A Dynamic Credit Index System for TSMEs in China Using the Delphi and Analytic Hierarchy Process (AHP) Methods

A high-quality credit index system is essential for technological small and medium-sized enterprises (TSMEs) to obtain financing from various institutions, such as banks, venture capital. Some attempts have made to construct the credit index system for TSMEs. However, the current credit index systems for TSMEs have placed too much emphasis on their financial ability with few prominent technological and talent indicators. Therefore, this study has proposed a dynamic credit index system for TSMEs in China using the Delphi and the Analytic Hierarchy Process (AHP) methods. This credit index system covers a wide range of indicators to measure the enterprises’ controller ability, operation and management ability, financial ability, and innovation capacity. This study made some contributions in the following aspects: (1) This study proposed a credit index system for TSMEs that highlights the main characteristics of technological innovation and talents of enterprises in China. (2) The credit index system is also highly adaptable as it can dynamically adjust the index weight according to the life cycles of TSMEs. (3) A case study of evaluating the credit of three TSMEs in China was selected to verify the feasibility and the effectiveness of this system. The results show that the credit index system constructed in this study provides a comprehensive and systematic model for evaluating the credit of TSMEs in China.The research was funded by Sichuan University and Chengdu Administration China (Sichuan) Pilot Free Trade Zone. And the APC was funded by Sichuan University

Manufacturing Quality Function Deployment: Literature Review and Future Trends

A comprehensive review of the Quality Function Deployment (QFD) literature is made using extensive survey as a methodology. The most important results of the study are: (i) QFD modelling and applications are one-sided; prioritisation of technical attributes only maximise customer satisfaction without considering cost incurred (ii) we are still missing considerable knowledge about neural networks for predicting improvement measures in customer satisfaction (iii) further exploration of the subsequent phases (process planning and production planning) of QFD is needed (iv) more decision support systems are needed to automate QFD (v) feedbacks from customers are not accounted for in current studies

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Pengukuran Efisiensi Pembangkit Listrik Dengan Menggunakan Two Stage Modeling Data Envelopment Analysis Dan Artificial Neural Network Studi Kasus Pembangkit Di PT X)

Pengukuran efisiensi industri manufaktur termasuk pembangkit listrik umumnya diukur melalui Overall Equipment Effectiveness (OEE) yang merupakan perhitungan terhadap Availability (ketersediaan waktu operasi sesuai kapasitas produksi), Performance (lamanya waktu yang dibutuhkan untuk menghasilkan produk) dan Quality (produk tanpa cacat yang dihasilkan). Perhitungan efisiensi pembangkit dengan menggunakan OEE menjadi sulit dibandingkan kesetaraan untuk masing-masing pembangkit mengingat pembangkit listrik yang akan diukur memiliki variasi yang cukup besar untuk kapasitas, teknologi, penggunaan energi primer dan lokasi yang berbeda pula. Penelitian terkait pengukuran produktifitas pembangkit umumnya menggunakan Data Envelopment Analysis (DEA) yaitu pendekatan non-parametric linear programming untuk mengukur efisiensi dan pemanfaatan kapasitas yang secara efektif mengidentifikasi batasan produksi untuk masing-masing Decision Making Unit (DMU). Meskipun sering digunakan sebagai alat untuk membandingkan efisiensi, DEA tidak memiliki kemampuan melakukan prediksi sehingga dibutuhkan kombinasi dengan metodologi yang dapat melakukan prediksi adaptif. Artificial Neural Network (ANN) memiliki kekuatan melakukan pemetaan nonlinier dan fungsi prediksi adaptif. Penelitian ini bertujuan menggunakan ANN yang dikaitkan dengan DEA dalam melakukan pengukuran efisiensi serta pemodelan untuk melakukan prediksi efisiensi pembangkit listrik di PT X agar dapat melakukan perbandingan efisiensi pembangkit dengan berbagai variasi tersebut. Untuk penelitian ini, DEA-ANN digabungkan sehingga kelebihan menggabungkan metode yang saling melengkapi dalam pengukuran efisiensi terpadu dan membuat model untuk memprediksi efisiensi pembangkit tersebut. Untuk pendekatan gabungan ini, model Charnes, Cooper dan Rhodes (CCR) dari DEA digunakan untuk mengevaluasi efisiensi setiap DMU dan untuk menangkap trend efisiensi setiap pembangkit. Berdasarkan hasil DEA tersebut, DMU akan dievaluasi dengan ANN untuk memprediksi skor efisiensi dan target output setiap DMU. Kerangka penelitian yang akan dihasilkan adalah sistem perbandingan dan pendukung pengambilan keputusan yang efektif yang menambahkan kemampuan prediksi adaptif terhadap efisiensi pembangkit tersebut. Pemetaan efisiensi pembangkit. Hasil pengukuran efisiensi dengan Two Stages DEA menggambarkan pemetaan pembangkit ke dalam 4 (empat) kuadran. Masing-masing kuadran akan menjelaskan kategori efisiensi masing-masing kuadran pembangkit untuk menentukan strategi dan kebijakan terkait alokasi anggaran investasi sehubungan dengan mempertahankan, meningkatkan atau menghapusbukukan pembangkit tersebut. Perbandingan efisiensi dengan DEA dan prediksi efisiensi dengan ANN memiliki korelasi R yang sangat tinggi dengan MAPE yang sangat kecil sehingga model prediksi ANN yang dibangun menjadi valid untuk digunakan sebagai alat pengukuran efisiensi pembangkit listrik. =============================================================================================================================== The performance measurement of manufacturing industries including power plants is generally measured through Overall Equipment Effectiveness (OEE) which is a calculation of Availability (availability of operating time according to production capacity), Performance (the length of time needed to produce a product) and Quality (product produced without defects). The performance calculation of the power plant by using OEE is difficult to compare because the power plant to be measured has a various variable like capacity, technology, primary energy usage and different locations. Research related to the power plant productivity measurement generally uses Data Envelopment Analysis (DEA), which is non-parametric linear programming to predict efficiency and capacity utilization that effectively identifies production limits for each Decision Making Unit (DMU). Although often used as a tool to compare performance, DEA does not have the ability to make predictions so it requires a combination of methodologies that can make adaptive predictions. Artificial Neural Network (ANN) has the power to perform nonlinear mapping and adaptive prediction functions. This study aims to use ANN that is associated with DEA in measuring and modeling performance to predict the performance of power plants in PT X in order to be able to compare the performance of the power plant with these variations. For this study, DEA-NN is combined approach so that the advantages of combining complementary methods in integrated performance measurement and modeling to predict the performance of the power plant. For this combined approach, the Charnes, Cooper and Rhodes (CCR) of DEA models are used to evaluate the efficiency of each DMU and to capture the efficiency trends of each power plant. Based on the results of the DEA, DMU will be evaluated by NN to predict the output and efficiency target of each DMU. The result of research framework is a system of comparison and support for effective decision making that have adaptive predictive capabilities to the performance of the power plant. Efficiency measurement using Two Stages DEA describes power plants mapping in to four quadrants. Each quadrant will explain power plant efficiency categories and describe power plant efficiency map to set up strategy and policy for investment budget allocation to keep, invest, or demolish the power plant. Efficiency comparison between DEA and efficiency prediction using NN has strong correlation when measure by Pearson Correlation Coeficient (R) and small Mean Absolute Percentage Error (MAPE) then Artificial Neural Network model is valid for power plant efficiency measurement