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Komori-Zevallos, Adrian Ricardo [Author]; Montedoro-Garay, Fernando Matias [Author]; Garcia-Lopez, Yvan Jesus [Author]; Quiroz Flores, Juan Carlos [Author]; Herberger, David [Author]; Hübner, Marco [Author]

A Production Model based in Lean 4.0 Principles And Machine Learning To Enhance The Productivity Of Small And Medium-Sized Enterprises (SMEs) In Peru's Food Manufacturing Sector - [published Version]

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  • Media type: E-Article; Text
  • Title: A Production Model based in Lean 4.0 Principles And Machine Learning To Enhance The Productivity Of Small And Medium-Sized Enterprises (SMEs) In Peru's Food Manufacturing Sector
  • Contributor: Komori-Zevallos, Adrian Ricardo [Author]; Montedoro-Garay, Fernando Matias [Author]; Garcia-Lopez, Yvan Jesus [Author]; Quiroz Flores, Juan Carlos [Author]; Herberger, David [Author]; Hübner, Marco [Author]
  • Published: Hannover : publish-Ing., 2023
  • Published in: Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 2 ; https://doi.org/10.15488/15326
  • Issue: published Version
  • Language: English
  • DOI: https://doi.org/10.15488/15256; https://doi.org/10.15488/15326
  • Keywords: Lean 4.0 ; Konferenzschrift ; Productivity ; Demand Forecasting ; Food Manufacturing Sector ; Machine Learning
  • Origination:
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  • Description: New technologies, increasing competition, and changing consumer preferences in the food manufacturing sector have forced companies to generate customized products in dynamic demand and thus remain competitive in the market. As a result, companies have had to rethink their processes and product designs to optimize their manufacturing operations. In addition, moving from a conventional production model to processes supported by intelligent systems to generate efficiency improvements in the demand planning and productivity in their activities is necessary. This paper aims to introduce the development of an integrated model of lean 4.0 practices, demand forecasting using SARIMAX and DSS in a manufacturing SME. In addition, a literature review allowed identifying the variables that would be affected, such as inventory, waste, obsolete products, and productivity. Finally, a case study in the food manufacturing sector is considered to validate the model. The results will be presented through a visual analytics dashboard to streamline plant team decision-making.
  • Access State: Open Access