Indikator adalah ukuran kuantitatif dan kualitatif yang menggambarkan tingkat pencapaian suatu sasaran atau tujuan yang telah ditetapkan. Fakultas Teknik Universitas Diponegoro yang merupakan Fakultas terbesar sekaligus sebagai penyumbang hasil riset terbesar setiap tahunnya telah mempunyai Renstra 2015-2020 sebagai pedoman. Didalam Renstra tersebut terdapat IKFT, yaitu indikator-indikator untuk mengukur kapasitas FT serta pencapaian FT setiap tahunnya. Indikator khusus untuk menilai kapasitas riset dan pencapaian riset masih minim dan masih perlu dikembangkan. Penelitian ini bertujuan mengembangkan indikator kunci untuk menilai kapasitas riset Fakultas Teknik Universitas Diponegoro. Kapasitas riset dapat ditinjau dari empat faktor utama keberlanjutan penelitian yaitu antara lain : faktor keuangan, manajemen organisasi, pendukung riset dan infrastruktur. Dalam penelitian ini terdapat sembilan indikator kunci yang dikembangkan dari jurnal dan sumber literatur lainnya. Terdapat 11 KPI sumber Renstra FT UNDIP, 9 KPI yang dikembangkan. Indikator tersebut digolongkan sesuai faktor keberlanjutan penelitian antara lain :  6 KPI faktor financial, 3 KPI faktor manajemen organisasi, 6 KPI pendukung riset dan 5 KPI infrastructure. Setelah dikembangkan dilakukan penilaian dengan terlebih dahulu melakukan pembobotan KPI menggunakan metode AHP dan kemudian dilakukan scoring dengan metode OMAX. Hasil dari penilaian tersebut didapatkan Indeks Pencapaian setiap kategori antara lain yaitu : financial 7,906, manajemen organisasi 5,628, pendukung riset 4,713 dan infrastruktur 8,171. Berdasarkan penilaian tabel OMAX setiap kategori didapatkan 7 KPI yang posisi lampu merah, 4 KPI yang posisi lampu kuning dan 9 KPI posisi lampu hijau. Tahap selanjutnya dirancang rekomendasi perbaikan dari beberapa literatur untuk meningkatkan kapasitas riset dan kemudian divalidasi dengan menggunakan metode delphi.

The design of performance assessment and measurement models is an important factor for any organization in an effort to achieve the expected goals in the future and as a tool to assess the success of the company. One of the most important performance measures is quality management. An assessment model that can be used to measure the performance of quality management is the Quality Management Maturity (QMM) Model. Until now, there has been no research related to the design of the QMM assessment model in the manufacturing industry sector. Until now, research related to the QMM Model only examines the construction industry sector, and the number is still relatively small. Even though, according to the 2020 Statistical Yearbook of Indonesia sources, the industrial sector that provides the largest contribution to Indonesia's national income in 2019 is the manufacturing industry sector. Therefore, the aim of this study is to design a QMM assessment/conceptual model in the manufacturing industry sector by identifying variables related to quality management. In this study, a literature study approach was used by examining ten journals that have a relationship with quality management. The results showed that the QMM model consisted of several levels, each of which was built by a number of quality management variables. Based on the results of the analysis of all available literature, the results of the variables at each level are obtained, namely at level 1 consisting of 2 variables, namely corporate-level quality and project level quality. Whereas at level 2, project quality level variables are divided into two variables, namely product quality and service quality. At level 3, corporatelevel quality is divided into two variables, namely external management and internal management.In addition, at level 3, product quality is divided into two variables, namely physical quality and perceived quality. Meanwhile, at level 4, each variable result from level 3 is further divided into a number of variables. In external management, there are six variables, in internal management, there are 28 variables, seven variables in physical quality, four variables in perceived quality, and 19 variables in service quality.