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Selecting the Distribution System using AHP and Fuzzy AHP Methods
(Springer Nature, 2024)
Saucedo-Martínez, Jania Astrid
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Salais-Fierro, Tomás Eloy
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Marmolejo Saucedo, José Antonio
In this research, we present a supporting tool for decision making by designing a distribution system for a trading company of supplies for the welding industry in Mexico. The case study encompasses a distribution system with shortage problems and poor fleet capacity. To address these problems, improvement options were grouped into three possible scenarios through a third-party logistics (3PL) service. Furthermore, for the evaluation and selection of one of the scenarios, the Analytic Hierarchy Process (AHP) methodology was proposed integrating fuzzy logic as a tool for decision making, including factors of uncertainty and subjectivity as well as a comparison with traditional AHP obtaining the best scenario, meeting the requirements of the company, and showing potential improvements in the desired service level for its distribution system. © 2024 Springer Nature
Scopus© Citations 3 8 1
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Industry 4.0 framework for management and operations: a review
(2017)
Saucedo-Martínez, Jania Astrid
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Pérez-Lara, Magdiel
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Marmolejo Saucedo, José Antonio
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Salais-Fierro, Tomás Eloy
;
Vasant, Pandian
The evolution of markets and customer requirements with highest level of precision, has been achieved created with technology and information systems, a new way of make the operations in the companies, and just the companies with the ability to adapt faster to technological innovations may remain on the market. The last industrial revolution, known as industry 4.0 perceives the operations as a holistic system, its represents a challenge that must be fulfilled and faced in order to achieve stability and permanence in the market, from the point of view of the world economies. In the organizational and business area all the operations must be linked to computer systems and management of information in the network, which causes greater efficiency in the flow. With this new perception of industry and the business, it involves different analytical tools that aim at bigger efficiency in the service to the consumers, resulting in a greater competitiveness in the market and making the differentiator. This work is one of the first surveys that provides an extensive analysis and review of 110 publications that appear from 1/01/12 to 20/02/17. The purpose of the study is to analyze categorically recent advances through qualitative and segmentation methods allowing to reveal trends and areas of opportunity in the industry sense of 4.0 in order to obtain research gaps that may be executed in organizational systems on the value chain. Another point is to be a reference in future research related to the categories selected in this study. © Springer Nature
Scopus© Citations 224 18 2
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Organizational Systems Convergence with the Industry 4.0 Challenge
(Springer International Publishing, 2018)
Pérez-Lara, Magdiel
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Saucedo-Martínez, Jania Astrid
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Marmolejo Saucedo, José Antonio
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Salais-Fierro, Tomás Eloy
The concept of Industry 4.0 consists of the digital technologies introduction in companies. It is the way to call the phenomenon of digital transformation applied to the production industry. The problem faced by organizations is that they assimilate the global idea of Industry 4.0 and the technical aspects associated with it. Industry 4.0 is highly linked to technologies such as the Internet of things, which favors predictive analysis, based on the information collected through these solutions, making it easier for companies to anticipate consumer requests. It is necessary that companies can readjust and plan their operations quickly and accurately to respond to the new demands of consumers. The products proliferation will continue to increase in the coming years, in a context that will allow greater customization. Therefore, companies must take advantage of market analysis to improve their supply chains and meet the demands of their users, since the customization of products requires a clear connection with production capabilities. The objective of the research is to describe the properties of Industry 4.0, to propose a measurement system according to the principles of the supply chain, by means of the descriptive and comparative research method.
Scopus© Citations 3 8 2
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Optimization of the Storage Location Assignment and the Picker-Routing Problem by Using Mathematical Programming
(2020)
Bolaños Zuñiga, Johanna
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Saucedo Martínez, Jania Astrid
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Salais-Fierro, Tomás Eloy
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Marmolejo Saucedo, José Antonio
The order picking process involves a series of activities in response to customer needs, such as the selection or programming of orders (batches), and the selection of different items from their storage location to shipment. These activities are accomplished by a routing policy that determines the picker sequence for retrieving the items from the storage location. Therefore, the order picking problem has been plenty investigated; however, in previous research, the proposed models were based on demand fulfilling, putting aside factors such as the product weight-which is an important criterion-at the time of establishing routes. In this article, a mathematical model is proposed; it takes into account the product's weight derived from a case study. This model is relevant, as no similar work was found in the literature that improves the order picking by making simultaneous decisions on the storage location assignment and the picker-routing problem, considering precedence constraints based on the product weight and the characteristics of the case study, as the only location for each product in a warehouse with a general layout. © 2020 by the authors, Applied Sciences (Switzerland), MDPI AG
Scopus© Citations 26 9 2
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Vertical and horizontal integration systems in Industry 4.0
(2018)
Pérez-Lara, Magdiel
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Saucedo-Martínez, Jania Astrid
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Marmolejo Saucedo, José Antonio
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Salais-Fierro, Tomás Eloy
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Vasant, Pandian
Industrial and technological growth, sponsored by the new organizational systems generated by the fourth industrial revolution, require adapt new business management ways in the companies. Within the organizational and business area we can conceive all activities as an operations set that are linked to computer systems and information management in the network, achieving more efficiency in the flow, in addition, this new industry perception and businesses includes different analytical tools which are useful to support the customer service efficiency improvement. The research objective is to propose and validate a methodological tool, for evaluating the technological and operational criteria within companies and place them in the right level for a transfer to the new industrial revolution, considering as well the vertical and horizontal systems in Industry 4.0.
Scopus© Citations 64 17 1
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Demand prediction using a soft-computing approach : a case study of automotive industry
(2020)
Salais-Fierro, Tomás Eloy
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Saucedo-Martínez, Jania
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Vela-Haro, Jose Manuel
According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data. © 2019 by the authors.
Scopus© Citations 13 14 2

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