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Applies time‐series forecasting, a traditional operations analysis methodology, to develop a forecasting procedure and ordering policy for a natural‐gas customer of Columbia Gas of Ohio, USA. Evaluates six time‐series methods and four operating policies against four commonly used measures of error and the cost consequences of error to the customer. Demonstrates that time‐series forecasting and decision theory developed by operations and applied in an actual industrial situation can become a powerful marketing technique. Provides further insights into evaluating forecasting models and ordering policies, demonstrating that introducing optimal planned bias is a robust decision‐making/forecasting approach within services. There are three parts to the study. The first is a straightforward testing of forecasting methods, using the forecasts as the natural‐gas ordering policy. Results vary depending upon how well forecasts are fitted to the data. For example, one inaccurate forecast with a poor fit incurs a penalty cost of $179,270, while the best forecast results in a penalty cost of $27,081. The second part evaluates two additional complex ordering rules with the same forecasting methods, further reducing the lowest cost to $17,709. The third part is a technical analysis reflecting a redesign of the study, demonstrating the difficulty of generalizing when characteristics of the underlying demand change. Concludes that the best forecasting model/operating policy is to use the very basic forecasting model of simple moving average (or the equivalent, first‐order exponential smoothing) combined with an optimal planned bias ordering policy, i.e. with the planned introduction of bias.

Research in the area of forecasting and stock inventory control for intermittent demand is designed to provide robust models for the underlying demand which appears at random, with some time periods having no demand at all. Croston’s method is a popular technique for these models and it uses two single exponential smoothing (SES) models which involve smoothing constants. A key issue is the choice of the values due to the sensitivity of the forecasts to changes in demand. Suggested selections of the smoothing constants include values between 0.1 and 0.3. Since an ARIMA model has been illustrated to be equivalent to SES, an optimal smoothing constant can be selected from the ARIMA model for SES. This chapter will conduct simulations to investigate whether using an optimal smoothing constant versus the suggested smoothing constant is important. Since SES is designed to be an adapted method, data are simulated which vary between slow and fast demand.

The Coronavirus (COVID-19) is one of the major pandemic diseases caused by a newly discovered virus that has been directly affecting the human respiratory system. Because of the gradually increasing magnitude of the COVID-19 pandemic across the world, it has been sparking emergencies and critical issues in the healthcare systems around the world. However, predicting the exact amount of daily reported new COVID cases is the most serious issue faced by governments around the world today. So, the purpose of this current study is to propose a novel hybrid grey exponential smoothing model (HGESM) to predicting transmission dynamics of the COVID-19 outbreak properly.

As a result of the complications relates to the traditional time series approaches, the proposed HGESM model is well defined to handle exponential data patterns in multidisciplinary systems. The proposed methodology consists of two parts as double exponential smoothing and grey exponential smoothing modeling approach respectively. The empirical analysis of this study was carried out on the basis of the 3rd outbreak of Covid-19 cases in Sri Lanka, from 1st March 2021 to 15th June 2021. Out of the total 90 daily observations, the first 85% of daily confirmed cases were used during the training, and the remaining 15% of the sample.

The new proposed HGESM is highly accurate (less than 10%) with the lowest root mean square error values in one head forecasting. Moreover, mean absolute deviation accuracy testing results confirmed that the new proposed model has given more significant results than other time-series predictions with the limited samples.

The findings suggested that the new proposed HGESM is more suitable and effective for forecasting time series with the exponential trend in a short-term manner.

When forecasting intermittent demand the method derived by Croston (1972) is often cited. Previous research favorably compared Croston's forecasting method for demand with simple exponential smoothing assuming a nonzero demand occurs as a Bernoulli process with a constant probability. In practice, however, the assumption of a constant probability for the occurrence of nonzero demand is often violated. This research investigates Croston's method under violation of the assumption of a constant probability of nonzero demand. In a simulation study, forecasts derived using single exponential smoothing (SES) are compared to forecasts using a modification of Croston's method utilizing double exponential smoothing to forecast the time between nonzero demands assuming a normal distribution for demand size with different standard deviation levels. This methodology may be applicable to forecasting intermittent demand at the beginning or end of a product's life cycle.

A method is developed for evaluating forecasting models with respect to both error and complexity in forecasting. Several types of forecasting accuracy measures (MSE, MPE, MAPE, Theil′s U‐Statistic and a loss cost function) are examined and the approach is illustrated using short‐term forecasting methods, and weekly and four‐weekly data. The approach can, however, be applied equally to immediate, medium‐ and long‐term forecasting.

This research examines the use of a number of time series model structures of a moderate allocation mutual fund, PRWCX. PRWCX was rated as the top fund in its category during the past five years. The fund invests at least 50% of its total assets that the fund manager believes that have above average potential for capital growth. The remaining assets are generally invested in convertible securities, corporate and government debt bank loans, and foreign securities. Forecasting the total NAV of such a moderate allocation mutual fund, composed of an extremely large number of investments, requires a method that produces accurate results. These models are exponentially smoothing (single, double, and Winter’s Method), trend models (linear, quadratic, and exponential) are Box-Jenkins models.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

This article presents a study of revenue forecasting in a Florida municipal government. Seven techniques, including the budget officers' judgmental approach, time series models, a deterministic model, and an optimized model, are employed with franchise and utility receipts in the Town of Davie. The authors found that simple time series models outperformed deterministic models and the judgmentally derived forecasts of local officials. Consistent with prior research, findings here suggest that the time series models are not only accurate, but also easy to implement and readily comprehensible by local officials.

Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization.

Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total.

From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods.

Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry.

This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods.

The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

Through a case study, this paper reports on a research project to develop a risk integrated methodology for forecasting the cost of electricity in a National Health Service (NHS) acute care hospital building. The paper is formed of two strands. Strand one presents a rationale for selecting an appropriate time series forecasting method and strand two looks at the implementation of probabilistic modelling of the forecasts generated in strand one. The results of the research revealed that the Holt‐Winters multiplicative forecasting method produced the most reliable forecasts. The probabilistic modelling of the forecasts revealed that after a pair‐wise comparison between data collected at the hospital used as the case study and data collected from NHS acute care trusts nationwide, the forecasts were most likely to belong to the Weibull distribution. The results could then be used as inputs into a whole life cycle cost model or as a stand‐alone forecasting technique for predicting future electricity costs for use in the NHS Trust Financial Proforma returns.