Determining the Tiers of a Supply Chain Using Machine Learning Algorithms

Abstract

Companies in the same supply chain influence each other, so sharing information enables more efficient supply chain management. An efficient supply chain must have a symmetry of information between participating entities, but in reality, the information is asymmetric, causing problems. The sustainability of the supply chain continues to be threatened because companies are reluctant to disclose information to others. If companies participating in the supply chain do not disclose accurate information, the next best way to improve the sustainability of the supply chain is to use data from the supply chain to determine each enterprise’s information. This study takes data from the supply chain and then uses machine learning algorithms to find which enterprise the data refer to when new data from unknown sources arise. The machine learning algorithms used are logistic regression, random forest, naive Bayes, decision tree, support vector machine, k-nearest neighbor, and multi-layer perceptron. Indicators for evaluating the performance of multi-class classification machine learning methods are accuracy, confusion matrix, precision, recall, and F1-score. The experimental results showed that LR and MLP accurately predicted companies (tiers), but NB, DT, RF, SVM, and K-NN did not accurately predict companies. In addition, the performance similarity of machine learning algorithms through experiments was classified into LR and MLP groups, NB and DT groups, and RF, SVM, and K-NN groups.

Pemodelan Klasifikasi Lama Waktu Pencapaian Jabatan Fungsional Lektor Kepala menggunakan Optimizer Parameter Support Vector Machine

“…Pengujian model dengan menggunakan k-fold cross validation pada implementasi setiap data berkesempatan menjadi data latih atau data evaluasi atau validasi sesuai jumlah fold atau iterasi (FUADAH, dkk, 2022). Hasil evaluasi yang diperoleh berupa nilai confusion matrik akurasi, presisi dan recall (Park, 2021). Adapun Persamaan untuk menghitung nilai confusion matrik multiclass meliputi akurasi, presisi dan recall seperti Persamaan (3) -Persamaan( 5)…”

“…Selanjutnya, kondisi False Negatif (FNi) adalah model tidak berhasil melakukan klasifikasi kelas negative ke-i sebagai kelas negative. True Positif (TPi) adalah suatu keadaan ketika model berhasil melakukan klasifikasi kelas positif ke-i secara benar (Park, 2021). Dalam menghitung akurasi, presisi, dan recall menggunakan komponen sebagaimana tersaji pada Tabel 2.…”

The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach

“…Since process development has become faster, industries are very well adapted to the just-in-time production system. [15,16,43] Order fulfillment…”

“…Any MS at present has several steps to follow, where the thorough inspection and timely production of goods can satisfy well-developed supply chain management techniques [15]. The processes range from the collection of raw material through to the supply of the final products to the consumers, and the monitoring techniques involved in these processes can be easily applied through the implementation of machine learning to the existing system [2].…”

Use of Artificial Intelligence Techniques in Supply Chain Risk Management

“…Park in his study, analyzed the data produced in the supply chain using a machine learning algorithm and tried to manage the flexibility of the supply chain in order to ensure the sustainability of the supply chain model consisting of manufacturers, distributors and wholesalers (Park 2021). Femi et al, in their study, stated that developing algorithms and analytical tools that predict where potential risks are in supply networks and operations using artificial intelligence techniques reduce financial risks (Femi et al 2021).…”