Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

Analysis of steel industries in thailand : Case study of production planning and control by determination of time limited free back orders.

รายงานวิจัย--มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร,2553The objectives of this research were to find the distribution of food demand for laying hens and to find the optimum amount of that food production which had minimum cost. The data consisted of customer purchase orders for the food of laying hens, price of food for laying hens, cost per unit for the food inventory, cost related to food of laying hens in which the food was out of stock, such as fine, overtime, urgent purchase for material. They were collected in 2008-2009 form a factory in Nakhonratchasima province. The collected data were analized in order to explore the distribution of the monthly food demand for the laying hens and to see the rate of inventory per unit. The results were used in the stochastic linear programming model for aggregate planning in which the optimum production, minimum cost, could be obtained. Programming algorithm in MATLAB and tools in Linprog software were used to get the solution. The distribution of the food demand for laying hens and the random numbers were used in the model. The study found that the distribution of food demand for laying hens every month has normal distribution (January to December). The monthly average amount of production from January to December were 10936.94, 12913.74, 11898.12, 13096, 15443.26, 14916.33, 10322.64, 10818.21, 11393.11, 11700.72, 9706.05 and 11702.05 respectively. The minimum total cost average for 12months was Baht 62,422,386.98.Rajamagala University of Technology Phra Nakho

การวิเคราะห์ระบบสินค้าคงคลังโดยใช้ตัวแบบสินค้าคงคลังเชิงสโตคาสติก: กรณีศึกษาอุตสาหกรรมเหล็กและเหล็กกล้าของไทย

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2555The objectives of this research were to select the most appropriate forecasting model for uncertain demand of hot-rolled steel and to find the purchase amount and the reorder point of some raw materials in order to minimize the total cost of production in an iron and steel production planning. The three forecasting models which were exponential model, ARIMS model, and the proposed Bayesian model were studied. An iron and steel factory in Samutprakarn province was a prototype for this study. Because of their uncertain high demand, the Steel H-Beam, Steel plate, steel round bar, and black steel round bar with various sizes were selected, and their parameters including demand rate, inventory cost, back order cost, and setup cost were collected. The study found that the proposed Bayesian forecasting model is the most appropriate. The predicted demand and all parameter values were used to find the purchase amounts and the reorder points of the raw materials under the (r,Q) policy of the stochastic inventory model.Rajamangala University of Technology Phra Nakho

การวิเคราะห์ผลผลิตมันสำปะหลัง และยางพาราในประเทศไทยอย่างมีประสิทธิภาพด้วยตัวแบบ GEE และ LMM ที่มีอิทธิพลเชิงพื้นที่รวมอยู่ด้วย

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2560The objectives of this research are to propose an efficient and proper model that fits the cassava and rubber yields data. A generalized estimating equation (GEE) and a linear mixed model (LMM) with spatial correlation following the conditional autoregressive model (CAR) were adopted. The dependent variables are the cassava and rubber yields collected each month in every province of Thailand. The factors considered are rainfall, averaged temperatures, and regions. The results from GEE and LMM show that the factors influencing on the cassava and rubber yields are rainfall, averaged temperature, and region. Both GEE and LMM fit the correlated data. The GEE is used to explain the influence of factors on the yields in all provinces while the LMM is used to explain the influence of factors on the yields in each province.Rajamangala University of Technology Phra Nakho

การวิเคราะห์สาเหตุของการผลิตสินค้าบกพร่องและเสียด้วยตัวแบบการถดถอยโลจิสติกส์แบบเบย์: กรณีศึกษาโรงงานผลิตชิ้นส่วนรถยนต์

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2557This research proposes a Bayesian logistic regression model which is applied to the data from autoparts manufacturing machines. Factors related to defective and bad products are investigated. The proposed model is compared with the logistic regression using maximum likelihood method for parameter estimation. The data were collected from 132 machines in an autoparts manufacturing factory. The research found that useful life, machine type 6, worker group 3 and 4, working step 1 and 2 influence to the risk of producing defective and bad products. When the useful life is increased by 1 month the risk of producing defective and bad products will be increased by 2.2%. The risk that the machine type 6 will produce defective and bad products is 4.078 times greater than the risk that the machine type will do. The risk that the worker group 3 will produce defective and bad products is 61.7% less than the risk that the worker group 12 will do. The risk that the worker group 4 will produce defective and bad products is 61.5% less than the risk that the worker group 12 will do. The risk that the working step 1 will produce defective and bad products is 2.831 times greater than the risk that the working step 4 will do. The risk that the working step 2 will produce defective and bad products is 13.8 % greater than the risk that the working step 4 will do. The parameter estimates from the Bayesian logistic regression are very close to the ones from the logistic regression using maximum likelihood method for parameter estimationRajamangala University of Technology Phra Nakho

