Otomotiv endüstrisi için araç özelliklerini kullanarak proses sürelerinin hat bazlı tahmini

Dünyadaki üretim sektörleri arasında lokomotif görevi gören sektörlerden biri de otomotivdir [1]. Bu sektöre hizmet eden fabrikalarda günlük, hatta saatlik araç çıkış sayıları önem arz etmektedir. Araçların üretilip son kullanıcıya ulaştırılmasında, hem üretim hem de tedarik lojistiği kullanılmaktadır. Hattan çıkan araçların hammadde ve yarı mamullerinin iç lojistikte beslenmesindeki en önemli etkenlerden biri de araçların hatlarda geçirdikleri zaman ve üretim süreleridir. Her araç için farklı hatlardaki üretim hızının doğru belirlenmesi, fabrikanın planlamasını etkileyen faktörlerden biridir. Burada kullanılan klasik yaklaşım, her iş için belirli bir sürenin belirlenmesi ve bu sürelerin toplanarak hat sürelerinin bulunmasıdır. Fakat öngörülemeyen durumlar, daha önce gerçekleştirilmemiş işlemler ve arızalar nedeniyle, sürelerde sapmalar meydana gelebilmektedir. Beklenmeyen durumların gerçekleşmesi, otomotiv sektöründeki üretim sürelerinin oluşturduğu verilerin uç değerler alabileceği ve bu değerlerin de modelden çıkartılmasının gerekliliğini göstermektedir. Bu çalışmada klasik yaklaşımdan farklı olarak istatistik ve yapay zeka tekniklerinden regresyon, regresyon ve sınıflandırma ağaçları ile tam bağlı yapay sinir ağları kullanılarak kaynak, montaj, boyahane bölümlerinin araç proses ve tampon bölümlerinin bekleme sürelerinin tahmini gerçekleştirilmiştir. Yapılan tahminler için 61 farklı model oluşturulmuş ve montaj transit ile kaynak tampon üst kat dışındaki hatlarda en düşük ortalama mutlak yüzdesel (OMYH), ortalama mutlak ve ortalama karesel hatalara sahip tekniğin sınıflandırma ağaçları olduğu görülmüştür. Montaj hatlarında süre tahmini ortalama %7,42 OMYH ile elde edilirken, boyahane hatları için ortalama %21,24 OMYH ve kaynak hatlarında için ortalama %22,49 OMYH ile belirlenmiştir. Araca mahsus öznitelikler kullanıldığından, araçların bekletildiği tampon bölgelerde süre tahmininin montaj alt kat haricinde ortalama %82,89 OMYH ile gerçekleştiği tespit edilmiştir. Montaj alt kat tampon bölgesinin %1000 OMYH değerinden daha yüksek değer belirlenmesi, bu alanın süre tahmininin uygun olmadığını göstermiştir.

Anahtar Kelimeler:

Otomotiv endüstrisi, regresyon ve sınıflandırma ağaçları, çoklu doğrusal regresyon, yapay sinir ağları, proses süre tahmini

Line-based process duration prediction using the vehicles features for the automotive industry

The automotive sector is an industry that acts as one of the locomotive among the manufacturing sectors in the world [1]. In these factories, daily, even hourly vehicle production speeds are important. Both production and supply logistics are very important for the vehicles to be produced and delivered to the end user. One of the most important factors in feeding the raw materials and semi-finished products of the vehicles coming off the line in internal logistics is the time they spend on the lines and production times of the vehicles. The accurate determination of the production speed in different lines for each vehicle affects the planning of the entire factory. The traditional approach here is to determine a certain time for each job and to find the line times by summing up manually. However, as a result of unpredictable events, breakdowns and processes that have not been performed before, deviations in periods may occur. It has been observed that the data generated by process times in the automotive sector can take extreme values, therefore, it is important to remove them from the model. In this study, unlike the traditional approach, using regression, regression and classification trees, and fully connected artificial neural networks, which are among statistical and artificial intelligence techniques, the waiting times of the vehicle, process and buffer sections of the source, assembly, dye-house sections were estimated. 61 different models were created for the predictions and it is seen that the classification trees technique performed the lowest mean absolute percent (MAPE), mean absolute (MAE) and mean square errors (MSE) in the lines other than the assembly transit and the welding buffer upper floor. While the estimation of duration in assembly lines was obtained with an average of 7.42% MAPE, it was determined with an average MAPE of 21.24% for dye-house and 22.49% MAPE for welding lines. Since the features specific to the vehicle are used, it has been determined that the time estimation in the buffer zones where the vehicles are kept is realized with an average of 82.89% MAPE except for the assembly lower floor buffer. The determination of a value higher than 1000% MAPE of the assembly lower floor buffer showed that the duration estimation of this zone was not suitable.

Keywords:

Automotive industry, regression and classification trees, multiple linear regression, artificial neural networks, process duration prediction,

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