Use of quantitative models in UK economic appraisal and policy-making

Quantitative models are used extensively in economic policy-making and forecasting. It is argued that the way that they are used is rather different from the usual formal account of their role, because of the political and social realities of the appraisal and policy-making process. After reviewing the standard account of the use of models, the paper provides an alternative account and attempts to document it with quotations from politicians and economic policy advisers. The conclusion discusses possible implications for other disciplines where quantitative models are an input into the policymaking process.     

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.     

Improving the supply chain performance: Use of forecasting models versus early order commitments

This paper evaluates the impact of forecasting models and early order commitment in a supply chain with one capacitated manufacturer and four retailers under demand uncertainty. Computer simulation models were used to simulate different demand forecasting and inventory replenishment decisions by the retailers as well as production decisions by the manufacturer under a variety of demand patterns and capacity tightness scenarios. This study found that early order commitments significantly affected the total costs and service levels, to various degrees, for the manufacturer and the retailers, suggesting that the benefits of early order commitment could be influenced by a combination of forecasting models, demand patterns and capacity tightness.     

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.     

Adaptive exponential smoothing versus conventional approaches for lumpy demand forecasting: case of production planning for a manufacturing line

Production planning in a lumpy demand environment can be tenuous, with potentially costly forecasting errors. This paper addresses the issue of selecting the smoothing factor used in lumpy demand forecasting models. We propose a simple adaptive smoothing approach to replace the conventional industrial practice of choosing a smoothing factor largely based on the analyst or engineer's experience and subjective judgment. The Kalman filter approach developed in this study processes measurements to estimate the state of a linear system and utilises knowledge from states of measurements and system dynamics. Performances of an array of forecasting models that have been shown to work well in lumpy demand environments are compared with respect to the proposed adaptive smoothing factor and the conventional smoothing constant across a spectrum of lumpy demand scenarios. All models using the adaptive smoothing factor based on Kalman filter weighting function generate smaller errors than their conventional counterparts, especially under high lumpiness demand environments. Our proposed approach is particularly useful when production management is concerned about simplicity and transferability of knowledge due to constant personnel turnaround and low retention rate of expertise.     

Political decision making in science and technology: A controversy about the release of genetically engineered organisms

In the policy-making process, there is increasing demand for scientific and technical information. But one of the major insights about science and technology recently highlighted by historians and philosophers is that in their development, science and technology are accompanied as much by internal controversy as by consensus. This creates a problem for the policy decision maker. How can a consensus in the policy making process be achieved in the context of scientific and technological controversy? This problem can be approached by analyzing the types of discussions in which scientific and technical experts participate, and is the topic of this paper.     

INAR implementation of newsvendor model for serially dependent demand counts

The classic newsvendor model was developed under the assumption that period-to-period demand is independent over time. In real-life applications, the notion of independent demand is often challenged. In this paper, we propose a dynamic implementation of the newsvendor model based on a class of integer-valued autoregressive (INAR) models when facing correlated discrete demand. Motivated by application, we consider INAR models with underlying Poisson error innovations and with underlying negative-binomial error innovations to accommodate overdispersion scenarios. We numerically compare our proposal with the standard newsvendor solution and with a standard autoregressive-based newsvendor solution. Our results show that an appropriately specified INAR-based newsvendor solution not only outperforms the standard case but also the approximating forecasting approaches. Moreover, even in the presence of autocorrelation, the use of the standard autoregressive model as an approximating approach can lead to increased costs over and above the standard implementation of the newsvendor model based on no forecasting.     

Short-term forecasting of electricity prices in the colombian electricity market

The restructuring of the electricity-generating industry from protected monopoly to an open competitive market has presented producers with a problem scheduling generation: finding the optimal bidding strategy to maximise their profits. In order to solve this scheduling problem, a reliable system capable of forecasting electricity prices is needed. This work evaluates the forecasting capabilities of several modelling techniques for the next-day-prices forecasting problem in the Colombian market, measured in USD/MWh. The models include exogenous variables such as reservoir levels and load demand. Results show that a segmentation of the prices into three intervals, based on load demand behaviour, contribute to an important standard deviation reduction. Regarding the models under analysis, Takagi?Sugeno?Kang models and ARMAX models identified by means of a Kalman filter perform the best forecasting, with an error rate below 6%.     

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).     

Stochastic characterization of upstream demand processes in a supply chain

In the supply chain management area, there has much recent attention to a phenomenon known as the bullwhip effect. The bullwhip effect represents the situation where demand variability increases as one moves up the supply chain. In this paper, we study this effect in an order-up-to supply-chain system when minimum Mean Square Error (MSE) optimal forecasting is employed as opposed to some commonly used simplistic forecasting schemes. We find that depending on the correlative structure of the demand process it is possible to reduce, or even eliminate (i.e., "de-whip"), the bullwhip effect in such a system by using an MSE-optimal forecasting scheme. Beyond the bullwhip effect, we also determine the exact time-series nature of the upstream demand processes.     

