Decision Support System

A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models:

A comparative analysis

Abstract:

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC) structure via neural techniques. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks and adaptive network-based fuzzy inference system techniques to manage the fuzzy demand with incomplete information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated using real-world data from a company which is active in durable consumer goods industry in Istanbul, Turkey.

1. Introduction

A supply chain (SC) has a dynamic structure involving the constant flow of information, product, and funds between different stages (Chopra & Meindl, 2001). Supply chain process has three important stages; supply, production, and distribution including not only manufacturer and suppliers, but also transporters, warehouses, retailers, and customers themselves. The flow of information, knowledge, product, or resources between and among these entities is to be managed appropriately to maximize the overall profitability. Specifically, information flow between departments is the most important connection links for a SC’s success. Forecasting is a part of the supply management picture and directly affects both quantity and delivery. Forecasts of usage, supply, market conditions, technology, prices, and so on, are always necessary to make good decisions (Leernders, Fearon,...