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Wiwik Sulistiyowati
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PROZIMA (Productivity, Optimization and Manufacturing System Engineering)
Published by Universitas Muhammadiyah Sidoarjo
ISSN : 25415115     EISSN : 25415115     DOI : https://doi.org/10.21070/prozima
Core Subject : Engineering,
Industrial & Manufacturing Engineering
Aim: to facilitate scholar, researchers, and teachers for publishing the original articles or review articles. Scope: Industrial Engineering included: Supply Chain Management Optimization and industry system Ergonomics Strategic Management Quality Engineering and Management Sustainability Experiment design Statistic Project Management Productivity Technology Management
Arjuna Subject : Teknik (semua kategori) - Tenik Industri dan Manufaktur
Articles 6 Documents
 1 
Search results for , issue "Vol 4 No 1 (2020): Juni" : 6 Documents clear
Clarisa Product Quality Control Using Methods Lean Six Sigma and Fmeca Method (Failure Mode And Effect Cricitality Analysis) (Case Study: Pt. Maspion Iii) Achmad Rifki Andriansyah; Wiwik Sulistyowati
PROZIMA (Productivity, Optimization and Manufacturing System Engineering) Vol 4 No 1 (2020): Juni
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/prozima.v4i1.1272

Abstract

PT. Maspion III is a company engaged in household appliances products, In carrying out its production process activities PT. Maspion III experiences various problems. Clarisa products are products that are found reject or defective. because it is caused by suboptimal quality control. This can be seen from the number of reject or defect products. This study aims to determine the type of waste that causes defects, determine the capability of the production process, and provide recommendations for improvement in the production process. The research methods are quantitative and qualitative approaches to lean six sigma and yang and FMECA (Failure Mode and Effect Criticality Analysis). lean six sigma is a systemic and systematic approach to identifying and eliminating waste. FMECA (Failure Mode and Effect Criticality Analysis) is used as a reference for companies to take corrective actions to identify product critical points in the production process. The results obtained are waste that affects product quality, namely waste defect, there are two highest defects, namely floi with a cumulative presentation of 51% and a breakage of 65%. In August the capability process is 1.5012, In September the capability process is 1.6818, In October the capability process is 1.3727, In November the capability process is 1.4275, In December the capability process is 1.4366
Controlling Vaname Shrimp (Litopenaeus Vannamei) Raw Material Inventories (Case Study at PT. Grahamakmur Ciptapratama Sidoarjo) Iffan Maflahah; Amalia Wahyu Pratiwi; Asfan
PROZIMA (Productivity, Optimization and Manufacturing System Engineering) Vol 4 No 1 (2020): Juni
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/prozima.v4i1.1273

Abstract

Raw material inventory control is used to place orders and ensure that shrimp raw materials arrive in a timely manner in accordance with consumer demand. This will expedite the course of the production process. This research was conducted at PT. Grahamakmur Ciptapratama Sidoarjo with the aim of knowing and analyzing the control of raw red ginger in frozen shrimp products. The method used is the Economic Order Quantity (EOQ) Method with the Lot For Lot (LFL) Technique. In addition, safety stock analysis, Maximum Inventory, warehouse capacity and Reorder Point analysis were carried out. The results of the study showed that inventory costs using the EOQ technique were lower than the company method or using the LFL technique. The EOQ technique produces an inventory cost of IDR 292,591.00 in 2017 with an order frequency of 105 times and in 2018 of IDR 289,750.00 with a booking frequency of 116 times. In the LFL technique inventory costs are more expensive, but the planning technique in the LFL method can be applied by companies in anticipation of raw material inventory if there is a small inventory. If the company applies the EOQ method, the company purchases raw materials in a larger amount, but the warehouse capacity in the company can still be met, this can be seen from the calculation of Maximum Inventory and warehouse capacity.
Balanced Scorecard Method: Analysis of Achievement of Manufacturing Services Companies Strategies in Pasuruan Muhamad abdul Jumali; Anita Kristina
PROZIMA (Productivity, Optimization and Manufacturing System Engineering) Vol 4 No 1 (2020): Juni
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/prozima.v4i1.1274

