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Статті в журналах з теми "Data Product"

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Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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1

Do, Namchul. "Education and Training of Product Data Analytics using Product Data Management System." Korean Journal of Computational Design and Engineering 22, no. 1 (March 1, 2017): 80–88. http://dx.doi.org/10.7315/cde.2017.080.

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2

Leong, K. K., K. M. Yu, and W. B. Lee. "Product data allocation for distributed product data management system." Computers in Industry 47, no. 3 (March 2002): 289–98. http://dx.doi.org/10.1016/s0166-3615(01)00152-x.

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3

Do, Namchul. "Integration of Social Media with Product Data Management for Collaborative Product Design." Journal of Korean Institute of Industrial Engineers 42, no. 1 (February 15, 2016): 50–56. http://dx.doi.org/10.7232/jkiie.2016.42.1.050.

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4

Krisnanto, Umbas, J. Juharsah, Purnama Putra, Andini Dani Achmad, and Elkana Timotius. "Utilizing Apriori Data Mining Techniques on Sales Transactions." Webology 19, no. 1 (January 28, 2022): 5581–90. http://dx.doi.org/10.14704/web/v19i1/web19376.

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The establishment of a marketing strategy is important for every business actor in the competitive world of business. Business operators must be able to develop sound marketing strategies to influence the attractiveness of consumers and to buy interest in the products provided so that the enterprise they operate can compete and have a market share and to maximize sales sales. To implement marketing strategies, references are required so that promotions can reach the right target, for example by seeking similarities between items. By using data mining techniques, these studies apply the a priori approach to the promotion of customer product recommendations by association rules on product sales transaction datasets to aid in the formation of applications between product items. The dataset represents a sample of sales of products for 2020. The application used for analyzing is RapidMiner, where a support value of > 20% and confidence of > 60% is determined. Each product package promoted is made up of 2 products from the calculation results. The two best rules that have value confidence is combined with 2 items (Cre1Cre2), (Cre1Cre12) and (Cre9Cre10). Based on the minimum support and confidence values that have been set, the results of the a priori method can produce association rules that can be used as a reference in product promotion and decision support in providing product recommendations to consumers.

5

Shaw, N. K., M. Susan Bloor, and A. de Pennington. "Product data models." Research in Engineering Design 1, no. 1 (March 1989): 43–50. http://dx.doi.org/10.1007/bf01580002.

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6

Vergeest, JSM. "Product Data Exchange." Computer-Aided Design 28, no. 8 (August 1996): 665. http://dx.doi.org/10.1016/0010-4485(96)86818-7.

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7

MOCHIZUKI, Tatsuya. "Product Data Management." Journal of the Japan Society for Precision Engineering 72, no. 2 (2006): 161–64. http://dx.doi.org/10.2493/jjspe.72.161.

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8

Werder, Karl, Stefan Seidel, Jan Recker, Nicholas Berente, John Gibbs, Nouredine Abboud, and Yossef Benzeghadi. "Data-Driven, Data-Informed, Data-Augmented: How Ubisoft’s Ghost Recon Wildlands Live Unit Uses Data for Continuous Product Innovation." California Management Review 62, no. 3 (April 27, 2020): 86–102. http://dx.doi.org/10.1177/0008125620915290.

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To stay ahead of the competition, firms must continuously learn from their customers and swiftly adopt those lessons to improve their products. A unit at Ubisoft, a leading game publisher headquartered in Paris, has established a three-pronged approach to drive product innovation based on three practices: data-driven exploration, data-augmented ideation, and data-informed validation. By establishing processes and capabilities for these practices and blending them in a portfolio approach to product design, they maximize the value generation potential of the data at their disposal. Product development in a variety of industries can benefit from the lessons of these data-oriented innovation practices.

9

Silvola, Risto, Arto Tolonen, Janne Harkonen, Harri Haapasalo, and Tarja Mannisto. "Defining one product data for a product." International Journal of Business Information Systems 30, no. 4 (2019): 489. http://dx.doi.org/10.1504/ijbis.2019.099308.

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10

Mannisto, Tarja, Arto Tolonen, Harri Haapasalo, Janne Harkonen, and Risto Silvola. "Defining one product data for a product." International Journal of Business Information Systems 30, no. 4 (2019): 489. http://dx.doi.org/10.1504/ijbis.2019.10020637.

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11

Do, Namchul, HyunChul Kang, and Heechul Bae. "Product Development Processes and Product Data Model for Supporting Development of Smart, Connected Products." Journal of the Korean Institute of Industrial Engineers 46, no. 2 (April 30, 2020): 82–93. http://dx.doi.org/10.7232/jkiie.2020.46.2.082.

