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Abstrak :
Tesis ini membahas tentang aplikasi dari metode penyusunan tata letak Systematic Layout Planning dan kemudian dievaluasi dengan Discrete Event Simulation menggunakan perangkat lunak Tecnomatix Plant Simulation untuk memberikan aternatif tata letak mesin pabrik yang lebih sesuai dengan produk yang ada di industri komponen alat berat saat ini dan lebih efisien dari sisi jarak perpindahan barang, waktu perpindahan barang, dan jumlah hasil produksi. Hasil penelitian ini menunjukkan bahwa aplikasi Systematic Layout Planning dan Discrete Event Simulation dapat memperpendek jarak perpindahan produk sebesar 32% setahun, mempercepat waktu perpindahan produk sebesar 27% setahun, dan meningkatkan jumlah hasil produksi sebesar 6% setahun.

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This study is about application of layout planning method the Systematic Layout Planning and then evaluated by Discrete Event Simulation method using software Tecnomatix Plant Simulation for getting more suitable machine layout to existing product in component industry of heavy equipment, and getting better efficiency on product moving distance, product moving time, and total product quantity. Study result shows that application of Systematic Layout Planning and Discrete Event Simulation could shortening product moving distance by 32% a year, shortening product moving time by 27% a year, and increasing total product quantity by 6% a year.

Abstrak :
Supply chain management merupakan aktivitas yang vital dalam mendukung proses produksi mobil di PT. XYZ. Pada laporan finansial fiscal year 2020, dilaporkan bahwa Inventory Turnover Ratio (ITR) di area part impor mengalami nilai yang rendah. Hal ini terjadi dikarenakan faktor eksternal, yakni terbatasnya frekuensi pengiriman part impor dan waktu pengiriman normal yang mencapai 20 hari. Setelah dilakukan FGD, rendahnya nilai ITR pada part impor berakibat meningkatnya nilai biaya pengiriman melalui udara (air freight) dikarenakan banyaknya Emergency order part impor, yang berdampak pada penurunan profit perusahaan. Pada beberapa penelitian sebelumnya, simulasi supply chain yang didukung oleh Big Data dapat melakukan optimasi supply chain dengan cara pengambilan keputusan strategis. Pada penelitian ini telah dilakukan studi terkait model simulasi part impor supply chain menggunakan simulasi kejadian diskrit, serta implementasi big data di PT. XYZ. Untuk menyelesaikan permasalahan tersebut, dibuat kerangka teoretis yang menghubungkan antara supply chain, Big Data, serta faktor penyebab terjadinya emergency order dalam terciptanya model simulasi dengan What-if analysis. Dalam proses pembuatan model simulasi supply chain, telah dilakukan analisa proses bisnis dan pengumpulan data menggunakan mix method dengan penyebaran kuesioner dan wawancara pakar. Big Data bertugas melakukan pengelolaan data supply chain, mengirimkan summary data berupa inisial stok, perencanaan produksi, dan perencanaan penerimaan barang harian ke dalam model simulasi dengan bantuan Cognos TM-1. Hasil simulasi yang ditampilkan pada Cognos TM-1, dijadikan sebagai referensi data dalam pengambilan keputusan strategik oleh manajemen, yakni dilakukan emergency order, normal order, production rearrangement, atau keputusan lainnya yang berkaitan dengan cost atau bisnis KPI lainnya dari perusahaan. Hasil Evaluasi yang didapatkan, proses data menggunakan big data hanya membutuhkan waktu 15-20 menit untuk mendapatkan data summary dengan akurasi 100% untuk data perencanaan produksi dan penerimaan barang, akan tetapi masih terdapat gap sekitar 130-unit mobil pada WIP (Work in Process) yang belum terkalkulasi. Untuk proses what if analysis, dapat dilakukan kurang dari 1 jam untuk mendapatkan keputusan oleh manajemen. ......Supply chain management is a vital activity for supporting the car manufacturing process at PT. XYZ. In the financial report fiscal year 2020, it was reported that the Inventory Turnover Ratio (ITR) on import part has a low value. It was caused by external factors, such as limited number of frequency delivery of import part and normal delivery time required 20 days. After conducted the FGD, the low value of ITR on import part has impact to air freight costs increase due to the large number of emergency orders, which resulted a decrease in company profits. In several previous research, supply chain simulations supported by Big Data, can optimize supply chains by making strategic decisions. In this research, it has been conducted study related with model simulation of import part supply chain using discrete-event simulation modeling, as well as the implementation of big data at PT. XYZ. To solve these problems, a theoretical framework is made that connects the supply chain, Big Data, and the factors that cause emergency orders to create a simulation model using what-if analysis. In the process of making a supply chain simulation model, business process analysis and data collection was carried out using a mix method with questionnaires and expert interviews, to determine the variables contained in the imported supply chain parts. Big Data is utilized to manage supply chain data, sending summary data of stock parts, daily stock requirements, and daily goods receipt into a simulation model inside Cognos TM-1 for the calculation and visualization process. The simulation results are displayed on the Cognos TM-1 which can be used as reference data in strategic decision making by management, whether to do emergency orders, normal orders, production rearrangements, or other decisions related to cost or other KPI business of the company. In evaluation results, data processing using big data only takes 15-20 minutes to get summary data with 100% accuracy for production planning data and goods receipts, but there is still a gap around 130 vehicle units in WIP (Work in Process) which has not been calculated. For the what if analysis process, it can take less than 1 hour to get a decision by management.

Abstrak :
Models that include a notion of time are ubiquitous in disciplines such as the natural sciences, engineering, philosophy, and linguistics, but in computing the abstractions provided by the traditional models are problematic and the discipline has spawned many novel models. This book is a systematic thorough presentation of the results of several decades of research on developing, analyzing, and applying time models to computing and engineering.