Effects of winter surface aeration on pelagic zooplankton communities in a small boreal foothills lake of Alberta, Canada

Holmes, Teslin G.; Tonn, William M.; Paszkowski, Cynthia A.; Scrimgeour, Garry J.

doi:10.1080/02705060.2014.956234

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…These three methods are widely applied in industries that demand forecasting [15,25,6,7,20]. However, in the past three years, the LME model has been broadly applied in various fields, such as the timber industry [30], medicine [9,49], and ecology [31], and to identify crucial influential factors. In addition, numerous studies have established models for forecasting [38,46].…”

Section: Forecasting Methodologymentioning

confidence: 99%

Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

Liao

Chiu

2017

Croat. Oper. Res. Rev.

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Abstract. In the past few decades, demand forecasting has become relatively difficult due to rapid changes in the global environment. This research illustrates the use of the make-to-stock (MTS) production strategy in order to explain how forecasting plays an essential role in business management. The linear mixed-effect (LME) model has been extensively developed and is widely applied in various fields. However, no study has used the LME model for business forecasting. We suggest that the LME model be used as a tool for prediction and to overcome environment complexity. The data analysis is based on real data in an international display company, where the company needs accurate demand forecasting before adopting a MTS strategy. The forecasting result from the LME model is compared to the commonly used approaches, including the regression model, autoregressive model, times series model, and exponential smoothing model, with the results revealing that prediction performance provided by the LME model is more stable than using the other methods. Furthermore, product types in the data are regarded as a random effect in the LME model, hence demands of all types can be predicted simultaneously using a single LME model. However, some approaches require splitting the data into different type categories, and then predicting the type demand by establishing a model for each type. This feature also demonstrates the practicability of the LME model in real business operations.

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Section: Forecasting Methodologymentioning

confidence: 99%

Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

Liao

Chiu

2017

Croat. Oper. Res. Rev.

View full text Add to dashboard Cite

show abstract

Section: Forecastingmentioning

confidence: 99%

Untitled

2017

Croat. Oper. Res. Rev.

View full text Add to dashboard Cite

In the past few decades, demand forecasting has become relatively difficult due to rapid changes in the global environment. This research illustrates the use of the make-to-stock (MTS) production strategy in order to explain how forecasting plays an essential role in business management. The linear mixed-effect (LME) model has been extensively developed and is widely applied in various fields. However, no study has used the LME model for business forecasting. We suggest that the LME model be used as a tool for prediction and to overcome environment complexity. The data analysis is based on real data in an international display company, where the company needs accurate demand forecasting before adopting a MTS strategy. The forecasting result from the LME model is compared to the commonly used approaches, including the regression model, autoregressive model, times series model, and exponential smoothing model, with the results revealing that prediction performance provided by the LME model is more stable than using the other methods. Furthermore, product types in the data are regarded as a random effect in the LME model, hence demands of all types can be predicted simultaneously using a single LME model. However, some approaches require splitting the data into different type categories, and then predicting the type demand by establishing a model for each type. This feature also demonstrates the practicability of the LME model in real business operations.

show abstract

Effects of winter surface aeration on pelagic zooplankton communities in a small boreal foothills lake of Alberta, Canada

Cited by 2 publications

References 18 publications

Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

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