{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T05:43:50Z","timestamp":1761975830218},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,25]]},"abstract":"<jats:p>Demand forecasting is an essential part of an efficient inventory control system. However, when the demand has an intermittent or lumpy behavior, forecasting it becomes a challenging task. Several methods have been developed to solve this issue, but nonetheless, they only consider the information about the occurrence of demand, failing to assess the drivers of the data behavior. With the current digitalization of the industry, more data is available and, therefore, the chances of finding a causal relationship between the available data and the demand increases. Considering that, this paper proposes a single-hidden layer neural network for forecasting irregularly spaced time series with attributes conveying information about the past demand, seasonality of the data and specialized knowledge about the process. The neural network proposed is compared with benchmark neural networks and traditional forecasting methods for intermittent demand using three different performance measures on actual demand data from an industry operating in the aircraft maintenance sector. Statistical analysis is conducted on comparison results to identify significant differences in the forecasting methods according to each performance measure.<\/jats:p>","DOI":"10.3233\/atde200113","type":"book-chapter","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T18:28:58Z","timestamp":1601490538000},"source":"Crossref","is-referenced-by-count":3,"title":["Neural Network with Specialized Knowledge for Forecasting Intermittent Demand"],"prefix":"10.3233","author":[{"given":"Alexandre Crepory Abbott","family":"de Oliveira","sequence":"first","affiliation":[{"name":"Mechatronic Systems Graduate Program, University of Bras\u00edlia"}]},{"given":"J\u00e9ssica Mendes","family":"Jorge","sequence":"additional","affiliation":[{"name":"Mechatronic Systems Graduate Program, University of Bras\u00edlia"}]},{"given":"Andrea Cristina","family":"dos Santos","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Bras\u00edlia"}]},{"given":"Geraldo Pereira Rocha","family":"Filho","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bras\u00edlia"}]}],"member":"7437","container-title":["Advances in Transdisciplinary Engineering","Transdisciplinary Engineering for Complex Socio-technical Systems \u2013 Real-life Applications"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/ATDE200113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T18:28:59Z","timestamp":1601490539000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/ATDE200113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,25]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/atde200113","relation":{},"ISSN":["2352-751X","2352-7528"],"issn-type":[{"value":"2352-751X","type":"print"},{"value":"2352-7528","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,25]]}}}