Peter G. Zhang

• Ph.D.
• Professor (Full) at Georgia State University

Intermittent demand forecasting is an important yet challenging task in many organizations. While prior research has been focused on traditional methods such as Croston’s method and its variants, limited research has been conducted using advanced machine learning or deep learning methods. In this study, we introduce Transformer, a recently develope...

Purpose This paper aims to reexamine the link between board racial diversity and firm performance. It focuses on the mechanism through which board racial diversity could affect performance. The paper proposes and empirically tests the role of employee productivity and R&D productivity in the relationship between board racial diversity and firm fina...

This paper examines service-level and distribution channel decisions for two competing supply chains with a focus on how service competition affects the channel structure. Each manufacturer can use either an integrated channel (i.e., sells products directly) or a decentralized channel (i.e., sells products through retailers). Service can be provide...

The Collaborative Planning, Forecasting, and Replenishment (CPFR) initiative is an increasingly popular paradigm that helps the supply chain better coordinate activities to serve customers with improved demand forecasting and production scheduling. CPFR provides a framework that covers a broad range of issues including demand forecasting, inventory...

We study the distribution channel decision of a manufacturer who considers whether to add an online channel (direct channel) to its brick-and-mortar retailer (indirect channel). The retailer faces the opportunity to invest in store assistance to help consumers choose products and thus reduce product returns. Special attention is given to the impact...

Product design has increasingly been recognized as an important source of competitive advantage. This paper empirically estimates the impact of effective design on the market value of the firm. We use a firm's receipt of a product design award as a proxy for its design effectiveness. Based on data from 264 announcements of design awards given to co...

Product design has increasingly been recognized as an important source of competitive advantage. This paper empirically estimates the impact of effective design on the market value of the firm. We use a firm’s receipt of a product design award as a proxy for its design effectiveness. Based on data from 264 announcements of design awards made betwee...

This article examines strategic outsourcing decisions for two competing manufacturers whose key components have quality improvement (QI) opportunities. We consider the effects of vertical and horizontal product differentiation on demand. In deriving the Subgame Perfect Nash Equilibria (SPNE), it is shown that either a symmetric outsourcing strategy...

In this article, we consider distribution channel strategies for an incumbent manufacturer who produces two complementary products and must determine whether or not to have another company to sell its products. We identify factors that affect the manufacturer's motivation to use dual‐channel distribution. Our results show that both complementarity...

Because of the changing competitive environment, quality might have lost some of its luster and emphasis in business. The research question we aim to address in this paper is: Does quality still pay in the new competitive environment? Using replication research, we re‐examine the impact of an effective total quality management (TQM) program on a fi...

This study examines the liability of foreignness (LOF) faced by multinational enterprises (MNEs), and the effects of strategies employed to overcome the liability. Based on a sample of 3,085 Sino-foreign joint ventures formed in manufacturing sectors in China, the authors find that Hong Kong investors, who are often perceived to have lower LOF than...

Despite the widely held belief of the importance of innovation, the connection between innovation and firm performance is empirically inconclusive, partially owing to the limitations of existing innovation measures, which tend to ignore the effectiveness of innovation programs. In this study, we use the winning of innovation awards as a proxy for t...

Neural networks has become an important method for time series forecasting. There is increasing interest in using neural networks to model and forecast time series. This chapter provides a review of some recent developments in time series forecasting with neural networks, a brief description of neural networks, their advantages over traditional for...

This study examines the liability of foreignness (LOF) faced by multinational enterprises (MNEs), and the effects of strategies employed to overcome the liability. Based on a sample of 3,085 Sino-foreign joint ventures formed in manufacturing sectors in China, the authors find that Hong Kong investors, who are often perceived to have lower LOF than...

A set of simulated time series is presented that is aimed toward evaluation of multi-step time series methods. Sixteen model forms are used, with thirty replications using common random numbers. The data set is tested, open, and extensible. Features of the data set, data generation and software methodologies are presented.

