Stream Analysis Research Articles

Discovering Unknown Non-Consecutive Double Byte Biases in RC4 Stream Cipher Algorithm

RC4 is one of the most widely used stream cipher algorithms. It is fast, easy and suitable for hardware and software. It is used in various applications, but it has a weakness in the distribution of generated key bytes. The first few bytes of Pseudo-Random Generation Algorithm (PRGA) key stream are biased or attached to some private key bytes and thus the analysis of key stream bytes makes it potential to attack RC4, and there is connection between the key stream bytes that make it weak and breakable by single- and double-byte biases attack. This work shows the analysis of RC4 key stream based on its non-consecutive double byte biases by using newly designed algorithm that calculates the bias in a standard time (seconds). The results are shown that the bias of RC4 keystream is proved and got the same results that were shown in the literature with less time and discover a set of new non-consecutive double byte biases in the positions (i) and (i+n). The analysis of 256 positions is required additional requirements such as supercomputer and the message passing interface environment that are not available in Iraq, therefore; the analysis is done for 32 positions.

Application of Lean Manufacturing to Minimize Waste in The Production Process of Tin Stabilizer

Lean Manufacturing concepts were being used by the manufacturing sector more and more in an effort to maximize resource usage, cut costs, and increase efficiency. The research dived into the underlying concepts of Lean Manufacturing and its various tools and strategies for identifying and removing waste, such as overproduction, defects, needless inventory, wasteful motion, transportation, overprocessing, and waiting periods. Lean manufacturing methods could be implemented in the continuous manufacturing environment to detect and reduce waste by using a process flow chart, value stream mapping tools, waste assessment model, value stream analysis tools, and the 5 why’s method. According to the findings of the study, the most prevalent waste in the manufacturing process was waste flaws, delays, and wasteful inventory. Process activity mapping (PAM), one of VALSAT’s extensive mapping technologies, was used to eliminate waste. The lead time was reduced by 12 days and 1 hour as a consequence of the improvement recommendations study. NVA activity was reduced from 75.3% to 59.5% based on the PAM study. Reduced lead time could result in a more efficient production process and lower production costs.

Development of Standardization Concepts for Packaging Machines

Packaging machines are designed to meet specific customer requirements and are usually produced on a one-off basis. To cater to consumer demands, packaging machines have become more and more complex. This extends the time taken for development and amplifies expenses. Therefore, novel approaches for standardization have to be explored beyond the established applications of mass production.To achieve this, an interdisciplinary value stream analysis is carried out within the relevant departments of a packaging machine company to identify specific constraints and requirements. These requirements are structured and documented to provide a foundation for targeted development of standardization concepts. Additionally, new possibilities are created to evaluate the effectiveness of standardization measures in the future and to establish a continual improvement process.The aim of this project is to establish a comprehensive strategy for standardization, enhanced communication, and data management that will be applied at all operational levels of the company. New communication methods, along with participatory and reflective methods, are expected to enhance acceptance by the employees and optimize business processes.

An Intelligent Edge Dual-Structure Ensemble Method for Data Stream Detection and Releasing

Edge intelligence is a critical enabler of intelligent application services in the Internet of Things (IoT). However, due to complex environmental factors, edge devices are subject to constant dynamic changes, which can result in security threats and sensitive information leakage. Therefore, it is essential to investigate data stream online analysis and detection strategies and implement an online releasing mechanism to ensure sensitive information is not leaked. Existing work rarely addresses these issues simultaneously or has poor performance, which poses a challenge. To address this challenge, we propose an intelligent edge dual-structure ensemble method (IEDSEM), consisting of three key components: data preprocessing, drift detection data analytics (IEDSEM-DDDA), and privacy-preserving data releasing (IEDSEM-PPDR). Data preprocessing is used primarily to enhance the quality of data streams to improve the performance of model learning. IEDSEM-DDDA involves three sequential operations: dynamic feature selection, model learning and selection, and online model ensemble deployment to achieve anomaly detection of online data streams. Meanwhile, IEDSEM-PPDR uses differential privacy and online optimization operations to achieve intelligent hierarchical protection of edge data. To validate the performance of our proposed IEDSEM method, we conducted two comprehensive simulation experiments on real data machines, verifying the accuracy of the concept drift component detection and the privacy optimization performance of the privacy-preserving component, respectively. Simulation results show that compared with several other advanced high-performance algorithms, our algorithm has over 99% accuracy in data stream analysis detection and more outstanding privacy-preserving ability.