Abstract

Bussiness iicompetition iiin iimanufacturing iisector iihas iipotential iito iidevelop, iiespecially iiin iieastern iiarea iiof iiEast iiJavaProvince, iiso iicompetency iiin iiservice iisector iiis iineeded, iibest iilevel, iiresistant iito iicompetition, iioptimizing iiresources iiowned iioptimally iiand iiachieving ii iicompany iistrategic. iiThe iiselection iiof iimanufacturing iiservice iicompanies iias iiresearch iiobject iiis iibased iion iitrusted iitrack iirecords. iiAim iiof iithis iistudy iiis iito iiknow ii4 iiprespctive, iicustomer iiprespective, iiinternal iibusiness iiprespective iiand iigrowth iilearning iiwith iibalanced iiscorecard ii iirules. iiUsing iiresponden iiwith iiproportionate iistratified iirandom iisampling iimethod iiusing iisolving iiformula. iiResult iiof iistudy iiis iiindicate iithat iithere iiare iia iinumber iivariations iidominated iiby iitotal iiplanning iicost iimanagement iiprocess iicarried iiout iiin iiaccordance iiwith iiperformance iimeasurement iistandarts, ii4 iiprespective iiof iithis iiperformance iimeasurement iihas iibeen iiguaranteed iiin iiexternal iiaudit iisystem iiand iineeds iito iigrow iiand iidevelop iithe iicompany’s iiexistence.
Determination of Production Instrumentation Equipment Maintenance Intervals In the Paper Industry Nurma M. Hidayatulloh; Tedjo Sukmono
PROZIMA (Productivity, Optimization and Manufacturing System Engineering) Vol 4 No 1 (2020): Juni
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/prozima.v4i1.1275

Abstract

PT. XYZ is a manufacturing industry engaged in paper processing with afval raw materials. The problem faced is machine failure that occurs suddenly without predictability, this is because there is no scheduled maintenance (preventive main-tenance). The object of this research is focused on production instrumentation equipment. This study uses the Failure Mode and Effect Analyzer (FMEA) method to identify the causes of failure and the effects of these failures by determining the critical value of the component, namely the Risk Priority Number (RPN) which is the largest, then the Reliability Centered Maintenance (RCM) II Decision Worsheet method for determine maintenance intervals of production instrumentation equipment. Based on the results of RPN calculations in the FMEA method to determine the critical components of the Instrumentation equipment, namely the Control Valve, it can be seen that the highest total RPN value is found in three components, namely Restrictor with an RPN value of 390, Power Supply with RPN of 297, and also a Pilot Positioner. with an RPN value of 240. And with optimum maintenance intervals, among others, the Restrictor every 40 hours, the Power Supply every 41 hours, and the Pilot Positioner every 47 hours.
Design Cost Control in Risk Management with the Expected Money Value (Emv) and Hirarc Method at Pt Xyz Jawa Timur Surabaya Rahardi Wardhana; Lukmandono
PROZIMA (Productivity, Optimization and Manufacturing System Engineering) Vol 4 No 1 (2020): Juni
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/prozima.v4i1.1276

Abstract

This Operation of production area A6 workshop in PT XYZ can protecally lead to risk of accidents due to contact between worker with machines that operate like geka machine. The accidents caused by companies not yet know how the potentially dangerous and risks of working in the garage area lathe, as well as the lack of good implementation of procedures before entering the work area. From the accident required a determinan of hazard and risk analysis on production A6 workshop conducted by using HIRARC (Hazard Identification and Risk Assessment Control) and conduct a risk assessment to provide an assessment of the result of multiplication Severity and Likelihood that refers to the Department of Ocupational Safety and Health Ministry Guidlines of Human Resources Malaysia
Detection and Classification of Canned Packaging Defects Using Convolutional Neural Network Rindi Kusumawardani; Putu Dana Karningsih
PROZIMA (Productivity, Optimization and Manufacturing System Engineering) Vol 4 No 1 (2020): Juni
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/prozima.v4i1.1280

Abstract

Packaging is one of the important aspects of a product’s identity. The good and adorable packaging can increase product competitiveness because it gives a perception to the customers of good quality products. Therefore, a good packaging display is necessary so that packaging quality inspection is very important. Automated defect detection can help to reduce human error in the inspection process. Convolutional Neural Network (CNN) is an approach that can be used to detect and classify a packaging condition. This paper presents an experiment that compares 5 network models, i.e. ShuffleNet, GoogLeNet, ResNet18, ResNet50, and Resnet101, each network given the same parameters. The dataset is an image of cans packaging which is divided into 3 classifications, No Defect, Minor Defect, and Major Defect. The experimental result shows that network architecture models of ResNet50 and ResNet101 provided the best result for cans defect classification than the other network models, with 95,56% for testing accuracy. The five models have the testing accuracy above 90%, so it can be concluded that all network models are ideal for detecting the packaging defect and defect classification for the cans product.

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