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12

Susanto, Rani, and Tati Harihayati M. "Pemodelan Data Warehouse Distribusi Produk di PT. X." INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi 3, no. 2 (July 1, 2019): 196. http://dx.doi.org/10.29407/intensif.v3i2.12769.

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The purpose of this study is to provide an overview of the Head of Distribution of PT. X to support product distribution monitoring activities in terms of the number of products to be distributed to each branch office as needed. The problem that occurs is the large number and diversity of products required where each branch office is located far apart, cause the company difficulty in collecting product data that functions for the monitoring process. The solution to this problem is there needs a model that can be used to collect all the necessary data for monitoring product distribution using the Data warehouse method. This method is used to collect diverse data into a storage area so that users can quickly analyze the data needed. OLTP is part of the Data Warehouse, which is the initial stage of modeling data sources, then with ETL processes, which are the basis for modeling data warehouse schemes. So, this model can be used as a reference to produce useful information for managerial parties PT. X in the next study.

13

Liu, Gang, Rongjun Man, and Yanyan Wang. "A Data Management Approach Based on Product Morphology in Product Lifecycle Management." Processes 9, no. 7 (July 16, 2021): 1235. http://dx.doi.org/10.3390/pr9071235.

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In the product life cycle from conception to retirement, there are three forms: conceptual products, digital products and physical products. The carriers of conceptual products are requirements, functions and abstract structures, and data management focuses on the mapping of requirements, functions, and structures. The carrier of digital products is digital files such as drawings and models, and the focus of data management is the design evolution of product. Physical products are physical entities, and their attributes and states will change over time. Existing data model research often focuses on one or two forms, and it is even impossible to integrate three forms of data into one system. So, a new data management method based on product form is presented. According to the characteristics of the three product form data, a conceptual product data model, a digital product data model, and a physical product data model are established to manage the three forms of data, respectively, and use global object mapping to integrate them into a unified data model. The conceptual product data model has a single data model for a single business stage. The digital product data model uses the core data model as the single data source, and uses one stage rule filter to add constraints to the core data model for each business stage. The physical product data model uses the core data model to manage the public data of the physical phase, and the phase private data model focuses on the private data of each business phase. Finally, a case of Multi-Purpose Container Vessel is studied to verify the feasibility of the method. This paper proposes three product forms of product data management and a unified data management model covering the three product forms, which provides a new method for product life cycle data.

14

Lei, X., Y. Wang, and T. Guo. "DOWNSCALING OF SMAP SOIL MOISTURE PRODUCT BY DATA FUSION WITH VIIRS LST/EVI PRODUCT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 355–60. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-355-2021.

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Abstract. Soil moisture is an essential variable of environment and climate change, which affects the energy and water exchange between soil and atmosphere. The estimation of soil moisture is thus very important in geoscience, while at same time also challenging. Satellite remote sensing provides an efficient way for large-scale soil moisture distribution mapping, and microwave remote sensing satellites/sensors, such as Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR), and Soil Moisture Active Passive (SMAP) satellite, are widely used to retrieve soil moisture in a global scale. However, most microwave products have relatively coarse resolution (tens of kilometres), which limits their application in regional hydrological simulation and disaster prevention. In this study, the SMAP soil moisture product with spatial resolution of 9km is downscaled to 750m by fusing with VIIRS optical products. The LST-EVI triangular space pattern provides the physical foundation for the microwave-optical data fusion, so that the downscaled soil moisture product not only matches well with the original SMAP product, but also presents more detailed distribution patterns compared with the original dataset. The results show a promising prospect to use the triangular method to produce finer soil moisture datasets (within 1 km) from the coarse soil moisture product.

15

You, Chun-Fong, and Tung-Hua Chan. "Assurance of Product Data." Computer-Aided Design and Applications 3, no. 1-4 (January 2006): 221–30. http://dx.doi.org/10.1080/16864360.2006.10738459.

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16

Lin, Chen. "Data driven product management." IEEE Engineering Management Review 46, no. 1 (March 1, 2018): 16–18. http://dx.doi.org/10.1109/emr.2018.2810099.

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17

Dong, Jun Hua. "Green Bicycle Product Designation Using Product Data Management System." Advanced Materials Research 630 (December 2012): 483–85. http://dx.doi.org/10.4028/www.scientific.net/amr.630.483.

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Environmental norms and the combination of market mechanism has become an international trend in recent years, therefore green product design is an important research topic. In this paper, we apply product data management system to the R&D of bicycle as a product design management tools, products and components to be established a database in order to generate bill of material to facilitate the assessment, re-use evaluation software inventory of green bicycle main parts, and to provide of the green bicycle industry reference for the design.

18

Zhang, Wubing. "Data Mining Technology for Equipment Machinery and Information Network Data Resources." Security and Communication Networks 2022 (August 3, 2022): 1–8. http://dx.doi.org/10.1155/2022/5928611.

