Forecasting, production planning, and control
Samrad Jafarian-Namin; Davood Shishebori; Alireza Goli
Abstract
The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the ...
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The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the advances in modelling capabilities. ARIMA is a popular model for describing the underlying stochastic structure of available data. Artificial Neural Networks (ANNs) can also be appropriate alternatives. In the literature, forecasting the temperature of Tehran using both techniques has not been presented so far. Therefore, this article focuses on modelling air temperatures in the Tehran metropolis and then forecasting for twelve months by comparing ANN with ARIMA. Particle Swarm Optimization (PSO) can help deal with complex problems. However, its potential for improving the performance of forecasting methods has been neglected in the literature. Thus, improving the accuracy of ANN using PSO is investigated as well. After evaluations, applying the seasonal ARIMA model is recommended. Moreover, the improved ANN by PSO outperforms the pure ANN in predicting air temperature.
Forecasting, production planning, and control
Obojobo Obukeajeta Donatus; Chima Uzorh
Abstract
Economic or local disruptions that affect organizations' production activities often result in unexpected losses. An excellent example is the recent COVID-19 pandemic disruption which affected many economies globally. This study presents a deterministic model and uses simple regression analysis to estimate ...
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Economic or local disruptions that affect organizations' production activities often result in unexpected losses. An excellent example is the recent COVID-19 pandemic disruption which affected many economies globally. This study presents a deterministic model and uses simple regression analysis to estimate the average condition for production losses. Its corresponding components' input resources impact the overall estimates for selected organizations in Nigeria. It is anticipated that variability in economic activities is always accompanied by unconventional stock returns whose behaviour indicates prevailing economic trends. Here we have looked at two organizations in the manufacturing sector as a case study; Nigerian Breweries and Nestle Nigeria, whose stock prices[X] upon analysis reveal that at[X]≤N30 and [X]≤N821 are estimated conditions for zero net profit for both organizations respectively. Therefore, for Nigerian Breweries, during the four quarters of the 2020 fiscal year, the following were assessed production losses,3.47 billion naira(Q1), 4.17 billion naira(Q2), 3.72 billion naira(Q3) and 0.68 billion naira(Q4) with a total of 12.04 billion naira annual estimated losses; with COGS,OPEX and SAEX having 39.6%,44.5% and 15.9% impact on the estimates. Nestle Nigeria records estimated production losses of 5.8 billion naira (Q1), 6.4 billion naira(Q2),4.2 billion naira(Q3), and -0.8 billion naira(Q4) (gain), resulting in a total 15.6 billion naira annual estimated loss; and COGS,OPEX, and SAEX having 45.9%, 48.2% and 5.9% impact on the estimates respectively. This implies, Selling and Advertising Expenses (SAEX) had the most negligible percentage impact on overall estimated production losses for both organizations compared to Costs of Goods Sold (COGS) and Operating Expenses (OPEX).This study, therefore, reiterates the position of other economic reports describing the adverse effects of the pandemic in Nigeria; while also serving as an investment analysis guide to potential investors..
Forecasting, production planning, and control
Akbar Abbaspour Ghadim Bonab
Abstract
Demand forecasting can have a significant impact on reducing and controlling companies' costs, as well as increasing their productivity and competitiveness. But to achieve this, accuracy in demand forecasting is very important. On this point, in the present study, an attempt has been made to analyze ...
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Demand forecasting can have a significant impact on reducing and controlling companies' costs, as well as increasing their productivity and competitiveness. But to achieve this, accuracy in demand forecasting is very important. On this point, in the present study, an attempt has been made to analyze the time series related to the demand for a type of women's luxury handbag based on a framework and using machine learning methods. For this purpose, five machine learning models including Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN), Discrete Wavelet Transform-Neural Networks (DWTNN), and Group Model of Data Handling (GMDH) were used. The comparison of the models was also based on the accuracy of the forecasting according to the values of forecasting errors. The RMSE, MAE error measures as well as the R, correlation coefficient were used to assess the forecasting accuracy of the models. The RBFNN model had the best performance among the studied models with the minimum error values and the highest correlation value between the observed values and the outputs of the model. But in general, by comparing the error values with the data range, it is concluded that the models performed reasonably well.
Forecasting, production planning, and control
Ejiroghene Kelly Orhorhoro; Andrew Amagbor Erameh; Rogers Ibunemisam Tamuno
Abstract
In this study, the effects of corrosion rate on post welded annealed heat-treated medium carbon steel in seawater was investigated. The medium carbon steel samples were butt-welded by using the Shielded Metal Arc Welding (SMAW) technique and, afterwards, heat treated by annealing at different annealing ...
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In this study, the effects of corrosion rate on post welded annealed heat-treated medium carbon steel in seawater was investigated. The medium carbon steel samples were butt-welded by using the Shielded Metal Arc Welding (SMAW) technique and, afterwards, heat treated by annealing at different annealing temperature was carried out. The microstructure of the unwelded and post welded heated samples was characterised by means of optical microscopy. The as received (control), unwelded and post welded annealed medium carbon steel samples were immersed in sea water for a duration of one hundred (100) days, and this was to stimulate the effect on equipment in offshore and food processing applications. Post welded heat treatment on the microstructure, weight loss and corrosion rate were evaluated. The results obtained showed an initial increase in both the weight loss and corrosion rate of samples up to 40 days and started decreasing afterwards. It was equally observed that the post welded annealed samples showed more corrosion activities than the un-welded annealed samples. Above and beyond, corrosion activity was more prominent in samples with the highest annealing temperature. More so, the unwelded annealed medium carbon steel showed a dispersion of coalescence cementite and ferrite grain while the post welded annealed medium carbon steel samples showed a martensite (light area marked by arrows) distributed in the ferrite (dark area) matrix.
Forecasting, production planning, and control
Adeniran Adetayo Olaniyi; Kanyio Olufunto Adedotun; Owoeye Adelanke Samuel
Abstract
Two years single moving average and simple exponential smoothing with smoothing constant of 0.9 were applied to forecast the 2018 demand for domestic air passenger in Nigeria. Also, the two methods of forecasting were evaluated and compared with Mean Squared Deviations (MSD) to determine which method ...
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Two years single moving average and simple exponential smoothing with smoothing constant of 0.9 were applied to forecast the 2018 demand for domestic air passenger in Nigeria. Also, the two methods of forecasting were evaluated and compared with Mean Squared Deviations (MSD) to determine which method gives the lowest deviation as it will produce best forecast for the year 2018 domestic air passenger demand in Nigeria. The study relied on data of domestic air passenger demand between the periods of the year 2010 to the year 2017. It was revealed that the MSD of two yearly single moving average gave the best year 2018 forecast as it has a lower MSD when compared to the MSD of simple exponential smoothing with the smoothing constant of 0.9. This study is useful in the planning process of an airport, airline, and other stakeholders involved in Nigeria’s air transportation. It will help to prevent problems of having excess air transport demand over air transport supply or having excess air transport supply over demand.