scholarly journals ABC classification of spare parts considering costs and service

2016 ◽  
Vol 22 (3/4/5) ◽  
pp. 244
Author(s):  
Manuel Cardós ◽  
Ester Guijarro ◽  
Eugenia Babiloni
Keyword(s):  
Spare Parts ◽  
Abc Classification

Researches on Spare Parts Management of Truck Assembly Shop

2011 ◽  
Vol 403-408 ◽  
pp. 1262-1265
Author(s):  
Shan Jiang ◽  
Fei Xu ◽  
Shu Qing Fan ◽  
Ying Jiang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Spare Parts ◽  
Final Conclusion ◽  
Fuzzy Evaluation ◽  
Abc Classification ◽  
Assembly Shop ◽  
Spare Parts Management ◽  
Better Than

This article is based on the ordinary ABC classification, uses the improved ABC classification to classify the company’s equipment and spare parts, and combine the results of those classifications to give the final conclusion. This classification is much better than the ordinary one, for it considers more important facts affecting manufacturing. This article uses Fuzzy Evaluation Analysis to classify its unique spare parts, and establishes the excess spare parts model to decrease quantities of the spare parts and therefore save the fluid funds of the company.

Demand Forecasting

2020 ◽  
pp. 1-21
Author(s):  
Natalie Nicole Mar Hernández ◽  
Patricia Cano-Olivos ◽  
Diana Sánchez-Partida ◽  
José-Luis Martínez-Flores ◽  
Santiago Omar Caballero-Morales
Keyword(s):  
Neural Networks ◽  
Demand Management ◽  
Demand Forecasting ◽  
Planning Horizon ◽  
Statistical Information ◽  
Production Data ◽  
Ann Model ◽  
Abc Classification ◽  
The City

This chapter provides a proposal for demand management in furniture SMEs located in the city of Puebla, México. The historical production data reviewed, and the classification of the most critical articles was made using the ABC classification methodology. Subsequently, the literature of SMEs was analyzed as well as the current situation and statistical information was sought. Additionally, it presented an overview of the models to forecast demand and applied to the most relevant articles. Due to the results and previous study, it was decided to implement a forecasting technique which is modelled by artificial neural networks. The ANN model developed with the TANSIGMOID transfer function by using MATLAB software. The appropriate forecasting techniques selected according to the variability of the demand of the articles takes a short-term planning horizon. This research will help the company reduce uncertainty, forecasting sales, and achieve better production planning through ANNs.

scholarly journals A review of multi-criteria classification of spare parts

2014 ◽  
Vol 25 (4) ◽  
pp. 528-549 ◽  
Author(s):  
Irene Roda ◽  
Marco Macchi ◽  
Luca Fumagalli ◽  
Pablo Viveros
Keyword(s):  
Operating Cost ◽  
Spare Parts ◽  
Current Situation ◽  
Field Analysis ◽  
Extensive Literature ◽  
Content Type ◽  
Spare Parts Management ◽  
The Status ◽  
Relevant Role

Purpose – Spare parts management plays a relevant role for equipment-intensive companies. An important step of such process is the spare parts classification, enabling properly managing different items by taking into account their peculiarities. The purpose of this paper is to review the state of the art of classification of spare parts for manufacturing equipment by presenting an extensive literature analysis followed by an industrial assessment, with the final aim to identify eventual discrepancies. Design/methodology/approach – Not only is the attention put on the literature about the subject, but also on an on-field analysis, that is presented comprehending an extensive survey and two in-depth exploratory case studies. The copper mining sector was chosen being representative for the case of capital intensive plants where the cost of maintenance has relevant weight on the total operating cost. Findings – The paper highlights the status of the scientific literature on spare parts classification by showing the current situation in the real industrial world. The paper depicts the existing barriers that leave gaps between theory and real practice for the application of an effective multi-criteria spare parts classification. Originality/value – The paper provides a review of the theory on spare parts classification methods and criteria, as well as empirical evidences especially for what concern current situation and barriers for an effective implementation in the industrial environment. The paper should be of interest to both academics and practitioners, since it provides original insights on the discrepancies between scientific and industrial world.

scholarly journals Classification Methods for Motor Vibration in Predictive Maintenance

2021 ◽  
Author(s):  
Christoph Kammerer ◽  
Michael Gaust ◽  
Pascal Starke ◽  
Roman Radtke ◽  
Alexander Jesser
Keyword(s):  
Spare Parts ◽  
Predictive Maintenance ◽  
Work Processes ◽  
Thermal Systems ◽  
Repair Work ◽  
Machine Maintenance ◽  
Reducing Costs ◽  
And Storage ◽  
Motor Vibration

Reducing costs is an important part in todays business. Therefore manufacturers try to reduce unnecessary work processes and storage costs. Machine maintenance is a big, complex, regular process. In addition, the spare parts required for this must be kept in stock until a machine fails. In order to avoid a production breakdown in the event of an unexpected failure, more and more manufacturers rely on predictive maintenance for their machines. This enables more precise planning of necessary maintenance and repair work, as well as a precise ordering of the spare parts required for this. A large amount of past as well as current information is required to create such a predictive forecast about machines. With the classification of motors based on vibration, this paper deals with the implementation of predictive maintenance for thermal systems. There is an overview of suitable sensors and data processing methods, as well as various classification algorithms. In the end, the best sensor-algorithm combinations are shown.

ABC analysis of the customers using axiomatic design and incomplete rough set

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1219-1232 ◽  
Author(s):  
Atena Gholami ◽  
Reza Sheikh ◽  
Neda Mizani ◽  
Shib Sankar Sana
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Success Factors ◽  
Rough Set Theory ◽  
Axiomatic Design ◽  
Target Market ◽  
Common Occurrence ◽  
Abc Classification ◽  
Abc Analysis

Customer’s recognition, classification, and selecting the target market are the most important success factors of a marketing system. ABC classification of the customers based on axiomatic design exposes the behavior of the customer in a logical way in each class. Quite often, missing data is a common occurrence and can have a significant effect on the decision- making problems. In this context, this proposed article determines the customer’s behavioral rule by incomplete rough set theory. Based on the proposed axiomatic design, the managers of a firm can map the rules on designed structures. This study demonstrates to identify the customers, determine their characteristics, and facilitate the development of a marketing strategy.

Multi-criteria classification of spare parts inventories – a web based approach

2015 ◽  
Vol 21 (4) ◽  
pp. 456-477 ◽  
Author(s):  
S. P. Sarmah ◽  
U. C. Moharana
Keyword(s):  
Fuzzy Rule ◽  
Spare Parts ◽  
Multiple Criteria ◽  
Classification Model ◽  
Web Based ◽  
Rule Based ◽  
Content Type ◽  
Total Cost ◽  
Rule Based Approach

Purpose – The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation. Design/methodology/approach – Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models. Findings – Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs. Practical implications – The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications. Originality/value – The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.

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