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Mathematics
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Applied Statistics in Management Sciences
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Applied Statistics in Management Sciences

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1045

Special Issue Editors

Prof. Dr. Chia-Ding Hou

E-Mail Website
Guest Editor
Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Interests: genetic statistics; simultaneous statistical inference
Dr. Rung-Hung Su

E-Mail Website
Guest Editor
Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Interests: practical application of statistics; quality management and control; inventory management; industrial management

Special Issue Information

Dear Colleagues,

Applied statistics is a type of of data analysis and is devoted to identifying reliable solutions based on analysis and estimation results. This Special Issue seeks high-quality studies focusing on the practice of statistical techniques in enabling the development of scientific decisions in management. We recommend that mathematical modeling, statistical skills, programming, algorithms, deep learning, and simulation are applied to make decisions that are more routed in science. An application example is provided to illustrate the practicality and applicability of the proposed method. Some managerial insights are also discussed. Novel applications from all fronts of management will also be considered. Topics include, but are not limited to, the following:

  • Marketing management;
  • Production management;
  • Inventory management;
  • Quality management and control;
  • Financial management;
  • Operations research;
  • Supply chain management;
  • Education policy;
  • Public health policy;
  • Environment and energy policy.

Prof. Dr. Chia-Ding Hou
Dr. Rung-Hung Su
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. Mathematics 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 2600 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

  • applied statistics
  • management sciences
  • decision-making
  • data analysis
  • deep learning

Published Papers (2 papers)

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Research

22 pages, 1777 KiB  
Article
Obtaining Conservative Estimates of Integrated Profitability for a Single-Period Product in an Own-Branding-and-Manufacturing Enterprise with Multiple Owned Channels
by Rung-Hung Su, Chia-Ding Hou and Jou-Yu Lee
Mathematics 2024, 12(13), 2080; https://doi.org/10.3390/math12132080 - 2 Jul 2024
Viewed by 61
Abstract
The achievable capacity index (ACI) is a simple and efficient approach for estimating the profitability of newsboy-type products, wherein profitability is defined as the probability of achieving the target profit by optimizing the order quantity. At present, the ACI is applicable to single [...] Read more.
The achievable capacity index (ACI) is a simple and efficient approach for estimating the profitability of newsboy-type products, wherein profitability is defined as the probability of achieving the target profit by optimizing the order quantity. At present, the ACI is applicable to single retail stores (i.e., single demand) but not to multiple sales channels (i.e., multiple demand). This paper presents an integrated achievable capacity index (IACI) by which to measure the aggregate profitability of multiple mutually independent channels under normally distributed demand. An unbiased IACI estimator is also developed, to which is applied the Taylor expansion to approximate its sampling distribution, wherein the sizes, means, and variances of demand differ in each channel. Furthermore, overestimates due to sampling error are avoided by deriving the lower confidence bound for the IACI. This paper also provides generic tables to aid managers seeking conservative estimates of profitability. The applicability of the proposed scheme is demonstrated numerically using a real-world example involving an own-branding-and-manufacturing (OBM) enterprise with multiple owned channels. Full article
(This article belongs to the Special Issue Applied Statistics in Management Sciences)
17 pages, 5891 KiB  
Article
Lifetime Distribution for a Mixed Redundant System with Imperfect Switch and Components Having Phase–Type Time-to-Failure Distribution
by Myung-Ki Baek and Heungseob Kim
Mathematics 2024, 12(8), 1191; https://doi.org/10.3390/math12081191 - 16 Apr 2024
Viewed by 499
Abstract
Recently, a mixed redundancy was introduced among the redundant design strategies to achieve a more reliable system within the equivalent resources. This study deals with a lifetime distribution for a mixed redundant system with an imperfect fault detector/switch. The lifetime distribution model was [...] Read more.
Recently, a mixed redundancy was introduced among the redundant design strategies to achieve a more reliable system within the equivalent resources. This study deals with a lifetime distribution for a mixed redundant system with an imperfect fault detector/switch. The lifetime distribution model was formulated using a structured continuous Markov chain (CTMC) and considers the time-to-failure (TTF) distribution of a component as a phase-type distribution (PHD). The model’s versatility and practicality are enhanced because the PHD can represent diverse degradation patterns of the components exposed to varied operating environments. The model provides accurate reliability for a mixed redundant system by advancing the approximate reliability function suggested in previous studies. Furthermore, the model would be useful in system design and management because it provides information such as the nth moment of the system’s lifetime distribution. In numerical experiments on some examples, the mixed redundancy was confirmed to devise a more reliable system than the existing active and standby redundancies, and the improvement effect increased as the number of redundant components decreased. The optimal structure for maximizing the expected lifetime of the system changes depends on the reliability of the components and fault detector/switch. Full article
(This article belongs to the Special Issue Applied Statistics in Management Sciences)
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