See Translated Mandarin VersionTechnological advancements in recent years have led to significant development for a variety of business applications. In particular, data-driven research, if utilized efficiently, provides ample opportunities for enterprises. To continuously promote the latest ideas in this strand of research, Prof. Hing Kai Chan, from the University of Nottingham Ningbo, China, and co-editor of the publication Supply Chain Management in the Big Data Era, shares how his research further advances his field of expertise in the IGI Global interview below.
What motivates you to conduct research activities in your area of expertise?
I think research itself is an interesting process. It may not bring you immediate benefits, but when you can find some useful research results, you will find it very interesting and satisfying. You will feel delighted that your efforts have not been wasted. In addition, conducting research can potentially advance theory in the field. In the case that the research studies and results can be employed well, they can help people having a better life, which should be the purpose of most scientific research.
What do you think was the most surprising discovery when conducting research?
I find that for many research questions, complex methods are not necessarily better than simple methods. Sometimes many researchers try to develop tedious research methods to solve some problems, but in fact, my research team and I have found that sometime simple methods can be more practical and may even perform better. For example, we compared the application of simple models and machine learning models in predicting port container throughput. Through analysis of the results, we found that the performance of machine learning models is not necessarily better than that of simple models under some circumstances.
As an expert in your field of study, what do you think you have learned so far?
Research is a process with uncertain outcomes, which can be surprising as well. Many times, we may not get the results we want to see. For perfectionists, many experiments and analysis can be frustrating. Therefore, we should balance the gains and losses of conducting research calmly.
What are your main expectations for the outcomes of your publication and research? How are they implemented?
I hope that my research results can be applied into practical applications. If my published articles can only make my resume look better and not contribute to practice, I will not be satisfied with the results of such research. This is also a problem of many theoretical studies, especially those of management/business studies. These studies are often contained at the theoretical level. If these studies cannot be implemented in practice they are of little real value. How are they implemented? This leads to the next question.
What are the main challenges of conducting research?
In fact, there are some obstacles to scientific research. If we lack project funding, advanced research cannot be conducted; if there is no teamwork/collaboration, particularly within the industry, we cannot better understand the actual problems in real-life applications and cannot obtain corresponding data for research. Another challenge to scientific research in management studies, which is different from other pure science domains, is the decoupling of the trailing for theoretical contributions from industrial needs; if we don’t focus on the needs of the industry and companies, our research outcomes are likely to be trapped in the academia and thus cannot create real impacts.
How does your research benefit for the community?
My research interests include sustainable operations and supply chain management, modeling and simulation, and the application of soft computing techniques in intelligent (industrial) systems. These research directions are consistent with the scope of the “Belt and Road Initiative” and “Made in China 2025.” For the industry, my research can support companies in formulating policies and processes. For the academic community, my research can enrich existing research content and provide theoretical support for more new research.
Why is your research area important to the field at large?
Most of my research is interdisciplinary, covering management, engineering, computer science and other disciplines. Through interdisciplinary research, my scientific research results not only provide theoretical contributions to management domain, but my research ideas also provide a practical basis for interdisciplinary research in a wide range of research fields.
How does your research differ from other fields in terms of innovation?
My research is mainly at the application level of management disciplines, not merely pure theoretical direction. This may be because my early training is in engineering, particularly the electronic engineering discipline. Therefore, I am more enthusiastic about data-driven scientific research rather than pure theoretical types of research.
What has your experience been like publishing with IGI Global?
I am very familiar with IGI Global. I have published various journal articles through IGI Global, and I also co-edited a volume. During the process, the staff at IGI Global were very professional and helpful. We collaborated very smoothly and fruitfully without encountering any problems.
IGI Global would like to thank Prof. Hing Kai Chan for sharing his research on the implementation of big data analytics for improved operations and supply chain processes. For more information about this research, view the publication here.
About Prof. Hing Kai Chan: Prof. Hing Kai Chan joined the Nottingham University Business School China in September 2014, and is a professor of operations management. He worked in the electronic manufacturing industry over 10 years before joining the academia.
Prof. Chan has published over 100 peer-reviewed academic articles and (co-)edited several special issues for reputable international journals. His publications appear in Production and Operations Management, European Journal of Operational Research, various IEEE Transactions, Decision Support Systems, International Journal of Production Economics, International Journal of Production Research, among others. He has been the co-editor of Industrial Management & Data Systems (SCI-indexed) since 2014, and is an associate editor of Transportation Research Part E: Logistics and Transportation Review (SCI-indexed) since 2018. He was the Associate Editor of the IEEE Transactions on Industrial Electronics (SCI-indexed) from 2009 to 2015, and the Associate Editor of the IEEE Transactions on Industrial Informatics (SCI-indexed) from 2014-2017. Professor Chan also serves as an Editorial Board Member (or similar) in a number of journals such as International Journal of Logistics Research and Applications (SSCI-indexed) and Online Information Review (SCI-indexed).