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

การวิเคราะห์ความอยู่รอด : การศึกษาการออกกลางคันของนักศึกษา ปวส.สายช่างอุตสาหกรรม มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร วิทยาเขตพระนครเหนือ

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2551The objectives of the study were to fine the survival probabilities, the haazard rates of dropping out of the tecnical diploma level students at Rajamangala University of Technology phra Nakhon, North Bangkok Campus, and the factors influencing on the university in 2005. The study foud tht the frist semester, the survival probabilities of students in all divisions were high, ranging from 0.9210 to 1.00 For the second semester, the survival probabilities of students in all divisions ranged from 0/7820 to 0.8815 except Industrial Technology which had a maximum value of 0.9295 aned Metal Technology which had aminimum value of 0.6271. For the third and the fourth semester, there were 6 division which had survival probabilities ranging from 0.7142 to 0.7971. They were Tool and Die Making, Computer technology, Eletrical Technology, Electronics, Auromatic Machine, and production design. They 4 were Mechanical Tool Technology, Business Computer, Mechanical Power, and Industrial Technolog. the hazard rates of dropping of students in all divising were low, ranging for 0.00 to 0.0822. For the second semester, the hazard rates of dropping of students in all divisions ranged from 0.1260 to 0.2458 except Industrial technology which had a minimum value of 0.0730 and Metal Technology which had a miximum value of 0.5221. For the third and the fourth semester, the hazard rates of students in all division ranged from 0.1355 to 0.2962 excepy metal Technology which had the maximum value of 0.5932 and Tool and Die Making which had the value of 0.3364. The factors influencing on hazard rates of students drpout were place of previous school, region of previos school, and grade point average of previons school. The hazard rate of dropping of students graduation from school in the central region (not including Bankok Metropolitan Area) was 43% (0.562 times) less than the hazard rate of dropping of students graduation from schools in Bangkok metropolitan area. The hazard rate of dropping of students having grade aveage of 3.10-3.50 and 3.51-4.00 from previous school was 43.1% (0.569 times) and 63.7% (0.377 times) less than the hazard rate of students having grade point average of 2.00-2.50 respectively.Rajamangala University of Technology Phra Nakho

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

การวิเคราห์ความน่าเชื่อถือได้ในการทำงานของเครื่องจักร และหาสาเหตุของเครื่องจักรเสีย: กรณีศึกษาโรงงานผลิตชิ้นส่วนรถยนต์

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2557This research proposes reliability analysis in order to find the probability that a machine will work properly, the failure rates of a machine, and the factors related to the failure rates. The data were collected from 8 types of machines in an autoparts manufacturing factory. They consist of the times measured from the first day of the study until the machine produces defective product, workers and production steps. The study duration is 90 days. The results show that the probability that the machine type 4 will work properly more than days is 0.40, machine type 5 more than 68 days is 0.32, machine type 6 more than 72 days is 0.31, machine type 8 more than 85 days is 0.26, machine type 9 more than 109 days is 0.80, machine type 10 at more than 52 days is 0.38, machine type 11 more than 71 days is 0.32, and the probability that the machine type 12 will work properly more than 85 days is 0.26. The failure rate of the machine type 4 at day 51 is 6.61, machine type 5 at day 68 is 1.61, machine type 6 at day 72 is 0.77, machine type 8 at day 85 is 1.26, machine type 9 at day 109 is 1.31, machine type 10 at day 52 is 0.92, machine type 11 at day 71 is 1.24, and the failure rate of the machine type 12 at day 85 is 2.15. The factors influencing on the failure rates are worker group 2 and production step 3 at the significance of 0.05.Rajamangala University of Technology Phra Nakho