• Industrial Engineering and Management Systems
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• v.10 no.2
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• pp.109-114
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• 2011

The number of small and medium enterprises (SMEs), especially those involved with research and development (R&D) programs and employed virtual teams to create the greatest competitive advantage from limited labor are increasing. Global and localized virtual R&D teams are believed to have high potential for the growth of SMEs. Due to the fast-growing complexity of new products coupled with new emerging opportunities of virtual teams, a collaborative approach is believed to be the future trend. This research explores the effectiveness of virtuality in SMEs' virtual R&D teams. Online questionnaires were emailed to Malaysian manufacturing SMEs and 74 usable questionnaires were received, representing a 20.8 percent return rate. In order to avoid biases which may result from pre-suggested answers, a series of open-ended questions were retrieved from the experts. This study was focused on analyzing an open-ended question, whereby four main themes were extracted from the experts' recommendations regarding the effectiveness of virtual teams for the growth and performance of SMEs. The findings of this study would be useful to product design managers of SMEs in order to realize the key advantages and significance of virtual R&D teams during the new product development (NPD) process. This is turn, leads to increased effectiveness in new product development's procedure.

• Journal of Global Scholars of Marketing Science
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• v.20 no.3
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• pp.239-248
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• 2010

This paper describes a collaborative project between academia and industry which focused on improving the marketing and product development strategies for two private label apparel brands of a large regional department store chain in the southeastern United States. The goal of the project was to revitalize product lines of the two brands by incorporating student ideas for new solutions, thereby giving the students practical experience with a real-life industry situation. There were a number of key players involved in the project. A privately-owned department store chain based in the southeastern United States which was seeking an academic partner had recognized a need to update two existing private label brands. They targeted middle-aged consumers looking for casual, moderately priced merchandise. The company was seeking to change direction with both packaging and presentation, and possibly product design. The branding and product development divisions of the company contacted professors in an academic department of a large southeastern state university. Two of the professors agreed that the task would be a good fit for their classes - one was a junior-level Intermediate Brand Management class; the other was a senior-level Fashion Product Development class. The professors felt that by working collaboratively on the project, students would be exposed to a real world scenario, within the security of an academic learning environment. Collaboration within an interdisciplinary team has the advantage of providing experiences and resources beyond the capabilities of a single student and adds "brainpower" to problem-solving processes (Lowman 2000). This goal of improving the capabilities of students directed the instructors in each class to form interdisciplinary teams between the Branding and Product Development classes. In addition, many universities are employing industry partnerships in research and teaching, where collaboration within temporal (semester) and physical (classroom/lab) constraints help to increase students' knowledge and experience of a real-world situation. At the University of Tennessee, the Center of Industrial Services and UT-Knoxville's College of Engineering worked with a company to develop design improvements in its U.S. operations. In this study, Because should be lower case b with a private label retail brand, Wickett, Gaskill and Damhorst's (1999) revised Retail Apparel Product Development Model was used by the product development and brand management teams. This framework was chosen because it addresses apparel product development from the concept to the retail stage. Two classes were involved in this project: a junior level Brand Management class and a senior level Fashion Product Development class. Seven teams were formed which included four students from Brand Management and two students from Product Development. The classes were taught the same semester, but not at the same time. At the beginning of the semester, each class was introduced to the industry partner and given the problem. Half the teams were assigned to the men's brand and half to the women's brand. The teams were responsible for devising approaches to the problem, formulating a timeline for their work, staying in touch with industry representatives and making sure that each member of the team contributed in a positive way. The objective for the teams was to plan, develop, and present a product line using merchandising processes (following the Wickett, Gaskill and Damhorst model) and develop new branding strategies for the proposed lines. The teams performed trend, color, fabrication and target market research; developed sketches for a line; edited the sketches and presented their line plans; wrote specifications; fitted prototypes on fit models, and developed final production samples for presentation to industry. The branding students developed a SWOT analysis, a Brand Measurement report, a mind-map for the brands and a fully integrated Marketing Report which was presented alongside the ideas for the new lines. In future if the opportunity arises to work in this collaborative way with an existing company who wishes to look both at branding and product development strategies, classes will be scheduled at the same time so that students have more time to meet and discuss timelines and assigned tasks. As it was, student groups had to meet outside of each class time and this proved to be a challenging though not uncommon part of teamwork (Pfaff and Huddleston, 2003). Although the logistics of this exercise were time-consuming to set up and administer, professors felt that the benefits to students were multiple. The most important benefit, according to student feedback from both classes, was the opportunity to work with industry professionals, follow their process, and see the results of their work evaluated by the people who made the decisions at the company level. Faculty members were grateful to have a "real-world" case to work with in the classroom to provide focus. Creative ideas and strategies were traded as plans were made, extending and strengthening the departmental links be tween the branding and product development areas. By working not only with students coming from a different knowledge base, but also having to keep in contact with the industry partner and follow the framework and timeline of industry practice, student teams were challenged to produce excellent and innovative work under new circumstances. Working on the product development and branding for "real-life" brands that are struggling gave students an opportunity to see how closely their coursework ties in with the real-world and how creativity, collaboration and flexibility are necessary components of both the design and business aspects of company operations. Industry personnel were impressed by (a) the level and depth of knowledge and execution in the student projects, and (b) the creativity of new ideas for the brands.

