• A decision tree–based classifier to provide nutritional plans recommendations

      Aguilar-Loja, Omar; Dioses-Ojeda, Luis; Armas-Aguirre, Jimmy; Gonzalez, Paola A. (IEEE Computer Society, 2022-01-01)
      The use of machine learning algorithms in the field of nutritional health is a topic that has been developed in recent years for the early diagnosis of diseases or the recommendation of better nutritional habits. People with poor diets are more prone to chronic diseases and, in the long term, this can lead to dead. This study proposes a model for the recommendation of nutritional plans using the decision tree technique considering the patient data, in complement with the BMI (Body Mass Index) and BMR (Basal Metabolic Rate) to evaluate and recommend the best nutritional plan for the patient. The algorithm used in the model was trained with a dataset of meal plan data assigned by specialists which were obtained from the Peruvian food composition table, and the data from the diets that were assigned and collected from the nutrition area of the Hospital Marino Molina Sccipa in Lima, Peru. Preliminary results of the experiment with the proposed algorithm show an accuracy of 78.95% allowing to provide accurate recommendations from a considerable amount of historical data. In a matter of seconds, these results were obtained using Scikit learn library. Finally, the accuracy of the algorithm has been proven, generating the necessary knowledge so that it can be used to create appropriate nutritional plans for patients and to improve the process of creating plans for the nutritionist.
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    • A novel hybrid approach of gravitational search algorithm and decision tree for twitter spammer detection

      Vives, Luis; Tuteja, Gurpreet Singh; Manideep, A. Sai; Jindal, Sonika; Sidhu, Navjot; Jindal, Richa; Bhatt, Abhishek (World Scientific, 2022-05-01)
      With the increasing popularity of online social networking platforms, the amount of social data has grown exponentially. Social data analysis is essential as spamming activities and spammers are escalating over online social networking platforms. This paper focuses on spammer detection on the Twitter social networking platform. Although existing researchers have developed numerous machine learning methods to detect spammers, these methods are inefficient for appropriately detecting spammers on Twitter due to the imbalance of spam and nonspam data distribution, the involvement of diverse features and the applicability of data mechanisms by spammers to avoid their detection. This research work proposes a novel hybrid approach of the gravitational search algorithm and the decision tree (HGSDT) for detecting Twitter spammers. The individual decision tree (DT) algorithm is not able to address the challenges as it is unstable and ineffective for the higher level of favorable data for a particular attribute. The gravitational search algorithm (GSA) constructs the DTs with improved performance as the gravitational forces act as the information-transferring agents through mass agents. Moreover, the GSA is efficient in handling the data of higher dimensional search space. In the HGSDT approach, the construction of the DT and splitting of nodes are performed with the heuristic function and Newton's laws. The performance of the proposed HGSDT approach is determined for the Social Honeypot dataset and 1KS-10KN dataset by conducting three different experiments to analyze the impact of training data size, features and spammer ratio. The result of the first experiment shows the need of a higher proportion of training data size, the second experiment signifies the more importance of textual content-based features compared to the other feature categories and the third experiment indicates the requirement of balanced data to attain the effective performance of the proposed approach. The overall performance comparison indicates that the proposed HGSDT approach is superior to the incorporated machine learning methods of DT, support vector machine and back propagation neural network for detecting Twitter spammers.
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    • An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

      Aboamer, Mohamed Abdelkader; Sikkandar, Mohamed Yacin; Gupta, Sachin; Vives, Luis; Joshi, Kapil; Omarov, Batyrkhan; Singh, Sitesh Kumar (Hindawi Limited, 2022-01-01)
      Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, "convolutional neural network"or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0-7), filters (10-40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10-12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.
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    • Blockchain-based Website Solution for Controlling the Authorized Sale of Drugs in Peru

      Garcia, Herbert Melendez; Cortez, Manuel Maza; Amaya, Edgar Diaz (Institute of Electrical and Electronics Engineers Inc., 2020-10-21)
      Drug counterfeiting or adulteration is a worldwide concern due to the serious consequences they generate, especially in the health and economic sectors. This concern is greater in Peru, as it is among the top five countries with drug counterfeiting incidents in the Americas, according to a study carried out in 2018 by the Pan American Health Organization. In this paper, we present our project, which aims at implementing a technological solution that provides reliable information on the origin and authenticity of these products in Peru to the drug consumer user, preserving the security and integrity of the exposed information using Blockchain technology. Likewise, it allows showing detailed drug characteristics, such as: composition, pharmaceutical form, active ingredients, among other relevant information. The technological solution, proposed by our project, aims at publishing the commercial origin of drugs from their sale in laboratories and distributors to the sale to the public in pharmacies. In the development of this paper, a bibliographic review of research on the use of blockchain technology is presented, as well as its benefits in the health sector, the architecture used by the system and the conceptual commercialization chain that supports it, and the qualitative and quantitative validation for the drug query service is shown.
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    • Business information architecture for successful project implementation based on sentiment analysis in the tourist sector

