的优势, 本论文还提出了一个指南, 来指导当新科技被引进时如何克服挑战。本论文通过对BDAI文献的梳理, 其文献综述结果对经理、学者、和消费者都有重要的启示作用。本论文是首篇在BDAI领域的系统综述, 对拓展电子商务营销、零售、和电子旅游科研有着重大贡献。

Details

Journal of Hospitality and Tourism Technology, vol. 11 no. 2
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 10 April 2019

Eleonora Bottani, Piera Centobelli, Mosé Gallo, Mohamad Amin Kaviani, Vipul Jain and Teresa Murino

The purpose of this paper is to propose an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler…

1668

Abstract

Purpose

The purpose of this paper is to propose an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks (OOSs) by jointly formulating price policies and forecasting retailer’s demand.

Design/methodology/approach

The framework is based on the cascade implementation of two artificial neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a real wholesale supply chain.

Findings

The application of the ANN framework to a real wholesale supply chain shows that the proposed methodology has the potential to decrease economic loss due to OOS occurrence by more than 56 percent.

Originality/value

The combined use of ANNs is a novelty in supply chain operation management. Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to more dependable demand forecasts.

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Article
Publication date: 30 August 2021

Hassan Younis, Balan Sundarakani and Malek Alsharairi

The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains…

4956

Abstract

Purpose

The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof.

Design/methodology/approach

Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM).

Findings

AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness.

Research limitations/implications

This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue.

Practical implications

This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore.

Originality/value

Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

Details

Journal of Modelling in Management, vol. 17 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

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