The paper is intended to develop a model to predict the number of damaged buildings and casualties due to earthquake using ANN (Artificial Neural Network). This model is expected to be able to generate the type and amount of relief supplies required by those affected during the emergency phase. This research develops ANN using supervised learning paradigm, and backpropagation learning algorithm. The applied ANN network architecture is a multiple-layer system, with 1 (one) neuron used in both input and output layer, and 95 (ninety-five) neurons used in the hidden layer yielding 0.99971 as the greatest value of the correlation coefficient. The output variable in this study is the earthquake impact consisting of six variables. While the input variables (predictors) in this study consisting of eight variables. The model in this study utilizes 123 seismic datasets, divided into 100 data (80%) for the training process and 23 data (20%) for the testing process. This research adds to the existing research and demonstrates the application of ANN in predicting the numbers of damaged buildings and casualties. The model is useful in supporting and strengthening preparedness and emergency relief activities due to earthquake disaster.

Industry 4.0 becomes more and more important in recent years because manufacturing industries are facing huge challenges in improving productivity for global competition. Industry 4.0 provides new opportunities to improve the resource and process efficiencies by combining information and communication technologies such as autonomous robots, internet of things, cloud computing, big data, augmented reality, additive manufacturing, etc. This integrated cyber-physical production system raise complexity within production system that implies new competencies required for industrial engineers which has been proven for years that the role of industrial engineer greatly influences a manufacturing industry’s success by designing, implementing, improving and optimizing a complex processes of an integrated systems that consist of people, money, knowledge, information, equipment, energy and materials. The competencies required in Industry 4.0 could be categorized under technical competencies and social competencies. A learning factory with real world system is often used to train students a new set of competencies by hands-on and direct experiences. Therefore, a learning factory can make a substantial contribution to competencies enhancement for industrial engineering students. This paper presents a conceptual of Industry 4.0 Lab as a learning factory for industrial engineering education as an enabler of students’ competencies in industry 4.0 era. A fully automated small production of filling bottle system integrating and demonstrating various Industry 4.0 concepts technologies is chosen. The learning modules and didactic approach are developed by integrating industrial engineering body of knowledge, Industry 4.0 value drives and Industry 4.0 levers in the creation of technical and social competencies required.

The impact of disasters can disrupt people’s lives, both natural and non-natural, resulting in human casualties, environmental damage, property loss, and psychological impact. Besides that, disasters that occur can also cause damage to health facilities, worship, education, and damage to homes, both severely, moderately, and lightly. The impact of disasters is so large, so a logistics warehouse is needed to handle the disaster. One of the countries prone to disasters, Indonesia which has the fourth largest population in the world with 34 provinces and 502 regions or cities. The purpose of this research is to determine the clustering of areas in Indonesia with a very high-risk, high-risk, moderate risk, low risk, and very low risk of disaster based on disaster data in Indonesian National Agency for Disaster Managementin 2010-2019 using K-Means calculations by Excel and the RapidMiner application. The results of both clustering methods are 6 cities that have a very high-risk index, 79 cities that have a high-risk index, 29 cities that have a medium risk index, 19 cities that have a low-risk index, and 369 cities have a very low-risk index. Thisresult can be considered for the construction of logistics warehouses for disaster management and K-Means method also can be used to know the clustering risk.

Our purpose in this paper is to determine the minimum standards of emergency goods to be made available to persons adversely affected by earthquakes to implement quick, accountable emergency response activities. Our results show the minimum standards for emergency earthquake relief goods well-suited to Indonesia’s local population. Our results are expected to be used to help define the types of emergency goods required following a disastrous earthquake. Our research includes information about a variety of emergency goods determined through questionnaires distributed to the earthquake-affected persons once designated as internally displaced persons (IDPs). Those answering questionnaires were asked to rate the importance of goods based on their experience during postearthquake evacuation.

This research was conducted to analyze the supply chain using packing plants. This study uses the Supply Chain Operations Reference (SCOR) and Analytical Hierarchy Process (AHP) to determine supply chain performance. There are three SCOR levels which are the determining factors in this study. The result of calculating the score of 23 factors which are the Metrics in Cement Packing Plant. The problem in the supply chain at the packing plant is 10 metrics, with an average performance indicator of 4 metrics, marginal of 5 metrics and poor of 1 metric. There are 8 strategy maps to improve Metrics with a score below 70, with the hope of improving the performance of supply chain as a whole.  Keywords: Supply Chain Management, Analytical Hierarchy Process, Supply Chain Operation Reference