Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age

Tsiliyannis, Christos Aristeides

doi:10.1016/j.ejor.2018.05.026

Cited by 25 publications

(7 citation statements)

References 57 publications

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“…Thus, the scheduling of maintenance operations can be optimized [103]. By applying Markov chain, Tsiliyannis [104] improved the forecast of the return of products, which can be used for remanufacturing. Another approach is the Pattern Recognition, used by Chin [105] for automated visual inspection in manufacturing.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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“…For OEMs, the potential cannibalization effect induced by remanufactured products on new product sales and uncertainties about the volume and quality of returned products remain the major challenges deterring the in-house remanufacturing capability development (Chen et al, 2015). In this regard, several attempts have been made to develop forecasting approaches that enable firms to make better and more accurate predictions; see Tsiliyannis (2018), Clottey et al (2012), andGoltsos et al (2019) for forecasting models for remanufacturing operations. Similarly, practice-oriented approaches such as seeding -selling new products as remanufactured at the start of a new product's life-cycle -have been reported in the extant literature, providing benefits to OEMs in terms of increased core recovery quantities, enabling the efficient remanufacturing earlier, and enhancing OEMs' ability to fulfill demands for remanufactured products throughout the product's life-cycle (Abbey et al, 2019).…”

Section: Remanufacturing Scmmentioning

confidence: 99%

Multi-dimensional circular supply chain management: A comparative review of the state-of-the-art practices and research

Zhang

Wang

Farooque

et al. 2021

Transportation Research Part E: Logistics and Transportation Re

100

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The circular economy (CE) concept has gained wide attention in practice as well as in academia in recent years. This paper reviews the state-of-the-art practices and research in "circular supply chain management" (CSCM), i.e., the integration of CE thinking into supply chain management (SCM) with the goal of achieving "zero wastes". The review covers 68 real-life CE implementation cases collected by the Ellen MacArthur Foundation and 124 publications in well-established, highranking academic journals in operations and supply chain management. The comparative review shows that CSCM encompasses multiple dimensions, including closed-loop SCM, reverse SCM, remanufacturing SCM, recycling SCM, and industrial symbiosis. A multi-dimensional CSCM (MD-CSCM) framework is developed to synthesize their interrelationships and to categorize academic publications into multiple research themes. Based on the identified research-practice gaps and pressing research needs, this study discusses important directions for future studies to advance supply chain circularity.

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“… Yuan et al, 2020b , Little, 2011 , Altizer and Nunn, 2006 , Pell et al, 2018 , Ross, 1916 , Tsiliyannis, 2018 …”

Section: Uncited Referencesmentioning

confidence: 99%

A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws

Tsiligianni¹,

Tsiligiannis²,

Tsiliyannis³

2023

European Journal of Operational Research

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A critical operations management problem in the ongoing COVID-19 pandemic is cognizance of (a) the number of all carriers at large (CaL) conveying the SARS-CoV-2, including asymptomatic ones and (b) the infection rate (IR). Both are random and unobservable, affecting the spread of the disease, patient arrivals to health care units (HCUs) and the number of victims. A novel, inventory perspective of COVID-19 is proposed, with random inflow, random losses and retrials (recurrent cases) and delayed/distributed exit, with randomly varying fractions of the exit distribution. A minimal construal, it enables representation of COVID-19 evolution with close fit of national incidence profiles, including single and multiple pattern outbreaks, oscillatory, periodic or non-periodic evolution, followed by retraction, leveling off, or strong resurgence. Furthermore, based on asymptotic laws, the minimum number of variables that must be monitored for identifying CaL and IR is determined and a real-time identification method is presented. The method is data-driven, utilizing the entry rate to HCUs and scaled, or dimensionless variables, including the mean residence time of symptomatic carriers in CaL and the mean residence time in CaL of patients entering HCUs. As manifested by several robust case studies of national COVID-19 incidence profiles, it provides efficient identification in real-time under unbiased monitoring error, without relying on any model. The propagation factor, a stochastic process, is reconstructed from the identified trajectories of CaL and IR, enabling evaluation of control measures. The results are useful towards design of policies restricting COVID-19 and encumbrance to HCUs and mitigating economic contraction.

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Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age

Cited by 25 publications

References 57 publications

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Multi-dimensional circular supply chain management: A comparative review of the state-of-the-art practices and research

A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws

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