Stok Kontrol Benzetimi JAVA Uygulaması

Stok Seviyeleri Kontrol Yöntemleri isimli yazımda daha önce anlatmış olduğum ve  “Sürekli Gözden Geçirme Esasına Dayalı Stok Kontrol Sistemi ile İlgili Örnek Benzetim Algoritması” olarak paylaştığım algoritmaya biraz daha görsellik katmak ve performansını arttırmak adına biraz ekleme yaptım. Programın çalışma mantığını anlamak için bu konuyu inceleyiniz.

NetBeans IDE 8.1 ve JAVA GUI kullanarak aşağıda resmini görmüş olduğunuz program oluştrulmuştur. Ayrıca Java’nın Look And Feel özelliklerinden biri olan Nimbus kullanılmıştır.

Stok Kontrol Benzetimi

Iterasyon, minumum Q, maksimum Q, minumum R, maksimum R gibi kullanıcıdan girilmesini istenen değerler girildikten sonra hesapla butonuna basıldığında alt bölümde yer alan tablo alanında her bir Q ve R değeri için hesaplanan maliyetler gösterilecektir. Ayrıca Talep için Uniform dağılım ve sabit talep olmak üzere iki adet talep yöntemi programlanmıştır. Seçtiğiniz yöntem aktif olacak ve hesaplama buna göre yapılacaktır.

Elde Bulundurma Maliyeti ve Kayıp Satış Maliyeti dışında kalan bütün alanlarda tamsayı kullanılması zorunludur aksi halde program sizden bu değerleri düzeltmenizi isteyecektir.

Stok Kontrol Benzetimi

Programı çalıştırabilmek için bilgisayarınızda Java SE Development Kit yüklü olmalıdır. Değil ise tıklayınız.. Jar uzantılı dosyaları Java Development Kit ile çalıştırabilirsiniz. Continue reading

Productivity&Quality Issues in Automotive Company

Quality is about an excellent product or service that fulfills our expectation or requirement. These expectations are depend on the intended use and the selling price. When a product meets our expectation, we can consider the product has the quality. Quality can be improves by several method such as failure mode and effect analysis (FMEA), quality function deployment (QFD), and benchmarking. In order to improve the quality of a product or service, the problems that affect that quality need to be determined. In determine those problems; we may use the statistical process control (SPC) tool such as Pareto diagram and the cause-and-effect diagram. In this context, we had been evaluating the quality issues in Honda Production Line by using these tools. The evaluation process is done by using the data of defected parts for month April, May, June and July 2015. The Pareto chart constructed in order to determine the critical problem face and it is constructed using the stated data. The critical problem regarding the quality issues in Honda Production Line are determined as dented and humped of the parts. This two problems quite similar and thus only one combined cause-and-effect diagram constructed. Construction of this diagram is a way in define the root cause of those problems.

Since 15 November 2000, a partnership between Honda Motor Co. Ltd. of Japan, DRB-HICOM Berhad and Oriental Holdings Berhad resulted in the birth of Honda Malaysia Sdn Bhd, a company committed to offering the “Highest Customer Satisfaction in Malaysia”. Since then, Honda Malaysia has been a solid and aggressive player in the Malaysian automotive market. Each year, it progresses so IOrapidly that it set up a plant in Pegoh, Melaka. With this new plant, not only is Honda Malaysia optimistic of achieving high sales every year, it has further strengthened Honda’s reputation in Malaysia.

Honda Malaysia Sdn Bhd is one of the biggest companies in car manufacturing. Every day, Honda Malaysia Sdn Bhd produces approximately around 200 units of cars. In industry involving high target of mass production, established company such as Honda too are not able to avoid quality issue in production line. There are many factors affecting the issue of the quality and one of the major factors is the defect of components. Whenever defects were detected during production, operators’ job process needs to be interrupted in order to deal with the defects item either to repair or to sort it out to other section. Due to the situation, technically, every time defects were encountered, man-power that initially should be focused on the production will decrease and causing a delay to the production line.

Data were collected from my friend who made his industrial training with Honda Malaysia Sdn Bhd. From him, we acquire information such as losses, number and type of defects for each rejected components. Figure below shows example of form of data we obtained. Continue reading

Analysis for Bounces of the Ball using Factorial Design and Taguchi Method

This work is only for understanding Taguchi Method and Factorial Design, in this context, daily subject is selected like “bounces of ball” for making it easy to understand.

Each ball is designed with specific materials, making it appropriate for a particular sport. The balls also have different numbers of bounce that caused by several factors. To measure the number of bounce for the ball, an experiment was conducted by dropping the ball from difference height onto the ground. The heights that used for this experiment are 1 meter and 2 meter. The experiment was comparing a basketball and a football ball. The surface for the balls to bounce also affects the number of ball to bounce before stop. There are two types of surface that being considered in this experiment, rough surface (on grass) and smooth surface (on cement).

To get the accurate results, the experiments are repeated three times for each run. The numbers of ball is counted from when the ball is released until the ball stops bouncing. The experiment is conducted by the same student drop the ball and the others student count the numbers of bounce for the ball. The data obtained from experiment is analyzed by using Factorial method (2 levels and 3 factors) and Taguchi method (4 orthogonal arrays).

The objectives of this experiment are:

  • To design of experiment that has at least 3 factors.
  • To evaluate the response by using full factorial design and Taguchi method.
  • To evaluate the analysis by using Minitab software.

Conditions or scopes of the experiment are:

  • Types of the ball which are basketball or football ball.
  • The height from the ball released which is 1 meter or 2 meter.
  • Types of surface which are rough or smooth.

Factorial Design

Factorial designs can be analyze in certain levels and certain factors depend on the experiment itself. Factorial design usually been analyze with 2 level and 2 or more factor. This method will involve ANOVA table, main effect, main interaction and regression model.

For the 22   design geometry and test matrix:

The combination of these two geometry and test matrix used to solve the problem is shown as below:

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