改进BASS模型应用于短生命周期产品需求预测

总结了目前国内外文献对短生命周期产品需求预测的研究,分析了短生命周期产品需求相关的特点.介绍了BASS模型及其发展,通过可行性分析,将BASS模型应用于短生命周期产品预测.利用类似产品补充预测所需信息,并对模型进行了季节性修正.算例预测结果表明,改进后的BASS模型应用于短生命周期产品的需求预测在MAD、RMSE、MAPE等各项误差指标方面都优于其他方法.     

Failure and reliability prediction by support vector machines regression of time series data

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.     

On statistical inference in time series analysis of the evolution of road safety

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research.     

One step-ahead ANFIS time series model for forecasting electricity loads

In electric industry, electricity loads forecasting has become more and more important, because demand quantity is a major determinant in electricity supply strategy. Furthermore, accurate regional loads forecasting is one of principal factors for electric industry to improve the management performance. Recently, time series analysis and statistical methods have been developed for electricity loads forecasting. However, there are two drawbacks in the past forecasting models: (1) conventional statistical methods, such as regression models are unable to deal with the nonlinear relationships well, because of electricity loads are known to be nonlinear; and (2) the rules generated from conventional statistical methods (i.e., ARIMA), and artificial intelligence technologies (i.e., support vector machines (SVM) and artificial neural networks (ANN)) are not easily comprehensive for policy-maker. Based on these reasons above, this paper proposes a new model, which incorporates one step-ahead concept into adaptive-network-based fuzzy inference system (ANFIS) to build a fusion ANFIS model and enhances forecasting for electricity loads by adaptive forecasting equation. The fuzzy if-then rules produced from fusion ANFIS model, which can be understood for human recognition, and the adaptive network in fusion ANFIS model can deal with the nonlinear relationships. This study optimizes the proposed model by adaptive network and adaptive forecasting equation to improve electricity loads forecasting accuracy. To evaluate forecasting performances, six different models are used as comparison models. The experimental results indicate that the proposed model is superior to the listing models in terms of mean absolute percentage errors (MAPE).     

Prognose von 15 Zeitreihen der DGOR mit Neuronalen Netzen

In 1982, the working group “Forecasting Methods” of the Deutsche Gesellschaft für Operations Research (DGOR) carried out a forecasting comparison between 12 various models which were applied to 15 time series. The results of this study can be considered as a good benchmark for further prediction techniques. This paper reports upon the prediction of these 15 time series by using a Neural Network which was developed by the Backpropagation algorithm. The four highest autocorrelated lag-variables were used as the input variables of the Neural Network. The results show that the Neural Network delivered worse predictions than the other methods including the naive prediction by forecasting non-stationary time series. Stationary time series could be predicted better than the naive prediction, but in comparison to the other techniques the results were only average. After regarding the problem of non-stationarity by using the Dickey-Fuller-Test, first differences were chosen as the input-variables of the Neural Network. In this case, there was a considerable improvement, but the best method (Box-Jenkins' ARIMA technique) could not be surpassed.     

Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market

The parameter selection is very important for successful modelling of input-output relationship in a function approximation model. In this study, support vector machine (SVM) has been used as a function approximation tool for a price series and genetic algorithm (GA) has been utilised for optimisation of the parameters of the SVM model. Instead of using single time series, separate time series for each trading interval has been employed to model each day-s price profile, and SVM parameters of these separate series have been optimised using GA. The developed model has been applied to two large power systems from National electricity market (NEM) of Australia. The forecasting performance of the proposed model has been compared with a heuristic technique, a linear regression model and the other reported works in the literature. Effect of price volatility on the performance of the models has also been analysed. Testing results show that the proposed GA-SVM model has better forecasting ability than the other forecasting techniques.     

BASS预测模型与库存控制集成研究

根据短生命周期产品的特征调整BASS扩散模型,并将其应用于短生命周期产品的需求预测,同时考虑产品需求对无形变质和短缺拖后量的影响,进而建立短生命周期产品多周期订货模型。通过算例分析获得短生命周期产品的成熟期开始时间和持续时间,进而求订货次数和订货量,给出多周期最优订货策略。数据实验结果显示基于BASS需求函数的库存控制模型可以提高需求预测精确度,有效降低库存成本,对企业库存控制具有指导意义。     

A combined model predictive control and time series forecasting framework for production-inventory systems

Model Predictive Control (MPC) has been previously applied to supply chain problems with promising results; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing insight in the future. In this paper this possibility is explored, by proposing a complete management framework for production-inventory systems that is based on MPC and on a neural network time series forecasting model. The proposed framework is tested on industrial data in order to assess the efficiency of the method and the impact of forecast accuracy on the overall control performance. To this end, the proposed method is compared with several alternative forecasting approaches that are implemented on the same industrial dataset. The results show that the proposed scheme can improve significantly the performance of the production-inventory system, due to the fact that more accurate predictions are provided to the formulation of the MPC optimization problem that is solved in real time.     

