Modeling and forecasting of Brazilian reservoir inflows via dynamic linear models

This work focuses on developing a forecasting model for the water inflow at an hydroelectric plant’s reservoir for operations planning. The planning horizon is 5 years in monthly steps. Due to the complex behavior of the monthly inflow time series we use a Bayesian dynamic linear model that incorporates seasonal and autoregressive components. We also use climate variables like monthly precipitation, El Niño and other indices as predictor variables when relevant. The Brazilian power system has 140 hydroelectric plants. Based on geographical considerations, these plants are collated by basin and classified into 15 groups that correspond to the major river basins, in order to reduce the dimension of the problem. The model is then tested for these 15 groups. Each group will have a different forecasting model that can best describe its unique seasonality and characteristics. The results show that the forecasting approach taken in this paper produces substantially better predictions than the current model adopted in Brazil (see Maceira & Damazio, 2006), leading to superior operations planning.     

基于灰色模型的怀来公路货运量预测的研究

为了把怀来建设成为京西物流港,以系统工程学、交通规划为基础,采用灰色预测模型法,先对怀来公路货物运输量进行实地调查．通过把调查的数据整理聚类分析,以数据为依据构造怀来公路货运灰色模型,用该模型对怀来的货运量进行预测,并对灰色模型进行了验证,最后确定了近期怀来公路货运量,同时也为交通部门制定相关政策和科学决策提供了强有力的依据。     

Stability in the inefficient use of forecasting systems: A case study in a supply chain company

Computer-based demand forecasting systems have been widely adopted in supply chain companies, but little research has studied how these systems are actually used in the forecasting process. We report the findings of a case study of demand forecasting in a pharmaceutical company over a 15-year period. At the start of the study, managers believed that they were making extensive use of their forecasting system that was marketed based on the accuracy of its advanced statistical methods. Yet most forecasts were obtained using the system’s facility for judgmentally overriding the automatic statistical forecasts. Carrying out the judgmental interventions involved considerable management effort as part of a sales & operations planning (S&OP) process, yet these often only served to reduce forecast accuracy. This study uses observations of the forecasting process, interviews with participants and data on the accuracy of forecasts to investigate why the managers continued to use non-normative forecasting practices for many years despite the potential economic benefits that could be achieved through change. The reasons for the longevity of these practices are examined both from the perspective of the individual forecaster and the organization as a whole.     

Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems

Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance capacity planning of complex product systems (CoPS), intended to value data that is routinely generated and stored, but often disregarded by MROs. State space formulations with multiplicative errors for the simple exponential smoothing (SES), Holt’s linear method (HLM), additive Holt-Winters (AHW), and multiplicative Holt-Winters (MHW) are assessed using real data, comprised of 171 maintenance projects collected from a major Portuguese aircraft MRO. A state space formulation of the MHW, selected using the bias-corrected Akaike information criterion (AICc), is integrated in a Decision Support System (DSS) for capacity planning with probabilistic inference capabilities and used to forecast the workload probability distribution of a future and unprecedent maintenance intervention. The developed tool is validated by comparing forecasted values with workloads of a particular maintenance intervention and with a model simulating current forecasting practices employed by MROs.     

CPFR中的联合预测研究

文章研究了联合计划、预测和补货（CPFR）中的联合预测流程,并建立了相关的预测模型。在建模的过程中,使用了状态空间方程来描述实际市场需求和观测到的市场需求（销售量）,并通过卡尔曼滤波来预测零售商下期的销售量．结合零售商库存策略,预测出零售商下期的订单量。     

木材加工厂原料需求量预测分析

木质原料需求量预测是木材加工企业库存控制的关键环节,甚至会直接影响到加工厂未来运营计划的整体规划。文中以福建某造板厂木材原料需求量为基础数据,建立组合灰色预测和三次指数平滑预测的预测模型,采用误差平方和极小化的方法确定单项预测模型最优权重,并对造板厂木材原料需求量加以预测和分析。     

Analytical derivation and behavioral interpretation of a model combining trip generation and distribution

The disjointedness of the planning sequence of trip generation and trip distribution is the main subject of this paper. We approach this disjointedness problem by analyzing the central properties of the independently discovered balancing methods of trip-distribution models in relation to two critical issues. First, in the current planning sequence, it is usual to start by forecasting how many trips will begin (production) and end (attraction) in each zone. This forecasting is done by estimating the production of trips independently from the attraction of trips and vice versa, and then forcing some mechanical balance of total trips being generated in the urban system. Second, in the same planning sequence, the output of this trip-generation process is the input to the next one (trip-distribution process): forecasting the matrix that describes the number of trips between each pair of zones. This forecasting is done in general by updating an equivalent obsolete matrix that was obtained from an origin-and-destination survey. The updating is generally accomplished by adjusting the outdated matrix with the so-called balancing factors. It is the purpose of this paper to support the balancing-factors approach in forecasting trip-distribution matrices with a methodological interpretation and to explain behaviorally the balancing factors; and in the process, to show the spatial interaction between trip production and attraction and the emerging need for simultaneous specification and estimation of the whole trip-generation process.     

The integration of forecasting and strategic planning

The paper analyzes the use of information in companies planning strategically versus those which are not. This contrast is used to build the case for developing strategic forecasting capability which focuses on a variety of environments, is proactive and interactive, and creates a need for different kinds of data bases and forecasting techniques.     

