scholarly journals Contribution for increasing the manufacturing companies productivity using a tracking and planning production program

2018 ◽  
Vol 184 ◽  
pp. 03006
Author(s):  
Iaroslav Francisc Pişec ◽  
Alin Pop
Keyword(s):  
Production Process ◽  
Real Time ◽  
Machine Tools ◽  
Critical Path ◽  
Planning Method ◽  
Manufacturing Companies ◽  
Production Program ◽  
The Cost ◽  
Complex Parts ◽  
Planning Production

In this paper is presented roughly a tracking and planning production program. The major challenge for manufacturing companies is to have the highest efficiency regarding the productivity. For this reason, the best way to achieve this is to track all elements from production process and to plan all phases as well as possible. The method used for planning production, is the Critical Path Method (CPM) because this kind of planning method determinates in advance the time of fabrications of complex parts. Using this tracking and planning production program, the companies can check the machine tools workload, can analyse the capacity, can assign the proper equipment for part’s fabrication, can estimate the delivering products time and also the cost of it. One of the advantages is that this information is provided on real time.

IMPLEMENTASI JOB ORDER COSTING METHOD DALAM PERHITUNGAN HARGA POKOK PRODUKSI MAP

2018 ◽  
Vol 4 (2) ◽  
pp. 43-55
Author(s):  
Ika Yulianti ◽  
Endah Masrunik ◽  
Anam Miftakhul Huda ◽  
Diana Elvianita
Keyword(s):  
Production Process ◽  
Quantitative Data ◽  
Production Cost ◽  
Qualitative Data ◽  
Cost Data ◽  
Method Calculation ◽  
Cost Of Production ◽  
The Right ◽  
The Cost

This study aims to find a comparison of the calculation of the cost of goods manufactured in the CV. Mitra Setia Blitar uses the company's method and uses the Job Order Costing (JOC) method. The method used in this study is quantitative. The types of data used are quantitative and qualitative. Quantitative data is in the form of map production cost data while qualitative data is in the form of information about map production process. The result of calculating the cost of production of the map between the two methods results in a difference of Rp. 306. Calculation using the company method is more expensive than using the Job Order Costing method. Calculation of cost of goods manufactured using the company method is Rp. 2,205,000, - or Rp. 2,205, - each unit. While using the Job Order Costing (JOC) method is Rp. 1,899,000, - or Rp 1,899, - each unit. So that the right method used in calculating the cost of production is the Job Order Costing (JOC) method

Modeling And Optimization Of The Construction Organization's Production Program

2020 ◽  
pp. 49-56
Keyword(s):  
Software Package ◽  
Target Function ◽  
Optimization Models ◽  
Labor Resource ◽  
Production Program ◽  
Construction Organizations ◽  
Source Data ◽  
Labor Resources ◽  
Construction Organization ◽  
Planning Production

In the article, the author considers the problems of complex algorithmization and systematization of approaches to optimizing the work plans of construction organizations (calendar plans) using various modern tools, including, for example, evolutionary algorithms for "conscious" enumeration of options for solving a target function from an array of possible constraints for a given nomenclature. Various typical schemes for modeling the processes of distribution of labor resources between objects of the production program are given, taking into account the array of source data. This data includes the possibility of using the material and technical supply base (delivery, storage, packaging) as a temporary container for placing the labor resource in case of released capacity, quantitative and qualification composition of the initial labor resource, the properties of the construction organization as a counterparty in the contract system with the customer of construction and installation works etc. A conceptual algorithm is formed that is the basis of the software package for operational harmonization of the production program ( work plans) in accordance with the loading of production units, the released capacities of labor resources and other conditions stipulated by the model. The application of the proposed algorithm is most convenient for a set of objects, which determines the relevance of its implementation in optimization models when planning production programs of building organizations that contain several objects distributed over a time scale.

ANALISIS PERHITUNGAN HARGA POKOK PRODUKSI DAN PENENTUAN HARGA JUAL ATAS PRODUK (Studi Kasus Pada PT Dasa Windu Agung)

2016 ◽  
Vol 1 (2) ◽  
pp. 183-190
Author(s):  
Dwi Urip Wardoyo
Keyword(s):  
Manufacturing Industry ◽  
Sampling Method ◽  
Manufacturing Companies ◽  
Test Analysis ◽  
Automotive Components ◽  
Convenience Sampling ◽  
Cost Of Production ◽  
The Cost ◽  
Keywords Cost

