Spektrum Industri

Systematic Risk Analysis of Railway Component Quality: Integration of Failure Mode & Effect Analysis (FMEA) and Fault Tree Analysis (FTA)

2024-08-15T07:16:53+00:00

Quality assurance is a critical aspect in the production systems, affecting product quality and safety. Defects and failures of manufactured components will diminish overall product quality, which could vulnerably risk consumer safety. This study focuses on quality assurance analysis of train component manufacturing systems. According to the quality control data, the number of defects recorded was about 10-12, on average, for each wagon produced. The defect mainly occurred while making the underframes, car body, and even the small components. This led to the tardiness of product delivery for 1-2 months. This study aims to analyze non-conformance report data and identify the potential failure modes, potential effects, and root causes. To do so, we integrated systematically FMEA (Failure Mode and Effect Analysis) and FTA (Fault Tree Analysis). First, RPN (Risk Priority Number) score was calculated to determine risk priority. Second, Pareto analysis was performed to select defects that most contributing to overall failures, which were then analyzed using FTA to obtain root causes. The results show that 8 defects exceed the critical RPN score of 209. Materials and personnel are identified as two major contributor failure events from three selected defects. The recommendation for further improvements is provided based on various defect categories to prevent similar defects. The findings demonstrate that the combined use of FMEA and FTA is effective in identifying failures and root causes within complex and long production cycle systems.

Facility Layout Planning of Sheet Metal Working Industry Using Metaheuristics

2023-10-20T03:30:02+00:00

The design of facility layout in a production floor determines the level of effectiveness and efficiency of the production process. Errors in arranging the layout in the production floor can disrupt the continuity of the production process and prevent optimal results. Production activities that occur over a long period of time make any inaccuracies in layout planning result in significant losses. In companies with Job Shop production type, which is characterized by identical products and varied processes, the production flow changes with each product made. Based on these issues, this research aims to optimize the layout in a company engaged in sheet metal working industry using metaheuristic algorithms such as Simulated Annealing (SA), Large Neighborhood Search (LNS), Adaptive Large Neighborhood Search (ALNS), and Ant Colony Optimization (ACO). The best total distance results were obtained by the SA, LNS, and ALNS algorithms, with a total travelled distance of 897,171 meters and a facility arrangement of 7-5-6-4-3-2-1 or 1-2-3-4-6-5-7. Additionally, considering computation time, the SA algorithm is the best choice as it has the fastest computation time compared to other algorithms.

Enhancing Pharma Manufacturing Efficiency: Integrating Lean Six Sigma and Fuzzy FMEA for Waste Reduction

2024-09-26T02:38:36+00:00

In pharmaceutical manufacturing, inefficiencies such as waiting times, excessive material usage, and packaging defects can significantly impact productivity and quality. This study adopts a Lean Six Sigma approach, integrating lean manufacturing and six sigma methodologies, to systematically address these challenges. Through Process Activity Mapping (PAM), it was determined that value-added (VA) activities account for approximately 63% of total production activities, while non-value-added (NVA) and essential non-value-added (ENVA) activities contribute about 34% and 4%, respectively. Critical waste was identified using the genba shikumi method, followed by Failure Modes and Effects Analysis (FMEA) to determine Risk Priority Numbers (RPNs). Fuzzy logic was applied to prioritize the suggested improvements for more accurate risk assessments. Key recommendations based on Fuzzy RPN rank include, enhancing bulk product quality before printing, implementing rigorous inspections of the printing process, optimizing machine utilization, and adjusting production schedules using the Shortest Processing Time (SPT) method.

Successful phytoremediation of simulated steel rolling industry heavy metals-contaminated soils using a Sorghum bicolor cultivar from Riko, Katsina, Nigeria

2024-09-27T02:39:48+00:00

The release of hazardous heavy metals (HMs) from industries and other sources threatens ecosystems in Katsina, Nigeria and beyond. Bioengineering through microbially-assisted phytoremediation (MAP) is the best innovative alternative to these industries for remediating HMs contaminated environments. Sorghum bicolor (L. Moench) had been reported to be efficient in heavy metals phytoremediation. This study evaluated the ability of a fast-growing local cultivar of S. bicolor (rirrik’a/rirritsa/mota in Hausa) from Riko village, Jibiya L.G.A., Katsina State, Nigeria to remediate mesocosms simulating mixed HMs contamination obtainable at the soils of the defunct DANA Steel Rolling Mills, Katsina industrial site, to residual concentrations matching USEPA/EU limits. A chronosequential, nutrient-poor phytoremediation approach was employed to study the restoration of the contaminated soils in greenhouse experiments. The bioremoval of HMs in individual (0.05-10 g/L Cr, 0.04-1 g/L Cu, 0.08-1 g/L Pb and 0.02-1 g/L Zn) and mixed mesocosms was studied over 8 weeks, in multiple replicates, with positive and negative controls. ANOVA, Mann-Whitney and Kruskal-Wallis (with Dunn’s post-hoc) tests were used to statistically analyse the obtained data. The results confirmed an overall bioremoval of 66.67% of the HMs. Bioremoval rates were statistically similar across all HMs (one-way ANOVA: p = 0.64); with 69.48% of Zn, 67.46% of Cu, 63.34% of Cr and 58.33% of Pb bioremoved. The final residual HMs were within limits set by EPA/EU (Mann Whitney U test: p = 0.23). Study verified the status of the local cultivar of S. bicolor as a suitable agent for safe, effective phytoremediation of industrial heavy metal contaminated sites. Thus, its use is recommended for on-the-field phytoremediation of hotspots of HM contamination within the study area and beyond. The study also contributes towards sustainable and eco-friendly practices by using phytoremediation to manage environmental wastes from industrial pollution.

