Demand Forecasting Tool For Inventory Control Smart Systems

With the availability of data and the increasing capabilities of data processing tools, many businesses are leveraging historical sales and demand data to implement smart inventory management systems. Demand forecasting is the process of estimating the consumption of products or services for future...

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Permalink: http://skupni.nsk.hr/Record/nsk.NSK01001163161/Details
Matična publikacija: Journal of communications software and systems (Online)
17 (2021), 2 ; str. 185-196
Glavni autori: Zohra Benhamida, Fatima (Author), Kaddouri, Ouahiba, Ouhrouche, Tahar, Benaichouche, Mohammed, Casado-Mansilla, Diego, Lopez-de-Ipina, Diego
Vrsta građe: e-članak
Jezik: eng
Online pristup: https://doi.org/10.24138/jcomss-2021-0068
Elektronička verzija članka
Elektronička verzija članka
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024 7 |2 doi  |a 10.24138/jcomss-2021-0068 
035 |a (HR-ZaNSK)001163161 
040 |a HR-ZaNSK  |b hrv  |c HR-ZaNSK  |e ppiak 
041 0 |a eng 
042 |a croatica 
044 |a ci  |c hr 
080 1 |2 2011 
100 1 |a Zohra Benhamida, Fatima  |4 aut  |9 HR-ZaNSK 
245 1 0 |a Demand Forecasting Tool For Inventory Control Smart Systems  |h [Elektronička građa]  |c Fatima Zohra Benhamida, Ouahiba Kaddouri, Tahar Ouhrouche, Mohammed Benaichouche, Diego Casado-Mansilla, Diego Lopez-de-Ipina. 
300 |b Ilustr. 
504 |a Bibliografija: 
504 |a Summary. 
520 |a With the availability of data and the increasing capabilities of data processing tools, many businesses are leveraging historical sales and demand data to implement smart inventory management systems. Demand forecasting is the process of estimating the consumption of products or services for future time periods. It plays an important role in the field of inventory control and Supply Chain, since it enables production and supply planning and therefore can reduce delivery times and optimize Supply Chain decisions. This paper presents an extensive literature review about demand forecasting methods for time-series data. Based on analysis results and findings, a new demand forecasting tool for inventory control is proposed. First, a forecasting pipeline is designed to allow selecting the most accurate demand forecasting method. The validation of the proposed solution is executed on Stock&Buy case study, a growing online retail platform. For this reason, two new methods are proposed: (1) a hybrid method, Comb-TSB, is proposed for intermittent and lumpy demand patterns. Comb- TSB automatically selects the most accurate model among a set of methods. (2) a clustering-based approach (ClustAvg) is proposed to forecast demand for new products which have very few or no sales history data. The evaluation process showed that the proposed tool achieves good forecasting accuracy by making the most appropriate choice while defining the forecasting method to apply for each product selection. 
700 1 |a Kaddouri, Ouahiba  |4 aut  |9 HR-ZaNSK 
700 1 |a Ouhrouche, Tahar  |4 aut  |9 HR-ZaNSK 
700 1 |a Benaichouche, Mohammed  |4 aut  |9 HR-ZaNSK 
700 1 |a Casado-Mansilla, Diego  |4 aut  |9 HR-ZaNSK 
700 1 |a Lopez-de-Ipina, Diego  |4 aut  |9 HR-ZaNSK 
773 0 |t Journal of communications software and systems (Online)  |x 1846-6079  |g 17 (2021), 2 ; str. 185-196  |w nsk.(HR-ZaNSK)000644741 
981 |b Be2021 
856 4 0 |u https://doi.org/10.24138/jcomss-2021-0068 
856 4 0 |u https://jcoms.fesb.unist.hr/10.24138/jcomss-2021-0068/  |y Elektronička verzija članka 
856 4 0 |u https://jcoms.fesb.unist.hr/pdfs/v17n2_2021-0068_fatima.pdf  |y Elektronička verzija članka 
856 4 1 |y Digitalna.nsk.hr