کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4943465 1437630 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Cluster-based hierarchical demand forecasting for perishable goods
ترجمه فارسی عنوان
پیش بینی تقاضای سلسله مراتبی در مورد کالاهای فاسد می شود
کلمات کلیدی
تجزیه و تحلیل پیش بینی، پشتیبانی تصمیم مواد غذایی تازه شسته و رفته، پیش بینی تقاضای سلسله مراتبی، آنالیز خوشه ای،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Demand forecasting is of particular importance for retailers in the context of supply chains of perishable goods and fresh food. Such goods are daily produced and delivered as they need to be provided as fresh as possible and quickly deteriorate. Demand underestimation and overestimation negatively affect the revenues of the retailer. Stock-outs have an undesired impact on consumers while unsold items need to be discarded at the end of the day. We propose a DSS that supports day-to-operations by providing hierarchical forecasts at different organizational levels based on most recent point-of-sales data. It identifies article clusters that are used to extend the hierarchy based on intra-day sales pattern. We apply multivariate ARIMA models to forecast the daily demand to support operational decisions. We evaluate the approach with point-of-sales data of an industrialized bakery chain and show that it is possible to increase the availability while limiting the loss at the same time. The cluster analysis reveals that substitutable items have similar intra-day sales pattern which makes it reasonable to forecast the demand at an aggregated level. The accuracy of top-down forecasts is comparable to direct forecasts which allows reducing the computational costs.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 76, 15 June 2017, Pages 140-151
نویسندگان
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