The use of the Internet as an additional sales channel offers traditional retailers opportunities to reach expanded markets while improving the efficiency of their operations. Although the potential benefits of the online channel are clear, there are significant variations in the scope and depth of online channel use among retailers. Drawing from d...

Neural networks have become standard and important tools for data mining. This chapter provides an overview of neural network models and their applications to data mining tasks. We provide historical development of the field of neural networks and present three important classes of neural models including feedforward multilayer networks, Hopfield n...

Despite a larger number of successful applications of artificial neural networks for classification in business and other areas, published research has not considered the effects of misclassification costs and group sizes. Without the consideration of uneven misclassification costs, the classifier development will be compromised in minimizing the t...

This paper examines a number of electronic data interchange (EDI) usage and implementation factors and their role in improving a firm's efficiency, productivity and competitiveness. Unlike other studies in the literature that use exclusively linear models, we apply nonlinear neural networks to model the relationship between performance improvement...

The use of electronic data interchange (EDI) and related coordination activities among supply chain members can improve supply chain performance. This study examines the relationship between a number of variables relating to EDI usage and supply chain coordination activities and performance improvements in efficiency, productivity, and competitiven...

Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time...

This study shows how artificial neural networks can be used to model consumer choice. Our study focuses on two key issues in neural network modeling — model building and feature selection. Using the cross-validation approach, we address these two issues together and specifically examine the effectiveness of a backward feature selection algorithm fo...

In building a decision support system (DSS), an important component is the modeling of each potential alternative action to predict its consequence. Decision makers and automated decision systems (i.e., modelbased DSSs) depend upon quality forecasts to assist in the decision process. The more accurate the forecast, the better the DSS is at helping...

Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Combining multiple models can be an effective way to improve forecasting performance. Recently, considerable research has been taken in neural network ensembles. Most of the work, however, is devoted to th...

Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach to forecasting quarterly time series. With a large data set of 756 quarterly time series from the M3 forecasting competition, we conduct a comprehensive investigation of the effectivenes...

This article examines production and outsourcing decisions for two manufacturers that produce partially substitutable products and play a strategic game with quantity competition. When both manufacturers outsource key components to the same upstream supplier, their products become more substitutable due to the increased commonality of the products....

Artificial neural networks (ANNs) have gained extensive popularity in recent years. Research activities are considerable, and the literature is growing. Yet, there is a large amount of concern on the appropriate use of neural networks in published research. The purposes of this paper are to: 1) point out common pitfalls and misuses in the neural ne...

In this paper we study the performance of a branch and bound enumeration procedure in solving a comprehensive mixed-integer linear programming formulation of a resource-sharing and scheduling problem (RSSP). The formulation is monolithic and deterministic. Various independent factors of an RSSP generally influence the size of the problem. An experi...

This paper presents a method for estimating a random walk which is observed subject to additive noise. The method is based on an optimal weighted average, conceptualized as defining a fuzzy set of "recent" data. A Monte Carlo experiment compares the method's effectiveness against the naive method, ordinary least squares regression, and regression w...

Neural networks have been widely used as a promising method for time series forecasting. However, limited empirical studies on seasonal time series forecasting with neural networks yield mixed results. While some find that neural networks are able to model seasonality directly and prior deseasonalization is not necessary, others conclude just the o...

A simulation experiment demonstrates the effectiveness of using a fuzzy set of "recent data" to train a forecasting model when the underlying process is time-varying.

Equity control is one of the key areas of research in international business. This study employs artificial neural networks (ANNs) to model foreign equity control. Comparisons are made with traditional statistical modeling approaches. It was found that ANNs produce a more parsimonious set of independent variables that yield higher classification ra...

Artificial neural networks have emerged as an important quantitative modeling tool for business forecasting. This chapter provides an overview of forecasting with neural networks. We provide a brief description of neural networks, their advantages over traditional forecasting models, and their applications for business forecasting. In addition, we...

