Forecasting Supply Chain Demand Using Machine Learning Algorithms

Forecasting Supply Chain Demand Using Machine Learning Algorithms

Réal Carbonneau (Department of Management Sciences, HEC Montréal, Canada), Rustam Vahidov (John Molson School of Business, Concorida University, Canada) and Kevin Laframboise (John Molson School of Business, Concorida University, Canada)
DOI: 10.4018/978-1-60566-144-5.ch018
OnDemand PDF Download:
$37.50

Abstract

Managing supply chains in today’s complex, dynamic, and uncertain environment is one of the key challenges affecting the success of the businesses. One of the crucial determinants of effective supply chain management is the ability to recognize customer demand patterns and react accordingly to the changes in face of intense competition. Thus the ability to adequately predict demand by the participants in a supply chain is vital to the survival of businesses. Demand prediction is aggravated by the fact that communication patterns between participants that emerge in a supply chain tend to distort the original consumer’s demand and create high levels of noise. Distortion and noise negatively impact forecast quality of the participants. This work investigates the applicability of machine learning (ML) techniques and compares their performances with the more traditional methods in order to improve demand forecast accuracy in supply chains. To this end we used two data sets from particular companies (chocolate manufacturer and toner cartridge manufacturer), as well as data from the Statistics Canada manufacturing survey. A representative set of traditional and ML-based forecasting techniques have been applied to the demand data and the accuracy of the methods was compared. As a group, Machine Learning techniques outperformed traditional techniques in terms of overall average, but not in terms of overall ranking. We also found that a support vector machine (SVM) trained on multiple demand series produced the most accurate forecasts.
Chapter Preview
Top

Introduction

Supply chain integration looks to combine resources in order to provide value to the end consumer by improving the flow and quality of information being passed between the participants in the chain (Zhao, Xie, & Wei, 2002). Thus, in an idealized case, where all participants adopt the integration philosophy and make efforts to implement it fully, the entire chain would perform effectively and efficiently in responding to end customer demands. However, although integration and sharing information can potentially reduce forecast errors, in reality they are neither ubiquitous nor complete and demand forecast errors still abound.

This is due to the fact that the original demand signal becomes distorted as it travels through the extended supply chain (a holistic notion of supply chain (Tan, 2001) that requires collaborative relationships (Davis & Spekman, 2004)). Demand forecast quality can be improved if done cooperatively by the partners in the chain. Collaborative forecasting and replenishment (CFAR) permits a firm and its supplier-firm to coordinate decisions by exchanging complex decision-support models and strategies, thus facilitating integration of forecasting and production schedules (Raghunathan, 1999). In the absence of CFAR, firms are relegated to traditional forecasting and production scheduling, a challenging task due to what the well-known phenomenon of “bullwhip effect” (Lee, Padmanabhan, & Whang, 1997a).

The value of information sharing across the supply chain is widely recognized as the means of combating demand signal distortion (Lee, Padmanabhan, & Whang, 1997b). However, there is a gap between the ideal of integrated supply chains and reality (Gunasekaran & Ngai, 2004).

Researchers have identified several factors that could hinder such long-term stable collaborative efforts. Premkumar (2000) lists some required critical issues that must be addressed to permit successful supply chain collaboration, including: (i) alignment of business interests, (ii) long-term relationship management, (iii) reluctance to share information, (iv) complexity of large-scale supply chain management, (v) competence of personnel supporting supply chain management and (vi) performance measurement and incentive systems to support supply chain management. Although these are important issues, in many companies, these issues have not yet been addressed in attempts to enable effective extended supply chain collaboration (Davis & Spekman, 2004). Additionally, in many supply chains there are power regimes and power sub-regimes that can prevent supply chain optimization (Cox, Sanderson, & Watson, 2001). The introduction of inaccurate information into the system could also lead to demand distortion, e.g., double forecasting and ration gaming by the partners, ordering more quantities than needed, despite the presence of a collaborative system and an incentive towards its usage (Heikkila, 2002).

Furthermore, the globalization trends and the advance of E-business increase the tendency towards more “dynamic” (Vakharia, 2002) and “agile” (Gunasekaran & Ngai, 2004; Yusuf, Gunasekaran, Adeleye, & Sivayoganathan, 2004) supply chains. While this trend enables the supply chains to be more flexible and adaptive, it could discourage companies from investing in long-term collaborative relationships among each other due to the restrictive nature of such commitments. The over-emphasis on investing in extensive relationships among the partners could lead to a “lock-in” situation, thus seriously jeopardizing the flexibility of the supply chain (Gossain, Malhotra, & El Sawy, 2005). Gossain et al. (2005) argue that developing robust and reconfigurable links would promote the agility of the chain in terms of offering and partnering flexibilities. In their study they found that while the quality of the information sharing in a supply chain could promote flexibility, the breadth of information shared has a detrimental effect on it. The modularity and loose couplings between the partners have been identified as positive factors in this regard. Overall, we see many realities that effectively form information exchange collaboration barriers, which limit the possibilities of information exchange within the supply chain.

