Lean Tools Selector - A Decision Support System

Small and Medium Enterprises (SMEs) contribute significantly to the economy of any country, but in return, they are limited in their resources. A large part of these companies, when setting out to promote the Lean Manufacturing (LM) have difficulties with the selection and analysis of Lean tools to implement. Eventually, if the improvement actions aren't properly planned, structured or supported by the whole organization then, they end up failing in their implementation. Besides this problem, the literature on LM does not provide enough information about how the selection of Lean tools or practices should be conducted. Therefore, this study presents a decision support system that can help organizations to identify waste and to select the most appropriate tools or Lean practices to implement. It should be noted that, before any implementation of a Lean tool or practice, the organization should take care of knowing its stakeholders, define its system, be informed of the current state of the organization, and identify all the processes that add value to the organization. The correct selection of Lean tools or practices does not ensure the success of the Lean philosophy in any organization, because there are some factors that must be required, namely, the commitment of top management, knowing how to lead and communicate with all employees, being the education and training a crucial point to ensure a good cultural change in the organization.This work was supported in part by Fundação para a Ciência e Tecnologia (FCT) and C-MAST- Centre for Mechanical and Aerospace Science and Technologies, under project UIDB/00151/2020.info:eu-repo/semantics/publishedVersio

Relevant occupational health and safety risks in the Portuguese food processing industry

The Agrifood Industry is the largest Portuguese Industry, constituted mainly by micro, small and medium-sized enterprises (SMEs). It is noted that more than any other type of organization SMEs have their own specificities that make it particularly appropriate to develop tools to facilitate communication and knowledge sharing for employers and workers. To this extent, identifying critical success factors is the key to increase SMEs productivity. Likewise, Occupational Safety and Health (OSH) in SMEs have their own characteristics, which difficult the prevention strategies implementation and aggravate the problematic of work accidents. This study analyses a fieldwork in 60 food processing companies in Portugal, related to the dairy, meat processing, bakery and horticultural subsectors. The analysis of the results allowed to identify that, at the national and regional level, the main failures are concerned with (1) lack of risk assessments regarding occupational noise, lighting, thermal environment and vibrations; (2) safety signaling, the circulation ways are not identified with appropriate safety colors; (3) general lighting, with too many shade areas and finally (4) complementary presence of associated risks to falls at the same level, falling of objects, thermal burns, the use of machines and equipment, fire, mechanical, ergonomic hazards and incorrect body postures. This study assesses the most relevant occupational health and safety risks in the Portuguese food processing industry to contribute to the improvement of OSH management and prevention of work accidents.info:eu-repo/semantics/publishedVersio

Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.Project Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).info:eu-repo/semantics/publishedVersio

Decision Support System in Dynamic Pricing of Horticultural Products Based on the Quality Decline Due to Bacterial Growth

A decision support system (DSS) was developed to help reduce food waste at traditional food retailers while selling fresh horticultural products, but also to promote food safety and quality. This computational tool includes two major functions: (1) the prediction of the remaining shelf life of fresh horticultural product, namely lettuce, onion, carrot, and cabbage based on its microbial growth status, governed by extrinsic and intrinsic parameters (temperature, water activity and pH, respectively). The remaining shelf life of the studied horticultural products is determined by using the online predictive food microbiology tool— the Combined Database for Predictive Microbiology (Combase). The time to reach the infectious doses of bacteria considered in the study for each of the four horticultural products are predicted; (2) the calculation of the dynamic price of the produce that should be set each day, depending on the predicted end of the marketing period to increase the demand and potential for sale to the final consumer. The proposed dynamic pricing model assumes a linear relation with the remaining shelf life of the analyzed vegetable to set the selling price. The shelf life determined by the DSS for optimal storage conditions is, in general, conservative, ensuring food safety. The automatic dynamic pricing gives new opportunities to small retailers to manage their business, fostering profit and simultaneously contributing to reduce food waste. Thus, this decision support system can contribute to the sustainable value of reducing food waste by providing information to small grocers and retailers on the safety of their perishable status depending on storage conditions and allowing them to suggest a fair price depending on that quality.This study is within the activities of project “PrunusPós—Optimization of processes for the storage, cold conservation, active and/or intelligent packaging and food quality traceability in post-harvested fruit products”, project n. º PDR2020-101-031695, Partnership n.º 87, initiaciative n.º 175, promoted by PDR 2020 and co-funded by EAFRD within Portugal 2020. P.D.G. acknowledges Fundação para a Ciência e a Tecnologia (FCT—MCTES) for its financial support via the project UIDB/00151/2020 (C-MASTinfo:eu-repo/semantics/publishedVersio

Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling

Deep learning has already been successfully used in the development of decision support systems in various domains. Therefore, there is an incentive to apply it in other important domains such as agriculture. Fertilizers, electricity, chemicals, human labor, and water are the components of total energy consumption in agriculture. Yield estimates are critical for food security, crop management, irrigation scheduling, and estimating labor requirements for harvesting and storage. Therefore, estimating product yield can reduce energy consumption. Two deep learning models, Long Short-Term Memory and Gated Recurrent Units, have been developed for the analysis of time-series data such as agricultural datasets. In this paper, the capabilities of these models and their extensions, called Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Units, to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and soil water content, to estimate end-of-season yield. The application of this technique was tested for tomato and potato yields at a site in Portugal. The Bidirectional Long Short-Term memory outperformed the Gated Recurrent Units network, the Long Short-Term Memory, and the Bidirectional Gated Recurrent Units network on the validation dataset. The model was able to capture the nonlinear relationship between irrigation amount, climate data, and soil water content and predict yield with an MSE of 0.017 to 0.039. The performance of the Bidirectional Long Short-Term Memory in the test was compared with the most commonly used deep learning method, the Convolutional Neural Network, and machine learning methods including a Multi-Layer Perceptrons model and Random Forest Regression. The Bidirectional Long Short-Term Memory outperformed the other models with an R2 score between 0.97 and 0.99. The results show that analyzing agricultural data with the Long Short-Term Memory model improves the performance of the model in terms of accuracy. The Convolutional Neural Network model achieved the second-best performance. Therefore, the deep learning model has a remarkable ability to predict the yield at the end of the season.Project Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).info:eu-repo/semantics/publishedVersio

