The Experts below are selected from a list of 9354 Experts worldwide ranked by ideXlab platform
Wenping Zhang - One of the best experts on this subject based on the ideXlab platform.
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parallel aspect oriented sentiment analysis for Sales Forecasting with big data
Production and Operations Management, 2018Co-Authors: Raymond Y. K. Lau, Wenping ZhangAbstract:While much research work has been devoted to supply chain management and demand forecast, research on designing big data analytics methodologies to enhance Sales Forecasting is seldom reported in existing literature. The big data of consumer-contributed product comments on online social media provide management with unprecedented opportunities to leverage collective consumer intelligence for enhancing supply chain management in general and Sales Forecasting in particular. The main contributions of our work presented in this paper are: (1) the design of a novel big data analytics methodology that is underpinned by a parallel aspect-oriented sentiment analysis algorithm for mining consumer intelligence from a huge number of online product comments; (2) the design and the large-scale empirical test of a sentiment enhanced Sales Forecasting method that is empowered by a parallel co-evolutionary extreme learning machine. Based on real-world big datasets, our experimental results confirm that consumer sentiments mined from big data can improve the accuracy of Sales Forecasting across predictive models and datasets. The managerial implication of our work is that firms can apply the proposed big data analytics methodology to enhance Sales Forecasting performance. Thereby, the problem of under/over-stocking is alleviated and customer satisfaction is improved. This article is protected by copyright. All rights reserved.
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Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data
Production and Operations Management, 2017Co-Authors: Raymond Y. K. Lau, Wenping ZhangAbstract:While much research work has been devoted to supply chain management and demand forecast, research on designing big data analytics methodologies to enhance Sales Forecasting is seldom reported in existing literature. The big data of consumer‐contributed product comments on online social media provide management with unprecedented opportunities to leverage collective consumer intelligence for enhancing supply chain management in general and Sales Forecasting in particular. The main contributions of our work presented in this study are as follows: (1) the design of a novel big data analytics methodology that is underpinned by a parallel aspect‐oriented sentiment analysis algorithm for mining consumer intelligence from a huge number of online product comments; (2) the design and the large‐scale empirical test of a sentiment enhanced Sales Forecasting method that is empowered by a parallel co‐evolutionary extreme learning machine. Based on real‐world big datasets, our experimental results confirm that consumer sentiments mined from big data can improve the accuracy of Sales Forecasting across predictive models and datasets. The managerial implication of our work is that firms can apply the proposed big data analytics methodology to enhance Sales Forecasting performance. Thereby, the problem of under/over‐stocking is alleviated and customer satisfaction is improved.
Raymond Y. K. Lau - One of the best experts on this subject based on the ideXlab platform.
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parallel aspect oriented sentiment analysis for Sales Forecasting with big data
Production and Operations Management, 2018Co-Authors: Raymond Y. K. Lau, Wenping ZhangAbstract:While much research work has been devoted to supply chain management and demand forecast, research on designing big data analytics methodologies to enhance Sales Forecasting is seldom reported in existing literature. The big data of consumer-contributed product comments on online social media provide management with unprecedented opportunities to leverage collective consumer intelligence for enhancing supply chain management in general and Sales Forecasting in particular. The main contributions of our work presented in this paper are: (1) the design of a novel big data analytics methodology that is underpinned by a parallel aspect-oriented sentiment analysis algorithm for mining consumer intelligence from a huge number of online product comments; (2) the design and the large-scale empirical test of a sentiment enhanced Sales Forecasting method that is empowered by a parallel co-evolutionary extreme learning machine. Based on real-world big datasets, our experimental results confirm that consumer sentiments mined from big data can improve the accuracy of Sales Forecasting across predictive models and datasets. The managerial implication of our work is that firms can apply the proposed big data analytics methodology to enhance Sales Forecasting performance. Thereby, the problem of under/over-stocking is alleviated and customer satisfaction is improved. This article is protected by copyright. All rights reserved.