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In order to solve the problem of aviation equipment system maintenance, it is very difficult to judge the faulty finished product according to the fault phenomenon, the author proposes a data mining-based prediction model for aviation equipment failure finished products. The model takes historical fault record data as input, clusters a large number of fault descriptions through text clustering to obtain fault phenomenon clusters, and establishes a many-to-many relationship between “fault phenomenon” and “fault finished product.” A probability distribution algorithm for faulty finished products is proposed, and by matching new fault phenomena and fault phenomenon clusters, the probability distribution of faulty finished products is calculated. The experimental results show that after calling the model to complete the clustering of the fault information database, 18966 fault phenomenon clusters are obtained, and each fault phenomenon cluster contains 2.9 fault records on average, the many-to-many relationship between the fault phenomenon and the faulty finished product of the fault information database is successfully constructed. The model can effectively predict the probability distribution of products that may fail according to the fault description, and the prediction accuracy can be improved with the increase of the amount of data to meet the actual security needs.

19

Trenevska Blagoeva, Kalina, and Marina Mijoska Belsoska. "DEVELOPING DATA DRIVEN PRODUCTS IN THE EMERGING MARKETS." KNOWLEDGE INTERNATIONAL JOURNAL 30, no. 1 (March 20, 2019): 197–202. http://dx.doi.org/10.35120/kij3001197t.

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The expansion of the digital economy and the rapid developments of technology induced the creation of new products and industries and drove a significant increase in data resources. The information products industry, including products based on data, information and knowledge, is intensely dynamic in terms of growth and the pace of new product introduction. The complexity in the variety of product offerings and the number of firms offering those products in this industry is increasing exponentially every day. Data-driven innovation forms a key pillar in the 21 century sources of growth. Large data sets are becoming a core asset in the economy, fostering new industries, processes, and products and creating significant competitive advantages (OECD, 2015). The past two decades have brought several reconfigurations of the information and knowledge economy. The recent technological breakthroughs have driven the emergence and the exponential growth of a digital economy with vast data assets. The changes have been accompanied by ongoing attempts to make sense of all the data through the use of analytics. Analytics add substantial value to intangible assets by making them easier to understand and apply. In a world in which information alone has become ubiquitous and somewhat commoditized, analytics provide a means of making information more useful and valuable. In this paper, we focus on new analytical capabilities and data assets that together form value-added information product offerings and new possibilities for emerging markets. These offerings are often called data products. In general, a data product is digital information that can be purchased. Data products incorporate data science into the operation of a product or service, using data in smart ways to provide value. In research, a data product is a large data set in a format that requires little or no processing or programming. Typical data products are predictive, descriptive or prescriptive models, as well as insights. The future of new product development reflects both developing new innovative products and data driven products typical for emerging markets that are large economies. For any organization, there is not only revenue, but competitive advantage to be gained in developing data products and new innovative products. Creating an effective development process for data products requires following well-established steps and data analytics helps adding a few new ones, which are explained in the paper. Further, this paper will focus on three important decisions for innovative process: decision 1- key enablers of emerging market innovation (R&D capability, market opportunity and executive champions), decision 2 - what product to develop (market need, portfolio fit, and product-capability fit) and decision 3 - how to develop the product (the decision matrix and bootstrapping). Organizations can follow several approaches to monetize their data like improving internal business processes and decisions, focusing information around core products and services, and selling information offerings to emerging and existing markets. Few remarks for our country will be made as further paths of development.

20

Lestari, Anissa, and Saruni Dwiasnati. "Implementation of Decision Tree for Making Decision of Claim Product from Steel Production." Journal of Systems Engineering and Information Technology (JOSEIT) 1, no. 1 (March 25, 2022): 1–9. http://dx.doi.org/10.29207/joseit.v1i1.2233.

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Product Claims is requests from consumers for products purchased from suppliers in accordance with agreements agreed by both parties. Products that have been claimed from consumers produce historical data sets that can be used as evaluations for producers to produce higher quality products. This study aims to process production data and shipment data then classify the types of products claimed based on the results of claim report from consumers. Data mining can be extracted information from a very large amount of data with specific methods to obtain information or new science. The method used in this study is the C4.5 algorithm method using the production code attribute as a claim or non-claim label attribute. This study produced a decision tree of 4 variables, there are thick of product, width of product, weight of product, destination of product, and type of product claim as label. This decision tree concept collects data which then calculates the value of entropy and gain to determine the rule. The conclusion from this study is the C4.5 algorithm helps classify the product claims and form a decision tree that can provide information about production results and can ensure with consumers related to product limits that may be claimed according to the agreed agreement. Evaluation of the results obtained that the algorithm C4.5 is 99.9% accuracy.

