Classification methodology for spare parts management combining maintenance and logistics perspectives

Teixeira, Catarina; Lopes, Isabel da Silva; Figueiredo, Manuel

doi:10.1080/23270012.2018.1436989

Cited by 30 publications

(28 citation statements)

References 41 publications

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“…[23] developed a risk framework for suppliers and proposed four criteria for the classification framework. [24] combined the characteristics of maintenance and logistics to propose a multi-standard classification method.…”

Section: Literature Review In the Field Of Manufacturing Managementmentioning

confidence: 99%

Application of Data Mining in the Field of Human Resource Management: A Review

Ma¹,

Zhang²,

Song³

et al. 2019

Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019)

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Data mining methods play an important role in customer management, manufacturing management, and financial management. This paper focuses on the latest research progress from 2012 to the present. The analysis of the progress of existing research will help researchers to better engage in research work in the future. The literature review is classified according to the research field and the data mining methods involved. In each of the research areas, we conduct research classification based on the data mining tools used. Through literature analysis, it can be seen that the degree of attention of neural networks has increased continuously in recent years.

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Section: Literature Review In the Field Of Manufacturing Managementmentioning

confidence: 99%

Application of Data Mining in the Field of Human Resource Management: A Review

Ma¹,

Zhang²,

Song³

et al. 2019

Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019)

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“…Traditional inventory models are based on a fixed demand (Teixeira et al, 2018). However, updated models should factor in the uncertainty of demand (Aloulou et al, 2014).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Peer Review #3 of "Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand (v0.1)"

2020

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This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zeroinflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston's method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

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“…MADM approaches identify how attribute information is to be processed to compute a suitable alternative or to rank the alternatives for supporting decision-making. It has been broadly applied in different domains, including engineering technology [1], operation research [2], and management science [3]. To solve decision-making problems having uncertainty, Atanassov [4] introduced the idea of intuitionistic fuzzy sets (IFSs) which involve both membership and non-membership functions, an efficient generalization of fuzzy sets [5] which characterize only membership function.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Decision-Making Based on m-Polar Fuzzy Hamacher Aggregation Operators

2019

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In this paper, we introduce certain aggregation operators, namely, the m-polar fuzzy (mF) Hamacher weighted average operator, mF Hamacher ordered weighted average (mFHOWA) operator, mF Hamacher hybrid average (mFHHA) operator, mF Hamacher weighted geometric (mFHWG) operator, mF Hamacher weighted ordered geometric operator, and mF Hamacher hybrid geometric (mFHHG) operator. We discuss some properties of these operators, inclusive of their ability to implement both symmetric and asymmetric treatments of the items. We develop an algorithmic model to solve multi-attribute decision-making (MADM) problems in mF environment using mF Hamacher weighted average operator (mFHWA) and mFHWG operators. They can compensate for the possible asymmetric roles of the attributes that describe the problem. In the end, to prove the validity and feasibility of the proposed work, we give applications for selecting the most affected country regarding human trafficking, selecting health care waste treatment methods and selecting the best company for investment. We also solve practical MADM problems by using ELECTRE-I method, and give a comparative analysis.

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Classification methodology for spare parts management combining maintenance and logistics perspectives

Cited by 30 publications

References 41 publications

Application of Data Mining in the Field of Human Resource Management: A Review

Application of Data Mining in the Field of Human Resource Management: A Review

Peer Review #3 of "Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand (v0.1)"

Multi-Attribute Decision-Making Based on m-Polar Fuzzy Hamacher Aggregation Operators

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