Solving the integrated raw material procurement and lot-sizing problem with the aid of big data analytics tools

• Grant number: 19/10824-7
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: October 01, 2019
• End date: June 30, 2024
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Maristela Oliveira dos Santos
• Grantee: Caio Paziani Tomazella
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID
• Associated scholarship(s): 21/12200-0 - Methods for solving the integrated procurement and lot-sizing problem with perishable inventory and uncertain demand, BE.EP.DR

Abstract

This project studies the integration of two problems from the supply chain management area, material procurement and production lot-sizing, while using big data analytics in order to obtain information for the model. The integrated planning of these two activities allows a more efficient decision-making process, reducing procurement and production costs and lead times. Furthermore, the use of big data analytics has been rising in the supply chain management literature for the past decade, and it is shown be a method that provides consistent and reliable data regarding suppliers, such as prices, capacity and availability. A series of factors related to the supply chain planning are modelled in order to analyse their impacts on the final solution and its related costs, such as: supplier selection and discount policies, which allow for more flexible purchasing policies; different inventory accounting methods, taking into consideration the raw material prices; production recipe selection, which makes the production process more flexible by allowing different material consumption; and client order selection, making a trade-off between revenue and process flexibility. The objectives of this project are to propose exact and heuristic methods for solving this problem, showing the efficiency of the integrated model when compared to decoupled models and to integrate the application of big data analytics in order to obtain the input information for the model. (AU)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

TOMAZELLA, Caio Paziani. Modeling Approaches and Solution Methods for the Lot-Sizing and Raw Material Procurement Problem. 2024. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.