21

Zhou, Z. D., Q. S. Ai, Q. Liu, W. Z. Yang, and S. Q. Xie. "A STEP-compliant product data model for injection moulding products." International Journal of Production Research 47, no. 16 (June 3, 2009): 4497–520. http://dx.doi.org/10.1080/00207540701851780.

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22

MEIJER, A. H. "Electronic data interchange for product structure data." International Journal of Computer Integrated Manufacturing 2, no. 4 (July 1989): 220–28. http://dx.doi.org/10.1080/09511928908944405.

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23

Sari, Juni Nurma, Lukito Edi Nugroho, Paulus Insap Santosa, and Ridi Ferdiana. "Product Recommendation Based on Eye Tracking Data Using Fixation Duration." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 4 (December 24, 2021): 109. http://dx.doi.org/10.22146/ijitee.58693.

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E-commerce can be used to increase companies or sellers’ profits. For consumers, e-commerce can help them shop faster. The weakness of e-commerce is that there is too much product information presented in the catalog which in turn makes consumers confused. The solution is by providing product recommendations. As the development of sensor technology, eye tracker can capture user attention when shopping. The user attention was used as data of consumer interest in the product in the form of fixation duration following the Bojko taxonomy. The fixation duration data was processed into product purchase prediction data to know consumers’ desire to buy the products by using Chandon method. Both data could be used as variables to make product recommendations based on eye tracking data. The implementation of the product recommendations based on eye tracking data was an eye tracking experiment at selvahouse.com which sells hijab and women modest wear. The result was a list of products that have similarities to other products. The product recommendation method used was item-to-item collaborative filtering. The novelty of this research is the use of eye tracking data, namely the fixation duration and product purchase prediction data as variables for product recommendations. Product recommendation that produced by eye tracking data can be solution of product recommendation’s problems, namely sparsity and cold start.

24

Zi-Yang Ye, Zi-Yang Ye, Xuan Ji Zi-Yang Ye, Ming-Zi Ye Xuan Ji, Yu-Tong Shan Ming-Zi Ye, and Xiang-Rong Shi Yu-Tong Shan. "Data Analysis of Amazon Product Based on LSTM and GPR." 電腦學刊 33, no. 4 (August 2022): 015–27. http://dx.doi.org/10.53106/199115992022083304002.

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<p>In this paper, we propose a method that combines models such as GPR with PSO optimization to predict the time series data. We use LSTM and TOPSIS with entropy weight method modification to process vari-ous types of data from various aspects, taking into account both tabular and textual data, and to mine valuable contents from them. Based on shopping data, we analyze the historical situation and predict the future sales of products. So that we can recommend the most suitable products for customers. At the same time, for merchants, this paper provides directions for product optimization and improvement of advertising and marketing strategies.</p> <p> </p>

25

Burkett, William C. "Product data markup language: a new paradigm for product data exchange and integration." Computer-Aided Design 33, no. 7 (June 2001): 489–500. http://dx.doi.org/10.1016/s0010-4485(01)00048-3.

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26

Peng, Bao Hua, J. L. Zhou, and Jing Feng. "Product Reliability Assessment Method Combining Degradation Data and Lifetime Data." Advanced Materials Research 44-46 (June 2008): 795–802. http://dx.doi.org/10.4028/www.scientific.net/amr.44-46.795.

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Reliability of products has long been considered as an important quality characteristic. Traditional methods of product reliability assessment are based on lifetime data. With products being much more reliable and the growing need for developing new products within shorter period and at lower cost, we can hardly get enough lifetime data in many cases. Performance degradation data can also be used for reliability assessment. Recently, they are found to be useful in some cases where they are easier to collect. But when performance degradation data are also limited and some lifetime data are available, it is preferred to utilize both information sources. This paper deals with the problem of reliability assessment combining both performance degradation data and lifetime data. It is assumed that two samples from the same product are tested differently. Degradation data are collected from one sample and lifetime data from the other. First, the performance degradation model is established, using either statistical methods or methods from physics of failure. Then lifetime of the product, which is defined as the first time when the performance crosses the known failure threshold, is calculated. The MLE method is used for parameter estimation where the maximum likelihood function is multiplication of the one from degradation data and the one from lifetime data. To illustrate the proposed method, an example of the metallized film capacitor, which is used in inertial confinement fusion (ICF) facility, is given. We model the performance degradation data of metallized film capacitor with Wiener process with drift. The failure of the capacitor is defined as the first time when its capacitance drops below a threshold. The lifetime distribution is deduced and the parameters are estimated from the joint maximum likelihood function. A comparison is conducted between the assessment results of degradation data only and those of combination of degradation data and lifetime data. In conclusion we propose that both degradation information and lifetime information should be used when neither of them is sufficient enough for reliability assessment. Some directions for future work are also discussed.

