Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence

Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review

peer reviewedThe notion of Cyber-Physical-Social System (CPSS) is an emerging concept developed as a result of the need to understand the impact of Cyber-Physical Systems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS was mainly attributed to the increasing use of sensor enabled smart devices and the tight link with the users. The concept of CPSS has been around for over a decade and it has gained an increasing attention over the past few years. The evolution to incorporate human aspects in the CPS research has unlocked a number of research challenges. Particularly human dynamics brings additional complexity that is yet to be explored. The exploration to conceptualise the notion of CPSS has been partially addressed in few scientific literatures. Although its conceptualisation has always been use-case dependent. Thus, there is a lack of generic view as most works focus on specific domains. Furthermore the systemic core and design principles linking it with the theory of systems are loose. This work aims at addressing these issues by first exploring and analysing scientific literatures to understand the complete spectrum of CPSS through a Systematic Literature Review (SLR). Thereby identifying the state-of-the-art perspectives on CPSS regarding definitions, underlining principles and application areas. Subsequently, based on the findings of the SLR, we propose a domain-independent definition and a meta-model for CPSS, grounded in the Theory of Systems. Finally a discussion on feasible future research directions is presented based on the systemic notion and the proposed meta-models

A holistic approach for selecting appropriate manufacturing shop floor KPIs

In the era of globalization, manufacturing industries need to monitor their manufacturing operations acutely in order to remain competitive. Manufacturers seek to engineer highly flexible, robust, and efficient manufacturing processes enabling the production of high-quality goods at competitive costs while always addressing and adapting to evolving challenges. As a result, manufacturing industries in the present time have realized the significance of shop floor data analysis. They are implementing performance measurement systems to continually assess and improve the operational state of their manufacturing operations. These systems comprise a set of Key Performance Indicators (KPIs), which can enumerate the effectiveness, competence, efficiency, and proficiency of manufacturing processes. There is a lack of KPI understanding by the manufacturers and no framework or methodology available in the literature to select KPIs systematically, methodically, and/or scientifically for a manufacturing facility. This deficiency typically leads to failures in reporting and monitoring critical performance measures, with resultant losses to achieve key business objectives. Viewing the current industrial needs and limitations highlighted in the literature, this research presents a holistic approach that enables manufacturers to systematically understand, analyze, and select appropriate KPIs for their shop floor operations assessment. The approach is mainly centered on the premise that KPIs can be chosen based on a set of measures that are theoretically grounded. First, a manufacturing shop floor exploration model is developed to 1) recognize the key business objectives, 2) identify the bottlenecks in the manufacturing shop floor facility that negatively impacts the throughput, 3) point out the problems and challenges, and 4) list the KPIs used for monitoring shop floor performance. The model uses a set of questionnaires and structured interviews to collect the required data (i.e., data related to manufacturing shop floor performance) along with the real-time data extracted from the manufacturing shop floor. Second, a novel KPI guideline is developed to systematically guide the manufactures to understand, analyze, and select appropriate KPIs. These guidelines consist of five stages: information stage, discernment stage, scheming stage, the origin of the data stage, and assisting technology to capture the data stage. Every stage consists of a set of measures and their corresponding elements dedicated to providing vital information to help manufacturers better monitor their shop floor operations and improve decision-making capabilities. Last, to streamline the decision-making by prioritizing key business objectives and KPIs, the SMART criteria technique is prudently selected. The practicality of the proposed approach is demonstrated through its application to an automotive seat manufacturing company. It is sensible to indicate that the complete methodology of selecting appropriate KPIs and reviewing the manufacturing shop floor performance is a continuous process. After suggesting and implementing the KPIs, the manufacturers should evaluate the performance regularly since, in the current complex manufacturing environment, both internal and external business factors change over time. Hence it is necessary to incorporate these changes and provide continuous improvement, evaluating the shop floor performance on a regular basis