LEAN SIX SIGMA APPROACH TO IMPROVE THE PRODUCTION PROCESS IN THE GARMENT COMPANY: A CASE STUDY

PT “X” is a company engaged in the garment industry. The company is facing problems that inhibit the production process. These problems comprise an excess of product defects beyond the specified limits, a considerable rate of product returns, delays in product shipment, and disorderly arrangements in storing raw material. This study aims to determine the root causes of problems in the PT “X” and provide recommendations for improvement using the concept of lean six sigma. The waste assessment model is used to identify the dominant waste, while the value stream analysis tools is a method used to analyze the causes of the dominant waste. From the research results, four dominant wastes are identified, namely waste defects, unnecessary inventory, waiting and unnecessary motion, with percentages of 27.02%, 15.92%, 14.32% and 14.0% respectively. The results of the analysis using mapping tools show the details of waste in each dominant waste. Proposed recommendations for improvement are made using the failure mode and effect analysis based on the highest risk priority number for each dominant waste. The recommendations proposed by the researchers include implementing a standard operating procedure and work instruction for the sewing process; scheduling machine maintenance using reliability centered maintenance II; planning and scheduling materials using material requirement planning; implementing line balancing; and designing name tags, visual displays, and storage rack layout.

Quantitative morphometric analysis of streams in extreme humid areas a case study of the Um-Mawiong river basin, Mawsynram, Meghalaya

Quantitative morphometric analysis of the drainage system is essential to characterising a watershed, as all the hydrologic and geomorphic processes occur within the watershed. Consequently, this plays a crucial role in understanding the geo-hydrological attributes of a drainage basin to the terrain feature and its flow patterns, thus enabling the estimate of the incidence of infiltration and runoff and other related hydrological characteristics of a watershed, which strongly impacts natural resource conservation. The study area selected is the Um-Mawiong River basin in Mawsynram, Meghalaya. The basin shows a dendritic pattern that highlights the homogeneity in the texture of the basin. Results suggest that the stream frequency of the basin is 19.10 km², suggesting a faster surface runoff and less infiltration. In addition, it has an Elongation ratio of 0.75 indicating an elongated basin shape. The current study demonstrates that the implementations of GIS techniques are trustworthy, efficient, and capable of managing extensive databases for managing river basins. The present study tries to analyse the linear, areal and relief aspects of the basin using a GIS environment and manipulated for different calculations. The analysis reveals that the total number of stream segments and length are maximum in first-order streams and decrease as stream order increases. The drainage density exhibits a high degree of positive correlation, i.e., 0.87, with its frequency suggesting an increase in stream population concerning increasing drainage density and vice versa.

Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study.

In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.

Audio-Video Analysis Method of Public Speaking Videos to Detect Deepfake Threat

Aim: The purpose of the article is to present the hypothesis that the use of discrepancies in audiovisual materials can significantly increase the effectiveness of detecting various types of deepfake and related threats. In order to verify this hypothesis, the authors proposed a new method that reveals inconsistencies in both multiple modalities simultaneously and within individual modalities separately, enabling them to effectively distinguish between authentic and altered public speaking videos. Project and methods: The proposed approach is to integrate audio and visual signals in a so-called fine-grained manner, and then carry out binary classification processes based on calculated adjustments to the classification results of each modality. The method has been tested using various network architectures, in particular Capsule networks – for deep anomaly detection and Swin Transformer – for image classification. Pre-processing included frame extraction and face detection using the MTCNN algorithm, as well as conversion of audio to mel spectrograms to better reflect human auditory perception. The proposed technique was tested on multimodal deepfake datasets, namely FakeAVCeleb and TMC, along with a custom dataset containing 4,700 recordings. The method has shown high performance in identifying deepfake threats in various test scenarios. Results: The method proposed by the authors achieved better AUC and accuracy compared to other reference methods, confirming its effectiveness in the analysis of multimodal artefacts. The test results confirm that it is effective in detecting modified videos in a variety of test scenarios which can be considered an advance over existing deepfake detection techniques. The results highlight the adaptability of the method in various architectures of feature extraction networks. Conclusions: The presented method of audiovisual deepfake detection uses fine inconsistencies of multimodal features to distinguish whether the material is authentic or synthetic. It is distinguished by its ability to point out inconsistencies in different types of deepfakes and, within each individual modality, can effectively distinguish authentic content from manipulated counterparts. The adaptability has been confirmed by the successful application of the method in various feature extraction network architectures. Moreover, its effectiveness has been proven in rigorous tests on two different audiovisual deepfake datasets. Keywords: analysis of audio-video stream, detection of deepfake threats, analysis of public speeches

