A System Modelling Approach Optimization Process and Machine Utilization In Casting Plant Using Lean Manufacturing Simulation Model

Perbaikan berkelanjutan, reduksi dan eliminasi waste dalam proses bisnis menjadi salah satu aspek yang dilakukan agar dapat terus memiliki daya saing yang sustainable. PT. X saat ini sedang dalam proses melakukan transformasi, reduksi dan eliminasi NVA serta perbaikan berkelanjutan di sisi proses guna meningkatkan daya saing yang sustainable. penelitian ini bertujuan dalam menganalisis dan mengajukan formulasi perbaikan proses menggunakan metode kerangka kerja pemodelan sistem dan simulasi. Temuan dari penelitian diketahui bahwa peningkatan Utilisasi Dies rata-rata 82,82 % relative meningkat 36% dibanding simulasi sebelumnya (52,9%); dengan rata-rata output 21,04 pcs/jam relative meningkat 42% dibanding simulasi sebelumnya (12,9 pcs/jam), hal ini dengan melakukan improvement proses semi auto dalam proses eject produk yang sekaligus berdampak dalam pengurangan manpower, serta mereduksi loss time akibat lama proses pendinginan dengan sistem heat transfer conveyor system

Optimizing Rapid Granulator Machines Utilization Using Overall Equipment Effectiveness and Capacity Measurement in Injection Molding

Low productivity can be affected by several conditions like machine downtime, operator performance, inefficient capacity planning, etc. The most effective way to find out the solution to this problem is to calculate machine utilization. The purpose of this research is to optimize Rapid Granulator machines in the injection molding area by using Day in the Life Of (DILO) observation, Overall Equipment Effectiveness (OEE), and capacity measurement. The research will analyze the suitable calculation metric to measure Rapid Granulator machines utilization by comparing machine capacity, planned run time, planned preventive maintenance, and the number of machines needed. In the last two years, the expected efficiency rate is always increasing up to 95% with the average of PT. MT Indonesia utilization rate of 85%. However, there are no standards or unified way to measure a machine’s utilization rate and due to the huge variety of machinery not all of them have the calculation metrics. Further observation shows that Rapid Granulator machines that located in the Injection Molding area of PT. MT Indonesia has never been calculated and the low utilization rate can be seen after a quick time study. A sample of 16 machines is measured with only 23% of utilization rate in one shift operation time. The result then shows that the improvement activities to reduce the number of machines from 105 to 24 will increase the utilization rate up to 87% with the OEE score increasing from 1.8% to 39%. Thus, PT. MT Indonesia can minimize cost as expected in the cost calculation and optimize Rapid Granulator machines usage.

Using a Flexible IoT Architecture and Sequential AI Model to Recognize and Predict the Production Activities in the Labor-Intensive Manufacturing Site

Under the pressures of global market uncertainty and rapid production changes, the labor-intensive industries demand instant manufacturing site information and accurate production forecasting. This research applies sensor modules with noise reduction, information abstracting, and wireless transmission functions to form a flexible internet of things (IoT) architecture for acquiring field information. Moreover, AI models are used to reveal human activities and predict the output of a group of workstations. The IoT architecture has been implemented in the actual shoe making site. Although there is a 5% missing data issue due to network transmission, neural network models can successfully convert the IoT data to machine utilization. By analyzing the field data, the actual collaboration among the worker team can be revealed. Furthermore, a sequential AI model is applied to learn to capture the characteristics of the team working. This AI model only requires training by 15 min of IoT data, then it can predict the current and next few days’ productions within 10% error. This research confirms that implementing the IoT architecture and applying the AI model enables instant manufacturing monitoring of labor-intensive manufacturing sites and accurate production forecasting.

Biobjective Scheduling for Joint Parallel Machines with Sequence-Dependent Setup by Taking Pareto-Based Approach

Modern factories have been moving toward just-in-time manufacturing paradigm. Optimal resource scheduling is therefore essential to minimize manufacturing cost and product delivery delay. This paper therefore focuses on scheduling multiple unrelated parallel machines, via Pareto approach. With the proposed strategy, additional realistic concerns are addressed. Particularly, contingencies regarding product dependencies as well as machine capacity and its eligibility are also considered. Provided a jobs list, each with a distinct resource work hour capacity, this novel scheduling is aimed at minimizing manufacturing costs, while maintaining the balance of machine utilization. To this end, different computational intelligence algorithms, i.e., adaptive nearest neighbour search and modified tabu search, are employed in turn and then benchmarked and validated against combinatorial mathematical baseline, on both small and large problem sets. The experiments reported herein were made on MATLAB™ software. The resultant manufacturing plans obtained by these algorithms are thoroughly assessed and discussed.

Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Task scheduling on the cloud involves processing a large set of variables from both the task side and the scheduling machine side. This processing often results in a computational model that produces efficient task to machine maps. The efficiency of such models is decided based on various parameters like computational complexity, mean waiting time for the task, effectiveness to utilize the machines, etc. In this paper, a novel Q-Dynamic and Integrated Resource Scheduling (DAIRS-Q) algorithm is proposed which combines the effectiveness of DAIRS with Q-Learning in order to reduce the task waiting time, and improve the machine utilization efficiency. The DAIRS algorithm produces an initial task to machine mapping, which is optimized with the help of a reward & penalty model using Q-Learning, and a final task-machine map is obtained. The performance of the proposed algorithm showcases a 15% reduction in task waiting time, and a 20% improvement in machine utilization when compared to DAIRS and other standard task scheduling algorithms.

DEVELOPMENT OF A MULTI-OBJECTIVE PRODUCTION PLANNING MODEL FOR A SAWMILL

Based on the objectives set out for a Sawmill, a goal programming model was developed to simultaneously consider the production volumes goal, sales revenue goal, production cost goal, and machine utilization goal in order to develop its production plans for a horizon. The unwanted deviations from the goals served as the objective function to be optimized subject to the goals constraints, operational constraints, and non-negativity constraints. Three independent pre-emptive goal priority structures, GP1, GP2, and GP3, were considered with each prioritizing the objectives differently. The goal programming model was tested for its utility using data gathered from the mill to the three-goal priority structures. The results obtained indicated that, for GP1, the product volume goals for all products were achieved, and all but one, volume goals were achieved for both GP2 and GP3. The viability test showed that all priority structures used were profitable with GP1, GP2, and GP3 recording 1.099, 1.102, and 1.095 respectively. This indicates that the three priority structures considered are approximately profitable at the same level. The goal programming model for production planning offers the decision-maker a variety of options as to its application.

Operational Costs of Mid-Performance Forwarders in Czech Forest Bioeconomy

Cut-to-length (CTL) operations are expanding in Central European bio-economies. However, they are costly, so efficiency must be maximized. The objective of this study was to analyze direct operational costs of three forwarders from the year 2006 until 2019. Annual amortization, services, materials, and personnel costs were analyzed and compared through ANOVA, trends were analyzed through linear regression. Forwarders LVS 5, John Deere 1010, and John Deere 1110E were deployed in coniferous forest stands with a mean stem volume between 0.10 and 0.84 m3/stem, forwarding distance between 261 and 560 m. The machines forwarded between 4045 and 34,604 m3 of timber per year, over operational times between 490 and 3896 MH (machine hours)/year, reaching machine utilization between 58% and 89%, machine productivity between 3.5 and 12.3 m3/MH, and costs between 20.95 and 84.39 €/MH. The most substantial were personnel costs (35 to 66% of the total costs), followed by materials (14.9–27.1%), amortization (12.5–15.7%), and services (3.3–22.1%). Differences between total operational costs per m3 of machines with different engine powers were not observed.

Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

Machine Rate Estimates and Equipment Utilization

As mechanization increases, the percentage of the total cost of the logging operation due to equipment purchase and operation increases. This makes assumptions about machine life, machine maintenance costs, and fuel consumption more critical in understanding the costs of logging operations. For many years machine rate calculations have followed a fixed format based on the concept of scheduled and productive machine hours. When equipment utilization is less than 100%, the traditional machine rate calculation assumes that the machine continues to depreciate and machine wear occurs during the non-productive time at the same rate as during the productive time. This can lead to overestimates of the hourly cost of machine operation by effectively shortening the machine lifetime productive hours as the utilization decreases. The use of inflated machine rates can distort comparisons of logging systems, logging strategies, equipment replacement strategies, and perhaps the viability of a logging operation. We propose adjusting the life of the machine to account for non-productive time: machine life in years should be increased with a decrease in machine utilization, while cumulative machine life in hours remains the same. Once the life has been adjusted, the traditional machine rate calculation procedure can be carried out as is normally done. We provided an example that shows the traditional method at 50% utilization yielded a machine rate per productive hour nearly 30% higher than our modified method. Our sample analysis showed the traditional method consistently provided overestimates for any utilization rate less than 100%, with lower utilization rates yielding progressively increasing overestimates. We believe that our modified approach yields more accurate estimates of machine costs that would contribute to an improved understanding of the machine costs of forest operations.