Data-driven manufacturing: An assessment model for data science maturity

Today, data science presents immense opportunities by turning raw data into manufacturing intelligence in data-driven manufacturing that aims to improve operational efficiency and product quality together with reducing costs and risks. However, manufacturing firms face difficulties in managing their data science endeavors for reaping these potential benefits. Maturity models are developed to guide organizations by providing an extensive roadmap for improvement in certain areas. Therefore, this paper seeks to address this problem by proposing a theoretically grounded Data Science Maturity Model (DSMM) for manufacturing organizations to assess their existing strengths and weaknesses, perform a gap analysis, and draw a roadmap for continuous improvements in their progress towards data-driven manufacturing. DSMM comprises six maturity levels from “Not Performed” to” Innovating” and twenty-eight data science processes categorized under six headings: Organization, Strategy Management, Data Analytics, Data Governance, Technology Management, and Supporting. The applicability and usefulness of DSMM are validated through multiple case studies conducted in manufacturing organizations of various sizes, industries, and countries. The case study results indicate that DSMM is applicable in different settings and is able to reflect the organizations’ current data science maturity levels and provide significant insights to improve their data science capabilities.     

Prediction methods and applications in the science of science: A survey

Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions.     

A knowledge based component/service repository to enhance analysts’ domain knowledge for requirements analysis

Knowledge of the business domain (e.g., insurance claim, human resources) is crucial to analysts’ ability to conduct good requirements analysis (RA). However, current practices afford little assistance to analysts in acquiring domain knowledge. We argue that traditional reuse repositories could be augmented by adding rich faceted information on component/services and artifacts such as business-process templates to help analysts acquire domain knowledge during RA. In this paper, we present the design of a Knowledge Based Component Repository (KBCR) for facilitating RA. Then, we report on the design and development of a KBCR prototype. We illustrate its application in a system that is populated with components and process templates for the auto insurance claim domain. An empirical study was conducted to assess its effectiveness in improving RA. Results showed that KBCR enhanced analysts’ business domain knowledge and helped them better prepare for RA. Our key research contribution is to offer analysts a rich repository (i.e., KBCR) containing domain knowledge that they could utilize to acquire domain knowledge that is crucial for carrying out RA. While repositories of reusable components have been employed for some time, no one has used such repositories to help analysts acquire domain knowledge in order improve the RA of the system.     

A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model

As the manufacturing industry is approaching implementation of the 4th industrial revolution, changes will be required in terms of scheduling, production planning and control as well as cost-accounting departments. Industry 4.0 promotes decentralized production and hence, cost models are required to capture costs of products and jobs within the production network considering the utilized manufacturing system paradigm A new mathematical cost model is proposed for assessing the cost-benefit analysis of introducing Industry 4.0 elements to the manufacturing facility, specifically, integrating and connecting external suppliers as strategic partners and establishing an infrastructure for communicating information between the manufacturing company and its strategic suppliers. The mathematical model takes into consideration the bi-directional relationship between hourly rates and total hours assigned to workcentres/activities in a certain production period. A case study, from a multinational machine builder, is developed and solved using the proposed model. Results suggest that though an additional cost is required to establish infrastructure to connect suppliers, the responsiveness and agility achieved resulting from uncertainty outweighs the additional cost.     

Systematic literature review on augmented reality in smart manufacturing: Collaboration between human and computational intelligence

Smart manufacturing offers a high level of adaptability and autonomy to meet the ever-increasing demands of product mass customization. Although digitalization has been used on the shop floor of modern factory for decades, some manufacturing operations remain manual and humans can perform these better than machines. Under such circumstances, a feasible solution is to have human operators collaborate with computational intelligence (CI) in real time through augmented reality (AR). This study conducts a systematic review of the recent literature on AR applications developed for smart manufacturing. A classification framework consisting of four facets, namely interaction device, manufacturing operation, functional approach, and intelligence source, is proposed to analyze the related studies. The analysis shows how AR has been used to facilitate various manufacturing operations with intelligence. Important findings are derived from a viewpoint different from that of the previous reviews on this subject. The perspective here is on how AR can work as a collaboration interface between human and CI. The outcome of this work is expected to provide guidelines for implementing AR assisted functions with practical applications in smart manufacturing in the near future.     

Applying knowledge engineering techniques to customer analysis in the service industry

Using the customer relationship management perspective to investigate customer behavior, this study differentiates between customers through customer segmentation, tracks customer shifts from segment to segment over time, discovers customer segment knowledge to build an individual transition path and a dominant transition path, and then predicts customer segment behavior patterns. By using real-world data, this study evaluates the accuracy of predictive models. The concluding remarks discuss future research in this area.     

Critical IS professional activities and skills/knowledge: A perspective of IS managers

This study utilized the activity competency model to investigate the perceived importance of critical professional activities and skills/knowledge required by three levels of information system (IS) managers. Our findings indicated that the perceived importance of critical IS professional activities were significantly different among the management levels, but not significantly different for various industry types. Carrying a critical IS activity out involves the use of a number of different skills and/or knowledge. A portfolio of the professional activities and the needed skills/knowledge at each management level are identified. The results of this study have implications for IS professional development and training. They can also serve as a guideline for recruiting the right IS managers and developing IS curriculums.     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

Data mining and knowledge discovery in materials science and engineering: A polymer nanocomposites case study

In this study, data mining and knowledge discovery techniques were employed to validate their efficacy in acquiring information about the viscoelastic properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites solely from data derived from a designed experimental study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. SOMs also showed how to prepare different VGCNF/VE nanocomposites with the same storage and loss modulus responses. A clustering technique, i.e., fuzzy C-means algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens (i.e., specimens containing no VGCNFs) in separate clusters. Most importantly, the results from data mining are consistent with previous response surface characterizations of this nanocomposite system. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics.     

Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry

In recent years, Industry 4.0 has been introduced as a popular term to describe the trend towards digitisation and automation of the manufacturing environment. Despite its potential benefits in terms of improvements in productivity and quality, this concept has not gained much attention in the construction industry. This development is founded in the fact that the far-reaching implications of the increasingly digitised and automated manufacturing environment are still widely unknown. Against this backdrop, the primary objective of this paper is to explore the state of the art as well as the state of practice of Industry 4.0 relating technologies in the construction industry by pointing out the political, economic, social, technological, environmental and legal implications of its adoption. In this context, we present the results of our triangulation approach, which consists of a comprehensive systematic literature review and case study research, by illustrating a PESTEL framework and a value chain model. Additionally, we provide recommendations for further research within a research agenda.     

A knowledge extraction and representation system for narrative analysis in the construction industry

Many researchers advocate that the real-world narratives shared by experts or knowledge workers are helpful in teaching and educating novices to learn new knowledge and skills. Narrative analysis is a useful method for experts to understand narratives. However, it does not produce any clear or explicit layouts. This is not easy for a new learner without prior knowledge to glean the right messages from narratives within a short time. In this paper, a narrative knowledge extraction and representation system (NKERS) is presented to extract and represent narrative knowledge in an effective manner. The NKERS is composed of a narrative knowledge element extraction algorithm, a narrative knowledge representation method and a narrative knowledge database. A prototype system has been built and trial implemented in the construction industry. The results show that the domain experts agree that the narrative maps generated by the NKERS can effectively represent narrative elements and flows. Three-quarters of respondents expressed that they will use the produced narrative maps in their training courses to facilitate students’ learning.     

NTRU中间距失败的概率分析及其影响

NTRU是一种极有发展潜力的公钥密码体制,然而它的解密机制是不完善的,可能出现合法密文无法解密的现象,这严重影响了该体制的安全性.间距失败是导致解密失败的一个重要原因,对此,在p=2相关多项式均为系数为0或1的二元多项式条件下,讨论了卷积系数的弱线性相关性,在此基础上对间距失败的概率计算进行了介绍和分析,得到了相关的计算公式,并介绍了一种利用间距失败对NTRU的攻击方法.     

Data structures and program correctness: Bridging the gap

An assertion language for data structures is presented, leading to the following results: formal semantics of operations on data structures are given in terms of the weakest precondition formula for assignment statements; input/output specifications for data-structure manipulating algorithms can be stated with precision; there is a clear relationship between the output specification and intermediate assertions; and knowledge about standard types of data structures can be schematized. These ideas are illustrated on an algorithm to reverse the arcs on a one-way linked list, and on a threaded tree example.     

Mass customized/personalized manufacturing in Industry 4.0 and blockchain: Research challenges,main problems,and the design of an information architecture

Mass customized and mass personalized production has become facilitated by the fourth industrial revolution. The resulting industrial environments require the development of information systems able to take the specifications of customers and convey them to the production system in such a way as to contribute to the coordination of all the stakeholders and activities required to fulfill the orders of the customers. This is beyond the capabilities of traditional systems based on MRP and ERP, since the information should be managed in a flexible and decentralized way to exploit the Smart Manufacturing facilities of Industry 4.0. Blockchain, instead, is a technology that provides those features constituting a sound information supporting basis for mass customized/personalized production. Consequently, we explore the potential of blockchain as an information technology able to support industries that base their business models on mass customized/personalized production processes. This survey allows us to identify important challenges for further developments, highlighting three issues in the production setting: (i) to deepen the interoperability of systems, (ii) to generate more implementations, and (iii) to develop efficient consensus protocols. As a response to these insights we provide a conceptual design of how blockchain contributes to managing efficiently mass customized production systems. In our design the information of customer specifications can be fused with data from the production process to generate a plan to fulfill the demand. This design arises as a solution approach to three stated problem, which are faced by mass customized production systems.     

Applications of virtual reality in maintenance during the industrial product lifecycle: A systematic review

As a state-of-the-art computer technology, virtual reality (VR) is considered to play an important role in helping manufacturing companies stay competitive in the international market. However, despite the achievements made in the field of VR, it is still an emerging technology that lacks deeper exploration and development in industrial application scenarios, especially in the coming fourth industrial revolution (Industry 4.0). This paper aims to systematically investigate the applications of VR in industrial maintenance to discover evidence of its values, limitations, and future directions so that VR can be guided to better serve manufacturing enterprises in remaining competitive in the coming Industry 4.0. A systematic literature review (SLR) methodology is adopted to review primary studies on this topic, by which 86 studies are ultimately included. The results show that VR has proved its value in benefiting maintenance issues through the product lifecycle. However, VR is still not an indispensable element for the lifecycle management of products regarding maintenance-related issues. Several key findings are concluded based on the analysis of the 86 studies. This review is valuable for researchers who are interested in the application of VR technology in maintainability design, maintenance training or maintenance task assistance.     

A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0

This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study.     

A computer-based system for the acquisition and analysis of data from a field tillage study

A computer-based data acquisition system is described that enables both continuous real-time control of data collection from some 250 sensors deployed at a remote field site, and immediate conversion from analogue data to the required physical parameter. In addition, data analysis programs can be developed and run concurrently with data collection. Important aspects of the computer operating system are discussed, as is the overall programming strategy for a field experiment. An account is given of the control of scanning and measuring instruments (connected to the computer via a standard parallel-interface bus), and of the technique adopted for utilizing the file management software to store processed data in an easily retrievable form. A FORTRAN program to control the measurement of soil temperatures is discussed; and an assessment is given of the equipment's performance in use.