The application of machine learning algorithms in the estimation of production lead times: A case study of a steel

Şeyma Duymaz; Ali Fuat Güneri

Makale detayları

The application of machine learning algorithms in the estimation of production lead times: A case study of a steel

Yazarlar:

Şeyma Duymaz | Ali Fuat Güneri

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Optimization in Manufacturing | Manufacturing and Industrial Engineering (Other)

Dergi:

Journal of Advances in Manufacturing Engineering Vol.5 Issue 1, pp.21-28

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322622

pISSN:

2717-7203

Anahtar kelimeler:

Özet:

 In companies with a diverse range of products, it can be challenging to regulate production times. Having an understanding of production lead times is crucial for addressing issues such as deadlines, cost, production scheduling, and customer satisfaction. It is challenging for the company to provide the customer with an accurate estimation of the time required to produce and deliver a product that has not been produced before. One of the advantages of knowing the production times is to be able to adjust the machines to use them more efficiently when preparing the production plan. In this study, input data were obtained from a steel construction company, including dimensions such as size, diameter, and weight. Additionally, the times required for the production of different products were measured. Based on these times, production times were estimated using machine learning algorithms, including Decision Tree, Random Forest, and Gradient Boosting. Consequently, precise predictions were generated with an accuracy rate of approximately 96.9. A test data set was then created with the objective of estimating the time required to produce a product that has never been produced. Additionally, the times of products that have not yet been produced were estimated. For each new product ordered, the machine must be adjusted and calibrated separately, which represents a significant loss of time and cost for the company. The objective of this research is to develop a model that can predict the time required to deliver a new product once it has been ordered. Furthermore, the aim is to enhance the efficiency of machine utilization.

 In companies with a diverse range of products, it can be challenging to regulate production times. Having an understanding of production lead times is crucial for addressing issues such as deadlines, cost, production scheduling, and customer satisfaction. It is challenging for the company to provide the customer with an accurate estimation of the time required to produce and deliver a product that has not been produced before. One of the advantages of knowing the production times is to be able to adjust the machines to use them more efficiently when preparing the production plan. In this study, input data were obtained from a steel construction company, including dimensions such as size, diameter, and weight. Additionally, the times required for the production of different products were measured. Based on these times, production times were estimated using machine learning algorithms, including Decision Tree, Random Forest, and Gradient Boosting. Consequently, precise predictions were generated with an accuracy rate of approximately 96.9. A test data set was then created with the objective of estimating the time required to produce a product that has never been produced. Additionally, the times of products that have not yet been produced were estimated. For each new product ordered, the machine must be adjusted and calibrated separately, which represents a significant loss of time and cost for the company. The objective of this research is to develop a model that can predict the time required to deliver a new product once it has been ordered. Furthermore, the aim is to enhance the efficiency of machine utilization.

 In companies with a diverse range of products, it can be challenging to regulate production times. Having an understanding of production lead times is crucial for addressing issues such as deadlines, cost, production scheduling, and customer satisfaction. It is challenging for the company to provide the customer with an accurate estimation of the time required to produce and deliver a product that has not been produced before. One of the advantages of knowing the production times is to be able to adjust the machines to use them more efficiently when preparing the production plan. In this study, input data were obtained from a steel construction company, including dimensions such as size, diameter, and weight. Additionally, the times required for the production of different products were measured. Based on these times, production times were estimated using machine learning algorithms, including Decision Tree, Random Forest, and Gradient Boosting. Consequently, precise predictions were generated with an accuracy rate of approximately 96.9. A test data set was then created with the objective of estimating the time required to produce a product that has never been produced. Additionally, the times of products that have not yet been produced were estimated. For each new product ordered, the machine must be adjusted and calibrated separately, which represents a significant loss of time and cost for the company. The objective of this research is to develop a model that can predict the time required to deliver a new product once it has been ordered. Furthermore, the aim is to enhance the efficiency of machine utilization.

 In companies with a diverse range of products, it can be challenging to regulate production times. Having an understanding of production lead times is crucial for addressing issues such as deadlines, cost, production scheduling, and customer satisfaction. It is challenging for the company to provide the customer with an accurate estimation of the time required to produce and deliver a product that has not been produced before. One of the advantages of knowing the production times is to be able to adjust the machines to use them more efficiently when preparing the production plan. In this study, input data were obtained from a steel construction company, including dimensions such as size, diameter, and weight. Additionally, the times required for the production of different products were measured. Based on these times, production times were estimated using machine learning algorithms, including Decision Tree, Random Forest, and Gradient Boosting. Consequently, precise predictions were generated with an accuracy rate of approximately 96.9. A test data set was then created with the objective of estimating the time required to produce a product that has never been produced. Additionally, the times of products that have not yet been produced were estimated. For each new product ordered, the machine must be adjusted and calibrated separately, which represents a significant loss of time and cost for the company. The objective of this research is to develop a model that can predict the time required to deliver a new product once it has been ordered. Furthermore, the aim is to enhance the efficiency of machine utilization.

Makale detayları

The application of machine learning algorithms in the estimation of production lead times: A case study of a steel

Yazarlar:

Şeyma Duymaz | Ali Fuat Güneri

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Optimization in Manufacturing | Manufacturing and Industrial Engineering (Other)

Dergi:

Journal of Advances in Manufacturing Engineering Vol.5 Issue 1, pp.21-28

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322622

pISSN:

2717-7203

Machine learning

production time

random forest regressor

regression

steel construction.

Sayı

Makale detayları

The application of machine learning algorithms in the estimation of production lead times: A case study of a steel

Yazarlar: Şeyma Duymaz | Ali Fuat Güneri

Yayıncı: Yildiz Technical University

Ana Başlıklar: Optimization in Manufacturing | Manufacturing and Industrial Engineering (Other)

Dergi: Journal of Advances in Manufacturing Engineering Vol.5 Issue 1, pp.21-28

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001322622

pISSN: 2717-7203

Machine learning

production time

random forest regressor

regression

steel construction.

Sayı

Yazarlar:

Şeyma Duymaz | Ali Fuat Güneri

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Optimization in Manufacturing | Manufacturing and Industrial Engineering (Other)

Dergi:

Journal of Advances in Manufacturing Engineering Vol.5 Issue 1, pp.21-28

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322622

pISSN:

2717-7203