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Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data
Production and Operations Management, 2017Co-Authors: Raymond Y. K. Lau, Wenping ZhangAbstract:While much research work has been devoted to supply chain management and demand forecast, research on designing big data analytics methodologies to enhance Sales Forecasting is seldom reported in existing literature. The big data of consumer‐contributed product comments on online social media provide management with unprecedented opportunities to leverage collective consumer intelligence for enhancing supply chain management in general and Sales Forecasting in particular. The main contributions of our work presented in this study are as follows: (1) the design of a novel big data analytics methodology that is underpinned by a parallel aspect‐oriented sentiment analysis algorithm for mining consumer intelligence from a huge number of online product comments; (2) the design and the large‐scale empirical test of a sentiment enhanced Sales Forecasting method that is empowered by a parallel co‐evolutionary extreme learning machine. Based on real‐world big datasets, our experimental results confirm that consumer sentiments mined from big data can improve the accuracy of Sales Forecasting across predictive models and datasets. The managerial implication of our work is that firms can apply the proposed big data analytics methodology to enhance Sales Forecasting performance. Thereby, the problem of under/over‐stocking is alleviated and customer satisfaction is improved.
John T. Mentzer - One of the best experts on this subject based on the ideXlab platform.
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Motivating the industrial Sales force in the Sales Forecasting process
Industrial Marketing Management, 2011Co-Authors: Teresa M. Mccarthy Byrne, Mark A. Moon, John T. MentzerAbstract:Abstract Previous research has recognized the value of the industrial Salesperson's role in the Sales Forecasting process, and offered normative descriptions of what that role should be. However, no studies have been conducted to determine the variables that motivate industrial Sales force involvement in and contribution to the Sales Forecasting process. This study employed depth interviews and survey research to develop and test a conceptual model of industrial Sales force Forecasting motivation. The research identifies five environmental signals that can be employed by managers to impact an industrial Salesperson's level of satisfaction with, effort directed towards, and seriousness placed in the Sales Forecasting process.
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Organizational factors in Sales Forecasting management
International Journal of Forecasting, 2007Co-Authors: Donna F. Davis, John T. MentzerAbstract:Abstract Over the past three decades, significant advances have been made in developing Sales Forecasting techniques that more accurately reflect marketplace conditions. However, surveys of Sales Forecasting practice continue to report only marginal gains in Sales Forecasting performance. This gap between theory and practice has been identified as a significant issue for Sales Forecasting research. The Forecasting literature suggests that the issue should be addressed by examining organizational factors in Sales Forecasting management. In this paper, we propose a theory-based framework of organizational factors in Sales Forecasting management that integrates research on organizational climate, organizational capabilities, organizational learning and Sales Forecasting. Empirical evidence of the fit between Sales Forecasting practice and the conceptual framework is provided by a content analysis of interview texts from an extensive field study of Sales Forecasting management that involved 516 practitioners at 18 global manufacturing firms.
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Sales Forecasting management
2005Co-Authors: John T. Mentzer, Carol C BienstockAbstract:Managing the Sales Forecasting Process Sales Forecasting Performance Measurement Time Series Forecasting Techniques Regression Analysis Qualitative Sales Forecasting Sales Forecasting Systems Benchmarking Studies The Surveys Benchmarking Studies In-Depth Analysis Multicaster Book Version Managing the Sales Forecasting Function