• The Journal of Society for e-Business Studies
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• v.4 no.3
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• pp.25-42
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• 1999

As todays manufacturers and business organizations are struggling to compete in the global marketplace, they are concentrating on the efficient use of numerous information on design, development production, testing, distribution, and maintenance of products throughout their whole life cycles. To meet these organizations demands on information integration, the CALS has been recently focused as a dominant philosophy. In this paper, we introduce the computer supports for engineering CALS in which the engineering process at an initial phase of product development is simultaneously and collaboratively peformed. The proposed system supports the following functions: a virtual prototyping, a distributed collaborative design, and engineering information management. We have conformed to CORBA (Common Object Request Broker Architecture) standard to support interoperability between distributed objects and have used JAVA to support cross-platform and distributed user access to the system on the Web. Under this system, multidisciplinary design teams in engineering CALS environment can collaboratively perform their tasks, share design information and communicate with each other on the Web.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.148-156
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• 2015

Increased competitive challenges are forcing companies to find better ways to bring their applications to market faster. Distributed development environments can help companies improve their time-to-market by enabling parallel activities. Although, such environments still have their limitations in real-time communication and real-time collaboration during the product development process. This paper describes a web-based collaborative framework which has been developed to support the decision making on a 3D design developing process. The paper describes 3D design file for the discussion that contains all relevant annotations on its surface and their visualization on the user interface for design changing. The framework includes a native CAD data converting module, 3D data based real-time communication module, revision control module for 3D data and some sub-modules such as data storage and data management. We also discuss some raised issues in the project and the steps underway to address them.

• Natural Product Sciences
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• v.8 no.1
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• pp.1-15
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• 2002

An International Cooperative Biodiversity Group (ICBG) program based at the University of Illinois at Chicago initiated its activities in 1998, with the following specific objectives: (a) inventory and conservation of of plants of Cuc Phuong National Park in Vietnam and of medicinal plants of Laos; (b) drug discovery (and development) based on plants of Vietnam and Laos; and (c) economic development of communities participating in the ICBG project both in Vietnam and Laos. Member-institutions and an industrial partner of this ICBG are bound by a Memorandum of Agreement that recognizes property and intellectual property rights, prior informed consent for access to genetic resources and to indigenous knowledge, the sharing of benefits that may arise from the drug discovery effort, and the provision of short-term and long-term benefits to host country institutions and communities. The drug discovery effort is targeted to the search for agents for therapies against malaria (antimalarial assay of plant extracts, using Plasmodium falciparum clones), AIDS (anti-HIV-l activity using HOG.R5 reporter cell line (through transactivation of the green fluorescent protein/GFP gene), cancer (screening of plant extracts in 6 human tumor cell lines - KB, Col-2, LU-l, LNCaP, HUVEC, hTert-RPEl), tuberculosis (screening of extracts in the microplate Alamar Blue assay against Mycobacterium tuberculosis $H_{37}Ra\;and\;H_{37}Rv),$ all performed at UIC, and CNS-related diseases (with special focus on Alzheimer's disease, pain and rheumatoid arthritis, and asthma), peformed at Glaxo Smith Kline (UK). Source plants were selected based on two approaches: biodiversity-based (plants of Cuc Phuong National Park) and ethnobotany-based (medicinal plants of Cuc Phuong National Park in Vietnam and medicinal plants of Laos). At mc, as of July, 2001, active leads had been identified in the anti-HIV, anticancer, antimalarial, and anti- TB assay, after the screening of more than 800 extracts. At least 25 biologically active compounds have been isolated, 13 of which are new with anti-HIV activity, and 3 also new with antimalarial activity. At GSK of 21 plant samples with a history of use to treat CNS-related diseases tested to date, a number showed activity against one or more of the CNS assay targets used, but no new compounds have been isolated. The results of the drug discovery effort to date indicate that tropical plant diversity of Vietnam and Laos unquestionably harbors biologically active chemical entities, which, through further research, may eventually yield candidates for drug development. Although the substantial monetary benefit of the drug discovery process (royalties) is a long way off, the UIC ICBG program provides direct and real-term benefits to host country institutions and communities.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.3
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• pp.215-221
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• 2011

To improve the naval ship acquisition process, systems engineering, modeling and simulation, etc. have been introduced, and there has been ongoing research on acquisition systems for effective support of it. However, due to characteristics of the naval ship acquisition process, development process mainly carried out at a shipyard such as basic design, detailed design, construction and test is difficult to integrate with the acquisition systems of IPT(Integrated Project Team). In addition, research aimed to improve this is rather lacking. In this paper, the naval ship product structure concept proposed in previous research was applied to the basic design system at shipyard, and basic research for expanding the coverage of naval ship acquisition systems to the basic design phase is performed. A data structure of modeling system appropriate to the basic design phase was proposed through research findings and the prototype system based on it was implemented.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.83-90
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• 2019

People constantly strive for better choices. For this reason, recommender system has been developed since the early 1990s. In particular, collaborative filtering technique has shown excellent performance in the field of recommender systems, and research of recommender system using machine learning has been actively conducted. This study constructs recommender system using collaborative filtering and machine learning based on stacking model which is one of ensemble methods. The results of this study confirm that the recommender system with the stacking model is useful in aspects of recommender performance. In the future, the model proposed in this study is expected to help individuals or firms to make better choices.

• International Journal of CAD/CAM
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• v.5 no.1
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• pp.69-81
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• 2005

As collaborative design and configuration design gain increasing importance in product development, it becomes essential to exchange parametric CAD models among participants. Parametric CAD models can be represented and exchanged in the form of a macro file or a part file that contains the modeling history of a product. The modeling history of a parametric CAD model contains feature specifications and each feature has selection information that records the name of the referenced topological entities. Translating this selection information requires solving the problems of how to identify the referenced topological entities of a feature (persistent naming problem) and how to convert the selection information into the format of the receiving CAD system (naming mapping problem). The present paper introduces the problem of exchanging parametric CAD models and proposes a solution to naming mapping.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.