27

Dickopf, T., and C. Apostolov. "Closed-Loop Engineering Approach for Data-Driven Product Planning." Proceedings of the Design Society 2 (May 2022): 373–82. http://dx.doi.org/10.1017/pds.2022.39.

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AbstractThis contribution introduces an approach for data-driven optimization of products and their product generations through a Closed-Loop Engineering approach resulting from the German research project DizRuPt. The approach focuses on data-driven product planning by ensuring data consistency and traceability between product planning, product development, and product operation by combining aspects and functions from Product Lifecycle Management (PLM) and the Internet of Things (IoT). The presented approach is illustrated and validated by pilot applications from the research project.

28

Suwarsono. "Pengembangan Basis Data Untuk Mengelola Komponen Perkakas Bantu Perakitan." Jurnal Teknik Industri 3, no. 1 (April 26, 2010): 1. http://dx.doi.org/10.22219/jtiumm.vol3.no1.1-11.

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Welding assembly jig company's activities are classified as job-order. Their products (jigs & fixtures) were depending on type of product, which was assembled. There are loot-of variations of product designs, process planning, tools, shop-floor control etc. to handling all customer order. One of the solutions is standardization product, to reduce product variation and the control problems.

29

YAMADA, SHIGERU, and AKIHIRO KAWAHARA. "STATISTICAL ANALYSIS OF PROCESS MONITORING DATA FOR SOFTWARE PROCESS IMPROVEMENT." International Journal of Reliability, Quality and Safety Engineering 16, no. 05 (October 2009): 435–51. http://dx.doi.org/10.1142/s0218539309003484.

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In recent years, software development has become more large-scaled, complicated, and diversified. At the same time, customer requirement of high quality and shortened delivery has increased. Therefore, we have to manage process quality and control product quality in the early-stage of software development in order to produce highly quality software products during the limited period. In this paper, we conduct multivariate linear analyses by using process monitoring data, derive effective process factors affecting the final product quality, and discuss the significant process factors with respect to software management measures of quality, cost, and delivery (QCD). Then, we discuss project management on the significant process factors affecting QCD and show its effect on QCD.

30

Tobin, Kenneth J., and Marvin E. Bennett. "Adjusting Satellite Precipitation Data to Facilitate Hydrologic Modeling." Journal of Hydrometeorology 11, no. 4 (August 1, 2010): 966–78. http://dx.doi.org/10.1175/2010jhm1206.1.

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Abstract Significant concern has been expressed regarding the ability of satellite-based precipitation products such as the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 products (version 6) and the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s (CPC) morphing technique (CMORPH) to accurately capture rainfall values over land. Problems exist in terms of bias, false-alarm rate (FAR), and probability of detection (POD), which vary greatly worldwide and over the conterminous United States (CONUS). This paper directly addresses these concerns by developing a methodology that adjusts existing TMPA products utilizing ground-based precipitation data. The approach is not a simple bias adjustment but a three-step process that transforms a satellite precipitation product. Ground-based precipitation is used to develop a filter eliminating FAR in the authors’ adjusted product. The probability distribution function (PDF) of the satellite-based product is adjusted to the PDF of the ground-based product, minimizing bias. Failure of precipitation detection (POD) is addressed by utilizing a ground-based product during these periods in their adjusted product. This methodology has been successfully applied in the hydrological modeling of the San Pedro basin in Arizona for a 3-yr time series, yielding excellent streamflow simulations at a daily time scale. The approach can be applied to any satellite precipitation product (i.e., TRMM 3B42 version 7) and will provide a useful approach to quantifying precipitation in regions with limited ground-based precipitation monitoring.

31

Guo, Jingzhi, and Chengzheng Sun. "Transforming ad hoc product data into canonical product representation." International Journal of Internet and Enterprise Management 3, no. 2 (2005): 117. http://dx.doi.org/10.1504/ijiem.2005.007636.

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32

Tang, Yan. "Studies on Broad-Sense Sample Method in Data Mining." Advanced Materials Research 989-994 (July 2014): 1453–55. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1453.

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With the development of science and technology, people pay more and more attention to the reliability of the products, especially in some special field, such as aerospace, military products, and some products of high reliability and long life. As a part that runs through the whole life cycle of products, reliability test provides an important source of data for the design, batch production and residual life assessment of the product development. For some expensive, new products put into use, they are not quite little in amount, having the characteristics of small sample. In this case, how to use the existing data to predict product life, reliability of calculating the reliability of a product more accurately and other related parameters is particularly important.