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Sales Forecasting Management: A Demand Management Approach
2004Co-Authors: John T. Mentzer, Michael MoonAbstract:PREFACE 1. MANAGING THE Sales Forecasting PROCESS INTRODUCTION A DEMAND MANAGEMENT APPROACH TO Sales Forecasting Sales Forecasting MANAGEMENT FORECASTS VERSUS PLANS VERSUS TARGETS THE ROLE OF Sales Forecasting IN Sales AND OPERATIONS PLANNING (S&OP) WHY IS A Sales FORECAST NEEDED? SUMMARY: ORGANIZATIONAL Sales Forecasting NEEDS THE TOOLS OF Sales Forecasting MANAGEMENT Sales Forecasting MANAGEMENT QUESTIONS Sales Forecasting AND PLANNING: AN ITERATIVE PROCESS FUNCTIONAL SILOS OVERVIEW OF THIS BOOK OVERVIEW OF THIS BOOK 2. Sales Forecasting PERFORMANCE MEASUREMENT INTRODUCTION Sales Forecasting ACCURACY Sales FORECOSTING COSTS CUSTOMER SATISFACTION PUTTING IT ALL TOGETHER--A Forecasting ROI DECISION CONCLUSIONS 3. TIMES SERIES Forecasting TECHNIQUES INTRODUCTION FIXED-MODEL TIME SERIES TECHNIQUES FIXED MODEL TIME SERIES TECHNIQUES SUMMARY OPEN-MODEL TIME SERIES TECHNIQUES SUMMARY 4. REGRESSION ANALYSIS INTRODUCTION HOW REGRESSION ANALYSIS WORKS THE PROCESS OF REGRESSION ANALYSIS FOR Forecasting FURTHER EVALUATION OF CANDIDATE MODELS MODEL VALIDATION AN EXAMPLE CONCLUSION NOTES 5. QUALITATIVE Sales Forecasting INTRODUCTION QUALITATIVE Forecasting: ADVANTAGES AND PROBLEMS SUMMARY: QUALITATIVE TECHNIQUE ADVANTAGES AND PROBLEMS QUALITATIVE TECHNIQUES AND TOOLS MARKET RESEARCH TOOLS FOR QUALITATIVE Forecasting DECISION ANALYSIS TOOLS FOR QUALITATIVE Forecasting SUMMARY APPENDIX 6. Sales Forecasting SYSTEMS INTRODUCTION THE Sales Forecasting SYSTEM AS A COMMUNICATION VEHICLE MULTICASTER Sales Forecasting SYSTEMS: SUMMARY APPENDIX 7. BENCHMARK STUDIES: THE SURVEYS INTRODUCTION STUDIES PRIOR TO PHASE 1 PHASE 2 METHODOLOGY FINDINGS COMPARING PHASE 1 TO PHASE 2 CONCLUSIONS FROM COMPARING PHASE 1 AND PHASE 2 Sales Forecasting SYSTEMS Sales Forecasting MANAGEMENT Forecasting IN CONSUMER VERSUS INDUSTRIAL MARKETS CONCLUSIONS: INDUSTRIAL VERSUS CONSUMER Forecasting CONCLUSIONS FROM PHASE 2 8. BENCHMARK STUDIES: WORLD-CLASS Forecasting INTRODUCTION FUNCTIONAL INTEGRATION APPROACH SYSTEMS PERFORMANCE MEASUREMENT CONCLUSIONS APPENDIX: Sales Forecasting AUDIT PROTOCOL 9. BENCHMARK STUDIES: CONDUCTING A Forecasting AUDIT INTRODUCTION THE ROLE OF AUDITING MANAGEMENT RESPONSE TO AUDITS CONCLUSIONS 10. MANAGING THE Sales Forecasting FUNCTION INTRODUCTION THE ROLE OF THE Sales Forecasting CHAMPION THE SEVEN KEYS TO BETTER Forecasting WHY THE CEO SHOULD CARE THE CEO SHOULD CARE AT TELLABS CONCLUSIONS REFERENCES INDEX
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C a Sales Forecasting audit
2003Co-Authors: Mark A. Moon, John T. Mentzer, Carlo D. SmithAbstract:Continuous improvement in Sales Forecasting is a worthy goal for any organization. This paper describes a methodology for conducting a Sales Forecasting audit, the goal of which is to help a company understand the status of its Sales Forecasting processes and identify ways to improve those processes. The methodology described here has been developed over a 5-year period, involving multiple auditors, and has been implemented (to date) at 16 organizations. This methodology revolves around three distinct phases: the 'as-is' phase, in which the audit team seeks to understand fully a company's current Forecasting process; the 'should-be' phase, in which the audit team presents a vision of what world-class Forecasting should look like at the audited company, and the 'way-forward' phase, in which the audit team presents a roadmap of how the company can change its Forecasting processes to achieve world-class levels. Those companies that have responded positively to the audit process have experienced significant improvement in their Forecasting performance. The paper concludes by presenting lessons from audits conducted to date, as well as implications for management practice and future research. 2002 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved.
Tsan-ming Choi - One of the best experts on this subject based on the ideXlab platform.
