Applied Stochastic Models in Business and Industry最新文献

{"title":"Modelling Task Durations Towards Automated, Big Data, Process Mining","authors":"Malcolm Faddy,&nbsp;Lingkai Yang,&nbsp;Sally McClean,&nbsp;Mark Donnelly,&nbsp;Kashaf Khan,&nbsp;Kevin Burke","doi":"10.1002/asmb.2933","DOIUrl":"https://doi.org/10.1002/asmb.2933","url":null,"abstract":"<p>Business processes are generally time-sensitive, impacting factors such as customer expectations, cost efficiencies, compliance requirements, supply chain constraints, and timely decision-making. Time analysis is therefore crucial for customer understanding and process congestion minimisation. Existing process mining methods mainly employ basic statistics, process discovery and data mining techniques. These approaches often lack a structured model or profile to characterise the data related to the duration of individual process tasks. Consequently, it can be difficult to comprehensively understand critical observations such as trends, peaks, and valleys of task durations. This paper proposes a parsimonious generic representation of task duration data that addresses these limitations. A mixture model comprising gamma, uniform and exponential distributions is proposed that allows for peaked components corresponding to durations terminating near a particular value (the peak) with, in addition, flatter components for durations terminating more randomly between the peaks. The modelling is validated using examples from patient billing and the telecom industry. In each scenario, the corresponding fitted models offer a good representation of the underlying process tasks. The model can therefore be used to improve knowledge of these tasks in terms of the mixture components and what they might represent, such as the root causes of task termination. The paper also considers information criteria more appropriate for large data sets where very small effects can appear “significant” using techniques developed for smaller data sets.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.2933","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Forecasting of Value-at-Risk and Expected Shortfall in Cryptocurrency Markets: A Nonlinear Semi-Parametric Framework","authors":"Cathy W. S. Chen,&nbsp;Po-Hui Chen,&nbsp;Ying-Lin Hsu","doi":"10.1002/asmb.2926","DOIUrl":"https://doi.org/10.1002/asmb.2926","url":null,"abstract":"<div>\u0000 \u0000 <p>Cryptocurrencies exhibit high volatility, emphasizing the importance of accurately measuring tail risk in their markets. This research incorporates a threshold-switching mechanism into Taylor's ES-CAViaR models that unveil features such as asymmetry and jump phenomena. These enhancements effectively capture the diverse tail risks of cryptocurrencies while enabling the simultaneous forecasting of both Value-at-Risk (VaR) and Expected Shortfall (ES). The proposed models incorporate two types of functions to address the VaR and ES nexus with the option to use the rolling standard deviation of returns as a short-term volatility proxy as a regressor. We estimate the parameters and forecast tail risk within a Bayesian framework. Taking the two largest cryptocurrencies by market capitalization, Bitcoin and Ethereum, we assess the one-step-ahead forecasting performance over a four-year out-of-sample period using a rolling window approach. The comparative results from backtests and five scoring functions among eight competing models support the conclusion that models with a threshold mechanism capture the tail risk of cryptocurrencies more accurately than other risk models.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foreword Special Issue on New Frontiers in Reliability and Risk Analysis: A Tribute to Nozer Darabsha Singpurwalla","authors":"Subrata Kundu,&nbsp;Thomas Mazzuchi,&nbsp;Kimberly F. Sellers,&nbsp;Refik Soyer","doi":"10.1002/asmb.70000","DOIUrl":"https://doi.org/10.1002/asmb.70000","url":null,"abstract":"&lt;p&gt;Nozer D. Singpurwalla (1939–2022)&lt;/p&gt;&lt;p&gt;We are honored to be guest editors for this special issue of &lt;i&gt;Applied Stochastic Models in Business and Industry&lt;/i&gt; which is a tribute to Nozer D. Singpurwalla's scholarly work and achievements. The special issue contains seventeen papers. Four of these papers were based on presented talks at the 2-day conference entitled &lt;i&gt;New Frontiers in Reliability and Risk Analysis&lt;/i&gt;, held on October 13–14, 2023 at The George Washington University in Washington, DC. The conference, which was dedicated to Nozer, brought together leading experts and young researchers in the fields in which Nozer was a major contributor, that is reliability, risk analysis, and Bayesian statistics. The special issue includes contributions on these topics from Nozer's friends and colleagues as well as from other researchers.