Improving health care means spending more time with patients and less time with inventory

In most hospitals and medical establishments, about 35 percent of their budgets are spent on supplies and labor to manage the inventories, material, and information flows; typically, these are managed as multiechelon systems. The importance of reducing inventory costs, among others, has become relevant in today's health care management. This research applies just-in-time concepts to health care inventories.     

GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processes

This report discusses methods for forecasting hourly loads of a US utility as part of the load forecasting track of the Global Energy Forecasting Competition 2012 hosted on Kaggle. The methods described (gradient boosting machines and Gaussian processes) are generic machine learning/regression algorithms, and few domain-specific adjustments were made. Despite this, the algorithms were able to produce highly competitive predictions, which can hopefully inspire more refined techniques to compete with state-of-the-art load forecasting methodologies.     

A brief history of forecasting competitions

Forecasting competitions are now so widespread that it is often forgotten how controversial they were when first held, and how influential they have been over the years. I briefly review the history of forecasting competitions, and discuss what we have learned about their design and implementation, and what they can tell us about forecasting. I also provide a few suggestions for potential future competitions, and for research about forecasting based on competitions.     

安徽省城镇居民收入与消费结构的关联分析

采用灰色关联分析方法对安徽省2001~2006年间城镇居民收入和消费结构的关系进行了量化分析,得出了收入对各项消费支出的影响程度,从大到小依次是:居住、衣着、食品、文教娱乐、交通通讯、其他商品、医疗保健、家庭设备用品及服务。又运用灰色预测方法对2007~2011年间的收入水平和各项消费支出进行了预测,并且对预测值进行了灰色关联分析,得出了收入对各项消费支出的影响程度,从大到小依次是:医疗保健、衣着、交通通讯、食品、居住、文教娱乐、家庭设备、其他商品及服务。最后,针对消费结构的升级提出建议。     

Applicability of the M5 to Forecasting at Walmart

The M5 Forecasting Competition, the fifth in the series of forecasting competitions organized by Professor Spyros Makridakis and the Makridakis Open Forecasting Center at the University of Nicosia, was an extremely successful event. This competition focused on both the accuracy and uncertainty of forecasts and leveraged actual historical sales data provided by Walmart. This has led to the M5 being a unique competition that closely parallels the difficulties and challenges associated with industrial applications of forecasting. Like its precursor the M4, many interesting ideas came from the results of the M5 competition which will continue to push forecasting in new directions.In this article we discuss four topics around the practitioners view of the application of the competition and its results to the actual problems we face. First, we examine the data provided and how it relates to common difficulties practitioners must overcome. Secondly, we review the relevance of the accuracy and uncertainty metrics associated with the competition. Third, we discuss the leading solutions and their implications to forecasting at a company like Walmart. We then close with thoughts about a future M6 competition and further enhancements that can be explored.     

Regional hospital needs projection: An input-output approach

Steeply rising medical costs and the inadequacies of health care for the needy have recently stirred up unprecedented concern over the health industry in this country. In order to achieve more efficient allocation of medical resources and, hence, more equitable distribution of medical services, regional planning and regulatory authorities have been established and periodically reorganized in the health field in this country. This paper develops a systematic input-output model utilizing a clinical approach to project the impatient services needed for a given population by age, sex, residence and medical specialty. On the basis of empirical discharge data, relationships between regional demographic characteristics and diagnoses, between diagnoses and inpatient services, and between inpatient services and the production capacity of hospitals were derived. Given the projected regional population, the coefficients so derived are adjusted and employed to project future inpatient service needs. By comparing the existing to projected capacity, the location and investment decisions for hospitals can then be properly made, and the possibility of resource misallocation may be minimized. The applicability of this model is demonstrated in forecasting the hospital needs in the Kansas City Metropolitan Area, 1975–1995.     

Forecasting by factors,by variables,by both or neither?

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation. A forecast-error taxonomy for factor models highlights the impacts of location shifts on forecast-error biases. Forecasting US GDP over 1-, 4- and 8-step horizons using the dataset from Stock and Watson (2009) updated to 2011:2 shows factor models are more useful for nowcasting or short-term forecasting, but their relative performance declines as the forecast horizon increases. Forecasts for GDP levels highlight the need for robust strategies, such as intercept corrections or differencing, when location shifts occur as in the recent financial crisis.     