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Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model
IEEE Transactions on Systems Man and Cybernetics: Systems, 2015Co-Authors: Shuyun Ren, Tsan-ming Choi, Na LiuAbstract:In this paper, we propose and explore a novel panel data-based particle-filter (PDPF) model to conduct fashion Sales Forecasting. We evaluate the performance of proposed model by using real data collected from the fashion industry. The experimental results indicate that the proposed panel data models outperform both the traditional statistical and intelligent methods, which provide strong evidence on the importance of employing the panel-data approach. Further analysis reveals that: 1) our proposed PDPF method yields a better Forecasting result in item-based Sales Forecasting than in color-based Sales Forecasting; 2) a larger degree of Granger causality relationship between Sales and price will imply a better Sales Forecasting result of the PDPF model; 3) increasing the amount of historical data does not necessarily improve Forecasting accuracy; and 4) the PDPF method is suitable for conducting fashion Sales Forecasting with limited data. These findings provide novel insights on the use of panel data for conducting fashion Sales Forecasting.
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Sales Forecasting for Fashion Retailing Service Industry: A Review
Mathematical Problems in Engineering, 2013Co-Authors: Na Liu, Tsan-ming Choi, Chi-leung Hui, Shuyun Ren, Sau-fun NgAbstract:Sales Forecasting is crucial for many retail operations. It is especially critical for the fashion retailing service industry in which product demand is very volatile and product’s life cycle is short. This paper conducts a comprehensive literature review and selects a set of papers in the literature on fashion retail Sales Forecasting. The advantages and the drawbacks of different kinds of analytical methods for fashion retail Sales Forecasting are examined. The evolution of the respective Forecasting methods over the past 15 years is revealed. Issues related to real-world applications of the fashion retail Sales Forecasting models and important future research directions are discussed.
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An intelligent fast Sales Forecasting model for fashion products
Expert Systems With Applications, 2011Co-Authors: Yong Yu, Tsan-ming Choi, Chi-leung HuiAbstract:Research highlights? We devise a fast and efficient Sales Forecasting model for fashion products. ? We propose a systematic framework to determine the suitable parameters of the model. ? We illustrate the features and the efficiency of the model with two sets of real data. Sales Forecasting is crucial in fashion business because of all the uncertainty associated with demand and supply. Many models for Forecasting fashion products are proposed in the literature over the past few decades. With the emergence of artificial intelligence models, artificial neural networks (ANN) are widely used in Forecasting. ANN models have been revealed to be more efficient and effective than many traditional statistical Forecasting models. Despite the reported advantages, it is relatively more time-consuming for ANN to perform Forecasting. In the fashion industry, Sales Forecasting is challenging because there are so many product varieties (i.e., SKUs) and prompt Forecasting result is needed. As a result, the existing ANN models would become inadequate. In this paper, a new model which employs both the extreme learning machine (ELM) and the traditional statistical methods is proposed. Experiments with real data sets are conducted. A comparison with other traditional methods has shown that this ELM fast Forecasting (ELM-FF) model is quick and effective.
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a hybrid sarima wavelet transform method for Sales Forecasting
Decision Support Systems, 2011Co-Authors: Tsan-ming ChoiAbstract:Time series Forecasting, as an important tool in many decision support systems, has been extensively studied and applied for Sales Forecasting over the past few decades. There are many well-established and widely-adopted Forecasting methods such as linear extrapolation and SARIMA. However, their performance is far from perfect and it is especially true when the Sales pattern is highly volatile. In this paper, we propose a hybrid Forecasting scheme which combines the classic SARIMA method and wavelet transform (SW). We compare the performance of SW with (i) pure SARIMA, (ii) a Forecasting scheme based on linear extrapolation with seasonal adjustment (CSD+LESA), and (iii) evolutionary neural networks (ENN). We illustrate the significance of SW and establish the conditions that SW outperforms pure SARIMA and CSD+LESA. We further study the time series features which influence the Forecasting accuracy, and we propose a method for conducting Sales Forecasting based on the features of the given Sales time series. Experiments are conducted by using real Sales data, hypothetical data, and publicly available data sets. We believe that the proposed hybrid method is highly applicable for Forecasting Sales in the industry.
