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Now showing items 49 - 64 of 201

  • Selection and industrial applications of panel data based demand forecasting models

    Ren, Shuyun   Choi, Tsan-Ming  

    Purpose - Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of variousmain streampanel data-based demand forecastingmodels. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed. Design/methodology/approach - It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed. Findings - This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed. Research limitations/implications - This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered. Practical implications - The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications. Originality/value - This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.
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  • Sales Rebate Contracts in Fashion Supply Chains

    Chiu, Chun-Hung   Choi, Tsan-Ming   Yeung, Ho-Ting   Zhao, Yingxue  

    We explore in this paper the performance of sales rebate contracts in fashion supply chains. We conduct both analytical and numerical analyses via a mean-variance framework with reference to real empirical data. To be specific, we evaluate the expected profits and variance of profits (risk) of the fashion supply chains, fashion retailers, and manufacturers under (1) the currently implemented sales rebate practices, (2) the case without sales rebate, and (3) the theoretical coordination situation (if target sales rebate is adopted). In addition, we analyze how sales effort affects the performances of the supply chain and its agents. Our analysis indicates that the rebate contracts may hurt the retailer and the manufacturer of a fashion supply chain when it is inappropriately set. Moreover, a properly designed sales rebate contract not only can coordinate the supply chain (with retail sales effort) but can also improve expected profits and lower the levels of risk for both the manufacturer and the retailer.
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  • Pricing and Benefit of Decentralization for Competing Supply Chains With Fixed Costs

    Xiao, Tiaojun   Choi, Tsan-Ming   Cheng, T. C. Edwin  

    This paper develops two duopoly game models to explore price decisions and the channel benefit of decentralization for two supply chains, and investigates the channel structure decisions in the presence of fixed marketing and manufacturing costs. We give the price decisions and discuss price distortion. Under the downward decentralization model, our analysis reveals that first, symmetric decentralization improves supply chain profit only when the fixed marketing costs are high or product substitutability is high; second, decentralization improves channel profit, and symmetric decentralization (i.e., both manufacturers use decentralization) is an equilibrium if the fixed marketing (or unit production) costs are sufficiently high and the market scales are sufficiently small; and third, the presence of fixed marketing costs and asymmetry of supply chains support the existence of asymmetric equilibrium. Under the upward decentralization model, we find that symmetric outsourcing emerges when the fixed manufacturing cost or the unit production cost is low while both market scale and product substitutability are large in the supplier-led setting, the channel profit under downward decentralization is higher than that under symmetric outsourcing only if product substitutability is high, and the sourcing strategy largely depends on the specific game sequence.
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  • Green manufacturing and distribution in the fashion and apparel industries

    Choi, Tsan-Ming   Lo, Chris K. Y.   Wong, Christina W. Y.   Yee, Rachel W. Y.  

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  • Recent Development in Big Data Analytics for Business Operations and Risk Management

    Choi, Tsan-Ming   Chan, Hing Kai   Yue, Xiaohang  

    "Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.
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  • Optimal Return Service Charging Policy for a Fashion Mass Customization Program

    Choi, Tsan-Ming  

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  • Color Trend Forecasting of Fashionable Products with Very Few Historical Data

    Choi, Tsan-Ming   Hui, Chi-Leung   Ng, Sau-Fun   Yu, Yong  

    In time-series forecasting, statistical methods and various newly emerged models, such as artificial neural network (ANN) and grey model (GM), are often used. No matter which forecasting method one would apply, it is always a huge challenge to make a sound forecasting decision under the condition of having very few historical data. Unfortunately, in fashion color trend forecasting, the availability of data is always very limited owing to the short selling season and life of products. This motivates us to examine different forecasting models for their performances in predicting color trend of fashionable product under the condition of having very few data. By employing real sales data from a fashion company, we examine various forecasting models, namely ANN, GM, Markov regime switching, and GM+ANN hybrid models, in the domain of color trend forecasting with a limited amount of historical data. Comparisons are made among these models. Insights on the appropriate choice of forecasting models are generated.
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  • A hybrid SARIMA wavelet transform method for sales forecasting

    Choi, Tsan-Ming   Yu, Yong   Au, Kin-Fan  

    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. (C) 2010 Elsevier B.V. All rights reserved.
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  • An intelligent fast sales forecasting model for fashion products

    Yu, Yong   Choi, Tsan-Ming   Hui, Chi-Leung  

    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. (c) 2010 Elsevier Ltd. All rights reserved.
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  • A cross-cluster and cross-region analysis of fashion brand extensions

    Choi, Tsan-Ming   Liu, Shuk-Ching   Tang, Christopher   Yu, Yong  

    To obtain sustainable growth in revenue and market share, many fashion brands deploy category extensions and line extensions. In this paper, we examine how different fashion brands in Europe, North America, and Asia execute their brand extension strategies over different periods. By classifying different fashion brands into four clusters according to different price points and fashion contents, we conduct a cross-region and cross-cluster analysis to examine how different fashion brands execute their brand extension strategies. Our analysis is based on publicly available data associated with 48 fashion brands over a 90-year period. We discuss our findings along with managerial insights.
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  • Impact of e-marketplace on supply chain under the markdown policy