&lt;/p&gt;&lt;p&gt;The first article by Soyer and Spizzichino presents an overview of Nozer's work in reliability and risk analysis as well as his interests in foundational aspects of statistics, probability, and decision analysis. The paper by Li, Tierney, Hellmayr, and West deals with sequential Bayesian analysis of multivariate time series models with a focus on causal inference which were both areas of interest to Nozer.&lt;/p&gt;&lt;p&gt;The next two papers are on topics that attracted Nozer's attention due to their foundational implications. Sellers and Booker describe their collaborations with Nozer regarding the connections of fuzzy sets with probability and reliability theory. The authors further discuss subsequent advances in this space and the perceptions across disciplines (particularly among statisticians and data scientists) over the last 20 years. The article by Polson and Sokolov presents an introduction to the notions of negative probability, which was of interest to Nozer during his final years, and the authors give a version of Bayes rule for such probabilities.&lt;/p&gt;&lt;p&gt;The article by Arkadani, Asadi, and Soofi builds on earlier work by Nozer on the comparison of informativeness of failures versus survivals in life testing. The authors consider a comparison of the information on moments and the model parameters and develop information measures. Finkelstein and Cha present an overview of mixture failure rates (that Nozer often referred to as “predictive failure rate”) to model heterogeneity in reliability and discuss recent developments on the topic including the stochastic intensity paradox.&lt;/p&gt;&lt;p&gt;The articles by Limnios, and Palayangoda and Balakrishnan deal with gamma processes for degradation modeling. Nozer used the gamma process in his study of Bayesian life testing, and failure processes in dynamic and multiple failure mode environments. Limnios considers a gamma process for degradation under a random environment modeled by a Markov process and presents results for averaging and normal deviation. Palayangoda and Balakrishnan consider a complete likelihood for the gamma processes and develop inference using the EM","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IIoT and Digital Twin: A Systematic Literature Review and Looking Beyond the State","authors":"Thomas Bleistein,&nbsp;Moritz Paulus,&nbsp;Kiran Gani,&nbsp;Robert Becker,&nbsp;Dirk Werth","doi":"10.1002/asmb.2923","DOIUrl":"https://doi.org/10.1002/asmb.2923","url":null,"abstract":"<div>\u0000 \u0000 <p>The fourth industrial revolution has driven the emergence of Digital Twins (DTs) and Industrial Internet of Things (IIoT) in manufacturing. However, the use of different definition has led to varied interpretations and inconsistent understanding of DTs. Thus, by exploring the gap between theoretical frameworks and practical implementations of IIoT-based DTs in manufacturing, this paper aims to shed light on the DT phenomenon by considering the historical evolution and fundamental concepts of IIoT-based DTs. Therefore, a systematic literature review was conducted to assess the ambiguity concerning DTs, particularly in distinguishing architectures and types. Therefore, this paper identifies IIoT-based DTs in manufacturing by reviewing application-oriented literature. As a result of a subsequent classification, this paper proposes a hierarchical classification based on communication dynamics (i.e., Uni-directional and Bi-directional) and information processing (i.e., use or non-use of machine learning). Conclusively, this study proposes a comprehensive classification approach for IIoT-based DTs and thus contributes to a more consistent understanding of the DT phenomenon. Moreover, this paper discusses key findings, as well as implications for research and practice. Finally potential avenues for future research are derived and the limitations of this study are discussed.