Approaches to displaying information to assist decisions under uncertainty

The estimation of the costs of a product or project and the decisions based on these forecasts are subject to much uncertainty relating to factors like unknown future developments. This has been addressed repeatedly in research studies focusing on different aspects of uncertainty; unfortunately, this interest has not yet been adopted in practice. One reason can be found in the inadequate representation of uncertainty. This paper introduces an experiment, which engages different approaches to displaying cost forecasting information to gauge the consideration of uncertainty in the subsequent decision-making process. Three different approaches of displaying cost-forecasting information including the uncertainty involved in the data were tested, namely a three point trend forecast, a bar chart, and a FAN-diagram. Furthermore, the effects of using different levels of contextual information about the decision problem were examined. The results show that decision makers tend to simplify the level of uncertainty from a possible range of future outcomes to the limited form of a point estimate. Furthermore, the contextual information made the participants more aware of uncertainty. In addition, the fan-diagram prompted 75.0% of the participants to consider uncertainty even if they had not used this type of diagram before; it was therefore identified as the most suitable method of graphical information display for encouraging decision makers to consider the uncertainty in cost forecasting.     

The Effects of Presenting Imprecise Probabilities in Intelligence Forecasts

How to assess and present analytic uncertainty to policymakers has emerged as an important topic in risk and policy analysis. Due to the complexity and deep uncertainty present in many forecasting domains, these reports are often fraught with analytic uncertainty. In three studies, we explore the effect of presenting probability assessments and analytic uncertainty through probability ranges. Participants were presented with mock intelligence forecasts that include narrative evidence as well as numerical probability assessments. Participants were sensitive to the ambiguity communicated through the confidence range. The narrative appeared to have a smaller effect on judgments when accompanied by a probability range as opposed to a point assessment. In one study, participants also thought that the probability range was more useful for decision making at a higher probability whereas the point estimate was more useful at a lower probability. When evaluating a forecast in hindsight, decisionmakers tended to report lower levels of blame and higher levels of source credibility for forecasts that reported ranges as compared to point assessments. These findings suggest that decisionmakers are not necessarily “ambiguity averse” in the forecasting context. Presenting ranges of probability may have distinct advantages as a way to communicate probability and analytic confidence to decisionmakers.     

An appreciation of alternative sales forecasting models: Recent techniques based on historical data

The subject of statistical sales forecasting has recently been brought to prominence as a major area of management decision-making by the growth of a substantive literature and the establishment of several research groups to investigate the building of forecasting models. Whilst a lot of attention has been focused on the relatively newer disciplines of technological forecasting and multiple-equation model building of macro-economic systems, statistical sales forecasting methods have also been subject to considerable development and a number of radically new techniques have emerged. The authors believe that it is timely to review the current ‘state of the art’ of sales forecasting methodology. This study examines a wide range of models in use although it is not intended as a comprehensive guide.     

Impact of Reseller's Forecasting Accuracy on Channel Member Performance

This article studies a three‐layer supply chain where a manufacturer sells a product through a reseller who then relies on its own salesperson to sell to the end market. The reseller has superior capability in demand forecasting relative to the manufacturer. We explore the main trade‐offs between the risk‐reduction effect and the information–asymmetry–aggravation effect of the improved forecasting accuracy. We show that under the optimal wholesale price contract, both the manufacturer and the reseller are always better off as the reseller's forecasting accuracy improves. Nevertheless, under the menu of two‐part tariffs, the manufacturer prefers the reseller to be either uninformed or perfectly informed about the market condition. We further find that the improved forecasting accuracy is beneficial for the reseller if its current forecasting system is either very poor or very good.     

