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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

When do I want to know and why? Different demands on sugarcane yield predictions

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Author(s):
Bocca, Felipe Ferreira [1] ; Antunes Rodrigues, Luiz Henrique [1] ; Modesto Arraes, Nilson Antonio [1]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Sch Agr Engn, Campinas, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: AGRICULTURAL SYSTEMS; v. 135, p. 48-56, MAY 2015.
Web of Science Citations: 12
Abstract

The production planning processes of sugarcane mills require quantitative information to support decisions on sugarcane yield and the effects of decisions made during planning. An exploratory study was conducted at a sugarcane mill with the goals of identifying the main decisions influenced by the prospects of future yield and of evaluating the manner in which those forecasts affect planning. Key decisions and their characteristics were identified based on a series of interviews and activity monitoring. These decisions are presented and discussed in relation to various solutions proposed by the scientific community for planning, as well as within the concept of Advanced Planning Systems. The yield forecasts used to inform budgeting and harvesting plans are of critical importance because actions taken based on those forecasts affect the entire value chain, highlighting the need for a decision-making framework that assess the effects of decisions on subsequent processes. Advanced Planning Systems design to the sugar value chain should incorporate the use of yield forecasts for production and must address the uncertainties throughout the entire system. These improvements can enhance the performances of Advanced Planning Systems by producing an integrated planning approach that is based on a comprehensive assessment of the sugar value chain. (C) 2014 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 12/50049-3 - Data Mining Techniques Applied to the Analysis and Prediction of Sugarcane Yield
Grantee:Luiz Henrique Antunes Rodrigues
Support Opportunities: Program for Research on Bioenergy (BIOEN) - Research Partnership for Technological Innovation (PITE)