区间时间序列预测准确度探讨

区间时间序列在决策过程中提供重要的信息,特别是在经济发展、人口政策、规划管理或金融监管等方面,因此如何计算出预测区间的精确度成为一个重要议题。本文提出两种区间预测准确度分析的方法,通过估计预测结果的平均区间误差平方和及平均相对区间误差和,比较不同预测方法的优劣。并由预测区间与实际区间的重叠位置,充分说明预测方法所具有的有效性。这些分析预测区间准确度的方法,将为管理者提供更客观的决策空间。     

Modeling the demand and supply in a new B2B-upstream market using a knowledge updating process

Business-to-Business (B2B) services companies invest heavily in acquiring very expensive assets that they hire out to serve their clients (e.g. UPS buys huge warehouses and hires them out to companies), and hence they engage in careful long-term planning and forecasting, especially when it concerns a new market. It is interesting to note that the client-firms, on the other hand, decide to hire those assets based mostly on the prevailing short-term market forces. Hence, it is important for the companies which provide the assets for hire to also build the prevailing short-term market trends into their long-term forecasting and planning. In this paper, we develop a model for tracking these two simultaneously evolving and interacting patterns, namely the asset-availability (i.e. supply) and utilization (i.e. demand) patterns, in order to better understand the underlying processes, and thereby provide a basis for better forecasting. We test our models using three sets of data collected from the oil drilling industry, and find the proposed model to provide a good fit and forecasting efficiency.     

灰色理论在城市总体规划中的应用研究

在编制城市总体规划时收集的资料和信息往往是不完全和不对称的，很多数据需要通过科学的理论去建立合理的模型来推算，本文采用灰色理论建模，以GDP预测为例详细说明了灰色预测模型GM（1，1）的建模过程。该模型对于预测城市总体规划中水文、人口、用地规模、城市化水平、交通运输发展、公共交通以及客货运量等具有普遍指导意义。     

Demand forecasting and order planning for humanitarian logistics: An empirical assessment

Humanitarian aid organizations are most known for their short-term emergency relief. While getting aid items to those in need can be challenging, long-term projects provide an opportunity for demand planning supported by forecasting methods. Based on standardized consumption data of the Operational Center Amsterdam of Médecins Sans Frontières (MSF-OCA) regarding nineteen longer-term aid projects and over 2000 medical items consumed in 2013, we describe and analyze the forecasting and order planning process. We find that several internal and external factors influence forecast and order planning performance, be it indirectly through demand volatility and safety markup. Moreover, we identify opportunities for further improvement for MSF-OCA, and for humanitarian logistics organizations in general.     

FORMASY FOrecasting and Recruitment in MAnpower Systems

In this paper the tools are developed for forecasting and recruitment planning in a graded manpower system. Basic features of the presented approach are:
- the system contains several grades or job categories in which the employees stay for a certain time before being promoted or leaving the system,
- promotability and leaving rate for any employee depend on time spent in the job category and personal qualifications (like education, experience, age),
- recruitment is not necessarily restricted to the lowest level in the system,
- several planning aims and restrictions are allowed.
The approach is based on a generalized Markov model for the dynamic behaviour of an individual employee. A forecasting procedure and a recruitment-scheduling procedure are based on this Markov model.     

基于GM（1，1）模型的营口港物流需求预测

港口物流规划是港口城市发展现代物流的一项重要内容,其中至关重要的一项就是对港口物流需求进行分析和预测。文中根据营口港物流发展现状,建立了基于GM（1,1）模型的港口物流需求灰色预测模型,并对预测结果进行了检验,通过检验可知GM（1,1）模型有很好的预测精度;同时预测了2012—2015年未来4年的物流需求,为政府进行港口物流规划以及制定相关的港口物流产业政策,促进区域经济与港口物流的整体协调和健康发展提供必要的决策数据。     

Use and misuse of information in supply chain forecasting of promotion effects

Demand forecasting is critical to sales and operations planning (S&OP), but the effects of sales promotions can be difficult to forecast. Typically, a baseline statistical forecast is judgmentally adjusted on receipt of information from different departments. However, much of this information either has no predictive value or its value is unknown. Research into base rate discounting has suggested that such information may distract forecasters from the average uplift and reduce accuracy. This has been investigated in situations in which forecasters were able to adjust the statistical forecasts for promotions via a forecasting support system (FSS). In two ecologically valid experiments, forecasters were provided with the mean level of promotion uplift, a baseline statistical forecast, and quantitative and qualitative information. However, the forecasters were distracted from the base rate and misinterpreted the information available to them. These findings have important implications for the design of organizational S&OP processes, and for the implementation of FSSs.     

GIS在城市户外游憩规划中的应用——以海口市为例

作为一种技术手段,GIS为规划者提供了全新的视角和工具,可应用于城市户外游憩规划的游憩资源与设施清查与现状分析,社会经济技术指标统计计算,土地利用功能区划,预测分析,可持续性评估,辅助规划决策方面。文章以海口市为例对GIS在城市户外游憩规划中的应用作了案例研究。     

INPUT–OUTPUT ANALYSIS FOR BUSINESS PLANNING: A CASE STUDY OF THE UNIVERSITY OF SYDNEY

We present a multi-region input–output (MRIO) model of the University of Sydney embedded in the Australian economy, which forms the centrepiece of a new data-driven framework for strategic forecasting and planning of the University's financial operations. This framework incorporates both Leontief's well-known demand-pull, as well as Ghosh's supply-push exercise. It is therefore able to estimate the immediate financial implications for the University, and the economy-wide flow-on effects, for example as a result of changes in demand for courses by students, or as a result of supply-side changes such as wage increases. We report on recent scenario studies on the financial performance of the teaching and research functions of the University, and the lessons learned for management practice.     

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results.