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Forecasting models in the manufacturing processes and operations management: Systematic literature review
Author(s) -
Agostino Icaro Romolo Sousa,
Silva Wesley Vieira,
Pereira da Veiga Claudimar,
Souza Adriano Mendonça
Publication year - 2020
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2674
Subject(s) - context (archaeology) , field (mathematics) , computer science , systematic review , identification (biology) , management science , portfolio , empirical research , operations research , data science , process management , risk analysis (engineering) , engineering , business , paleontology , philosophy , botany , mathematics , medline , finance , epistemology , political science , pure mathematics , law , biology
The purpose of this paper is to present the result of a systematic literature review regarding the application and development of forecasting models in the industrial context, especially the context of manufacturing processes and operations management. The study was conducted considering the preparation of an established research protocol to know, discuss, and analyze the main approaches adopted by researchers in the field. To achieve this objective, we analyzed 354 recent papers published in periodicals between 2008 and 2018. This paper makes three main contributions to the field: (i) it presents an updated portfolio of prediction models in the industrial context, providing a reference point for researchers and industrial managers; (ii) it presents a characterization of the field of study through the identification of publication vehicles, frequency, and the principal authors and countries related to the development of research on the theme; (iii) it proposes a unified framework, listing the characteristics of the prediction models with their respective application contexts, identifying the current research directions to provide theoretical aids for the development of new approaches to forecasting in industry. The results of this study provide an empirical base for further discussions on studies that focus on forecasting in the industrial context.