A joint decision model of variant selection and inventory control based on demand forecasting

This paper attempts to develop a joint decision model to determine the product variety (PV) decision with inventory control (IC) decision for multiple brands of a certain product for a centralized two-echelon distribution system with a general dealer and a retailer, in which the demand forecasting i...

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Bibliographic Details
Published in:2008 IEEE International Conference on Automation and Logistics pp. 362 - 367
Main Authors: Gao Junjun, Hu Yongping
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2008
Subjects:
Demand forecasting
distribution systems
Forecasting
Integrated circuit modeling
Inventory control
Joints
Marketing and sales
Predictive models
product variety
variant selection
ISBN:9781424425020, 1424425026
ISSN:2161-8151
Online Access:Get full text
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Summary:This paper attempts to develop a joint decision model to determine the product variety (PV) decision with inventory control (IC) decision for multiple brands of a certain product for a centralized two-echelon distribution system with a general dealer and a retailer, in which the demand forecasting is taken into consideration and the impact of different statistical forecasting methods on decision results and distribution system performance is also investigated. Demand forecasts derived from a demand generation function of time, price, shelf-space, seasonal factor, promotional effecting factor and random factor, a nonlinear integer programming model is established to maximize total multi-period profits while considering costs of ordering, shipping, purchasing and carrying inventory as well as lost sales, subject to given stocking space constraints and service level constraints. The results indicate that using Winterpsilas exponential method (WIN) model to forecast may be beneficial and the optimal variant selection decision is single-brand policy for this assortment in this distribution system. The findings can also help supply chain managers select suitable forecasting models to improve distribution systems performance and obtain right PV and IC policy.
ISBN:9781424425020
1424425026
ISSN:2161-8151
DOI:10.1109/ICAL.2008.4636176