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Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products
Handbook of Research on Machine Learning Applications and Trends, 2010Co-Authors: Yong Yu, Tsan-ming Choi, Kin-fan Au, Zhan-li SunAbstract:The evolutionary neural network (ENN), which is the hybrid combination of evolutionary computation and neural network, is a suitable candidate for topology design, and is widely adopted. An ENN approach with a direct binary representation to every single neural network connection is proposed in this chapter for Sales Forecasting of fashionable products. In this chapter, the authors will first explore the details on how an evolutionary computation approach can be applied in searching for a desirable network structure for establishing the appropriate Sales Forecasting system. The optimized ENN structure for Sales Forecasting is then developed. With the use of real Sales data, the authors compare the performances of the proposed ENN Forecasting scheme with several traditional methods which include artificial neural network (ANN) and SARIMA. The authors obtain the conditions in which their proposed ENN outperforms other methods. Insights regarding the applications of ENN for Forecasting Sales of fashionable products are generated. Finally, future research directions are outlined.
Chijie Lu - One of the best experts on this subject based on the ideXlab platform.
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Sales Forecasting by combining clustering and machine-learning techniques for computer retailing
Neural Computing and Applications, 2017Co-Authors: I-fei Chen, Chijie LuAbstract:Sales Forecasting is a critical task for computer retailers endeavoring to maintain favorable Sales performance and manage inventories. In this study, a clustering-based Forecasting model by combining clustering and machine-learning methods is proposed for computer retailing Sales Forecasting. The proposed method first used the clustering technique to divide training data into groups, clustering data with similar features or patterns into a group. Subsequently, machine-learning techniques are used to train the Forecasting model of each group. After the cluster with data patterns most similar to the test data was determined, the trained Forecasting model of the cluster was adopted for Sales Forecasting. Since the Sales data of computer retailers show similar data patterns or features at different time periods, the accuracy of the forecast can be enhanced by using the proposed clustering-based Forecasting model. Three clustering techniques including self-organizing map (SOM), growing hierarchical self-organizing map (GHSOM), and K-means and two machine-learning techniques including support vector regression (SVR) and extreme learning machine (ELM) are used in this study. A total of six clustering-based Forecasting models were proposed. Real-life Sales data for the personal computers, notebook computers, and liquid crystal displays are used as the empirical examples. The experimental results showed that the model combining the GHSOM and ELM provided superior Forecasting performance for all three products compared with the other five Forecasting models, as well as the single SVR and single ELM models. It can be effectively used as a clustering-based Sales Forecasting model for computer retailing.
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a clustering based Sales Forecasting scheme by using extreme learning machine and ensembling linkage methods with applications to computer server
Engineering Applications of Artificial Intelligence, 2016Co-Authors: Chijie LuAbstract:Sales Forecasting has long been crucial for companies since it is important for financial planning, inventory management, marketing, and customer service. In this study, a novel clustering-based Sales Forecasting scheme that uses an extreme learning machine (ELM) and assembles the results of linkage methods is proposed. The proposed scheme first uses the K-means algorithm to divide the training Sales data into multiple disjointed clusters. Then, for each cluster, the ELM is applied to construct a Forecasting model. Finally, a test datum is assigned to the most suitable cluster identified according to the result of combining five linkage methods. The constructed ELM model corresponding to the identified cluster is utilized to perform the final prediction. Two real Sales datasets of computer servers collected from two multinational electronics companies are used to illustrate the proposed model. Empirical results showed that the proposed clustering-based Sales Forecasting scheme statistically outperforms eight benchmark models, and hence demonstrates that the proposed approach is an effective alternative for Sales Forecasting.
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a clustering based Sales Forecasting scheme using support vector regression for computer server
Procedia Manufacturing, 2015Co-Authors: Yangyu Chuang, Chijie LuAbstract:In this study, a clustering-based Sales Forecasting scheme based on support vector regression (SVR) is proposed. The proposed scheme first uses k-means algorithm to partition the whole training Sales data into several disjoint clusters. Then, for each group, the SVR is applied to construct Forecasting model. Finally, for a given testing data, three similarity measurements are used to find the cluster which the testing data belongs to and then employee the corresponding trained SVR model to generate prediction result. A real aggregate Sales data of computer server is used as an illustrative example to evaluate the performance of the proposed model. Experimental results revealed that the proposed clustering-based Sales Forecasting scheme outperforms the single SVR without data clustering and hence is an effective alternative for computer server Sales Forecasting.