Forecasting with gradient boosted trees: augmentation,tuning, and cross-validation strategies: Winning solution to the M5 Uncertainty competition

Deep neural networks and gradient boosted tree models have swept across the field of machine learning over the past decade, producing across-the-board advances in performance. The ability of these methods to capture feature interactions and nonlinearities makes them exceptionally powerful and, at the same time, prone to overfitting, leakage, and a lack of generalization in domains with target non-stationarity and collinearity, such as time-series forecasting. We offer guidance to address these difficulties and provide a framework that maximizes the chances of predictions that generalize well and deliver state-of-the-art performance. The techniques we offer for cross-validation, augmentation, and parameter tuning have been used to win several major time-series forecasting competitions—including the M5 Forecasting Uncertainty competition and the Kaggle COVID19 Forecasting series—and, with the proper theoretical grounding, constitute the current best practices in time-series forecasting.     

Forecasting data vintages

This article provides a discussion of Clements and Galvão’s paper “Forecasting with vector autoregressive models of data vintages: US output growth and inflation.” Clements and Galvão argue that a multiple-vintage VAR model can be useful for forecasting data that are subject to revisions. They draw a “distinction between forecasting future observations and revisions to past data,” which focuses forecasters’ attention on yet another real time data issue. This comment discusses the importance of taking data revisions into consideration, and compares the multiple-vintage VAR approach of Clements and Galvão to a state space approach.     

Aggregate nursing requirement planning in a public health care delivery system.

In this paper, we present an aggregate nursing requirement planning model for inpatient services to provide inputs for preparing yearly budgets in a public health care delivery system. A forecasting system using autoregressive integrated moving average time-series models forms the basis of project demands for nursing hours by medical specialities. These projections along with the institutional constraints and patient care requirements are all incorporated in a linear programming model for assessing needs for permanent staff, overtime pay and contracting temporary help—by medical service, nursing skill level and time period (month). We also expand the model to evaluate the sizing of a pool of float nurses. The model is developed within the framework of routine managerial planning process of the system under study (vis-à vis, the data base and the organizational structure). Historical data are used to estimate input parameters for the model. The staffing needs generated from the model for 1978 are compared with the actual system performance.     

Improving resilience of the healthcare supply chain in a pandemic: Evidence from Europe during the COVID-19 crisis

The severe scarcity of critical medical supplies caused by the COVID-19 pandemic led to considerable procurement challenges in the healthcare supply chain (HCSC). As ensuring the availability of such supplies during disruptions is critical, the debate on how to increase supply chain resilience in healthcare has gained new momentum. We present empirical evidence from a multi-tier case study spanning nine European medical supplies manufacturers and hospital groups. Based on the resource dependence theory, we investigated procurement-related strategies to improve medical supplies availability. We conducted semi-structured interviews with 39 procurement and supply chain management experts and derived seven propositions on buffering and bridging approaches for managing evolving resource dependencies and thereby strengthening supply chain resilience in a pandemic. Overall, we confirm the resource dependence theory's applicability for explaining companies' mitigation measures in a pandemic disruption. We find that bridging measures within the healthcare supply base, such as offering procurement support for suppliers or leveraging long-term buyer-supplier relationships, are more effective for securing medical supplies than buffering measures. Complementing bridging with buffering, such as extended upstream procurement or resource sharing among hospitals, can lead to superior risk mitigation as capacities of the present supplier base may not suffice. Furthermore, we extend the resource dependence theory by showing that the severity of disruptions caused by a pandemic triggers new forms of buffering external to the HCSC. Both traditional and new buffering measures establish novel flows of medical supplies in the HCSC that can enable higher supply security in a pandemic.     