Advancing Liquid-EM Techniques and Workflows for Operando Studies of Energy Materials

The need for clean, renewable energy has driven the expansion of research focused on new materials for electrocatalysts, fuel cells, and batteries. These materials need to be plentiful, inexpensive, and have high energy capacity operating lifetimes to power a growing consumer market that is seeking alternatives to fossil-based fuel sources.[1] However, energy materials often operate at the nanoscale, which involves structural or morphological changes that can only be observed using transmission electron microscopy (TEM). To get more insight in the nanoscale changes, operando TEM can be used to observe a catalyst or battery material under its native operating conditions.Operando TEM techniques enable dynamic, real-time imaging of a material’s nanoscale processes as they occur within their functional environment.[2] This enables researchers to simultaneously follow structural and morphological changes and correlate those to the material’s electrochemical behavior. Operando studies using liquid electrolytes can utilize liquid-EM. In this case, a sample is encapsulated in a liquid environment between electron transparent membranes, which enables researchers to study reactions as they occur in an electrolyte environment.[1] This technique has enabled researchers to better understand the mechanistic pathways that can result in electrocatalyst deactivation [3,4] battery capacity fading [5-7]and identify targets within these materials to improve their performance.[8,9,10] However, as operando TEM combines multiple challenging topics, e.g. battery studies, electrolyte degradation, radiolysis and dose management, it has so far been a laborious undertaking for any one researcher alone.Successful operando liquid-EM experiments are challenging due to many factors, beginning with experiment design and workflow, and culminating complex analysis of multiple streams of large, often cumbersome, datasets. Here, we have set out to address and develop hardware and software tools which mitigate some of the most common challenges associated with operando liquid-EM electrochemical studies. Using a machine vision software platform called AXON, a holistic, workflow-driven approach to address key challenges and pain points associated with in-situ and operando TEM studies has been developed. New hardware and MEMs-based features integrated into the liquid-EM solution, Poseidon Select (Figure 1A), include the ability to perform electrochemical studies beyond room temperature, and improved stabilization of electrochemical signals for correlation with bulk studies. Moreover, adding a Luggin-Haber metal probe for electrical measurements allows for the use of any reference electrode compared to the limitation of a pseudo-reference electrode, e.g. platinum (Figure 1B).[11] On top of this, AXON uses learning algorithms for improved stabilization and tracking of dynamic samples, accurate dose quantification and management, consolidation of experimental parameters and metadata, and an intuitive, free-to-use, visual data analysis tool, AXON Studio.In this talk, examples of liquid-EM microscopy work for energy materials will be shown, as shown in Figure 1C-D. In this figure, copper nanograins were used to convert CO2 to multicarbon products while observing the morphological changes to the nanograins over time and simultaneously observing the electrochemical changes in the sample. Moreover, explanations and examples will be shown on how machine-vision software solutions can improve the workflow of electrochemical in-situ experiments. These workflow methods elevate the operation of in-situ experiments so that any one researcher can now operate microscope and in-situ cell simultaneously.[1] Yang, Y. et al. (2021) ACS Catalysis 11, 1136–1178.[2] Chenna, S. et al. (2012) ACS Catalysis, 2, 2395–2402.[3] Shi, F. et al. (2020) Chem, 6, 2257–2271.[4] Impagnatiello, A. et al. (2020) ACS Applied Energy Materials, 3, 2360–2371.[5] Bhatia, A. et al. (2021) Small Methods, 2100891.[6] He, K. et al. (2018) Nano Energy, 49, 338–345[7] Sasaki, Y. et al. (2021) Journal of Power Sources, 481, 228831.[8] Pu, S.D. et al. (2020) ACS Energy Letters, 5, 2283–2290.[9] Balaghi, S.E. et al. (2021) ACS Applied Materials & Interfaces, 13, 19927–19937.[10] Yang, Y. et al. (2023) Nature, 614, 262-270[11] Choudhary, S. et al (2022) Journal of the Electrochemical Society, 169, 111505 Figure 1

Social Consumer Relation Management Using Social Media as a Marketing Scheme in University

Social media or called as ‘socmed’ is an online media that is frequently used by humans to improve in activities, which include promoting study programs of university. Most universities require a marketing scheme to promote certain study programs by using socmed. This study aims to investigate marketing strategies of universities using social costumer relation management (SCRM) and socmed. This is a qualitative study with a narrative model design. This study included about 2000 students from universities in South Sumatra, Lampung, Bengkulu, and Bangka Belitung, Indonesia. The samples were collected by proportional random sampling approach to attain the number of informants and data were collected through online interview questionnaire. Furthermore, the data were analyzed using coding approaches (open, axial, and selective coding) and value stream analysis. The findings revealed that SCRM can be an alternative marketing scheme for universities that utilize socmed to disseminate information owing to access easiness, complete and updated information, and attractive appearance.