      Zapata, Gianpierre; Murga, Javier; Raymundo, Carlos; Dominguez, Francisco; Moguerza, Javier M.; Alvarez, Jose Maria (Springer New York LLC, 2019-12-01)
      In the today’s market, there is a wide range of failed IT projects in specialized small and medium-sized companies because of poor control in the gap between the business and its vision. In other words, acquired goods are not being sold, a scenario which is very common in tourism retail companies. These companies buy a number of travel packages from big companies and due to lack of demand for these packages, they expire, becoming an expense, rather than an investment. To solve this problem, we propose to detect the problems that limit a company by re-engineering the processes, enabling the implementation of a business architecture based on sentimental analysis, allowing small and medium-sized tourism enterprises (SMEs) to make better decisions and analyze the information that most possess, without knowing how to exploit it. In addition, a case study was applied using a real company, comparing data before and after using the proposed model in order to validate feasibility of the applied model.
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    • Cloud-based open-source enterprise content management model at a SME operating in the manufacturing sector

      Montesinos-Rosales, Andrea; Salas-Villacorta, Sebastian; Mauricio-Sanchez, David; Raymundo-Ibañez, Carlos (Association for Computing Machinery, Inc, 2019-11-12)
      Every year, small- and medium-sized enterprises (SMEs) expand their participation in the Peruvian market, while facing high internal disorganization issues that stifle their growth. This problem is rooted on the fact that the contents generated by these companies are not usually adequately recorded, managed, and exploited, and thus negatively affecting the organization and their competitiveness levels. Now, although most of this issue may be solved through enterprise content management (ECM) solutions, they are not affordable for most SMEs because of their high prices. Within this context, this study proposes the implementation of a cloud-based open-source ECM model at a manufacturing SME in Peru. Through this model, the company was able to access the benefits of an ECM to restructure the way they manage content, gaining 67% more efficiency, establishing a collaboration channel between employees, suppliers, and customers, and reporting a 93% model adaptation rate among staff members.
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    • Comparing the impact of internet of things and cloud computing on organisational behavior: a survey

      García-Tadeo, Diego A.; Reddy Peram, Dattatreya; Suresh Kumar, K.; Vives, Luis; Sharma, Trishu; Manoharan, Geetha (Elsevier Ltd, 2022-01-01)
      Cloud computing is about delivery of different computing services involving databases, analytics, software, networking with the use of internet to enhance innovation, incorporate flexibility in resources and broaden profitability. However, Internet of Things (IoT) is an essential system for interrelating computer devices, digital machines, people and others which are offered with unique identifiers where data can be transferred with human involvement and wireless network. 42% of organisations in UK use cloud computing. The problem with cloud computing revolves around security and privacy issues as data is stored by a third party from inside or outside of the organisation leading to broken authentication, compromising of credentials and others. The use of IoT is vulnerable as it provides connectivity to devices, machines and people therefore, it needs to contain more storage that is made from cloud facilities. Survey has been conducted where primary quantitative method has been considered to obtain data from 101 managers of the organisation that has adopted cloud computing and IoT. However, 8 close-ended questions have been asked to 101 managers. Positivism philosophy has been used to make quantifiable observations along with descriptive design and others. The results and discussion will analyse responses of the respondents after conducting statistical analysis. However, research has been revolving around making a comparison between using cloud computing and IoT along with analysing organisational behaviour.
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    • Data Visualization Techniques for Monitoring Real-Time Information of Cold Chain

      Rivas Tucto, Jerson; Castillo Talexio, Nora; Shiguihara Juárez, Pedro (Springer Science and Business Media Deutschland GmbH, 2021-01-01)
      Real-time monitoring of temperature is a critical factor in ensuring the integrity of food during the cold chain. In this work, we compare techniques related to real-time data visualization to contribute to more efficient monitoring of the cold chain. Three real-time data display attributes were evaluated, and we constructed a dataset based on the Frisbee database (CDD). In this paper, we proposed graphics containing different line and area techniques to be evaluated for a specialist. The proposed graphs contained the line and area techniques that, when performing the experiment, obtained a higher success rate compared to the auto-charting technique. However, it was evidenced that elements such as color facilitate the detection of anomalies and trends in temperature change due to its high percentage of effectiveness in the results.
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    • Design of a hexapod robot using artificial intelligence for the routes of the peruvian andes