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Vuppalapati, Sai Mahesh. "Mastering Data Product Development: Strategies, Architectures, and Best Practices." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 3658–69. http://dx.doi.org/10.22214/ijraset.2024.60647.

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Abstract: The importance of data products for organizations cannot be overstated, as they enable valuable insights and informed decision-making. This article offers a thorough guide to constructing data products, with a specific emphasis on data warehouse architecture and data product management. Key concepts in data product management are explored, such as treating data as a product, understanding the data product lifecycle, and defining roles and responsibilities. The article explores the key elements of a data warehouse, including data integration and ETL processes, data modeling, and storage and retrieval techniques. This approach outlines a systematic process for developing data products, covering everything from identifying opportunities and defining requirements to design, implementation, testing, deployment, and maintenance. Emphasizing the significance of data governance, quality assurance, security, privacy, and regulatory compliance. Additionally, we delve into performance metrics, monitoring, and strategies for continuous improvement of data products. The article showcases case studies, such as Our World in Data [1], to demonstrate real-world applications and best practices. Finally, we will explore future trends, challenges, and opportunities in data p

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Yuliarnis, Sri Kurnia, Yeka Hendriyani, Denny Kurniadi, and M. Giatman. "APPLICATION OF DATA MINING FOR ANALYSIS OF CONSUMER PURCHASE DATA ON SALES TRANSACTION DATA AT HALAL MART HNI HPAI DHARMASRAYA." JURNAL PENDIDIKAN TEKNOLOGI KEJURUAN 3, no. 1 (February 26, 2020): 68–75. http://dx.doi.org/10.24036/jptk.v3i1.6923.

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The sales strategy determines the continuity of the business being run. The problems that occur are the sales archive data has not been analyzed in-depth, the information system has not been integrated with applications for sales data analysis, online media promotion has not been maximized, inadequate stock of goods, the layout of goods is not optimal, and the combination of the number of products is not optimal. This study aims to extract hidden information in the sales database using Data Mining. From the information generated, sales strategy recommendations are developed relating to promotions, inventory, catalogue design, item layout, and the combination of product quantities. The method used is the association rule with Apriori algorithm to find consumer purchase patterns through the resulting association. The importance of association can be identified by two benchmarks, namely support and confidence. The sales strategy analyzed includes product promotion, catalogue design, product layout, stock predictions, and product combinations for sale. Based on the research produced 7 strong rules which are the highest association rules which are then developed into a sales strategy recommendation.

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Janićijević, Stefana, Đorđe Petrović, and Miodrag Stefanović. "Sales prediction on e-commerce platform, by using data mining model." Serbian Journal of Engineering Management 5, no. 2 (2020): 60–76. http://dx.doi.org/10.5937/sjem2002060j.

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In this paper we applied twinning algorithm for product that are sold via e-commerce platform. To establish relatively homogenous product groups that were on sale on this e-commerce platform during the last year, it was necessary to form predictive mathematical model. We determined set of relevant variables that will represent group attributes, and we applied K-means algorithm, Market Basket model and Vector Distance model. Based on analysis of basic and derived variables, fixed number of clusters was introduced. Silhouette index was used for the purposes of detecting whether these clusters are compact. Using these cluster separations, we created models that detect similar products, and try to analyze probability of sales for each product. Analysis results can be used for planning future sales campaigns, marketing expenses optimization, creation of new loyalty programs, and better understanding customer behavior in general.

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Lewis, Scott, Michael Mateas, Susan Palmiter, and Gene Lynch. "Ethnographic data for product development." Interactions 3, no. 6 (December 1996): 52–69. http://dx.doi.org/10.1145/242485.242505.

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McKay, A., M. S. Bloor, and A. de Pennington. "A framework for product data." IEEE Transactions on Knowledge and Data Engineering 8, no. 5 (1996): 825–38. http://dx.doi.org/10.1109/69.542033.

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Skeate, Robert C. "Transfusion medicine data as product." Transfusion 53, no. 6 (June 2013): 1153–56. http://dx.doi.org/10.1111/trf.12226.

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Goldby, M. "Getting agile with product data." Manufacturing Engineer 81, no. 6 (December 1, 2002): 257–59. http://dx.doi.org/10.1049/me:20020603.

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Gott, Brian. "The product data management market." World Class Design to Manufacture 2, no. 4 (August 1995): 18–22. http://dx.doi.org/10.1108/09642369310091115.

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Zubritsky, Elizabeth. "Product Review: Chromatography data systems." Analytical Chemistry 71, no. 9 (May 1999): 333A—337A. http://dx.doi.org/10.1021/ac990357p.

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Guo, Jingzhi, and Chengzheng Sun. "Context representation of product data." ACM SIGecom Exchanges 4, no. 1 (March 2003): 20–28. http://dx.doi.org/10.1145/844357.844364.

