{rfName}
Qu

Indexed in

License and Use

Icono OpenAccess

Altmetrics

Analysis of institutional authors

Mula, JosefaAuthorSanchis, RaquelAuthor

Share

May 8, 2025
Publications
>
Article
Sí

Quantitative models for printing production planning in a lean manufacturing approach under uncertainty

Publicated to: Direccion y Organizacion. 85 (85): 93-101 - 2025-04-01 85(85), DOI: 10.37610/85.693

Authors:

Rojas, Tania; Mula, Josefa; Sanchis, Raquel
[+]

Affiliations

Univ Politecn Salesiana, Ind Engn Dept, Chambers 227, Guayaquil 090114, Ecuador - Author
Univ Politecn Valencia, Res Ctr Prod Management & Engn CIGIP, C-Alarcon 1, Alcoy 03801, Spain - Author

Abstract

Demand uncertainty is inherent to production planning processes in a manufacturing environment due to intermittent customer order acceptance, among others. Hence the need to provide approaches and tools capable of facing these challenges regarding uncertainties. This paper aims to present a comparative analysis of several quantitative modelling approaches for production planning in a lean manufacturing (LM) approach under uncertainty. It should be noted that we wish to focus approaches on the printing industry. The search methodology consisted of selecting articles that centre on LM and uncertainty, and the printing industry, or another industry with similar characteristics from a quantitative perspective. The main findings are related to the identification of the applied modelling approaches and lean tools. After analysing the selected articles, the use of six modelling approaches was identified by highlighting stochastic programming (SP) and mixed integer linear programming (MILP). The identified models aim to minimise costs, optimise production and satisfy customer demand in an uncertain environment. Using LM tools improves stability and resource efficiency and should include more of them. The reviewed models offer several approaches to deal with uncertainty in production systems, which can be very useful for the printing industry and other sectors.
[+]

Keywords

Lean manufacturingPrintingProduction planningQuantitative modellingUncertainty

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Direccion y Organizacion, and although the journal is classified in the quartile Q4 (Agencia WoS (JCR)), its regional focus and specialization in Management, give it significant recognition in a specific niche of scientific knowledge at an international level.

[+]

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-06:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 20 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
[+]

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Ecuador.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Rojas, Tania) and Last Author (Sanchis Gisbert, Raquel).

the author responsible for correspondence tasks has been Rojas, Tania.

[+]

Awards linked to the item

The research leading to these results received funding from Project "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; and from Grant PDC2022-133957-I00 (CADS4.0-II) funded by MCIN/AEI/10.13039/501100011033 and by European Union Next Generation EU/PRTR.
[+]