Fuzzy-FMECA: Right Solution for Jet Dyeing Machine Damage Prevention

2024-08-20T14:47:19+00:00

Jet dyeing machines, essential for producing high-quality and environmentally friendly textiles, face persistent issues with defects that lead to production stoppages, compromised cloth quality, and significant financial losses for companies. These challenges hinder operational efficiency and undermine the competitive edge of textile manufacturers in a rapidly evolving market. Jet Dyeing machines continue to innovate to produce high quality and environmentally friendly textiles, with the discovery of defects causing cloth production to stop, cloth quality to decline, and company losses. The Fuzzy-FMECA approach enhances accuracy and adaptability in identifying failure risks, improving maintenance for complex jet dyeing systems. This study aims to identify the root causes of jet dyeing machine damage for preventive maintenance design. Studies using robust fuzzy-FMECA can identify critical components of jet dyeing machines with a high degree of accuracy. This can improve machine reliability and reduce fabric quality failures. The dominant machine failures identified in jet dyeing components are leakage, short circuits, and installation errors. The Pareto analysis shows that leaks, tears, and short circuits are responsible for over 70% of total
failures. The most critical components include the main pump and electric socket, both with an RPN score of 7.42, representing a significant 30% of overall risk. Other high-risk components such as the steam pipe packing and heat exchanger steam pipe also have an RPN of 7.25. These findings indicate that over 60% of the failures arise from just a few key components. These findings have succeeded in identifying the critical components of the jet dyeing machine (main pump and socket) which have the highest potential risk of failure. The proposed preventive maintenance design can reduce these risks, but needs to be refined with consistent, competent and monitored inspections. The preventive maintenance design significantly mitigates risks, requiring ongoing refinement through regular, skilled, and supervised inspections to ensure optimal effectiveness.

Efficiency Evaluation in Indonesia's Quarrying Industry Using Variable Combinations DEA

2024-09-19T09:37:17+00:00

Data Envelopment Analysis (DEA) is a method considered to evaluate a company's performance. DEA applies multiplies the input and output variables for analyzing the efficiency but does not provide guidance in selecting those variables. As a rule, researchers use several methods. If the number of variables used is too many, it will affect the efficiency value. This will reduce the strength of the efficiency value, which can cause all DMU values to be efficient. DEA and variable selection are important in performance evaluation because DEA aids in determining relative efficiency, whereas variable selection guarantees that the evaluation is based on the most relevant and significant aspects. The purpose of this study is to suggest the variable combination method for subtracting the number of variables that will be utilized in implementing the DEA. The method used in this study is the Average Input Variable Combinations (VCs)-Variable Returns-to-Scale (VRS) DEA. The data were classified, defined, and processed with a view to computing efficiency scores and DMU classifications. The research result indicated that the proposed method (VCs-DEA) treats the variable reduction factor and the average calculation factor to obtain the final result of the efficiency score. These two factors contribute to the accuracy of the efficiency value. Some real-world implications of these findings, such as making better use of resources, streamlining operations, and coming up with new plans, Furthermore, the evidence may be used to benchmark performance as well as help decision-makers in creating more effective policy. This study finds that only 1 out of 12 DMUs is efficient (8%), while the remaining 11 are inefficient (92%). Indonesia quarrying establishment can be classified into 3 categories such as Optimal Category (S-Sand); Middle Category (LS-Lime-Stone; F-Feldspars; Gr-Granite; SA-Stone and Andesite; K-Kaolin; Q-Quartz; and G-Gravel); and Less Category (So-Soil; C-Clay; M-Marble; and O-Others).