Artificial neural networks have emerged as an important quantitative modeling tool for business forecasting. This chapter provides an overview of forecasting with neural networks. We provide a brief description of neural networks, their advantages over traditional forecasting models, and their applications for business forecasting. In addition, we...

This chapter presents an extended Self-Organizing Map (SOM) network and demonstrates how it can be used to forecast market segment membership. The Kohonen’s SOM network is an unsupervised learning neural network that maps n-dimensional input data to a lower dimensional (usually one- or two-dimensional) output map while maintaining the original topo...

This chapter presents a combined ARIMA and neural network approach for time series forecasting. The model contains three steps: (1) fitting a linear ARIMA model to the time series under study, (2) building a neural network model based on the residuals from the ARIMA model, and (3) combine the ARIMA prediction and the neural network result to form t...

Artificial neural networks have emerged as an important quantitative modeling tool for business forecasting. This chapter provides an overview of forecasting with neural networks. We provide a brief description of neural networks, their advantages over traditional forecasting models, and their applications for business forecasting. In addition, we...

In this chapter, we propose a neural network based weighted window approach to time series forecasting. We compare the weighted window approach with two commonly used methods of rolling and moving windows in modeling time series. Seven economic data sets are used to compare the performance of these three data windowing methods on observed forecast...

This study shows how neural networks can be used to model posterior probabilities of consumer choice and a backward elimination procedure can be implemented for feature selection in neural networks. Two separate samples of consumer choice situations were selected from a large consumer panel maintained by AT&T. Our findings support the appropriatene...

This chapter presents a combined ARIMA and neural network approach for time series forecasting. The model contains three steps: (1) fitting a linear ARIMA model to the time series under study, (2) building a neural network model based on the residuals from the ARIMA model, and (3) combine the ARIMA prediction and the neural network result to form t...

The purpose of this paper is to compare the accuracy of various linear and nonlinear models for forecasting aggregate retail sales. Because of the strong seasonal fluctuations observed in the retail sales, several traditional seasonal forecasting methods such as the time series approach and the regression approach with seasonal dummy variables and...

Previous research has documented that software projects are frequently prone to escalation. While the escalation literature acknowledges that project-related (as well as psychological, social, and organizational) factors can promote escalation behavior, there has been no investigation regarding the role that project management factors may have in d...

Previous research has documented that software projects are frequently prone to escalation. While the escalation literature acknowledges that project-related (as well as psychological, social, and organizational) factors can promote escalation behavior, there has been no investigation regarding the role that project management factors may have in d...

Traditionally, in ( Q , r ) inventory systems, when a shortage occurs, incoming demands are either filled by emergency orders or backordered. However, the backorder costs are usually time-dependent, hence it is costly to backorder early in the lead time. On the other hand, it is obviously expensive to fill the shortages with emergency orders alone....

Despite its great importance, there has been no general consensus on how to model the trends in time series data. Compared to traditional approaches, neural networks have shown some promise in time series forecasting. This paper investigates how to best model trend time series using neural networks. Four strategies (raw data, raw data with time ind...

Information system (IT) projects can often spiral out of control to become runaway systems that far exceed their original budget and scheduled due date. The majority of these escalated projects are eventually abandoned or significantly redirected without delivering intended business value. Because of the strategic importance of IT projects and the...

Bias and variance play an important role in understanding the fundamental issue of learning and generalization in neural network modeling. Several studies on bias and variance effects have been published in classification and regression related research of neural networks. However, little research has been done in this area for time-series modeling...

Autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. ARIMA models and ANNs are often compared w...

this paper to use a cross-validation scheme to accurately describe predictive performance of neural networks. Cross-validation is a resampling technique which uses multiple random training and test subsamples. The advantage of cross-validation is that all of the observations or patterns in the available sample are used for testing and most of them...

Equity control is one of the key determinants of performance in international joint venture. This study employs the transaction cost framework to predict equity control via artificial neural networks (ANNs). Comparisons are made with the traditional statistical modeling approaches. ANNs produce a more parsimonious set of independent variables that...