Industry 4.0 and Society 5.0: Opportunities and Threats

Industry 4.0 promises to revolutionize industrial production with increased operational efficiency, the development of new business models, services and products. It enables real-time production planning and dynamic optimization in contrast to conventional forecast. The tools and technological advances developed by Industry 4.0 will have a significant role to improving the quality of life in society in order to enable a happier, motivated, and satisfied and with more time for leisure. As a consequence, it will increase productivity and the mankind will choose the direction and the kind of society we want to create in the future to promote equal wealth distribution. As consequence of Industry 4.0 emerges Society 5.0, beginning in Japan, due to the concern of the ageing population. Society 5.0 focuses on the use of tools and technologies developed by Industry 4.0 to benefit the humankind. Intelligent systems, developed by Industry 4.0, could be seen by society as a beneficial rather than as adversaries. Future society could benefit from advanced technology in solving problems and economically. Society 5.0 has a special focus to position the human being at the centre of innovation, technological transformation and industrial automation, stimulated by Industry 4.0. This new Society 5.0 paradigm will play a predominant role in creating a happier, satisfied, fulfilled and consequently more productive society. Society 5.0, also called the super intelligent society, intends to use advanced technology of Industry 4.0 for enjoyment of humankind, in order to promote an interconnection between people and systems in cyberspace with optimization of results by artificial intelligence.Fundação para a Ciência e Tecnologia (FCT), under project UID/EMS/00151/2013 C-MAST, with reference POCI-01-0145-FEDER-007718.info:eu-repo/semantics/publishedVersio

Fostering Awareness on Environmentally Sustainable Technological Solutions for the Post-Harvest Food Supply Chain

This study presents a current status and future trends of innovative and environmentally sustainable technological solutions for the post-harvest food supply chain and the food industry, in terms of ecological packaging, active, and/or intelligent packaging. All these concerns are currently highlighted due to the strong increase in the purchase/sale of products on online platforms, as well as the requirements for stricter food security and safety. Thus, this study aims to increase the global awareness of agro-industrial micro, small, and medium size enterprises for the adoption of innovative food solutions though industry digitalization (Industry 4.0), associated logistics and circular economy, with a concern for cybersecurity and products information, communication and shelf-life extension. The adoption of these guidelines will certainly foster along the complete food chain (from producer to consumer, with all intermediary parties) the awareness on environmentally sustainable technological solutions for the post-harvest food supply chain, and thus, promoting the future food sustainability required by the population increase, the climate change, the exodus of rural population to urban areas, and food loss and waste.This study is within the activities of project S4Agro-Soluções Sustentáveis para o Setor Agroindustrial (Sustainable Solutions for the Agro-industrial sector) promoted by COMPETE 2020– POCI–SIAC: 02/SIAC/2019 (POCI-02-0853-FEDER-046425) and co-financed by FEDER under the Portugal 2020 initiative.info:eu-repo/semantics/publishedVersio

Cognitive Manufacturing in Industry 4.0 towards Cognitive Load reduction: A Conceptual Framework

Cognitive manufacturing utilizes cognitive computing, the industrial Internet of things (IoT), and advanced analytics to upgrade manufacturing processes in manners that were not previously conceivable. It enables associations to improve major business measurements, for example, productivity, product reliability, quality, and safety, while decreasing downtime and lowering costs. Considering all the facts that can prejudice the manufacturing performance in Industry 4.0, the cognitive load has received more attention, since it was previously neglected with respect to manufacturing industries. This paper aims to investigate what causes cognitive load reduction in manufacturing environments, i.e., human–computer interaction technologies that reduce the identified causes and the applications of cognitive manufacturing that use the referred technologies. Thus, a conceptual framework that links cognitive manufacturing to a reduction of the cognitive load was developed.This research was funded by the project 026653|POCI-01-0247-FEDER-026653—INDTECH—New technologies for smart manufacturing, co-financed by the Portugal 2020 Program (PT 2020), Compete 2020 Program, and the European Union through the European Regional Development Fund (ERDF). The authors wish to thank the relevant bodies for the opportunity and financial support that permitted carrying out this project: Fundação para a Ciência e Tecnologia (FCT) and C-MAST (Center for Mechanical and Aerospace Science and Technologies), under project UIDB/00151/2020.info:eu-repo/semantics/publishedVersio

Determination of the cell-free layer in circular PDMS microchannels

In microcirculation the cell-free layer is believed to reduce the friction between red blood cells (RBCs) and endothelial cells and consequently reduce blood flow resistance. However, the complex formation of the cell-free layer has not yet been convincingly described mainly due to multi-physical and hemorheological factors that affect this phenomenon. In this experimental work, we study the effect of hematocrit (Hct) on the thickness of the cell-free layer in straight circular polydimethylsiloxane (PDMS) microchannels. The channels studied are 73 ± 2 mm in diameter, flexible and circular to mimic blood vessels. The images are captured using confocal microscopy and are post-processed using Image J and MATLAB. The formation of a cell-free layer is clearly visible in the images captured and by using a combination of image analysis techniques we are able to detect an increase in the cell-free layer thickness as Hct decreases