Isotope Exchange of ND3 on Pt Catalyst–Loaded 5A Molecular Sieve

Recollecting and purifying unburned tritium is a preferred process for D-T fusion operations. However, tritium containing byproducts act as a potential holdup for the material, requiring additional processing to liberate the tritium. Using deuterium as a surrogate for tritium, this work demonstrates the capture and isotope exchange of deuterated ammonia using a Pt catalyst–loaded 5A molecular sieve, performed at ambient temperatures, verified by the analysis of the effluent gas streams and desorbed ammonia isotopologues post exchange.

A survey of episode mining

AbstractEpisode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode‐mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up‐to‐date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Association Rules Technologies > Association Rules

Stream analysis for a sub-catchment of Red River (Vietnam) using isotopic technique and recursive digital filter method

River streams, in general, comprise of two flows: quick flow and baseflow. The baseflow is closely related to geological catchment properties, and understanding the baseflow contribution to stream flow is very important in the planning of water resources management. The baseflow in a sub-catchment of the Red River in Vietnam was quantified using the isotopic technique, and results were compared with Eckhardt’s recursive digital filter (RDF) method. Results of the isotopic approach showed that groundwater is recharged from regions at 300 to 800 m above mean sea level. The upstream baseflow gains from Holocene and Pleistocene aquifers throughout the year, contributing to 65 ± 4 % of the river discharge. On the contrary, the midstream baseflow contributes 44.6 ± 6.5 % of the river’s annual discharge to both Holocene and Pleistocene aquifers. The downstream is more complicated, where the baseflow loses to the Pleistocene aquifer and gains 73 ± 17 % of the river's annual discharge from the Holocene aquifer. The loss of baseflow was attributed to the high rate of groundwater mining.Maintaining the vegetative cover over the recharge areas is recommended to reduce runoff and increase groundwater potential so that the baseflow could sustain the river stream.

Rapid identification of boiling crisis with event-based visual streaming analysis

The formation of a stable vapor layer during boiling processes triggers the boiling crisis that can damage the entire system. While there have been efforts to build a predictive model for critical heat flux on live images, unfortunately, images from conventional frame-cameras show limitations for performing real-time digital inference as they contain significant unnecessary information. A new type of imaging technique, neuromorphic event imaging, captures individual pixel-wise brightness changes, thereby recording motion-based phenomena in a sparse and efficient manner. In this study, we develop neural network models capable of predicting the onset of boiling crisis by utilizing neuromorphic-simulated event data. We reveal that event-based models demonstrate their proficiency and time efficiency in learning patterns of the boiling process from relatively small datasets. The event-based models also prove the generalizability to unseen surfaces by showing the potential for transfer learning. The results suggest that an extension of this work may prove effective in developing forecasting models that detect the incipient boiling crisis before it damages boiling surfaces. These findings highlight that event data can be effectively applied to real-time, motion-dependent contexts.

Participatory study of policy process for wildlife conservation and ecotourism in Eswatini: A multiple streams analysis

By the 1950 s, overhunting in the African Kingdom of Eswatini drove most of its wildlife to extinction. In 2022, Eswatini has one of the most robust conservation success stories in all of Southern Africa and yet has received minimal scholarly attention. Using the Multiple Streams Framework, to understand a complex system, we analyze 40 interviews and 34 policy documents to study variation in problems, policy, and politics across four different types of conservation areas: Community Based Wildlife Protected Areas, a Royal National Park, a Parastatal National Park, and a Privately Managed Protected Area. Our research is participatory, meaning Eswatini conservation professionals played a role in shaping research design and questions and collecting data. We found that policy solutions common to all institutions include 1) the absolute decision-making power of the king, 2) the stringent wildlife protection law in the 1953 Game Act, and 3) a surprisingly conducive space for communities to enact community-based conservation amidst an authoritarian monarchy. The most important problems include economic challenges (e.g. poverty) and climate change. Politics play a role, with unprecedented, violent pro-democracy protests in the summer of 2021 with activists calling for increased human rights considerations, a problem that spills into the wildlife conservation sector. Findings can be used by policy-makers seeking to replicate conservation successes elsewhere, or to enact reforms in the Eswatini system to enable its proven system of wildlife conservation to adapt and endure.

