Erni Puspanantasari Putri 
1, Erwin Widodo2 , Jaka Purnama3 , Bonifacius Raditya Sri Pramana Putra4 , Agatha Hannabel Avnanta Puteri3
1, Erwin Widodo2 , Jaka Purnama3 , Bonifacius Raditya Sri Pramana Putra4 , Agatha Hannabel Avnanta Puteri3
1- Department of Industrial Engineering, Universitas 17 Agustus 1945 Surabaya , erniputri@untag-sby.ac.id
2- Department of Industrial Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
3- Department of Industrial Engineering, Universitas 17 Agustus 1945 Surabaya, Surabaya, Indonesia
4- Department of Informatics Engineering, Universitas 17 Agustus 1945 Surabaya, Surabaya, Indonesia
2- Department of Industrial Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
3- Department of Industrial Engineering, Universitas 17 Agustus 1945 Surabaya, Surabaya, Indonesia
4- Department of Informatics Engineering, Universitas 17 Agustus 1945 Surabaya, Surabaya, Indonesia
Abstract: (280 Views)
Micro- and small-scale industries (MSIs) are the pillars of Indonesia’s national economy. MSIs face several issues as their businesses grow. Performance evaluation is one way to identify MSI’s effectiveness. The research objective is to evaluate the MSI’s performance in East Java Province, Indonesia. It is an effort to improve the MSI's performance. The stepwise modeling approach (SMA) and data envelopment analysis (DEA) methods were applied to identify MSIs' effectiveness, determine the classification of inefficient MSIs, and formulate an inefficient MSI development strategy. In the existing SMA concept, the remaining variables in the END step are the selected variables (model X-Y). This study proposes that variables from the initial step to step n+1 are considered in creating efficiency score models. There are five proposed models, including model 4X-3Y, model 3X-3Y, model 3X-2Y, model 2X-2Y, and model 2X-Y. The research result indicated that the proposed ES model 3X-3Y is the best. 54% inefficient and 46% efficient DMUs make up the model 3X-3Y. Six cities and fourteen regencies make up the inefficient SMI classification. Cluster_A (50%) consists of four cities and six regencies. Cluster_B (25%) consists of two cities and three regencies. Cluster_C contains two regencies (10%). Cluster_D comprises three regencies (15%).
Keywords: Performance evaluation, Stepwise modeling approach, Data envelopment analysis, Micro- and small-scale industries
Type of Study: Research |
Subject:
Productivity Improvement
Received: 2024/04/22 | Accepted: 2024/12/1
Received: 2024/04/22 | Accepted: 2024/12/1
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