Prof. Chan is a fellow of the Institution of Engineering and Technology (FIET) and the Higher Education Academy (FHEA). He is also a senior member of the Institute of Electrical and Electronics Engineers, and a member of the Chartered Institute of Marketing and the Chartered Institute of Logistics and Transport. Prof. Chan is a chartered engineer and a chartered marketer.
IGI Global would like to thank Prof. Hing Kai Chan for sharing his research on the implementation of big data analytics for improved operations and supply chain processes. For more information about this research, view the publication here.
Prof. Hing Kai Chan's publication, Supply Chain Management in the Big Data Era, is available through IGI Global’s Online Bookstore and world-renowned InfoSci®-Books, a database of 5,300+ reference books.
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Purchase or recommend this database to your institution’s librarian.
For your reference, find below a sample of related titles within supply chain management and big data, which are also featured in IGI Global’s award-winning InfoSci-Books database and are available for purchase in print and electronic format. Be sure to recommend these titles to your librarian, to ensure your institution can acquire the most emerging research.
| | Big Data Analytics for Entrepreneurial Success
Prof. Soraya Sedkaoui (Khemis Miliana University, Algeria & SRY Consulting Montpellier, France)
Copyright: © 2019 | Pages: 300 | ISBN: 9781522576099 | EISBN: 9781522576105
This scholarly publication provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques within business applications. Featuring coverage on a broad range of topics such as algorithms, data collection, and machine learning, this publication provides concrete examples and case studies of successful uses of data-driven projects as well as the challenges and opportunities of generating value from data using analytics. |
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| | Handbook of Research on Industrial Applications for Improved Supply Chain Performance
Prof. Jorge Luis García-Alcaraz (Universidad Autónoma de Ciudad Juárez, Mexico), et al.
Copyright: © 2020 | Pages: 549 | ISBN: 9781799802020 | EISBN: 9781799802044
This scholarly publication identifies techniques, tools, and methodologies that can improve supply chain performance and enable businesses to generate a competitive advantage in the globalized market. |
| |
|
Disclaimer: The opinions expressed in this article are the author’s own and do not reflect the views of IGI Global.
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了解商业和管理领域的研究成果: 对话陈庆佳教授
近年来，技术进步推动了各种商业应用的重大发展，尤其是，数据驱动研究如果得到有效利用，将为企业成长提供充足的机会。为了不断提供这一领域的最新研究成果，我们邀请了来自宁波诺丁汉大学的陈庆佳教授，暨《大数据时代的供应链管理》（Supply Chain Management in Big Data Era）一书的联合主编，分享了他的研究如何进一步提升了他的专业领域。
是什么激励您在自己的专业领域开展研究活动？
我认为研究本身就是一个很有趣的过程。它可能不会立刻给你带来实实在在的好处，但当你能找到一些有用的研究成果的时候，你会觉得很有趣和很有满足感，会感到自己的努力没有白费。此外，进行研究可以潜在地推进该领域的理论。在成果可以很好地利用的情况下，它们可以帮助人们得到更好的生活，这也应该是大部分科学研究的目的所在。
您认为在进行研究时最令人惊讶的发现是什么？
我发现针对很多研究问题，复杂的方法未必比简单的方法好。有时许多研究人员试图开发一些繁琐的研究方法来解决一些问题，但事实上我和我的研究团队发现，简单的方法可以更加实用，而且效果可能更好。例如，我们比较了简单模型和机器学习模型在预测港口集装箱吞吐量上的应用，通过分析结果发现在数据量有限的情况下，机器学习模型的表现不一定比简单模型好。
