A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company

Alemany, M. M. E.; Ortíz, Ángel; Fuertes-Miquel, Vicente S.

doi:10.1016/j.cie.2018.05.040

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…• Number of Subtypes (S.): The total number of existing subtypes of each LHP-item depends on the attributes used in the classification stage and their possible values. For instance, in the ceramics sector, each piece has to be inspected and classified, and individual models (products) are usually stored in homogeneous subgroups (subtypes) according to quality (aspect), tone (degree of colour) and calibre (thickness) (Alemany et al 2018;Alarcón et al 2011;Davoli et al 2010). The usual consideration of 3 quality grades, 2 tones and 3 calibres in the same model (finished good) could add up 13 different references.…”

Section: Environmentmentioning

confidence: 99%

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

Mundi

Alemany

Poler

et al. 2019

International Journal of Production Research

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Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.

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Section: Environmentmentioning

confidence: 99%

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

Mundi

Alemany

Poler

et al. 2019

International Journal of Production Research

Self Cite

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show abstract

“…Wu and Shen [20] evaluated the impacts of the MPS and throughput on scheduling in production environments with CPSs and the IoT. Alemany et al [21] presented a decision support tool based on a mathematical model to calculate the amount available to promise. Serrano-Ruiz et al [22] proposed a conceptual framework of the MPS 4.0 based on digital twins and machine learning for zero-defect management in the supply chain.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0

Tobon-Valencia¹,

Lamouri

Pellerin

et al. 2022

Sustainability

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The purpose of this article is to propose a guide for the digital transformation (4.0) of a manufacturing SME’s medium-term production planning process, the master production schedule (MPS). A model of the current MPS process of a group of SMEs is presented as a starting point toward digitization. The current state of this process reveals a lack of tools to support decision making and the need to increase the reliability of input data and to make the process more agile. Industry 4.0 technologies and process modeling could increase agility in the planning process. However, the digital transformation of medium-term planning activities in SMEs has not been studied. To fill this gap, a group of six experts was consulted. The novelty of this study was to identify the Industry 4.0 technologies that could improve medium-term planning and integrate them into a standardized MPS process model. This model is an ultimate point of digitization that cannot be achieved immediately by any SME, but only after several cycles of planning, deployment, and improvement. Therefore, this research also provides a method to help SMEs determine how to start the digitization of their MPS process.

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“…Indeed, the development of optimization and decision support tools is needed to obtain all the benefits of transactional information technology (IT), improving the economic performance and customer satisfaction of supply chains (Grossmann, 2005). Along these lines, mathematical programming models have been demonstrated to be powerful optimization tools to support decision makers in many supply chain processes such as: production planning (Alemany et al, 2013), order promising (Alemany et al, 2018;Grillo et al, 2017), shortage planning , supply chain production and transport planning (Mula et al, 2010), among others. The agriculture sector also faces many complex problems for optimization (Saranya & Amudha, 2017) as it has been reported in some recent works (Cid-Garcia & Ibarra-Rojas, 2019;Grillo et al, 2017;Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem

Garrido

Antonelli

Martín

et al. 2020

Computers and Electronics in Agriculture

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Mathematical programming models are invaluable tools at decision making, assisting managers to uncover otherwise unattainable means to optimize their processes. However, the value they provide is only as good as their capacity to capture the process domain. This information can only be obtained from stakeholders, i.e., clients or users, who can hardly communicate the requirements clearly and completely. Besides, existing conceptual models of mathematical programming models are not standardized, nor is the process of deriving the mathematical programming model from the concept model, which remains ad hoc. In this paper, we propose an agile methodology to construct mathematical programming models based on two techniques from requirements engineering that have been proven effective at requirements elicitation: the language extended lexicon (LEL) and scenarios. Using the pair of LEL + scenarios allows to create a conceptual model that is clear and complete enough to derive a mathematical programming model that effectively captures the business domain. We also define an ontology to describe the pair LEL + scenarios, which has been implemented with a semantic mediawiki and allows the collaborative construction of the conceptual model and the semi-automatic derivation of mathematical programming model elements. The process is applied and validated in a known fresh tomato packing optimization problem. This proposal can be of high relevance for the development and implementation of mathematical programming models for optimizing agriculture and supply chain management related processes in order to fill the current gap between mathematical programming models in the theory and the practice.

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A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company

Abstract: A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company. Computers

Cited by 11 publications

References 31 publications

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0

Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem

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