基于贝叶斯正则化的TDBPNN模型在中国外贸预报中的应用及评估

基于相空间重构的非线性预报思想,建立一个时滞的BP神经网络模型(TDBPNN),采用贝叶斯正则化方法提高BP网络的泛化能力,并将该模型应用于中国进出口贸易的预测,结果证明改进的TDBPNN模型具有较好的泛化能力,准确拟合了进出口贸易发展的历史值和趋势。区别于一般的预测评价,认为非线性预测不仅要注重数据拟合和精度改进,而且应该能够反映被预报系统的非线性特征。在分析模型预测精度的同时,通过计算拟合序列和原序列的非线性特征量进行模型评价,证实预测模型能够合理地“捕捉”到产生原序列的非线性系统的动力学特征。     

一种基于改进的Akaike信息准则的相关关系检测的新方法

本文基于改进的Akaike信息准则(AICC),建立了一种专门适用于小样本随机变星相关关系检测的新方法。这种方法不仅把AICC准则从AR模型推广到ARMA模型,而且增加了自变量的筛选过程,使方法既是最优的又是最简的。通过分析改革以来我国宏观经济变量的相关关系,证明了方法的有效性。     

Conundrums with Uncertainty Factors

The practice of uncertainty factors as applied to noncancer endpoints in the IRIS database harkens back to traditional safety factors. In the era before risk quantification, these were used to build in a “margin of safety.” As risk quantification takes hold, the safety factor methods yield to quantitative risk calculations to guarantee safety. Many authors believe that uncertainty factors can be given a probabilistic interpretation as ratios of response rates, and that the reference values computed according to the IRIS methodology can thus be converted to random variables whose distributions can be computed with Monte Carlo methods, based on the distributions of the uncertainty factors. Recent proposals from the National Research Council echo this view. Based on probabilistic arguments, several authors claim that the current practice of uncertainty factors is overprotective. When interpreted probabilistically, uncertainty factors entail very strong assumptions on the underlying response rates. For example, the factor for extrapolating from animal to human is the same whether the dosage is chronic or subchronic. Together with independence assumptions, these assumptions entail that the covariance matrix of the logged response rates is singular. In other words, the accumulated assumptions entail a log‐linear dependence between the response rates. This in turn means that any uncertainty analysis based on these assumptions is ill‐conditioned; it effectively computes uncertainty conditional on a set of zero probability. The practice of uncertainty factors is due for a thorough review. Two directions are briefly sketched, one based on standard regression models, and one based on nonparametric continuous Bayesian belief nets.     

Energy modeling as a tool for planning

This paper discusses the application of a general equilibrium model, developed at Stanford Research Institute, for use in long range forecasting and business planning. The value of the modeling approach is illustrated through application to a hypothetical natural gas liquefaction venture. Specific features and representative output of the SRI model are presented. The authors stress the use of a modeling approach in the planning process as a quantitative means of evaluating the importance of uncertainty in specific decision variables and the ability of such model to test explicitly the long term impact of changes in a given set of base assumptions.     

基于可变区间权重的中期用电量半参数预测模型

由于数据变化规律的多样性,中期电力负荷的波动有着不同于短期、长期负荷的特点。基于电力系统复杂性的研究视角,重点讨论了中期负荷预测过程中模型的不确定性、参数的时变特性以及负荷波动的周期性规律。根据中期负荷的数据特性,建立了基于非参数修匀的半参数模型,定义了函数区间的划分粒度以及模型权重的求解方法,提出了基于可变区间权重的动态预测方法,给出了基于经验模态分解和波动能量分析的噪声序列提取、检验方法。试验研究结果表明,气候因素对用电消耗的影响最大,经济因素次之;从选取的指标来看,不同时期的影响因素对于模型的解释能力是时变的;所提方法能够对电力负荷进行精确的多粒度、多维度分析,进而掌握其局部变化规律,可有效用于电力系统中期负荷预测。     

基于GERT随机网络的废弃回收预测模型研究

在阐述回收处理业务流程的基础上,分析其不确定性;运用随机网络系统模型的相关理论构建GERT网络模型,探讨产品回收再制造零部件和可再生材料的预测模型。该模型既可以预测出废弃产品返回数量、回收处理过程中的再造零部件和可再生材料的比例和数量,又可以预测出它们的期望返回时间。最后,以打印机的回收再制造为例,对模型进行了实际应用。     