    Ning, Zhong   Choi, Tsan-Ming   Xie, Charlene   Xie, Li   Dai, Junjun  

    Purpose - This paper aims to explore the effect of e-marketplace on the supply chain's performance under the markdown policy. Profit and risk analyses are both conducted and channel coordination issues are examined. Design/methodology/approach - The paper presents a markdown policy supply chain analytical model with e-marketplace and examines the optimal markdown policy. The mean-variance theory is employed to study both the risk and profit residing in the supply chain. Extensive numerical analysis is conducted. The paper investigates both the cases when e-marketplace selling price is exogenous and endogenous. Findings - The markdown policy can coordinate the supply chain as long as the parameters satisfy certain analytical conditions. The expected profit and risk in the supply chain are both increased when e-marketplace is introduced. The retailer shares a larger portion of the increased expected profit but at the same time bears a higher risk. Research limitations/implications - In this study, similar to the mainstream literature in the related area, the supply chain consists of one manufacturer and one retailer, and there is one single selling season with one product. Despite being able to generate interesting analytical results, this model fails to capture the more complicated real world practices. Practical implications - The existence of e-marketplace can be beneficial to the whole supply chain in terms of expected profit improvement. When the expected profit increase brought about by e-marketplace is large enough to compensate for both the operational cost of e-marketplace and the increase of the risk, the retailer could consider introducing e-marketplace to dispose of the excess inventory. Originality/value - This paper is an original work. It is based on the reviewed literature and the model with markdown policy is new. This could be a reference for further research into optimal performance in the supply chain with e-marketplace.
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  • A New and Efficient Intelligent Collaboration Scheme for Fashion Design

    Yu, Yong   Choi, Tsan-Ming   Hui, Chi-Leung   Ho, Tin-Kin  

    Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time-cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance.
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  • Recent Development in Big Data Analytics for Business Operations and Risk Management

    Choi, Tsan-Ming   Chan, Hing Kai   Yue, Xiaohang  

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  • An Effective Optimization Algorithm for Application Mapping in Network-on-Chip Designs

    Wang, Xinyu   Choi, Tsan-Ming   Yue, Xiaohang   Zhang, Mengji   Du, Wanyu  

    The application mapping problem is an NP-hard combinatorial optimization problem in network-on-chip (NoC) design. Applications of size (n > 30) cannot be solved optimally by an exact algorithm in reasonable time, and the evolutionary algorithms have drawn the attention of NoC researchers. In this paper, we propose a new effective optimization method based on the discrete particle swarm optimization framework, which includes the novel principles for representation, velocity computing, and position-updating of the particles. In our proposed method, particles are allowed to swing between elite and regular pools, and a simple local search procedure is applied on elite particles to exploit the promising solutions. Extensive computational studies using standard benchmark instances and task graphs for free (TGFF) random instances reveal that the proposed optimization algorithm is able to attain the best results, and thus competes very favorably with the previously proposed heuristic approaches. A stability analysis and the two-sided Wilcoxon rank sum tests are also presented to shed light on the robust behavior of the algorithm.
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  • Electronic price-testing scheme for fashion retailing with information updating

    Choi, Tsan-Ming   Chow, Pui-Sze   Xiao, Tiaojun  

    Pricing is a crucial decision for electronic fashion retailers. Motivated by various observed industrial practices in electronic retailing, we study in this paper the optimal Internet pricing schemes which employ price testing with Bayesian information updating following the Bernoulli process. This paper contributes to the literature and advancement of knowledge in a number of ways: (i) we propose an analytical model to study the Internet pricing problem with price-testing and Bayesian information updating for fashion retailers. (ii) We derive the closed-form expressions of the expected value of sampling information (EVSI) and the expected value of perfect information (EVPI) under the price testing scheme. (iii) We conduct the pre-posterior analysis and construct the optimal sampling plan with three different rules. (iv) We develop the optimal posterior pricing policies, with respect to the mean-risk and Value-at-Risk (VaR) objectives. Numerical analyses, which include the studies on EVPI and the efficient frontiers, are presented to generate more insights. (C) 2012 Elsevier B.V. All rights reserved.
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  • Supply Chain Contracts in Fashion Department Stores: Coordination and Risk Analysis

    Shen, Bin   Chow, Pui-Sze   Choi, Tsan-Ming  

    In the fashion industry, department stores normally trade with suppliers of national brands by markdown contract whilst developing private labels with cooperated designers by profit sharing contract. Motivated by this real industrial practice, we study a single-supplier single-retailer two-echelon fashion supply chain selling a short-life fashion product of either a national brand or a private label. The supplier refers to the national/designer brand owner and the retailer refers to the department store. We investigate the supply chain coordination issue and examine the supply chain agents' performances under the mentioned two contracts. We find the analytical evidence that there is a similar relative risk performance but different absolute risk performances between the national brand and the private label. This finding provides an important implication in strategic interaction for the risk-averse department stores in product assortment and brand management. Furthermore, we explore the impact of sales effort on the supply chain system and find that the supply chain is able to achieve coordination if and only if the supplier (i.e., the national brand or the private label) is willing to share the cost of the sales effort.
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