Complete Chapter List

Search this Book:
Reset
Editorial Review Board
Table of Contents
Preface
Vijayan Sugumaran
Chapter 1
Hong Lin
In this chapter a program construction method based on ?-Calculus is proposed. The problem to be solved is specified by first-order predicate logic... Sample PDF
Designing Multi-Agent Systems from Logic Specifications: A Case Study
$37.50
Chapter 2
Rahul Singh
Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers.... Sample PDF
Multi-Agent Architecture for Knowledge-Driven Decision Support
$37.50
Chapter 3
Farid Meziane
Trust is widely recognized as an essential factor for the continual development of business-to-customer (B2C) electronic commerce (EC). Many trust... Sample PDF
A Decision Support System for Trust Formalization
$37.50
Chapter 4
Mehdi Yousfi-Monod
The work described in this chapter tackles learning and communication between cognitive artificial agents and trying to meet the following issue: Is... Sample PDF
Using Misunderstanding and Discussion in Dialog as a Knowledge Acquisition or Enhancement Procecss
$37.50
Chapter 5
Sungchul Hong
In this chapter, we present a two-tier supply chain composed of multiple buyers and multiple suppliers. We have studied the mechanism to match... Sample PDF
Improving E-Trade Auction Volume by Consortium
$37.50
Chapter 6
Manoj A. Thomas, Victoria Y. Yoon, Richard Redmond
Different FIPA-compliant agent development platforms are available for developing multiagent systems. FIPA compliance ensures interoperability among... Sample PDF
Extending Loosely Coupled Federated Information Systems Using Agent Technology
$37.50
Chapter 7
H. Hamidi
The reliable execution of mobile agents is a very important design issue in building mobile agent systems and many fault-tolerant schemes have been... Sample PDF
Modeling Fault Tolerant and Secure Mobile Agent Execution in Distributed Systems
$37.50
Chapter 8
Xiannong Meng, Song Xing
This chapter reports the results of a project attempting to assess the performance of a few major search engines from various perspectives. The... Sample PDF
Search Engine Performance Comparisons
$37.50
Chapter 9
Antonio Picariello
Information retrieval can take great advantages and improvements considering users’ feedbacks. Therefore, the user dimension is a relevant component... Sample PDF
A User-Centered Approach for Information Retrieval
$37.50
Chapter 10
Aboul Ella Hassanien, Jafar M. Ali
This chapter presents an efficient algorithm to classify and retrieve images from large databases in the context of rough set theory. Color and... Sample PDF
Classification and Retrieval of Images from Databases Using Rough Set Theory
$37.50
Chapter 11
Lars Werner
Text documents stored in information systems usually consist of more information than the pure concatenation of words, i.e., they also contain... Sample PDF
Supporting Text Retrieval by Typographical Term Weighting
$37.50
Chapter 12
Ben Choi
Web mining aims for searching, organizing, and extracting information on the Web and search engines focus on searching. The next stage of Web mining... Sample PDF
Web Mining by Automatically Organizing Web Pages into Categories
$37.50
Chapter 13
John Goh
Mobile user data mining is about extracting knowledge from raw data collected from mobile users. There have been a few approaches developed, such as... Sample PDF
Mining Matrix Pattern from Mobile Users
$37.50
Chapter 14
Salvatore T. March, Gove N. Allen
Active information systems participate in the operation and management of business organizations. They create conceptual objects that represent... Sample PDF
Conceptual Modeling of Events for Active Information Systems
$37.50
Chapter 15
John M. Artz
Earlier work in the philosophical foundations of information modeling identified four key concepts in which philosophical groundwork must be further... Sample PDF
Information Modeling and the Problem of Universals
$37.50
Chapter 16
Christian Hillbrand
The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect... Sample PDF
Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence
$37.50
Chapter 17
Yongjian Fu
In this chapter, we propose to use N-gram models for improving Web navigation for mobile users. Ngram models are built from Web server logs to learn... Sample PDF
Improving Mobile Web Navigation Using N-Grams Prediction Models
$37.50
Chapter 18
Réal Carbonneau, Rustam Vahidov, Kevin Laframboise
Managing supply chains in today’s complex, dynamic, and uncertain environment is one of the key challenges affecting the success of the businesses.... Sample PDF
Forecasting Supply Chain Demand Using Machine Learning Algorithms
$37.50
Chapter 19
Teemu Tynjala
The present study implements a generic methodology for describing and analyzing demand supply networks (i.e. networks from a company’s suppliers... Sample PDF
Supporting Demand Supply Network Optimization with Petri Nets
$37.50
About the Contributors