Adaptive forecasting techniques

The class of exponential smoothing models which vary the values of their parameters to adapt to changing conditions in a time series are referred to as adaptive forecasting techniques. In this article criteria for evaluating forecasting models are presented and the features of a simple exponential smoothing model that are exploited by the adaptive techniques are discussed. Several adaptive forecasting schemes are described and classified, and examples of the performance of these techniques are presented.     

The use of time series forecasting in zone order picking systems to predict order pickers’ workload

In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. This paper introduces workload forecasting in a warehouse context, in particular a zone picking warehouse. Improved workforce planning can contribute to an effective and efficient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders, the assumption of a constant given demand is questioned in this paper. The objective of this study is to present time series forecasting models that perform well in a zone picking warehouse. A real-life case study demonstrates the value of applying time series forecasting models to forecast the daily number of order lines. The forecast of order lines, along with order pickers’ productivity, can be used by warehouse supervisors to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up and top-down approaches are evaluated in order to find the best-performing forecasting method.     

Clustering Empirical Failure Rate Curves for Reliability Prediction Purposes in the Case of Consumer Electronic Products

In this paper, a methodology based on the combination of time series modeling and soft computational methods is presented to model and forecast bathtub‐shaped failure rate data of newly marketed consumer electronics. The time‐dependent functions of historical failure rates are typified by parameters of an analytic model that grabs the most important characteristics of these curves. The proposed approach is also verified by the presentation of an industrial application brought along at an electrical repair service provider company. The prediction capability of the introduced methodology is compared with moving average‐based and exponential smoothing‐based forecasting methods. According to the results of comparison, the presented method can be considered as a viable alternative reliability prediction technique. Copyright © 2015 John Wiley & Sons, Ltd.     

Grey system theory and fuzzy time series forecasting for the growth of green electronic materials

The development of green materials is an important part of corporate social responsibility. Companies need to use resources legitimately and have environmental protection responsibility. Forecasting the growth trend of green copper clad laminate (CCL) material is crucial for manufacturers of printed circuit boards and green CCLs. Early and accurate understanding of such trends in this industry can lead to the early acquisition of opportunities for related markets and technological development. Because historical data samples associated with green CCL are small and typically lack a normal distribution, employing conventional regression analysis or time series models for forecasting is not suitable for testing-related presumptions. To address this issue, this study adopts grey system theory (GST) and a fuzzy time series (FTS) model as forecasting methods. The results show that effective forecasting can be achieved by applying either a GM (1,1) α model or heuristic FTS model to the data of a small and non-normally distributed sample.     

Complex-Valued Wavelet Lifting and Applications

Signals with irregular sampling structures arise naturally in many fields. In applications such as spectral decomposition and nonparametric regression, classical methods often assume a regular sampling pattern, thus cannot be applied without prior data processing. This work proposes new complex-valued analysis techniques based on the wavelet lifting scheme that removes “one coefficient at a time.” Our proposed lifting transform can be applied directly to irregularly sampled data and is able to adapt to the signal(s)’ characteristics. As our new lifting scheme produces complex-valued wavelet coefficients, it provides an alternative to the Fourier transform for irregular designs, allowing phase or directional information to be represented. We discuss applications in bivariate time series analysis, where the complex-valued lifting construction allows for coherence and phase quantification. We also demonstrate the potential of this flexible methodology over real-valued analysis in the nonparametric regression context. Supplementary materials for this article are available online.     

