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To increase competitiveness with its competitors at the national, regional, and global levels, The Manufacturing company in this research improves the efficiency of the production process. However, the efficiency in welding did not reach the target. From the target of 97%, the actual efficiency was 95.9%. The late process dominates Line Stop by 1.7%. The late process is a strong indication of waste. The research was conducted using the Value Stream Analysis Tools (VALSAT) method, and it was found that there were 3 critical wastes, including motion 24.4%, overproduction 18.5%, and inventory 15.8%. With the Root Cause Analysis (RCA) method combined with Failure Mode and Effect Analysis (FMEA), 4 priority scales of root causes are obtained, namely: Medium part assembling system is carried out manually, the Storage system (packing style part) using iron pallets, Production system using big lot system, and the number of parts placement containers reach 800 units. The countermeasures are making a full auto hanger, changing the packing style from iron pallets to poly boxes and dollies, changing the production system to a small lot system, and making dolly common (1 dolly to install 8 part variants). By comparing the Value Stream Mapping conditions before and after, the final results are: 178 minutes of working time reduction, 58 activities reduction, 2 forklift usage reduction, and 2,306 m2 of keeping space. So that it can increase the efficiency of the underbody assembly process from 95.9% to 98.4%. This research supports the company policy in terms of increasing the company's competitiveness at the national, regional, and global levels. One of the improvements in this research is direct supply using dolly common, a benchmark item for Toyota affiliates in the Asia Pacific. [ABSTRACT FROM AUTHOR]

Study case in the research is a company engaged in automotive manufacturing that produces radiators. This year, there has been an increase in the demand for radiator production where company has not been able to meet this demand with the current condition of the production line, especially in the Styrofoam section. Therefore, it is necessary to have a Lean Manufacturing approach to increase productivity by reducing waste (waste). The method used is DMAIC (Define, Measure, Analyze, Improve, Control) and Value Stream Mapping (VSM). The flow of current state values is generally shown through Current State Mapping (CSM). Processing Based on the data, the Inner DMAIC is obtained, and the types of waste accepted are Over process. Next, the root causes of waste use the Fishbone Diagram and determine the appropriate improvement solution proposal using 5W+1 H tools. After that, make a comparison before and after repairs. And it can be seen through the flow of future condition values of FSM (Future State Mapping) so that there is a decrease in the production of cutting thick Styrofoam materials with cycle times from 614 seconds to 534 seconds and repeated reductions to 1 work step in the process of cutting thick materials. [ABSTRACT FROM AUTHOR]

The automotive sub-sector industries are experiencing obstacles during Covid-19 pandemic situation. One of the obvious problems is imbalanced production line due to out-of-control cycle time, whereby the cycle time difference is too sparse between workstations. This causes bottleneck and increase in overtime cost. To solve this problem, the Toyota Production System (TPS) and line balancing methods were applied to analyze and improve the production line. The initial analysis using TPS method shows minor improvement, whereby the line efficiency can be improved from 87.72% to 91.03%, balance delay can be reduced from 12.28% to 8.97%, and the smoothing index from 10.00 to 5.00 but the bottleneck problem still occurs. Then, further analysis using Helgeson Birnie line balancing method was applied resulted in a significant improvement with line efficiency 98.61%, balance delay 1.39%, and smoothing index 1.00. Simulation using promodel software shows there are 2173 quantity of finish goods can be produced in run time 890 minutes. [ABSTRACT FROM AUTHOR]

Satellite-based early warning of extreme atmospheres through the website has been used for various purposes such as early warning of a flood, early warning of strong winds, early warning of tidal wave height, and early warning of transportation. One of the network-based extreme atmosphere early warning systems is the Satellite Disaster Early Warning System (SADEWA). SADEWA obtains data automatically from the Automatic Weather Station (AWS) displayed on the website. The weather parameters displayed are temperature, humidity, and rainfall. Technology Acceptance Model (TAM) and Best Worst Method (BWM) seem appropriate enough to examine individual perceptions of SADEWA acceptance because there has been no similar research in this field. Data on the 50 perceptions of user behavior were analyzed using multiple linear regression and BWM to determine the factors that influence and also the level of user acceptance of SADEWA. There are 4 (four) factors that influence user acceptance of SADEWA, namely Perceived Usefulness (PU), Perceived Risk (PR), Attitude to Use (AU), and Environmental Influence (EI). The level of acceptance is in receiving SADEWA. [ABSTRACT FROM AUTHOR]

This study was conducted in the manufacturing industry which focuses on producing automotive spare parts products, one of which is the pipe center cross. In 2020, the monthly production target for pipe center cross was frequently not achieved and became a company obstacle due to waste of activity. For this reason, it is necessary to minimize the waste or activities as non-added value to the company. The value stream mapping (VSM) was applied in determining material flow and information flow about the pipe center cross production process, the waste assessment model (WAM) and value stream mapping analysis tools (VALSAT) were utilized for searching for waste, fishbone diagram for root cause analysis (RCA), and five whys in finding the solution towards improvement. Based on the proposed improvement, the production process time decreased by 21.76%. [ABSTRACT FROM AUTHOR]