      Abarca, Arnold; Quispe, Grimaldo; Zapata-Ramirez, Gianpierre; Raymundo-Ibanez, Carlos; Rivera, Luis (Institute of Electrical and Electronics Engineers Inc., 2019-11-01)
      This paper presents an alternative solution to improve the locomotion system of a hexapod robot by artificial intelligence. Through an optimal design to achieve static stability, dynamic stability and optimize energy consumption through an autonomous system that is able to perform trajectories without any inconvenience. For the robot to move without flaws has certain restrictions in design (weight, size, materials, etc.) The hexapod has a high degree of movement and this allows many trajectories handle at the time of travel. Using sensors under certain working conditions we will obtain the necessary data and signals to satisfactorily comply with the hexapod robot design.
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    • Develop a Model for Assessing the Most Efficient Diseases Diagnosis using Machine Learning

      Vives, Luis; Basha, N. Khadar; Poonam; Gehlot, Anita; Chole, Vikrant; Pant, Kumud (Institute of Electrical and Electronics Engineers Inc., 2022-01-01)
      so, machine learning techniques are being developed to improve performance and maintenance prediction. Increasing our knowledge of the relationship between humans and algorithms, Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. Numerous researchers recently developed numerous computer-aided diagnostic algorithms employing various supervised learning approaches. Early identification of sickness may help to reduce the number of people who die as a result of these illnesses. Using machine learning techniques, this research creates an efficient automated illness diagnostic algorithm. We chose three key disorders in this paper: coronavirus, cardiovascular diseases, and diabetes. The data are inputted into a mobile application in the suggested model, the investigation is then done in a real-time dataset that used a pre-trained model machine learning technique trained within the same dataset then implemented in firebase, and lastly, the illness identification result can be seen in the mobile application. Logistic regression is a method of prediction calculation
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    • Efficient Grocery Shopping Using Geolocation and Data Mining

      Aliaga-Vasquez, Myrella; Bramon-Ayllon, Reyna; Ugarte, Willy (IEEE Computer Society, 2022-01-01)
      In the current pandemic, people are looking to leave their houses less frequently to prevent getting infected, but the absence of an app that shows the necessary information before going to the supermarket forces people to look in different supermarkets for the products they want to buy, thus increasing their chances of catching the virus, not to mention the waste of money and time. DoremyS is an app that allows you to create shopping lists that indicate to the user which supermarket to visit to find every product in them; it uses Geolocation to recommend supermarkets that are near the user and Data Mining to recommend shopping lists based on the user's interests.
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    • Enterprise architecture based on TOGAF for the aof educational institutions to e-learning using the DLPCA Methodology and Google Classroom

      Puntillo, Geraldine; Salazar, Alonso; Wong, Lenis (Springer Science and Business Media Deutschland GmbH, 2022-01-01)
      Given the current situation of online classes, it is necessary to implement a Business Architecture model in order to facilitate the adaptation of virtual teaching, since 97.4% of teachers give up the use of information systems for learning. In addition, up to 80% of students experience stress with this new modality of learning. Based on this context, we can identify the gap in the adaptation to the virtual class process as a latent problem. Therefore, a model composed of 3 stages (Analysis, design, and validation) is proposed. Stage 1 includes the analysis of components on which the model will be developed. Stage 2 describes the Open Group Architecture Framework (TOGAF) on which the model will be developed, and the Discover, Learn, Practice, Collaborate, and Assignment (DLPCA) e-learning Methodology as the basis of the business process to be proposed. Finally, in stage 3, the model was validated in a private school in Lima with 70 students, 2 teachers, and 1 director, where it was shown that our proposal increased user satisfaction by 18.97%, positively increased adaptation to virtual classes by 28.50%, and also obtained a 75.34% acceptance of our proposal by the subjects of study, which shows the effectiveness of our solution to the problem.
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    • Evaluation Method of Variables and Indicators for Surgery Block Process Using Process Mining and Data Visualization

      Rojas-Candio, Piero; Villantoy-Pasapera, Arturo; Armas-Aguirre, Jimmy; Aguirre-Mayorga, Santiago (2021-01-01)
      In this paper, we proposed a method that allows us to formulate and evaluate process mining indicators through questions related to the process traceability, and to bring about a clear understanding of the process variables through data visualization techniques. This proposal identifies bottlenecks and violations of policies that arise due to the difficulty of carrying out measurements and analysis for the improvement of process quality assurance and process transformation. The proposal validation was carried out in a health clinic in Lima (Peru) with data obtained from an information system that supports the surgery block process. Finally, the results contribute to the optimization of decision-making by the medical staff involved in the surgery block process.
      Acceso abierto
    • Extended Model for the Early Skin Cancer Detection Using Image Processing