Technological forecasting for power generation—A study using the Delphi technique

Technological forecasting is a powerful technique for obtaining insights into possible futuristic innovations. The forecast can provide a basis for long-range planning and policy formulation in organizations. Technological forecasting, though a useful and powerful tool, has yet to be applied widely in developing countries. This paper outlines a forecasting exercise carried out by the authors using the Delphi technique in India. The focus of the exercise was on electric energy generation and transportation. The findings have been compared with other available studies in India in similar areas. The paper also illustrates how additional information inputs can mould such forecasts. For this purpose, the authors have compared the results of the current study with the results of a similar study carried out in the same organization 8 years earlier. The change in perception regarding the timing of certain items is very evident and the underlying reasons have been given.     

基于动态模型平均的大豆期货价格预测研究

针对大豆期货价格波动的复杂性及影响因素的多元性，本文将动态模型平均理论引入大豆期货价格分析与预测研究中，通过动态选择解释变量和系数时变程度，在有效控制模型和系数不确定性的同时，最大限度综合利用大豆期货市场内外部信息，以提高大豆期货价格预测准确度。具体的，本文提出一套基于动态模型平均理论的大豆期货价格影响因素与预测分析框架，从期货市场和经济环境等两方面准确地识别出大豆期货价格影响因素的时变特征，进而构建大豆期货价格预测模型，并通过预测误差指标和Diebold-Mariano检验法评估其与基准模型的预测能力。研究结果表明，动态模型平均理论在有效剖析大豆期货价格影响因素的时变特征的同时，能明显提升大豆期货价格预测准确度。     

Accuracy in the long run—Where are we now

Long-term forecasting must be viewed as informative speculation about the future. It should be credited with relatively little scientific authenticity. At the present stage of theory and data, no magic methods can be expected to overcome the problem of satisfactory forecasting. Improvement, however, is certainly not just a question of putting more and more variables and more and more numbers into a computer. Methods which indicate how to cushion against uncertainty and methods which bring a greater awareness of options for the future are essential if we are to gain greater control over events. The hazy images of the long-term future which are generated by scenario and other forms of analysis form the guidelines within which short and especially medium-term choices must be made. But in the end we must recognise the inherent limitations of forecasting and forecasting methods and think of forecasting not so much as a method of prediction but as a contribution to tackling the future in a more integrated sense.     

Social forecasting: An integral part of corporate planning?

The increasing need for business to monitor the social dimensions of its environment and, hopefully make some forecasts of future trends has met with some constructive response from academics and consultants although not as yet on a very liberal scale. The published literature does not indicate to what extent companies in general attemp social forecasting and, where they do, the degree of integration which exists within their corporate planning systems. The authors, therefore, decided to survey a sample of British organizations to see if they could shed some light on these issues and thereby add some information to the excellent accounts of individual cases of social forecasting in, they suspect, the more advanced and atypical companies. The survey suggests a general picture of: awareness of the value of social forecasting; fairly widespread ignorance of the techniques which do exist, primitive though these may largely be; successful integration of social forecasting into the corporate planning systems of a substantial number of organizations but not in the majority.     

Mechanisms to Induce Buyer Forecasting: Do Suppliers Always Benefit from Better Forecasting?

