Work

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

Year: 2025

Type: article

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.

Source: Dirección y Organización

DOI: 10.37610/85.693

Authors: Tania Rojas, Josefa Mula, Raquel Sanchis

Institutions: Politecnica Salesiana University, Universitat Politècnica de València

Language: English

Topic: Management and Optimization Techniques

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

Funders: European Commission, Agencia Estatal de Investigación, NextGenerationEU