Sentiment Analysis and Emotional Language as Predictors of Drug Satisfaction in User Reviews

2024-09-07T01:18:54+00:00

This study investigates how emotional expressions in user-generated drug reviews predict satisfaction ratings using sentiment analysis and emotion detection. By analyzing over 370,000 reviews from the UCI Machine Learning Repository, the study aims to bridge gaps in understanding the emotional drivers behind user satisfaction across different drug categories. For sentiment analysis, VADER, a Python-based lexicon tool, was used to categorize sentiment polarity, while the NRC Word-Emotion Lexicon provided a nuanced mapping of emotions like joy, sadness, and anger. Results reveal that emotions such as joy and trust are positively correlated with higher ratings, while anger and disgust are linked to lower satisfaction. However, the R-squared value (~0.043) indicates that emotions alone do not fully predict ratings, highlighting the need to consider additional factors like drug efficacy and side effects. This low R-squared value suggests that while emotions significantly influence satisfaction, other elements play a substantial role. The study's findings have critical implications for pharmaceutical companies and healthcare providers, suggesting the need for emotion-driven marketing strategies and improved patient support systems. Future research could explore more advanced machine learning models, such as BERT or GPT-based approaches, and investigate specific user demographics or drug side effects to enhance predictive accuracy.

Corn Agroindustry Supply Chain Management in Indonesia: Increasing Added Value and Competitiveness through the Hayami Method

2024-09-07T01:18:15+00:00

The agro-industry serves as a crucial intermediary for transforming agricultural societies into industrial ones, contributing to a balanced economic structure. This sector processes agricultural products through various methods, adding value and yielding higher profits than raw commodities. Corn, a staple in Indonesian agriculture, is predominantly cultivated with limited post-harvest activities, impeding rural economic growth. This study examines the supply chain management of the corn agro-industry to enhance value addition and competitiveness. Utilizing the Hayami method, the research identifies stages in the corn processing chain, including cleaning, grinding, filtering, and drying, and evaluates the value added at each stage. The analysis reveals that the supply chain involves multiple stakeholders, from farmers to retailers, and highlights the disparity in value addition among different actors. The study concludes that effective supply chain management, risk mitigation, and strategic interventions are vital for sustaining the corn agro-industry. Recommendations include extended mentorship for farmers and the implementation of efficient production practices to ensure long-term sustainability and economic growth.

Cost Optimization for Logistics Services: A Simulation Approach to Delivery Alternatives

2024-06-26T08:44:37+00:00

An essential activity in the delivery of goods by logistics service companies is how to deliver goods to consumers according to the agreed time with minimal costs. A case study was conducted on one of the logistics service companies in Bandung, which has an exciting feature: promising goods to consumers within 24 hours. The interesting thing about this company is that it uses the rest of the luggage of travelers traveling to the destination city by plane. In existing conditions, problems often arise, namely, goods do not reach customers according to the agreed time. This causes losses to the company because it must pay a late penalty. Therefore, the author designed several alternatives to meet freight forwarding in less than 24 hours. This study aims to optimize the cost of shipping goods from various alternatives by considering the delivery time of less than 24 hours. This study uses an experimental method with a system model to conduct simulations. Parameters use primary data from the company and secondary data from websites. The author designed two alternatives to shipping goods if no match was found with the traveler. The first alternative is to use air cargo at Bandung Airport. The second alternative is that if it is predicted that the goods will not reach the customer within 24 hours through Bandung Airport, they will be sent to Soekarno Hatta Airport Jakarta using a truck. A match with the traveler at the airport will be sought. The second alternative is also considered if there is no match with the traveler, then the delivery of goods uses air cargo. The simulation results provide a total cost for alternatives 1 and 2 of IDR 69,779,084.40/month and IDR 107,025,296, respectively, for goods that do not meet the delivery of less than 24 hours for alternative 1, namely nine items/month or 1% of the total shipment and alternative 2, namely 19 goods or 2% of the total delivery. The simulation in this study resulted in choosing the first alternative as the best alternative with the lowest cost.

Tabu Search Algorithm for Solving a Location-Routing-Inventory Problem

2024-09-26T02:37:04+00:00

Location decisions, inventory control, and vehicle routing are interrelated decisions. Inventory control decisions, such as order lot size and order frequency, affect both inventory and transportation costs. Failure to take inventory and transportation costs into consideration when determining location decisions can lead to suboptimality since they have a large impact on inventory and transportation costs. Therefore, how to decide locations, determine vehicle routing, and control inventory optimally, or location-routing-inventory problem (LRIP), becomes an important issue to design logistics systems. The objective of this paper is to develop a heuristic method base on Tabu Search (TS) to solve a LRIP. The contribution of this paper which is the heuristic method based on TS to solve a LRIP has never been developed before. TS is a type of metaheuristic. The success of TS is due to its ability to direct the search process so as not to get trapped in the local optimum, in large part, like many other metaheuristics. TS has been widely used to solve complex combinatorial optimization problems. The result of the computational comparison show that the heuristic method can provide a relatively small average gap of 3.20% compared to the optimal method. Application of the proposed heuristic is done in DKI Jakarta.