In this study, we consider a supplier's contract offerings to a buyer who may obtain improved forecasts for her demand over time. We investigate how the supplier can take advantage of the buyer's better forecasts and what kind of contracts he should offer to the buyer in order to maximize his profits. We model a natural forecast evolution where the buyer can obtain a more accurate forecast closer to the selling season. We assume there is information asymmetry between the buyer and the supplier at all times in that the buyer understands her demand better than the supplier. Three types of contracts that the supplier can offer are considered: (1) one where a contract is offered before the buyer has a chance to obtain improved forecasts, (2) one where a contract is offered after the buyer has obtained improved forecasts, and (3) a contingent (dynamic) contract which offers an initial contract to the buyer before she obtains improved forecasts, followed by a later contract (contingent on the initial contract) offered after improved forecasts have been obtained. We consider two scenarios: (1) where the supplier is certain that the buyer can obtain more accurate forecasts over time, and (2) where the supplier is uncertain about the buyer's forecasting capability (or forecasting cost). In the first scenario, we show that among the three types of contracts, the contingent contract is always the most profitable for the supplier. Furthermore, using the contingent contract, the supplier always benefits from higher accuracy of the buyer's demand forecasts. In the second scenario, we explicitly model the supplier's level of certainty about the buyer's capability of obtaining better forecasts, and explore how the supplier can design contracts to induce the buyer to obtain better forecasts when she is capable.     

The Value of Weather Information for E‐Commerce Operations

To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.     

基于季节调整和Holt-Winters的月度负荷预测方法

针对负荷序列中异常数据会导致模型误设或参数估计发生偏差的问题，提出利用季节调整方法，先对原始负荷序列进行季节调整，获得消除离群值、节假日影响的季节调整后序列和季节成分序列；然后用改进的Holt-Winters方法对季节调整后成分进行预测，用虚拟回归方法预测季节成分序列；最后对各成分预测结果重构得到最终预测结果的月度负荷预测方法。通过实例检验，提出的方法能明显提高预测精度，预测效果要优于季节性Holt-Winters、SARIMA、神经网络、支持向量机等模型。     

Sales forecasting— using a combination of approaches

The authors of this article argue that when market researchers and managers are faced with two or more forecasts of the same event, the typical approach is to attempt to determine which is better (or best). The superior forecasting technique is then accepted and the rest are discarded. However, in today's uncertain markets such a procedure is generally inferior to combining different forecasting approaches. Not only will the composite forecast usually have a lower average error but significant information will be generated from the diverse forecasting sources, providing management with valuable insights and diagnostics.     

基于向量夹角余弦的组合预测模型的性质研究

基于向量夹角余弦的组合预测是一种相关性的组合预测模型,它是研究组合预测方法的一个新途经.针对基于向量夹角余弦准则下组合预测模型,研究它的基本结构特征.首先提出新的优性组合预测、预测方法优超、冗余度等概念.然后探讨了非劣性组合预测、优性组合预测以及冗余预测方法的存在性,并给出冗余信息的判定定理.最后进行实例分析,表明该方法有较大的实际应用价值.     

Bayesian Reanalysis of the Challenger O-Ring Data

A Bayesian forecasting model is developed to quantify uncertainty about the postflight state of a field-joint primary O-ring (not damaged or damaged), given the O-ring temperature at the time of launch of the space shuttle Challenger in 1986. The crux of this problem is the enormous extrapolation that must be performed: 23 previous shuttle flights were launched at temperatures between 53 °F and 81 °F, but the next launch is planned at 31 °F. The fundamental advantage of the Bayesian model is its theoretic structure, which remains correct over the entire sample space of the predictor and that affords flexibility of implementation. A novel approach to extrapolating the input elements based on expert judgment is presented; it recognizes that extrapolation is equivalent to changing the conditioning of the model elements. The prior probability of O-ring damage can be assessed subjectively by experts following a nominal-interacting process in a group setting. The Bayesian model can output several posterior probabilities of O-ring damage, each conditional on the given temperature and on a different strength of the temperature effect hypothesis. A lower bound on, or a value of, the posterior probability can be selected for decision making consistently with expert judgment, which encapsulates engineering information, knowledge, and experience. The Bayesian forecasting model is posed as a replacement for the logistic regression and the nonparametric approach advocated in earlier analyses of the Challenger O-ring data. A comparison demonstrates the inherent deficiency of the generalized linear models for risk analyses that require (1) forecasting an event conditional on a predictor value outside the sampling interval, and (2) combining empirical evidence with expert judgment.