作为您研究领域的专家，您认为到目前为止您学到了什么？
我觉得研究是一个无法有确定结果的过程，可能是惊喜，也可能是意外。很多时候我们可能无法获得我们希望看到的结果。对于有完美主义倾向的人来说，很多分析和实验可能会使人很沮丧。因此，我们应当平衡研究的得与失，放平心态。
您对出版物成果的主要期望是什么？它们是如何实现的？
我希望我的研究成果能够投入到实际的应用。如果我发表的文章只能够使我的简历更好看，而不能对实际的应用作出贡献，我不会对这样的研究成果感到满意。这也是许多理论研究的问题，尤其是管理和商业学的研究，这些研究常常停留在理论层面，然而无法经过实际应用的研究是缺乏真实价值的。如何实现这些研究也带出下一个问题。
进行研究的主要挑战是什么？
其实科学研究还是有一些阻碍的。如果我们缺乏项目资金，研究就无法推进；如果没有更多的团队合作/协作，尤其是业界的参与，我们就无法了解到更多市场上的实际问题，也无法得到相应的数据做研究；同时，管理学的科研跟自然科学研究不同，还存在与市场脱节的挑战：如果我们无法了解当今业界的发展和应用，我们的成果只会被局限于学数界，很可能无法配合市场和大部分公司的需求，从而无法实践我们科研应有的影响力。
您的研究对其用户社区有哪些好处？
我的研究方向主要包括可持续运营和供应链管理，建模与仿真，软计算技术在智能(工业)系统中的应用等。这些研究方向与 “一带一路”倡议和《中国制造2025》的研究内容相符。对于业界来说，我从事的研究可以为公司制定政策提供支持。对于学术界来说，我的研究可以丰富现有的研究内容，为更多的新研究提供理论支持。
为什么您的研究领域对广泛研究领域也很重要？
我从事的大多是交叉学科的研究，涵盖了管理学、计算机科学等学科。通过跨学科的研究，我的科研成果不仅为管理学提供了理论贡献，我的交叉学科研究思路也为广泛研究领域进行交叉学科的研究提供了实践依据。
您的研究在领域在创新方面与其它领域有何不同？
我的研究主要是在管理学科的应用层面，而不是理论方向。这可能是因为我是电子工程方向出身的。所以我更热衷于用数据去进行应用方向的科学研究而非理论研究。
您在IGI Global的出版经历如何？
我对IGI Global是非常熟悉的。我曾经在IGI Global上发表过期刊论文，我也参与了编写一本书。在这个过程中，IGI Global的工作人员是非常专业且乐于助人的。我们合作的非常愉快，没有遇到任何问题。
关于编辑: 陈庆佳教授于2014年9月加入诺丁汉大学商学院（中国），目前是运营管理学教授。加入学术界前，他在电子制造业工作了超过10年。陈教授已经发表了超过100篇同行评审学术文章，并为一些重要国际期刊兼任编辑。他已发表大量文章在生产与运营管理，欧洲运营研究期刊，IEEE Transactions，决策支持系统期刊，生产经济学国际期刊，生产研究国际期刊等。从2014年开始他担任《工业管理和数据系统》杂志的联合主编（SCI-indexed）, 和从2018年开始他也是《运输研究Part E：物流和交通评论》的副编辑（SCI-indexed）。2009年至2015年间他也是IEEE工业电子期刊的副编辑（SCI-indexed），和2014年至2017年间是IEEE工业信息学期刊的副编辑（SCI-indexed）。他同时还兼任一些重要期刊的编委会成员，如《物流研究与应用国际期刊》（SSCI-indexed），《网络信息评论》（SCI-indexed）。
他是英国工程技术学会会士（FIET）和英国高等教育学会会士(FHEA)。另外，他是美国电气和电子工程师学会的资深会员，同时作为认证工程师 (CEng) 和认证市务学员, 也是英国特许市务学会 (CIM) 和英国国际物流与运输学会(CILT)的会员。
IGI-Global感谢陈庆佳教授分享了他在实施大数据分析以改进运营和供应链流程方面的研究成果。有关此项研究的更多信息，请点击链接查看本书。
由陈庆佳教授编撰的《大数据时代的供应链管理》一书可通过IGI Global的在线书店和InfoSci®-图书数据库（含5,300+ 参考书）获取。
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欢迎您购买或将该数据库推荐给您的机构图书馆员。
以下是供应链管理和大数据中的相关书目，这些图书也出现在IGI Global屡获殊荣的InfoSci-图书数据库中，可供您以纸质和电子格式购买。请向你的图书管理员推荐这些书目，以确保你的机构能够获得最新的研究成果。
| | Big Data Analytics for Entrepreneurial Success
Prof. Soraya Sedkaoui (Khemis Miliana University, Algeria & SRY Consulting Montpellier, France)
Copyright: © 2019 | Pages: 300 | ISBN: 9781522576099 | EISBN: 9781522576105
This scholarly publication provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques within business applications. Featuring coverage on a broad range of topics such as algorithms, data collection, and machine learning, this publication provides concrete examples and case studies of successful uses of data-driven projects as well as the challenges and opportunities of generating value from data using analytics. |
| |
|
|
| | Handbook of Research on Industrial Applications for Improved Supply Chain Performance
Prof. Jorge Luis García-Alcaraz (Universidad Autónoma de Ciudad Juárez, Mexico), et al.
Copyright: © 2020 | Pages: 549 | ISBN: 9781799802020 | EISBN: 9781799802044
This scholarly publication identifies techniques, tools, and methodologies that can improve supply chain performance and enable businesses to generate a competitive advantage in the globalized market. |
| |
|
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