Comparison of a new bootstrapping method with parametric approaches for safety stock determination in service parts inventory systems

In this paper, we address the problem of forecasting and managing the inventory of service parts where the demand patterns are highly intermittent. Currently, there are two classes of methods for determining the safety stock for the intermittent item: the parametric and bootstrapping approaches. Viswanathan and Zhou (2008) developed an improved bootstrapping based method and showed through computational experiments that this is superior to the method by Willemain et al. (2004). In this paper, we compare this new bootstrapping method with the parametric methods of Babai and Syntetos (2007). Our computational results show that the bootstrapping method performs better with randomly generated data sets, where there is a large amount of (simulated) historical data to generate the distribution. On the other hand, with real industry data sets, the parametric method seems to perform better than the bootstrapping method.     

Auctions for transferable objects: Theory and evidence from the vehicle quota system in Singapore

This paper studies the hypothesis that auctions with resale markets result in higher prices. The vehicle quota system introduced in Singapore in May 1990 provides the setting. The Certificates of Entitlement (COEs) necessary to purchase new cars were initially transferable for all quota categories. After October 1991, COEs for four major categories became non-transferable. Our results indicate that while the conversion to non-transferability eliminated speculation, it has also intensified competition among car distributors. Auctions for non-transferable COEs in fact led to higher COE prices in three of the four categories.An earlier version of this paper was presented at the Symposium on Law and the Social Sciences, 7 August 1992, at the Regional English Language Centre, Singapore. We want to thank our colleagues for discussions on the subject, the Chief Editor and two anonymous referees of the journal for their constructive comments and suggestions, and the Registry of Vehicles of Singapore for providing us with the data for this study. Financial Support from NUS Grant RP900048 is gratefully acknowledged. We are solely responsible for the errors in the paper.     

Estimating and comparing learning curves in three Asian economies

This paper consists of two parts. In the first, a method is suggested to isolate the effects of scale economies on learning phenomena by integrating the conventionally specified learning curve with the production function in neoclassical economics. The problem of establishing the variable for measuring experience is dealt with in the estimation procedure. Using this method we estimated the learning curves of 20 industries in the manufacturing sector in Singapore using data taken from 1961 to 1991. More modern industries like electronics, precision instruments and pharmaceutics have steeper learning curves than older industries. Among traditional industries, the food and beverage industry has a greater learning potential than others. In the second part of the paper, the learning curves for 11 common industries in South Korea, Japan and Singapore were estimated and compared. The Japanese industries are found to have steeper learning curves compared to those in Singapore and South Korea.The authors are from the Department of Business Policy, Faculty of Business Administration, National University of Singapore. The authors would like to thank Ms Tan Mui Lee for her assistance in preparing the time series data used for this study.     

城市电网负荷预测方法研究

介绍了不同负荷预测方法的适用范围,分析了我国城市电网负荷发展的特点,给出了城市电网负荷预测的思路和重点;在此基础上,提出了城市规划不同阶段和城市不同地区的负荷预测实现方法,进一步研究了提高城市电网负荷预测结果准确性的主要措施。     

中国上海燃料油期货定价模型研究

采用协整理论及基于VAR的Grange因果关系检验与冲击反应函数方法对中国上海期货交易所燃料油期货价格作建模分析。单位根检验显示，选取的样本序列均为I（1）。Granger因果关系检验显示：美原油期货价格，新加坡180燃料油现货价格变量为燃料油期价的Granger原因；上海燃料油期货价格是黄埔现货价格的单向的Granger原因，期货价格对现货价格具有发现作用。最终作出的长期协整方程显示：美原油期货价格，新加坡180燃料油现货价格、欧元汇率与上海燃料油现货价构成长期显著的均衡关系。美原油期货价格对沪燃料油期价的弹性为0．85；新加坡180燃料油现货价格对沪燃料油期价的弹性为0．78；欧元汇率对沪燃料油期价的弹性为1．04。从最终建立动态模型来看，模型有较好的拟合及预测精度。因此，该模型对沪燃料油期价风险控制具有较好的参考作用。     

Some determinants and effects of FDI in Singapore

This article analyses the microeconomic determinants of effects of foreign direct investment (FDI) in Singapore by seven propositions, using the available secondary data. Variables discussed are related to the areas of industrial structure, economic growth, trade, international competitiveness, productivity and skill intensity. It attempts to draw a pattern on the determinants and effects of inward and outward FDI in Singapore.The research for this paper was partially funded by the Centre for International Business Studies, The University of Western Ontario. This paper is a revised version of a paper presented in Bellagio, Italy in October 1983 which will appear in Dunning (1985).The author is a Professor at the School of Business Administration, The University of Western Ontario.     