Demand forecasting and smoothing capacity planning for products with high random demand volatility

For those products that are heavily competitive in the marketplace, demand volatility and unpredictability have been growing. This has resulted in a sizeable deviation in demand forecasts when using the traditional forecasting methods. Accordingly, this study aims to develop a real option approach-based forecasting model for predicting demand during the upcoming planning horizon for products with high random volatility on demand. The real option approach can effectively deal with the long-term trends and random variation involved in a given demand stochastic diffusion process. Additionally, this study proposes taking Monte Carlo simulation as a numerical method to solve the demand-forecasting model. Monte Carlo simulation not only can accurately approximate almost any type of stochastic processes, but also can competently handle the path-dependant relationship existing between successive demands. Subsequently, these demand forecasts are used to determine the provisioned smoothing capacity during the upcoming planning horizon. To this end, this study also proposes several effective and practical smoothing capacity-planning approaches in accordance with the specified production strategy. Based on a numerical example, the integrated planning approach can obtain a plausible result.     

Demand forecasting and cost performance in a model of a real manufacturing unit

A series of experiments is reported in which the demand forecasting method used in a simulation model of an actual make-for-stock shop was varied, and the resulting impact on a cost function observed. The forecasting models employed were all of the multiple exponential smoothing type. Reducing the smoothing constant, i.e. changing the filtering characteristics, was found to lead to statistically significant cost savings across the full range of experimental conditions examined. However, the effect of increasing the order of the forecasting model, i.e. changing the assumption about the nature of the demand time series, was found to be sensitive to the stock-out cost rate used, with both increases and decreases in cost occurring.     

The inventory costs of common mis-specification of demand-forecasting models

In inventory planning, the use of exponential smoothing to forecast demand or the assumption that demand over consecutive time periods is i.i.d. is commonplace. In practice, these forecasting approaches are often invoked without justifying their appropriateness. In this paper we assert that, in many situations, the demand process may be different from that implicit in these commonly applied forecasting methods. In particular, we consider demand generated by a general ARM A process. For such a process, we derive expressions for the comparison of the steady-state sum of holding cost and stockout cost per unit time that results from using the correct forecasting model with that which results from the two commonly-used models mentioned above. This comparison indicates that correctly identifying the demand process is warranted and that popular efforts in batch-size reduction increase the benefits of doing so.     

Comparison of Missing Data Imputation Methods in Time Series Forecasting

Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks. In this study, we evaluate and compare the effects of imputation methods for estimating missing values in a time series. Our approach does not include a simulation to generate pseudo-missing data, but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom. In an experiment, therefore, several time series forecasting models are trained using different training datasets prepared using each imputation method. Subsequently, the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models. The results obtained from a total of four experimental cases show that the -nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.     

An Integrated Materials Management System: Its Design and Formulation

An integrated management control system which makes use of quantitative business methodology is discussed. Areas where decisions need to be made and requiring assistance from the system are: distribution, production planning, inventory control and production sequencing. Quantitative techniques employed include time series forecasting, linear and integer programming, inventory and production sequencing theory.     

MODELING THE EVOLUTION OF DEMAND FORECASTS ITH APPLICATION TO SAFETY STOCK ANALYSIS IN PRODUCTION/DISTRIBUTION SYSTEMS

In this paper, we propose a general probabilistic model for modeling the evolution of demand forecasts, referred to as the Martingale Model of Forecast Evolution (MMFE). We combine the MMFE with a linear programming model of production and distribution planning implemented in a rolling horizon fashion. The resulting simulation methodology is used to analyze safety stock levels for a multi-product/multi-plant production/distribution system with seasonal stochastic demand. In the context of this application we demonstrate the importance of good forecasting.     

Analysis and solution to the single-level batch production smoothing problem

Many companies use mixed-model production systems running under the Just-in-Time philosophy in order to efficiently meet customer demands for a variety of products. Such systems require demand be stable and production sequence be leveled. The production smoothing problem aims at finding level schedules in which the appearances of products are dispersed over the horizon as uniformly as possible. In this paper, the production smoothing problem is extended to a more general manufacturing environment where a single machine can be identified as either the final or the bottleneck stage of the system and products may have arbitrary non-zero setup and processing time requirements on this single machine. An optimization model is built for the problem and a two phase solution methodology is developed. The first phase problem is shown to be NP-hard and a parametric heuristic procedure is proposed for its solution. In contrast, the second phase problem is shown to be efficiently solvable and currently available solution methods are adopted from the literature. A computational study is designed to test the proposed two phase solution methodology and also the parametric heuristic procedure. Computational results show that the proposed two phase solution methodology enables effective and efficient control of the studied manufacturing system, and the heuristic procedure developed for the first phase problem is time efficient and promises near optimal solutions for a variety of test instances.