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Ruliah, Ruliah, Bahar Bahar, and Andita Suci Pratiwi. "PENGEMBANGAN DESAIN PEMBELAJARAN SISTEM BASIS DATA." Instruksional 2, no. 2 (July 29, 2021): 7. http://dx.doi.org/10.24853/instruksional.2.2.7-17.

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Instructional design plays an important role in improving the quality of learning and the learning experience of students. But in fact, many universities (especially in the field of Information Technology) have not developed a structured learning design, so that the learning process becomes ineffective. This paper presents a database system learning design model using the Borg and Gall step 1 development model and adapting all stages in the Dick and Carey model. The research consists of four main stages: conducting a preliminary study to find information about the learning product to be developed; develop products based on research findings; conduct field trials on the products developed; and revise products based on test results. The product quality of the developed learning design model is measured from aspects of validity and aspects of practicality. Validity is assessed through expert validation (database system learning material expert, instructional design expert, graphic media expert, and linguist). The measurement results obtained an average value of 5 (on a scale of 1-5), which means that the product of the learning design model developed is included in the valid category. Practicality is measured based on the responses of students and teachers. The results of the practicality measurement obtained an average value of 4.56 (on a scale of 1-5), which means that the product of the learning design model developed is included in the Practically used category

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Kim, Yijin, Hong Joo Lee, and Junho Shim. "Developing Data-Conscious Deep Learning Models for Product Classification." Applied Sciences 11, no. 12 (June 19, 2021): 5694. http://dx.doi.org/10.3390/app11125694.

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In online commerce systems that trade in many products, it is important to classify the products accurately according to the product description. As may be expected, the recent advances in deep learning technologies have been applied to automatic product classification. The efficiency of a deep learning model depends on the training data and the appropriateness of the learning model for the data domain. This is also applicable to deep learning models for automatic product classification. In this study, we propose deep learning models that are conscious of input data comprising text-based product information. Our approaches exploit two well-known deep learning models and integrate them with the processes of input data selection, transformation, and filtering. We demonstrate the practicality of these models through experiments using actual product information data. The experimental results show that the models that systematically consider the input data may differ in accuracy by approximately 30% from those that do not. This study indicates that input data should be sufficiently considered in the development of deep learning models for product classification.

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Peng, Wei Ping, Yuan Hua Zhong, Zhao Liu, Jing Li, and Rong Gao. "Research on Data Processing in PLM Product Based on Cloud Platform." Advanced Materials Research 1046 (October 2014): 469–76. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.469.

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To solve the problem of massive PLM product data analysis, a PLM product data analysis system based on OpenStack cloud platform was proposed.It includes a data analysis method of structured products based on data warehouse and a data analysis method of non-structured products based on Hadoop. By means of the former method, firstly product data was filtered, tranformed and loaded into the warehouse, then the required data cube was extracted, lastly the structured product data was analyzed with the analysis tools of data warehouse. By means of the latter method, the product data firstly was loaded into the distributed file system,and the non-structured massive PLM product data was analyzed by the data mining algorithm,which was programmed by JAVA language based on MapReduce. By applying the methods mentioned above to massive PLM product data analysis, it shows that these methods hava a higher efficiency.

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Mulhall, Douglas, Anne-Christine Ayed, Jeannot Schroeder, Katja Hansen, and Thibaut Wautelet. "The Product Circularity Data Sheet—A Standardized Digital Fingerprint for Circular Economy Data about Products." Energies 15, no. 9 (May 6, 2022): 3397. http://dx.doi.org/10.3390/en15093397.

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Background. Laws that enable a circular economy (CE) are being enacted globally, but reliable standardized and digitized CE data about products is scarce, and many CE platforms have differing exclusive formats. In response to these challenges, the Ministry of The Economy of Luxembourg launched the Circularity Dataset Standardization Initiative to develop a globalized open-source industry standard to allow the exchange of standardized data throughout the supply cycle, based on these objectives: (a) Provide basic product circularity data about products. (b) Improve circularity data sharing efficiency. (c) Encourage improved product circularity performance. A policy objective was to have the International Organization for Standardization (ISO) voted to create a working group. Methods. A state-of-play analysis was performed concurrently with consultations with industry, auditors, data experts, and data aggregation platforms. Results. Problem statements were generated. Based on those, a solution called Product Circularity Data Sheet (PCDS) was formulated. A proof of concept (POC) template and guidance were developed and piloted with manufacturers and platforms, thus fulfilling objective (a). For objective (b), IT ecosystem requirements were developed, and aspects are being piloted in third party aggregation platforms. Objective (c) awaits implementation of the IT ecosystem. The policy objective related to the ISO was met. Conclusions and future research. In order to fully test the PCDS, it is necessary to: conduct more pilots, define governance, and establish auditing and authentication procedures.

