From technological development to social advance: A review of Industry 4.0 through machine learning

Industry 4.0 has attracted considerable interest from firms, governments, and individuals as the new concept of future computer, industrial, and social systems. However, the concept has yet to be fully explored in the scientific literature. Given the topic's broad scope, this work attempts to understand and clarify Industry 4.0 by analyzing 660 journal papers and 3,901 news articles through text mining with unsupervised machine learning algorithms. Based on the results, this work identifies 31 research and application issues related to Industry 4.0. These issues are categorized and described within a five-level hierarchy: 1) infrastructure development for connection, 2) artificial intelligence development for data-driven decision making, 3) system and process optimization, 4) industrial innovation, and 5) social advance. Further, a framework for convergence in Industry 4.0 is proposed, featuring six dimensions: connection, collection, communication, computation, control, and creation. The research outcomes are consistent with and complementary to existing relevant discussion and debate on Industry 4.0, which validates the utility and efficiency of the data-driven approach of this work to support experts??? insights on Industry 4.0. This work helps establish a common ground for understanding Industry 4.0 across multiple disciplinary perspectives, enabling further research and development for industrial innovation and social advance

Customer process management A framework for using customer-related data to create customer value

Purpose The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services. Design/methodology/approach This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature. Findings The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature. Originality/value "Using data to advance service" is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company's use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data

Challenges of diet planning for children using artificial intelligence

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications.MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human-and AI-generated diets in 2 steps.RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human-and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information).CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria

Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records

Aggressive driving behavior (ADB) is a major cause of traffic accidents. As ADB is controllable, ADB-based driving risk assessment is an effective method for drivers and transportation companies to ensure driving safety. Conventionally, the relationships between ADBs and accident-related records are analyzed when assessing driving risk. However, such records typically overlook driver responsibility for driving risks and depend considerably on the person producing the data (e.g., police officers or insurance managers). Foremost, conventional approaches do not consider non-accident situations that comprise most driving scenarios. Thus, we propose a novel driving risk assessment method that uses only ADB data. In this method, interpretable latent risk factors are extracted from ADB data via sparse non-negative matrix factorization (NMF), and then the driving risk score is computed on a scale of 0-100. The proposed method was validated by adopting a real-world application to assess the driving risk of bus drivers in South Korea and by conducting an evaluation performed by transportation experts in conjunction with the Korea Transportation Safety Authority. Results revealed that the proposed method can discriminate between high-and low-risk driving, thus providing clear guidelines to improve driving. Then, the proposed driving risk score assessment method using NMF was compared with existing machine learning-based risk assessment methods. The proposed method outperformed the conventional methods in terms of driving risk discrimination and interpretability. This study can provide risk assessment guidelines based on driving behavior records and contribute to the application of machine learning in transportation safety management

SAO-based semantic mining of patents for semi-automatic construction of a customer job map

The Outcome-Driven Innovation (ODI) method based on the 'Jobs-to-be-done' concept is very useful in the identification of unmet customer needs and has been adopted widely in the industry. The Job Map, a tool of the ODI method, is used to understand customers by defining their behavioral process. Complications must be overcome before the Job Map can be applied to the specific problem in question, such as a time-consuming process, dealing with a large amount of data, and experts' biased work. To solve these problems, this study develops a patent mining-based method based on the subject-action-object (SAO) structure to support the creation of a Job Map by semi-automatizing data collection and analysis. This effort at better utilizing computers in customer analysis for product design will contribute to expanding computerized methods for solving design and engineering problems in practice

Multi-factor service design: identification and consideration of multiple factors of the service in its design process

Service design is a multidisciplinary area that helps innovate services by bringing new ideas to customers through a design-thinking approach. Services are affected by multiple factors, which should be considered in designing services. In this paper, we propose the multi-factor service design (MFSD) method, which helps consider the multi-factor nature of service in the service design process. The MFSD method has been developed through and used in five service design studies with industry and government. The method addresses the multi-factor nature of service for systematic service design by providing the following guidelines: (1) identify key factors that affect the customer value creation of the service in question (in short, value creation factors), (2) define the design space of the service based on the value creation factors, and (3) design services and represent them based on the factors. We provide real stories and examples from the five service design studies to illustrate the MFSD method and demonstrate its utility. This study will contribute to the design of modern complex services that are affected by varied factors

From data to value: A nine-factor framework for data-based value creation in information-intensive services

Service is a key context for the application of IT, as IT digitizes information interactions in service and facilitates value creation, thereby contributing to service innovation. The recent proliferation of big data provides numerous opportunities for information-intensive services (IISs), in which information interactions exert the greatest effect on value creation. In the modern data-rich economy, understanding mechanisms and related factors of data-based value creation in IISs is essential for using IT to improve such services. This study identified nine key factors that characterize this data-based value creation: (1) data source, (2) data collection, (3) data, (4) data analysis, (5) information on the data source, (6) information delivery, (7) customer (information user), (8) value in information use, and (9) provider network. These factors were identified and defined through six action research projects with industry and government that used specific datasets to design new IISs and by analyzing data usage in 149 IIS cases. This paper demonstrates the usefulness of these factors for describing, analyzing, and designing the entire value creation chain, from data collection to value creation, in IISs. The main contribution of this study is to provide a simple yet comprehensive and empirically tested basis for the use and management of data to facilitate service value creation