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Demand management model based on quantitative forecasting methods and continuous improvement to increase production planning efficiencies of SMEs Bakeries

  • Universidad Peruana de Ciencias Aplicadas
  • Universidad Rey Juan Carlos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In the last quarter of 2018, the manufacturing sector grew 11.4%, of which 19.6% was accounted for by the food industry. However, one of the main problems faced by micro and small companies is poor management, since many of these businesses plan based on experience. In addition, inaccurate demand forecasting generates losses for these organizations due to overproduction or understocking. While the former triggers losses from elevated operating costs, the latter leads to loss of revenue and unsatisfied customers and compromises future demand rates. Therefore, a demand management model was designed to provide accurate and relevant information, which may improve production planning based on the continuous improvement approach. The model increased the planning from 91.2% to 96.2% in a micro business dedicated to the production and sale of bread.

Original languageEnglish
Title of host publicationIntelligent Human Systems Integration - Proceedings of the 3rd International Conference on Intelligent Human Systems Integration IHSI 2020
Subtitle of host publicationIntegrating People and Intelligent Systems
EditorsTareq Ahram, Waldemar Karwowski, Alberto Vergnano, Francesco Leali, Redha Taiar
PublisherSpringer
Pages760-765
Number of pages6
ISBN (Print)9783030395117
DOIs
StatePublished - 2020
Event3rd International Conference on Intelligent Human Systems Integration, IHSI 2020 - Modena, Italy
Duration: 19 Feb 202021 Feb 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1131 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference3rd International Conference on Intelligent Human Systems Integration, IHSI 2020
Country/TerritoryItaly
CityModena
Period19/02/2021/02/20

Keywords

  • Demand forecasting continuous improvement
  • Demand management
  • Forecasting methods

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