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability Analysis of Load-Sharing Systems Using a Flexible Model With Piecewise Linear Functions","authors":"Shilpi Biswas,&nbsp;Ayon Ganguly,&nbsp;Debanjan Mitra","doi":"10.1002/asmb.2934","DOIUrl":"https://doi.org/10.1002/asmb.2934","url":null,"abstract":"<div>\u0000 \u0000 <p>A flexible model for analysing load-sharing data is developed by approximating the cumulative hazard functions of component lifetimes by piecewise linear functions. The proposed model is data-driven and does not depend on restrictive parametric assumptions on underlying component lifetimes. Maximum likelihood estimation and construction of confidence intervals for model parameters are discussed. Estimates of reliability characteristics such as reliability at a mission time, quantile function, mean time to failure and mean residual time for load-sharing systems are developed in this setting. As the proposed model is capable of providing a good fit for load-sharing data, it also results in a better estimation of these important reliability characteristics. The performance of the proposed model is observed to be quite satisfactory through a detailed Monte Carlo simulation study. The analyses of two load-sharing datasets, one pertaining to the lives of two-motor load-sharing systems and another related to basketball games, are provided as illustrative examples. In summary, this article presents a comprehensive discussion on a flexible model that can be used for load-sharing systems efficiently.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information About the Moments or the Likelihood Model Parameters? A Chicken and Egg Problem","authors":"Omid M. Ardakani,&nbsp;Majid Asadi,&nbsp;Ehsan S. Soofi","doi":"10.1002/asmb.2931","DOIUrl":"https://doi.org/10.1002/asmb.2931","url":null,"abstract":"<div>\u0000 \u0000 <p>This article compares the information content of a sample for two competing Bayesian approaches. One approach follows Dennis Lindley's Bayesian standpoint, where one begins by formulating a prior for a parameter related to the problem in question and incorporates a likelihood to transition to a posterior. This contrasts with the usual Bayesian approach, where one starts with a likelihood model, formulates a prior distribution for its parameters, and derives the corresponding posterior. In both cases, the sample information content is measured using the difference between the prior and posterior entropies. We investigate this contrast in the context of learning about the moments of a variable. The maximum entropy principle is used to construct the likelihood model consistent with the given moment parameters. This likelihood model is then combined with the prior information on the parameters to derive the posterior. The model parameters are the Lagrange multipliers for the moment constraints. A prior for the moments induces a prior for the model parameters; however, the data provides differing amounts of information about them. The results obtained for several problems show that the information content using the two formulations can differ significantly. Additional information measures are derived to assess the effects of operating environments on the lifetimes of system components.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Degradation Modeling Comparison From Statistical to Artificial Intelligence Models for Curing Oven Chains","authors":"Hasan Misaii,&nbsp;Amélie Ponchet Durupt,&nbsp;Hai Canh Vu,&nbsp;Nassim Boudaoud,&nbsp;Patrick Leduc,&nbsp;Yun Xu,&nbsp;Arnaud Caracciolo","doi":"10.1002/asmb.2930","DOIUrl":"https://doi.org/10.1002/asmb.2930","url":null,"abstract":"<div>\u0000 \u0000 <p>The limitations of physics-based models and the constraints posed by data-driven models have motivated the development of fusion models for degradation modeling. These fusion models are designed to overcome the shortcomings inherent to either type of these models when used in isolation. In reliability analysis, particularly for highly reliable systems or units, the available datasets often exhibit small sample sizes. In such instances, the amount of data may not suffice for training powerful data-driven models, which typically require large datasets. Additionally, physics-based models may fail to capture all relevant information present in the data. This article focuses on addressing small sample-size datasets related to highly reliable systems, exploring various statistical and machine learning models tailored for such datasets, from statistical and AI models to fusion models. Furthermore, to address the challenges of using these models in isolation, a combination approach is presented involving employing simple data-driven models accompanied by essential data preprocessing and a physics-based model. This combination enables the models to capture the majority of pertinent information within the data. Also, a time-windowed multilayer perceptron is adapted to the dataset, showing that a meticulously prepared artificial neural network model might surpass the performance of some robust data-driven and even fusion models.