The purpose of this paper is to compare the accuracy of various linear and nonlinear models for forecasting aggregate retail sales. Because of the strong seasonal fluctuations observed in the retail sales, several traditional seasonal forecasting methods such as the time series approach and the regression approach with seasonal dummy variables and...

Forecasting future retail sales is one of the most important activities that form the basis for all strategic and planning decisions in effective operations of retail businesses as well as retail supply chains. This chapter illustrates how to best model and forecast retail sales time series that contain both trend and seasonal variations. The effec...

This study examines the capability of neural networks for linear time-series forecasting. Using both simulated and real data, the effects of neural network factors such as the number of input nodes and the number of hidden nodes as well as the training sample size are investigated. Results show that neural networks are quite competent in modeling a...

This paper investigates the use of neural network combining methods to improve time series forecasting performance of the traditional single keep-the-best (KTB) model. The ensemble methods are applied to the dif®cult problem of exchange rate forecasting. Two general approaches to combining neural networks are proposed and examined in predicting the...

This study presents an experimental evaluation of neural networks for nonlinear time-series forecasting. The effects of three main factors — input nodes, hidden nodes and sample size, are examined through a simulated computer experiment. Results show that neural networks are valuable tools for modeling and forecasting nonlinear time series while tr...

Artificial neural networks (ANNs) have received more and more attention in time series forecasting in recent years. One major disadvantage of neural networks is that there is no formal systematic model building approach. In this paper, we expose problems of the commonly used information-based in-sample model selection criteria in selecting neural n...

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, l...

Forecasting future retail sales is one of the most important activities that form the basis for all strategic and planning decisions in effective operations of retail businesses as well as retail supply chains. This chapter illustrates how to best model and forecast retail sales time series that contain both trend and seasonal variations. The effec...

In this paper, we present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classification theory. The method of cross-validation is used to e...

The potential of neural networks for classification problems has been established by numerous successful applications reported in the literature. One of the major assumptions used in almost all studies is the equal cost consequence of misclassification. With this assumption, minimizing the total number of misclassification errors is the sole object...

This study utilizes an artificial neural network (ANN) approach to predict the performance of equity mutual funds that follow value, blend and growth investment styles. Using a multi-layer perceptron model and GRG2 nonlinear optimizer, fund-specific historical operating characteristics were used to forecast mutual funds' risk-adjusted return. Resul...

Cellular manufacturing systems achieve the economies of scope and scale approaching that of flexible and high-volume production when the machine/part clusters are totally independent of each other. However, most real systems contain bottleneck machines and exceptional parts (exceptional elements) that reduce these economies. Many grouping methods h...

We investigate the potential of artificial neural networks in diagnosing thyroid diseases. The robustness of neural networks with regard to sampling variations is examined using a cross‐validation method. We illustrate the link between neural networks and traditional Bayesian classifiers. Neural networks can provide good estimates of posterior prob...

Neural networks have successfully been used for exchange rate forecasting. However, due to a large number of parameters to be estimated empirically, it is not a simple task to select the appropriate neural network architecture for an exchange rate forecasting problem. Researchers often overlook the effect of neural network parameters on the perform...

Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise, they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs. This paper presents...

This paper considers the economic statistical design of X̄ control charts with Weibull failure properties. A Weibull failure model is appropriate for many systems in electrical and mechanical industries. The economic statistical control chart design model is developed and illustrated by two examples. It is shown that for some situations, the increa...

We developed a modified Conflict Style Inventory- Preferred Conflict-Handling Mode (PCHM) to explain how individuals deal with situations in which their desires are in conflict with another individual or group. The instrument, developed for this research, was based on the Managerial Grid developed by Van de Vliert & Kabanoff (1). The two variables...

Thesis (Ph. D.)--Kent State University, 1998. Includes bibliographical references (leaves 134-152).