      Poma, Jonathan Miguel Campos; Dominguez, Emily Yanira De La Cruz; Armas-Aguirre, Jimmy; Gonzalez, Leonor Gutierrez (IEEE Computer Society, 2020-06-01)
      In this research paper, we proposed an extended model for the early detection of skin cancer... The purpose is reduce the waiting time to obtaining a diagnosis, in addition, the function of the dermatoscope has been digitized by using a Smartphone and magnifying lenses as an accessory the mobile device. The proposed model has five phases: 1. The patient is attended by a general practitioner or nurse previously trained in any health center which has WiFi or mobile network connectivity to record their data and capture the skin lesion that will be analyzed. 2) The image will be in the cloud storage, which at the same time feeds an exclusive access website of dermatologists.3) Images are analyzed in real time using an image recognition service provided by IBM, which is integrated into a cloud-hosted web platform and an-Android application. 4)The result of the image processing is visualized by the dermatologist who makes a remote diagnosis.5) This diagnosis is received by the general practitioner or nurse, responsible for transmitting the diagnosis and treatment to the patient. This model was validated in a group of 60 patients, where 28 suffer from skin cancer in the early stage, 12 in the late stage and 20 are healthy patients, in a network of clinics in Lima, Peru. The obtained result was 97.5% of assertiveness on the analyzed skin lesions and 95% in healthy patients.
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    • Hybrid big bang-big crunch with ant colony optimization for email spam detection

      Natarajan, Rathika; Mehbodniya, Abolfazl; Ganapathy, Murugesan; Neware, Rahul; Pahuja, Swimpy; Vives, Luis; Asha (World Scientific, 2022-04-01)
      Electronic mails (emails) have been widely adapted by organizations and individuals as efficient communication means. Despite the pervasiveness of alternate means like social networks, mobile SMS, electronic messages, etc. email users are continuously growing. The higher user growth attracts more spammers who send unsolicited emails to anonymous users. These spam emails may contain malware, misleading information, phishing links, etc. that can imperil the privacy of benign users. The paper proposes a self-adaptive hybrid algorithm of big bang-big crunch (BB-BC) with ant colony optimization (ACO) for email spam detection. The BB-BC algorithm is based on the physics-inspired evolution theory of the universe, and the collective interaction behavior of ants is the inspiration for the ACO algorithm. Here, the ant miner plus (AMP) variant of the ACO algorithm is adapted, a data mining variant efficient for the classification. The proposed hybrid algorithm (HB3C-AMP) adapts the attributes of B3C (BB-BC) for local exploitation and AMP for global exploration. It evaluates the center of mass along with the consideration of pheromone value evaluated by the best ants to detect email spam efficiently. The experiments for the proposed HB3C-AMP algorithm are conducted with the Ling Spam and CSDMC2010 datasets. Different experiments are conducted to determine the significance of the pre-processing modules, iterations, and population size on the proposed algorithm. The results are also evaluated for the AM (ant miner), AM2 (ant miner2), AM3 (ant miner3), and AMP algorithms. The performance comparison demonstrates that the proposed HB3C-AMP algorithm is superior to the other techniques.
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    • Implementation of Lean Manufacturing Principles to increase Productivity in SMEs in the manufacturing sector of clothing

      Arica-Hernandez, Marco Antonio; Llagas-Llontop, Sebastian Eduardo; Khaburzaniya, Irakliy (Association for Computing Machinery, 2022-01-12)
      SMEs in the textile sector face many problems in their production flows, mainly due to the lack of production management systems caused by poor management of the production chain. Therefore, a diagnostic analysis is carried out in a textile SME to evaluate and define the deficiencies and factors that affect its competitiveness, which began with the analysis of the current situation of the company, where it was established the existence of poor-quality management, high waiting times and lack of procedures. Therefore, the use of lean manufacturing tools such as Jidoka, Single Minute Exchange of Die with respect to the production line and process management for the measurement and control of operations in the production area is proposed. The incorporation of these tools in block allows to decrease the rates of defective products, the excess of operative work and the set-up of the machines for the change of model. The main result of the research was that production increased to 0.091 und/PEN.
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    • Information security risk management model for mitigating the impact on SMEs in Peru