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Redundancy Allocation for Series and Parallel Systems: A Copula-Based Approach","authors":"Ravi Kumar,&nbsp;T. V. Rao,&nbsp;Sameen Naqvi","doi":"10.1002/asmb.2928","DOIUrl":"https://doi.org/10.1002/asmb.2928","url":null,"abstract":"<div>\u0000 \u0000 <p>The allocation of redundant components to a system is a common method for enhancing the system's lifetime. This study explores the optimal allocation of redundancies in series and parallel systems with two components by assuming components and redundancies are dependent. That is, we perform the stochastic comparisons of the series (parallel) systems in the case of two redundancies at the component level. Specifically, we examine the stochastic comparisons across three scenarios: (i) components (and redundancies) have dependent lifetimes but are independent of each other, and components (redundancies) have identical marginal distributions in the two generated systems; (ii) components (and redundancies) have dependent lifetimes and are independent of each other, but the marginal distributions of components (redundancies) are different in the two generated system; and (iii) components and redundancies are interdependent and the marginals of the components (redundancies) in the two generated systems are same. In this study, we model the dependency using the concept of copula and perform the desired stochastic comparisons using generalized distorted distribution functions. Furthermore, we demonstrate our findings through various examples and counterexamples. Finally, we provide a simulation-based study and a real data analysis to illustrate our findings.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimising by Simulation-Based Optimisation the Cycle Time for the Line Balancing Problem in Real-World Environments","authors":"Luis A. Moncayo–Martínez,&nbsp;Naihui He,&nbsp;Elias H. Arias–Nava","doi":"10.1002/asmb.2925","DOIUrl":"https://doi.org/10.1002/asmb.2925","url":null,"abstract":"<div>\u0000 \u0000 <p>In the context of Industry 4.0, a production line must be flexible and adaptable to stochastic or real-world environments. As a result, the assembly line balancing (ALB) problem involves managing uncertainty or stochasticity. Several methods have been proposed, including stochastic mathematical programming models and simulations. However, programming models can only incorporate a few sources of uncertainty that result in impractical or unfeasible solutions to implement due to overlooked complexities, while simulation is only used to test solutions from deterministic approaches or adjust parameters without maintaining their optimum value. The proposed methodology uses a deterministic mathematical model to minimize the cycle time, followed by the simulation to measure the impact of selected sources of uncertainty on the cycle time. Finally, the optimum value of the stochastic parameters is computed using simulation-based optimization to maintain the average cycle time close to the deterministic one. A real-life assembly line balancing problem for a motorcycle manufacturing company is solved to test the proposed methodology. The sources of uncertainty are the tasks' stochastic processing times, inter-arrival time, the number of workers in each station, and the speed of the material handling system. Results show that the average cycle time is above 2.7% from the deterministic value computed by the programming model when the inter-arrival time is set to 270 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 </mrow>\u0000 <annotation>$$ pm $$</annotation>\u0000 </semantics></math> 60 s; the processing times are allowed to increase or decrease by 3 s; the material handling system's speed is 1.5 m/s; and the number of workers in cells is between 4 and 6, with a speed of 2 m/s. The reader can download the source code and the simulation model to apply the methodology to other instances.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Thinking in Reliability and Risk Analysis: An Overview of Nozer D. Singpurwalla's Work","authors":"Refik Soyer,&nbsp;Fabio Spizzichino","doi":"10.1002/asmb.2927","DOIUrl":"https://doi.org/10.1002/asmb.2927","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, an overview of Nozer Singpurwalla's work in reliability and risk analysis is provided. Rather than presenting a chronological review of his work, the emphasis is given to those areas of his research which better reflect Nozer's scientific personality.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}