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Machine Learning Applications in Business

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3279

Special Issue Editors


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Guest Editor
Department of Information Systems and Operations Management, Florida Gulf Coast University, Ft. Myers, FL 33965, USA
Interests: machine learning; artificial intelligence; operations; metaheuristics
Economic Research Department, Federal Reserve Bank of Kansas City, Kansas City, MO 64198, USA
Interests: macroeconomics; monetary policy; machine learning; financial economics; operations; international finance

Special Issue Information

Dear Colleagues,

We invite research papers for publication in a special issue of the Sustainability journal (for the section on Economic and Business Aspects of Sustainability) on “Machine Learning Applications in Business.” Many problems in business apply machine learning algorithms to find good solutions. Businesses face problems in many domains including supply-chain management, logistics management, inventory management, transportation management, quality management, financial management, sales and marketing management, managerial accounting management etc. Machine learning techniques include Neural Networks (Supervised, Unsupervised, Semi-supervised, Hybrid), Deep Learning, Inductive learning, Deductive learning, Ensemble learning, Genetic Algorithms, NeuroGenetic algorithms, Support Vector Machines, decision trees etc. These techniques can be applied to a variety of tasks such as classification, clustering, optimization, curve fitting, text processing, image processing etc. in many different domains in business. Articles selected for publication must demonstrate successful application of some machine learning approach on some business problem. Preference will be given to an application that involves sustainability issues.

Prof. Dr. Anurag Agarwal
Mr. Sungil Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • business operations
  • sustainability

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Published Papers (1 paper)

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Research

21 pages, 1902 KiB  
Article
An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data
by Che-Yu Hung, Chien-Chih Wang, Shi-Woei Lin and Bernard C. Jiang
Sustainability 2022, 14(4), 2382; https://doi.org/10.3390/su14042382 - 19 Feb 2022
Cited by 6 | Viewed by 2375
Abstract
The problem of missing data is frequently met in time series analysis. If not appropriately addressed, it usually leads to failed modeling and distorted forecasting. To deal with high market uncertainty, companies need a reliable and sustainable forecasting mechanism. In this article, two [...] Read more.
The problem of missing data is frequently met in time series analysis. If not appropriately addressed, it usually leads to failed modeling and distorted forecasting. To deal with high market uncertainty, companies need a reliable and sustainable forecasting mechanism. In this article, two propositions are presented: (1) a dedicated time series forecasting scheme, which is both accurate and sustainable, and (2) a practical observation of the data background to deal with the problem of missing data and to effectively formulate correction strategies after predictions. In the empirical study, actual tray sales data and a comparison of different models that combine missing data processing methods and forecasters are employed. The results show that a specific product needs to be represented by a dedicated model. For example, regardless of whether the last fiscal year was a growth or recession year, the results suggest that the missing data for products with a high market share should be handled by the zero-filling method, whereas the mean imputation method should be for the average market share products. Finally, the gap between forecast and actual demand is bridged by employing a validation set, and it is further used for formulating correction strategies regarding production volumes. Full article
(This article belongs to the Special Issue Machine Learning Applications in Business)
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