A robust support vector regression model for electric load forecasting

Electric load forecasting is a crucial part of business operations in the energy industry. Various load forecasting methods and techniques have been proposed and tested. With growing concerns about cybersecurity and malicious data manipulations, an emerging topic is to develop robust load forecasting models. In this paper, we propose a robust support vector regression (SVR) model to forecast the electricity demand under data integrity attacks. We first introduce a weight function to calculate the relative importance of each observation in the load history. We then construct a weighted quadratic surface SVR model. Some theoretical properties of the proposed model are derived. Extensive computational experiments are based on the publicly available data from Global Energy Forecasting Competition 2012 and ISO New England. To imitate data integrity attacks, we have deliberately increased or decreased the historical load data. Finally, the computational results demonstrate better accuracy of the proposed robust model over other recently proposed robust models in the load forecasting literature.     

灰色预测模型在煤矿成本预测中的应用

本文通过搜集大量现场资料,对煤矿企业成本的复杂性进行分析,阐述了比较先进、符合煤矿实际状况的灰色预测模型,并且将这一模型应用到H煤矿的成本预测。通过实证分析充分说明这一模型的有效性。     

Forecasting third-party mobile payments with implications for customer flow prediction

Forecasting customer flow is key for retailers in making daily operational decisions, but small retailers often lack the resources to obtain such forecasts. Rather than forecasting stores’ total customer flows, this research utilizes emerging third-party mobile payment data to provide participating stores with a value-added service by forecasting their share of daily customer flows. These customer transactions using mobile payments can then be utilized further to derive retailers’ total customer flows indirectly, thereby overcoming the constraints that small retailers face. We propose a third-party mobile-payment-platform centered daily mobile payments forecasting solution based on an extension of the newly-developed Gradient Boosting Regression Tree (GBRT) method which can generate multi-step forecasts for many stores concurrently. Using empirical forecasting experiments with thousands of time series, we show that GBRT, together with a strategy for multi-period-ahead forecasting, provides more accurate forecasts than established benchmarks. Pooling data from the platform across stores leads to benefits relative to analyzing the data individually, thus demonstrating the value of this machine learning application.     

构建医药市场信息系统 提升医药企业竞争力

随着我国新医改政策的出台与逐步实施,以及国外医疗卫生机构进入我国的医疗市场,给我国医药企业发展带来机遇的同时,也带来了竞争的压力和冲击。在激烈的市场营销环境中,医药企业为了获得持续生存和发展,就要不断地开拓市场,调查与预测医药市场信息以满足消费者日益增长和变化的消费需求。因此,如何构建医药市场信息系统,对未来市场走势做出正确的判断,成为医药企业在市场竞争中提升竞争力的重要手段。     

A comparison of the performance of different univariate forecasting methods in a model of capacity acquisition in UK electricity supply

Forecasting competitions have usually compared the accuracy of different forecasting methods across a number of different time series. This paper describes a study of the application of ten forecasting methods to a single time series: that of peak electricity demand in England and Wales. The performance measure used, however, is not one of the usual forecast ones, e.g., MSE or MAPS, but a managerial one in that the impact of different forecast methods on the profitability of the Central Electricity Generating Board for England and Wales is assessed using a financial simulation model. As well as examing the effects of forecast method on profitability the effects of two other factors, namely the use of a temperature corrected data series and the impact of log transformation of the data are considered. All these effects are both statistically and practically significant. The results are then examined from a different standpoint: specifically the extent to which the financial impacts of alternative forecast methods can be explained using a number of conventional forecast accuracy measures. This question is of major importance in applications since accuracy is one of the few easily measured characteristics of a potential forecasting method. It is concluded that much, though not all, of the results can be explained by accuracy considerations.     

30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial

The seed of this special section was the workshop celebrated at FUNCAS in Madrid in February 2019 “30 Years of Cointegration and Dynamic Factor Models Forecasting and its Future with Big Data”. In this editorial, we describe the main contributions of the 13 papers published within the special section towards forecasting in the context of non- stationary Big Data using cointegration or Dynamic Factor Models.     

Linear transformations of vector ARMA processes

Linear transformations of stochastic processes are used in many ways in economic analyses, for example when linear aggregates or subprocesses are considered. It is demonstrated that a linear transformation of a vector ARMA process is again an ARMA process and conditions for stationarity are given. Three different predictors for a linearly transformed process are compared. Forecasting the original process and transforming the predictions is superior to forecasting the transformed process directly and to transforming univariate predictions of the components of the original process. Conditions for equality of the three different forecasts are provided.