A time-phased order-point system in environments with and without demand uncertainty: a comparative analysis of non-monetary performance variables

This study explored how the video assistant referee (VAR) has inﬂuenced match performance variables at Fédération Internationale de Football Association (FIFA) World Cup tournaments. Comparative analysis was undertaken of matches played during the FIFA 2018 World Cup ( n = 64) tournament, where VAR was employed, and those played during the 2014 World Cup ( n = 64) tournament, where VAR was not employed. The following performance variables were recorded and analysed for each of the matches played: goals, penalties, corner kicks, yellow cards, red cards, offsides, playing time during the first half, playing time during the second half and total playing time. After the introduction of VAR, there were significant ( p < 0.05) increases in the number of penalties, as well as playing time during the first half, second half and total playing time. In contrast, a signiﬁcant ( p < 0.05) decline was observed in the number of offsides after VAR was implemented. The current findings have practical implications for improvement of VAR implementation guidelines at FIFA World Cup competitions.

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Comparative analysis of short-term demand predicting models using ARIMA and deep learning

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i4.pp3319-3328 ◽

2021 ◽

Vol 11 (4) ◽

pp. 3319

Author(s):

Halima Bousqaoui ◽

Ilham Slimani ◽

Said Achchab

Keyword(s):

Neural Network ◽

Deep Learning ◽

Comparative Analysis ◽

Convolutional Neural Network ◽

Demand Uncertainty ◽

Short Term Memory ◽

Real Life ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network.

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