企业需求预测准确性存在的问题及其应对策略(二)

鉴于需求预测在企业经营活动中具有重要地位,且会受到受各种因素影响,本文在对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析的基础上,指出了通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和未来市场情况的偏差,从而持续提高需求预测的准确性,促进企业生产、销售的良性运行。     

Top-down versus bottom-up forecasting strategies

Some controversy exists about the advocacy of top-down versus bottom-up forecasting strategies. Top-down forecasting refers to the process of forecasting the demand for the aggregate of items in a class and then inferring individual demands according to a percentage of the total; bottom-up refers to separately forecasting the requirements for each individual item. This paper outlines the relative advantages of each strategy and indicates the situations in which each should be preferred.     

企业需求预测准确性存在的问题及其应对策略(一)

鉴于需求预测在企业经营活动中具有重要地位,且会受到受各种因素影响,本文在对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析的基础上,指出了通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和未来市场情况的偏差,从而持续提高需求预测的准确性,促进企业生产、销售的良性运行。     

A knowledge-based approach to the generation of IDEF0 models

Though a powerful tool for modelling manufacturing systems, IDEF0 is not without weaknesses. It is generally recognised that the process of IDEF0 modelling can be time-consuming and inconsistent. However, the process may be automated to improve time-effectiveness and consistency. This paper proposes a knowledgebased system for automating the process. The system, which is meant for the discrete manufacturing industry, is based on the concept of reference models. It is intended for the modellers of manufacturing systems, as an aid rather than as their replacement. A prototype of the proposed system has been developed. The paper explains the knowledge-based approach to the generation of IDEF0 models and describes the work that has been done at Gintic on the development of the prototype system.     

An innovative air-conditioning load forecasting model based on RBF neural network and combined residual error correction

Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. They have developed many forecasting methods, such as multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), grey model (GM) and artificial neural network (ANN), in the field of air-conditioning load prediction. However, none of them has enough accuracy to satisfy the practical demand. On the basis of these models existed, a novel forecasting method, called ‘RBF neural network (RBFNN) with combined residual error correction’, is developed in this paper. The new model adopts the advanced algorithm of neural network based on radial basis functions for the air-conditioning load forecasting, and uses the combined forecasting model, which is the combination of MLR, ARIMA and GM, to estimate the residual errors and correct the ultimate foresting results. A study case indicates that RBFNN with combined residual error correction has a much better forecasting accuracy than RBFNN itself and RBFNN with single-model correction.     

用户体验视角下的地铁乘客广告需求分析方法

目的 研究地铁乘客群体与地铁广告之间的关系及需求,以期形成综合乘客出行流程与地铁广告功能系统的广告服务体验闭环。方法 对地铁乘客完整出行旅程的体验要素以及空间广告的功能、内容及传播属性进行供需对应关系分析,构建“属性—层级—节点—引导”四维度的AHCG乘客广告需求分析模型,并结合武汉部分地铁线路实际状况,将乘客的出行、消费等综合生活需求结合地铁广告的功能特性及设置方式进行合理运用。结果 分析发现现有站点广告商业价值划分方式并不能为地铁广告投放利用带来更有效的帮助,精准投放还需要形成多维标准,以此对各站点进行评估。结论 对武汉部分地铁线路进行实际调研,得出的分析方法能够用于将乘客出行、消费等生活需求对应至地铁广告功能开发利用与设置方式改进上,对地铁广告运营乃至广告位基础设施建设都具有一定的参考价值。     

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.     

最优组合预测方法在家用汽车需求预测中的应用

为了提高预测的准确性,引入了组合预测模型,将几个单一预测模型有机地结合起来,综合各个预测模型的优点,对未来几年内家用轿车需求进行预测.通过使组合预测误差平方和最小,确定各个单一预测方法的权重系数,得出更为准确的预测结果.计算结果表明该方法具有较好的实用性.     

Operational storm surge forecasting in the Netherlands: developments in the last decade

The accurate forecasting of storm surges is an important issue in the Netherlands. With the emergence of the first numerical hydrodynamic models for surge forecasting at the beginning of the 1980s, new demands and possibilities were raised. This article describes the main phases of the development and the present operational set-up of the Dutch continental shelf model, which is the main hydrodynamic model for storm surges in the Netherlands. It includes a brief discussion of applied data-assimilation techniques, such as Kalman filtering, the model calibration process and some thoughts on quality assurance in an operational environment. After further describing some select recent investigations, the paper concludes with some remarks on future developments in a European context.     

基于Petri网的供应链协同需求预测流程模型

提出了一个集成化的供应链协同需求预测模型,该模型包括数据、组织、环境与例外、决策与方法、运作与计划以及协作与调整等6个部分,其特点是将不同成员的意见根据不同的重要性融合到预测中,且突出了多源信息的应用.通过基于有色Petri网对本模型的