A mediation system for continuous spatial queries on a unified schema using Apache Spark

ABSTRACT Recent advances in big and streaming data systems have enabled real-time analysis of data generated by Internet of Things (IoT) systems and sensors in various domains. In this context, many applications require integrating data from several heterogeneous sources, either stream or static sources. Frameworks such as Apache Spark are able to integrate and process large datasets from different sources. However, these frameworks are hard to use when the data sources are heterogeneous and numerous. To address this issue, we propose a system based on mediation techniques for integrating stream and static data sources. The integration process of our system consists of three main steps: configuration, query expression and query execution. In the configuration step, an administrator designs a mediated schema and defines mapping between the mediated schema and local data sources. In the query expression step, users express queries using customized SQL grammar on the mediated schema. Finally, our system rewrites the query into an optimized Spark application and submits the application to a Spark cluster. The results are continuously returned to users. Our experiments show that our optimizations can improve query execution time by up to one order of magnitude, making complex streaming and spatial data analysis more accessible.

Analysis of E-commerce Live Streaming and Traffic Generation

The Internet has become an integral part of our society. This paper focuses on TikTok, one of today's most popular Internet platforms, and analyzes its e-commerce live streaming structure and traffic generation methods. Through a comprehensive examination of TikTok's current status and prospects, this study aims to deepen our understanding of the current Internet model and identify emerging trends. TikTok's commercial liquidity is essential to its success as a commercial platform. However, balancing commercial interests with user experience has challenged the platform's development. The key to TikTok's growth is creating a sticky user base and fostering a unique platform culture. Rather than prioritizing commercial gain over user experience, TikTok aims to engage users in the commercial process, which is critical for sustainable development. Overall, this paper sheds light on the complexities of e-commerce live streaming and traffic generation on TikTok and highlights the platform's unique approach to balancing commercial interests with user experience.

Impacts of morphology parameters on the risk of rupture in intracranial aneurysms: statistical and computational analyses

The hemodynamic analysis of the blood stream inside the cerebral aneurysms reveals the risk of the aneurysm rupture. In addition, the high risk region prone to rupture would be determined by the hemodynamic analysis of the blood. In present article, computational fluid dynamic is used for the investigation of the hemodynamic effects on the aneurysm wall and risk of rupture. This study tries to find the connection between the risk of rupture with three geometrical features of aneurysm i.e., Ellipsoid Max semi-axis, Size ratio and Tortuosity. Statistical analysis is done over 30 different ruptured /unruptured ICA aneurysms to find meaningful relation between selected geometrical factors and rupture risk. The hemodynamic analysis is done over four distinct aneurysm models to attain more details on effects of chosen geometrical factors. The results of simulations indicate that the Ellipsoid Max semi-axis have meaningful impacts on the risk of rupture.

Interactions across Displays and Space: A Study of Virtual Reality Streaming Practices on Twitch

The growing live streaming economy and virtual reality (VR) technologies have sparked interest in VR streaming among streamers and viewers. However, limited research has been conducted to understand this emerging streaming practice. To address this gap, we conducted an in-depth thematic analysis of 34 streaming videos from 12 VR streamers with varying levels of experience, to explore the current practices, interaction styles, and strategies, as well as to investigate the challenges and opportunities for VR streaming. Our findings indicate that VR streamers face challenges in building emotional connections and maintaining streaming flow due to technical problems, lack of fluid transitions between physical and virtual environments, and not intentionally designed game scenes. As a response, we propose six design implications to encourage collaboration between game designers and streaming app developers, facilitating fluid, rich, and broad interactions for an enhanced streaming experience. In addition, we discuss the use of streaming videos as user-generated data for research, highlighting the lessons learned and emphasizing the need for tools to support streaming video analysis. Our research sheds light on the unique aspects of VR streaming, which combines interactions across displays and space.

PROPOSED IMPLEMENTATION OF LEAN MANUFACTURING TO REDUCE WASTE IN PLYWOOD PRODUCTION

CV. Treewood Abadi Group is a manufacturing company engaged in producing plywood products. In the plywood production process from raw materials to finished products there are still obstacles in the form of activities that do not have added value or waste that cause decreased efficiency and effectiveness of production activities. This study aims to identify the correlation relationship between the type of waste and waste that has the greatest effect on the plywood production process. The steps of this research are mapping the flow of the production process using waste assessment model, selection of mapping tools with value stream analysis tools and then providing proposed improvements based on the failure mode that has the highest risk priority number value. with failure mode and effect analysis, it is found that the proposed improvements are in the form of implementing visual displays, improving material handling tools, re-layout and increasing the number of workers.