This study aims to determine the determination of the cost of production for products produced by PT. DWA. The Company is engaged in the manufacturing industry specialized in automotive components. Its activity is carried out through a series of production processes, so that expenses spent in the production will be calculated into the cost of the production sold. The population in this study were all manufacturing companies in Jakarta. Convenience sampling method selected one of the companies that get the confidence to assemble three national car project in Indonesia, namely Timor, Bakrie and Maleo. Test analysis used in this study is to test the calculation of full costing with job order costing. This study shows that (a) determination of the cost elements associated with the cost of production and (b) determining the cost of production on a product-based job costing with full costing approach. Keywords: cost of production, full costing

scholarly journals Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 943 ◽  
Author(s):  
Il Bae ◽  
Jaeyoung Moon ◽  
Jeongseok Seo
Keyword(s):  
Real Time ◽  
High Performance ◽  
Autonomous Driving ◽  
Planning Method ◽  
Dynamics Simulation ◽  
Driving Experience ◽  
Optimal Velocity ◽  
Planning Approach ◽  
Velocity Planning ◽  
Time Optimal

The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

scholarly journals Evaluation of StrepBSelectChromogenic Medium and the Fast-Track Diagnostics Group B Streptococcus (GBS) Real-Time PCR Assay Compared to Routine Culture for Detection of GBS during Antepartum Screening

2017 ◽  
Vol 55 (7) ◽  
pp. 2137-2142 ◽  
Author(s):  
Deirdre L. Church ◽  
Heather Baxter ◽  
Tracie Lloyd ◽  
Oscar Larios ◽  
Daniel B. Gregson
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Fast Track ◽  
Group B Streptococcus ◽  
Culture Method ◽  
Predictive Values ◽  
Content Type ◽  
Standard Culture ◽  
Group B ◽  
The Cost

ABSTRACTLife-threatening infection in neonates due to group BStreptococcus(GBS) is preventable by screening of near-term pregnant women and treatment at delivery. A total of 295 vaginal-rectal swabs were collected from women attending antepartum clinics in Calgary, Alberta, Canada. GBS colonization was detected by the standard culture method (Strep B Carrot Broth subcultured to blood agar with a neomycin disk) and compared to recovery with Strep Group B Broth (Dalynn Biologicals) subcultured to StrepBSelectchromogenic medium (CM; Bio-Rad Laboratories) and the Fast-Track Diagnostics GBS real-time PCR (quantitative PCR [qPCR]) assay (Phoenix Airmid Biomedical Corp.) performed with broth-enriched samples and the Abbottm2000sp/m2000rt system. A total of 62/295 (21%) women were colonized with GBS; 58 (19.7%) cases were detected by standard culture, while CM and qPCR each found 61 (20.7%) cases. The qPCR and CM were similar in performance, with sensitivities, specificities, and positive and negative predictive values of 98.4 and 98.4%, 99.6 and 99.6%, 98.4 and 98.4%, and 99.6 and 99.6%, respectively, compared to routine culture. Both qPCR and CM would allow more rapid reporting of routine GBS screening results than standard culture. Although the cost per test was similar for standard culture and CM, the routine use of qPCR would cost approximately four times as much as culture-based detection. Laboratories worldwide should consider implementing one of the newer methods for primary GBS testing, depending on the cost limitations of different health care jurisdictions.

scholarly journals Real-Time Train Wheel Condition Monitoring by Fiber Bragg Grating Sensors

2011 ◽  
Vol 8 (1) ◽  
pp. 409048 ◽  
Author(s):  
Chuliang Wei ◽  
Qin Xin ◽  
W. H. Chung ◽  
Shun-yee Liu ◽  
Hwa-yaw Tam ◽  
...  
Keyword(s):  
Fiber Bragg Grating ◽  
Real Time ◽  
Visual Inspection ◽  
Condition Index ◽  
Bragg Grating ◽  
Time System ◽  
Real Time System ◽  
Fiber Bragg Grating Sensors ◽  
Extensive Field ◽  
The Cost

Wheel defects on trains, such as flat wheels and out-of-roundness, inevitably jeopardize the safety of railway operations. Regular visual inspection and checking by experienced workers are the commonly adopted practice to identify wheel defects. However, the defects may not be spotted in time. Therefore, an automatic, remote-sensing, reliable, and accurate monitoring system for wheel condition is always desirable. The paper describes a real-time system to monitor wheel defects based on fiber Bragg grating sensors. Track strain response upon wheel-rail interaction is measured and processed to generate a condition index which directly reflects the wheel condition. This approach is verified by extensive field test, and the preliminary results show that this electromagnetic-immune system provides an effective alternative for wheel defects detection. The system significantly increases the efficiency of maintenance management and reduces the cost for defects detection, and more importantly, avoids derailment timely.

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