The role of the forecasting process in improving forecast accuracy and operational performance

Several operations decisions are based on proper forecast of future demand. For this reason, manufacturing companies consider forecasting a crucial process for effectively guiding several activities and research has devoted particular attention to this issue. This paper investigates the impact of how forecasting is conducted on forecast accuracy and operational performances (i.e. cost and delivery performances). Attention is here paid on three factors that characterize the forecasting process: whether structured techniques are adopted, whether information from different sources is collected to elaborate forecasts, and the extent to which forecasting is used to support decision-making processes. Analyses are conducted by means of data provided by the fourth edition of the Global Manufacturing Research Group survey. Data was collected from 343 companies belonging to several manufacturing industries from six different countries. Results show that companies adopting a structured forecasting process can improve their operational performances not simply because forecast accuracy increases. This paper highlights the importance of a proper forecasting-process design, that should be coherent with how users intend to exploit forecast results and with the aim that should be achieved, that is not necessarily improving forecast accuracy.     

2种风电功率预测模型的比较

采用ARMA模型对风电功率进行了预测,并由ARMA方程推导出卡尔曼滤波状态方程和测量方程,从而将预测问题转化到状态空间,并利用卡尔曼滤波法预测了风电功率,比较了2种方法的预测效果。实例表明,卡尔曼滤波法能够提高风电功率的预测精度,并在一定程度上解决了时间序列分析法的预测时延问题,对电力系统的安全、稳定、经济运行以及提高运行效益具有重要意义。     

国内人才需求预测研究的进展与问题分析

根据研究目的,按照一定标准选取了国内2009～2013年关于人才需求预测的20篇文献,从预测的主要类型、主要思路和主要方法三个方面对这些文献进行了归纳总结和深入分析,得出了进行人才需求预测的基本类型、基本思路和基本方法,并对目前的研究进展进行了评价,指出了目前研究中存在的问题.     

线性预测模型的分布估计

本文讨论把线性预测模型修改成势分析预测模型,并用数值模拟方法给出预测值分布的方法。     

The impact of aggregation level on forecasting performance

Most operations decisions are based on some kind of forecast of future demand. Thus, forecasting is definitely a very traditional area in the operations and inventory management literature. While literature concerning forecast explores the adoption of various qualitative and quantitative methods, this paper tries to design new solutions to improve forecasting accuracy by focusing on the forecasting process that uses such algorithms. In particular, when forecasting demand one should always make clear exactly what he/she is trying to forecast, in terms of the time bucket (i.e., the period of time over which demand is aggregated), the forecasting horizon, the set of items the demand refers to (e.g., forecasting demand for a single item can be much harder than forecasting demand for a group of items), the set of locations the demand refers to (e.g., demand at the single store level is much less predictable than the demand for a whole chain of stores). Traditionally, these features of the final output of forecasting also influence the forecasting process. Indeed, when one wants to forecast demand at single store single item single day level it seems natural to analyse demand and causal factors at the same level of aggregation. On the contrary, in this paper we aim at showing that, first of all often aggregating and/or disaggregating data in the forecasting process can lead to substantial improvements; second, the choice of the appropriate level of aggregation depends on the underlying demand generation process.In addition, most forecasting algorithms tend to focus on a single demand variable. On the contrary, we can analyse analogous time series to improve the effectiveness of the forecasting process. Clustering techniques can be used to identify such homologous time series. Such clusters of homologous time series can provide, on the one hand, the sample size required to gain good statistical confidence and, on the other hand, relatively homogeneous data.In the paper, we use sales data from a food retailer at a very detailed level to test our hypotheses. This claims for relevance for both practitioners and researchers.     

Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House Prices

While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime-switching. The underlying intuition behind regime-switching models is that the series of interest behaves differently depending on the realization of an unobservable regime variable. Regime-switching models are a compelling choice for real estate markets that have historically displayed boom and bust cycles. However, we find that, while regime-switching models can perform better in-sample, simple ARIMA models generally perform better in out-of-sample forecasting.     

Extended revenue forecasting within a service industry

Revenue forecasting is an important topic for management to track business performance and support related decision making processes (e.g. headcount or capital expenditure). It focuses on how a business recognises operating revenue, which can differ from the point at which a sales order is won. Whilst there are many publications detailing forecasting theory, in a business context these largely focus on sales order recognition alone.This paper describes the development of a revenue forecasting tool appropriate for service provision. The organisation involved in the development of the revenue forecasting tool will remain anonymous for commercial reasons but will be referred to as “Organisation A”. The targeted outcome was to extend the forecast window from one month to three months with an error rate of no more than ±10%. The tool was required to consolidate supporting data, adopt appropriate analysis/projection techniques and extend the forecast window in a specific and complex business environment.The resulting tool returned high level results that were aligned to the original targets, and was developed with three components using a combination of projection approaches appropriate to the operating environment. Whilst limited to a specific service industry as a trial, the paper provides a useful reference point for revenue forecasting in complex service businesses and provides a basis for further research opportunities for extended revenue forecasting and business analysis approaches within other service industries.     