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Martin, John. "Data, Data Everywhere." Mechanical Engineering 137, no. 07 (July 1, 2015): 46–51. http://dx.doi.org/10.1115/1.2015-jul-3.

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This article explores evolution of product lifecycle management (PLM) and its advantages. PLM is commonly defined as a set of applications that enable the creation, design, and development of new products through rollout, servicing, upgrade, and end of life. PLM seller Dassault Systèmes, for example, said its 3DExperience platform is compliant with more than 40 standards requested by industry, including web, communication, visualization, and security standards. Most PLM software is able to generate reports from information located in a single system; but only skilled users are able to access, aggregate, and analyze real-time structured and unstructured data found in multiple applications across the organization. Social networks are cropping up in PLM, helping users quickly identify and construct communities with complementary skills to solve problems and enable processes. The experts comment that wherever the end user is working, behind the scenes, the PLM platform is ensuring real-time visibility and control—driving better products and reducing liability and risk.

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Wang, Bing, Timothy R. Anderson, and Wilson Zehr. "Competitive Pricing Using Data Envelopment Analysis — Pricing for Oscilloscopes." International Journal of Innovation and Technology Management 13, no. 01 (January 28, 2016): 1650006. http://dx.doi.org/10.1142/s0219877016500061.

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The research in this paper proposes a new technique for pricing products in competitive markets taking into account the features and prices of competing product offerings. This technique is based on a methodology known as data envelopment analysis (DEA) and is referred to as competitive pricing using data envelopment analysis (CPDEA). With the development of technology accelerating and new products coming to the market at an ever faster pace, prices of current products are often adjusted based on the state-of-the-art (SOA) technology in the market in order to remain competitive. CPDEA measures the product features that are most important to customers and calculates the performance efficiency values using the DEA method. CPDEA regards price as a performance feature, using this approach the manufacturer can adjust the price in order for a product to reach the SOA frontier and maintain competitive pricing. This research demonstrates the proposed method applied to a popular product category in the test and measurement industry: oscilloscopes. The authors investigated the features of oscilloscopes that are most important to users, then a feature dataset from different oscilloscope models was collected, and the performance efficiency values of the different models were calculated. The product prices are then adjusted in order for efficiency to be as close to 1 as possible which means that the products are considered SOA in the market. In this way, we obtain a more competitive price for the older products, while also setting the prices for the advanced products in a way that captures the value of their additional features.

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Astuti, Juli, and Trisna Yuniarti. "Data Mining Modeling in Clustering Car Products Sales Data in the Automotive Industry in Indonesia." Jurnal Manajemen Industri dan Logistik 7, no. 2 (November 4, 2023): 261–81. http://dx.doi.org/10.30988/jmil.v7i2.1258.

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The research aims to build a model based on sales data for all automotive products in Indonesia using data mining with a k-means approach. This study uses automotive product sales data from January 2017 to September 2022. The lowest Davis-Bouldin index shows that three clusters (k=3) have the best performance. Based on the clustering results, 92% of the items are in cluster 0, 1% in cluster 1, and 7% in cluster 2. In addition, the clustering results show that cluster 1 is a car product with high sales volume. Cluster 2 is a car product with medium sales volume. Furthermore, cluster 0 is a car product with low sales volume. Business people or related parties can use data visualization and extraction from clustering results to learn the latest insights and information in determining business strategies, policies, and decisions to improve business competitiveness.

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Schröter, Norbert, and Marieke Thome. "Product Data Templates of Repair Products for Building Information Modeling (BIM)." MATEC Web of Conferences 364 (2022): 04009. http://dx.doi.org/10.1051/matecconf/202236404009.

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Building Information Modeling (BIM) describes the digital and collaborative planning, construction and management of buildings by all stakeholders by means of a digital 3D building (data) model. This enables complex analyses, simulations and optimizations of cost, time and quality. For these reasons, BIM is becoming increasingly important in the construction industry and brings changes along the entire “construction value chain”. Such changing and unclear (data) requirements are major challenges for the manufacturers of construction chemicals, e.g. for products for “Concrete Repair and Protection”. As harmonized standards are still lacking and a wide variety of concepts are available on the market today. Therefore, demand-oriented BIM product data templates for two pilot groups of construction chemicals products were developed by the German association of manufacturers of construction chemical products (Deutsche Bauchemie e. V.). With all parties involved in the process, from planning to construction, the information requirements were recorded. Furthermore, characteristics were defined using existing standards and structured in an extensive list of characteristics. In addition, software templates were created in native but also in open file formats. The development of further lists of characteristics is currently in preparation, in addition to other product groups, also for repair mortars and other repair products.