      Garay, Daniel Felipe Carnero; Marcos Antonio, Carbajal Ramos; Armas-Aguirre, Jimmy; Molina, Juan Manuel Madrid (IEEE Computer Society, 2020-06-01)
      This paper proposes an information security risk management model that allows mitigating the threats to which SMEs in Peru are exposed. According to studies by Ernst Young, 90% of companies in Peru are not prepared to detect security breaches, and 51% have already been attacked. In addition, according to Deloitte, only 10% of companies maintain risk management indicators. The model consists of 3 phases: 1. Inventory the information assets of the company, to conduct the risk analysis of each one; 2. Evaluate treatment that should be given to each risk, 3. Once the controls are implemented, design indicators to help monitor the implemented safeguards. The article focuses on the creation of a model that integrates a standard of risk management across the company with a standard of IS indicators to validate compliance, adding as a contribution the results of implementation in a specific environment. The proposed model was validated in a pharmaceutical SME in Lima, Peru. The results showed a 71% decrease in risk, after applying 15 monitoring and training controls, lowering the status from a critical level to an acceptable level between 1.5 and 2.3, according to the given assessment.
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    • Intelligent gravitational search random forest algorithm for fake news detection

      Natarajan, Rathika; Mehbodniya, Abolfazl; Rane, Kantilal Pitambar; Jindal, Sonika; Hasan, Mohammed Faez; Vives, Luis; Bhatt, Abhishek (World Scientific, 2022-01-01)
      Online social media has made the process of disseminating news so quick that people have shifted their way of accessing news from traditional journalism and press to online social media sources. The rapid rotation of news on social media makes it challenging to evaluate its reliability. Fake news not only erodes public trust but also subverts their opinions. An intelligent automated system is required to detect fake news as there is a tenuous difference between fake and real news. This paper proposes an intelligent gravitational search random forest (IGSRF) algorithm to be employed to detect fake news. The IGSRF algorithm amalgamates the Intelligent Gravitational Search Algorithm (IGSA) and the Random Forest (RF) algorithm. The IGSA is an improved intelligent variant of the classical gravitational search algorithm (GSA) that adds information about the best and worst gravitational mass agents in order to retain the exploitation ability of agents at later iterations and thus avoid the trapping of the classical GSA in local optimum. In the proposed IGSRF algorithm, all the intelligent mass agents determine the solution by generating decision trees (DT) with a random subset of attributes following the hypothesis of random forest. The mass agents generate the collection of solutions from solution space using random proportional rules. The comprehensive prediction to decide the class of news (fake or real) is determined by all the agents following the attributes of random forest. The performance of the proposed algorithm is determined for the FakeNewsNet dataset, which has sub-categories of BuzzFeed and PolitiFact news categories. To analyze the effectiveness of the proposed algorithm, the results are also evaluated with decision tree and random forest algorithms. The proposed IGSRF algorithm has attained superlative results compared to the DT, RF and state-of-the-art techniques.
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    • IoT Watercare: Water Quality Control System in Unofficial Settlements of Peru Based in an IoT Architecture

      Salgado, Juanelv; Pizarro, Cesar; Wong, Lenis; Castillo, Jose (IEEE Computer Society, 2022-01-01)
      Many homes in the country of Peru, especially those located in unofficial settlements, are not connected to public service networks, and in the case of residential water, require tanker truck delivery. However, this water has often been contaminated from the upstream storage, conveyance and delivery systems that provide it, and thus will not comply with government water quality standards, ultimately compromising the health of the people who rely on it. While the topic of quality monitoring in traditional water networks has been studied, research has not focused on water quality control in under-developed and under-served unofficial settlements. This study introduces an IoT architecture and web-based system for real-Time monitoring of the key water quality parameters to help municipalities and other government entities to act early when large volumes of low-quality water are detected. The system proposed was implemented across five layers: capture, communication, processing, storage and presentation. Two experiments were conducted in a residential home with real time measurement of temperature, turbidity, TDS y pH. When comparing the results of both experiments, the pH parameter had a better precision with a 2% error rate. In addition, the survey results showed that the experts agree with the proposal.
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    • Lean Manufacturing Model Adapted for Waste Reduction in Peruvian Condiment Production Agri-Businesses

      Mesones-Guillén, Axel; Palacios-Jesús, Lizeth; Carvallo-Munar, Edgardo; Salas-Castro, Rosa; Cardenas-Rengifo, Luis (2021-01-01)
      The Peruvian export supply is primarily based on the segment of dressings and condiments. This paper seeks to adapt lean manufacturing tools using Poka-Yoke techniques and process standardization, which may align with existing processes to prevent production errors. This combination of tools is expected to reduce the percentage of waste generated throughout the condiment production process. Subsequent to an initial evaluation of the current method combined with the application of both the tools, 11.4% waste reduction was ultimately reported.
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