Are Virtual Markets Efficient Predictors of New Product Success? The Case of the Hollywood Stock Exchange*

Defining effective methods for determining consumer preferences for products prior to their launch has been a mainstay of marketing and management literature for decades. Virtual Stock Markets (VSMs) is an emerging method in new product forecasting that has been shown to produce reliable new product sales estimates by combining individual preferences via market‐based aggregation mechanisms. Due to the emerging popularity of VSMs among practitioners, this cross‐disciplinary study (combining insights from finance, marketing, and new product development fields) uses the example of the Hollywood Stock Exchange (HSX) and examines its predictive validity and potential systematic biases in its predictions to help think about the general applicability of these forecasting methods to other product areas, or how forecasts in other product areas may need to be modified to be more precise. This study finds evidence of overestimating the sales potential associated with products on the low end of the revenue expectation spectrum, which could be linked to the fact that in artificial exchanges, where no money changes hands, people tend to gamble hoping to make excessive returns. However, this explanation is weakened by the introduction of additional variables linked to the negative influence of information search costs (harder to utilize information is not fully reflected in the stock prices) and over‐utilization of highly visible/conspicuous information. Practical implications for managers considering using VSMs for new product forecasting in creative gestalt‐like settings are discussed.     

房地产投资的量度

根据天津市多年来房地产供求关系的有关资料，将最小二乘法和马尔可夫(Markov)预测法相结合，对天津市(城区)的房地产需求趋势进行了预测，并根据预测结果对未来的房地产需求情况进行了分析，经验证用这种数学模型预测出的结果与事实相符。     

A big data driven framework for demand-driven forecasting with effects of marketing-mix variables

This study aims to investigate the contributions of promotional marketing activities, historical demand and other factors to predict, and develop a big data-driven fuzzy classifier-based framework, also called “demand-driven forecasting,” that can shape, sense and respond to real customer demands. The availability of timely information about future customer needs is a key success factor for any business. For profit maximization, manufacturers want to sense demand signals and shape future demands using price, sales, promotion and others economic factors so that they can fulfil customer's orders immediately. However, most demand forecasting systems offer limited insight to manufacturers as they fail to capture contemporary market trends, product seasonality and the impact of forecasting on the magnitude of the bullwhip effect. This paper aims to improve the accuracy of demand forecasts. In order to achieve this, a back-propagation neural network-based model is trained by fuzzy inputs and compared with benchmark forecasting methods on a time series data, by using historical demand and sales data in combination with advertising effectiveness, expenditure, promotions, and marketing events data. A statistical analysis is conducted, and the experiments show that the method used in the proposed framework outperforms in optimality, efficiency and other statistical metrics. Finally, some invaluable insights for managers are presented to improve the forecast accuracy of fuzzy neural networks, develop marketing plans for products and discuss their implications in several fields.     

Some aspects of foreign exchange management in singapore

This paper describes the experience Singapore has had with the managed floating of its currency. The underlying policy of the Monetary Authority of Singapore (MAS) is shown to have been to maintain a strong and stable currency. This has been achieved in spite of the devaluation of the U.K. pound and subsequent floatation and two devaluations of the U.S. dollar. The last section of the paper shows that Singapore was able to maintain the competitiveness of its exports during the 1970s and 80s by controlling its inflation rate to offset the effects of her currency's appreciation.     

Robust Testing of the Utility‐based High‐technology Product Sales Forecasting Models Proposed by Decker and Gnibba‐Yukawa (2010)

Decker and Gnibba‐Yukawa (2010) propose an elegant utility‐based model for forecasting the sales of high‐technology products and suggest that the model yields forecasts that are highly accurate. However, this finding is based on forecasts for a total of only six holdout observations shared across three products. This number of observations is insufficient for reliable inferences to be drawn about the accuracy of a method and the use of such a small data set runs counter to an accepted principle of forecast evaluation. The authors’ proposed model was tested on more extensive data and sensitivity analysis applied to the results. No evidence was found that the utility‐based model could outperform a relatively simple extrapolative model despite the much greater effort involved in applying the proposed model. In addition, the utility‐based model is only applicable for forecasting sales during a narrow interval in a product's life cycle and requires several periods of historic sales data before it can be implemented. It also depends heavily on the accurate estimates of parameters that are determined outside the model (and which may depend on difficult judgments by managers) and assumes that consumers or households will only purchase the product once between the launch date and the forecast horizon. In light of this, it is argued that the utility‐based model is likely to have limited usefulness as a sales forecasting tool.     

Effects of correlation on intermittent demand forecasting and stock control

This study investigates the effects of three different types of correlation on forecasting and stock control of intermittent demand items. Applying appropriate forecasting and stock control methods to theoretically generated compound Poisson demand data we show that correlation in intermittent demand does play a role in forecast quality and stock control performance. Negative autocorrelation levels lead to higher service levels than positive values, while cost does not significantly change. Our results also show that high intermittency levels intensify these changes in service level. We also show that cross-correlation produces results in the opposite direction of autocorrelation in size or intervals; that is, positive (negative) cross-correlation leads to higher (lower) service levels.     

取向纳米改性包装材料阻隔性数学模型

取向纳米改性是提高包装材料阻隔性的一种有效的绿色化方法。本文阐述了蒙脱土的结构性能、纳米化的方法及其改性包装材料阻隔性的原理,重点介绍了多种预测取向纳米蒙脱土改性聚合物渗透性的模型,包括模型的构建条件、影响因素、数学建模和运用范围。讨论并比较了各种模型优缺点及与实验结果的符合程度,为取向纳米改性包装材料阻隔性的预测提供了理论依据。