A road map for applied data sciences supporting sustainability in advanced manufacturing: the information quality dimensions

Abstract Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. Sustainability is a critical asset of a manufacturing enterprise. It enables a business to differentiate itself from competitors and to compete efficiently and effectively to the best of its ability. This paper is a review of data analytics, and how it supports advanced manufacturing with an emphasis on sustainability. The objective is to present a context for a roadmap for applied data science addressing such analytic challenges. We start with a general introduction to advanced manufacturing and trends in modern analytics tools and technology. We then list challenges of analytics supporting advanced manufacturing and sustainability aspects. The information quality (InfoQ) framework is proposed as a backbone to evaluate the analytics needed in advanced manufacturing. The eight InfoQ dimensions are: 1) Data Resolution, 2) Data Structure, 3) Data Integration, 4) Temporal Relevance, 5) Chronology of Data and Goal, 6) Generalizability, 7) Operationalization and 8) Communication. These dimensions provide a classification of advanced manufacturing analytics domains. The paper provides a roadmap for the development of applied analytic techniques supporting advanced manufacturing and sustainability. The objective is to motivate researchers, practitioners and industrialists to support such a roadmap

Quality, Risk and the Taleb Quadrants

Abstract The definition and the management of quality has evolved and assumed a variety of approaches, responding to an increased variety of needs. In industry, quality and its control has responded to the need of maintaining an industrial process operating as "expected", reducing the process sensitivity to uncontrolled disturbances (robustness) etc. By the same token, in services, quality has been defined as "satisfied customers obtaining the services they expect". Quality management, like risk management, has a general negative connotation, arising from the consequential effects of "non-quality". Quality, just as risk, is measured as a consequence resulting from factors and events defined in terms of the statistical characteristics that underlie these events. Quality and risk may thus converge, both conceptually and technically, expanding the concerns that both domains are confronted with and challenged by. In this paper, we analyze such a prospective convergence between quality and risk, and their management. In particular we emphasize aspects of integrated quality, risk, performance and cost in industry and services. Throughout such applications, we demonstrate alternative approaches to quality management, and their merging with risk management, in order to improve both the quality and risk management processes. In the analysis we apply the four quadrants proposed by Nassim Taleb for mapping consequential risks and their probability structure. Three case studies are provided, one on risk finance, a second one on risk management of telecommunication systems and a third one on quality and reliability of web based services

Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures

Reallocations of time between daily activities such as sleep, sedentary behavior and physical activity are differentially associated with markers of physical, mental and social health. An individual’s most desirable allocation of time may differ depending on which outcomes they value most, with these outcomes potentially competing with each other for reallocations. We aimed to develop an interactive app that translates how self-selected time reallocations are associated with multiple health measures. We used data from the Australian Child Health CheckPoint study (n = 1685, 48% female, 11–12 y), with time spent in daily activities derived from a validated 24-h recall instrument, %body fat from bioelectric impedance, psychosocial health from the Pediatric Quality of Life Inventory and academic performance (writing) from national standardized tests. We created a user-interface to the compositional isotemporal substitution model with interactive sliders that can be manipulated to self-select time reallocations between activities. The time-use composition was significantly associated with body fat percentage (F = 2.66, P < .001), psychosocial health (F = 4.02, P < .001), and academic performance (F = 2.76, P < .001). Dragging the sliders on the app shows how self-selected time reallocations are associated with the health measures. For example, reallocating 60 minutes from screen time to physical activity was associated with -0.8 [95% CI -1.0 to -0.5] %body fat, +1.9 [1.4 to 2.5] psychosocial score and +4.5 [1.8 to 7.2] academic performance. Our app allows the health associations of time reallocations to be compared against each other. Interactive interfaces provide flexibility in selecting which time reallocations to investigate, and may transform how research findings are disseminated

Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models

The response to the COVID-19 pandemic has been highly variable. Governments have applied different mitigation policies with varying effect on social and economic measures, over time. This article presents a methodology for examining the effect of mobility restriction measures and the association between health and population activity data. As case studies, we refer to the pre-vaccination experience in Italy and Israel. Facing the pandemic, Israel and Italy implemented different policy measures and experienced different population behavioral patterns. Data from these countries are used to demonstrate the proposed methodology. The analysis we introduce in this paper is a staged approach using Bayesian Networks and Structural Equations Models. The goal is to assess the impact of pandemic management and mitigation policies on pandemic spread and population activity. The proposed methodology models data from health registries and Google mobility data and then shows how decision makers can conduct scenario analyses to help design adequate pandemic management policies

Implementing SCRUM using Business Process Management and Pattern Analysis Methodologies

The National Institute of Standards and Technology in the US has estimated that software defects and problems annually cost 59.5 billions the U.S. economy (http://www.abeacha.com/NIST_press_release_bugs_cost.htm). The study is only one of many that demonstrate the need for significant improvements in software development processes and practices. US Federal agencies, that depend on IT to support their missions and spent at least $76 billion on IT in fiscal year 2011, experienced numerous examples of lengthy IT projects that incurred cost overruns and schedule delays while contributing little to mission-related outcomes (www.gao.gov/products/GAO-12-681). To reduce the risk of such problems, the US Office of Management and Budget recommended deploying an agile software delivery, which calls for producing software in small, short increments (GAO, 2012). Consistent with this recommendation, this paper is about the application of Business Process Management to the improvement of software and system development through SCRUM or agile techniques. It focuses on how organizational behavior and process management techniques can be integrated with knowledge management approaches to deploy agile development. The context of this work is a global company developing software solutions for service operators such as cellular phone operators. For a related paper with a comprehensive overview of agile methods in project management see Stare (2013). Through this comprehensive case study we demonstrate how such an integration can be achieved. SCRUM is a paradigm shift in many organizations in that it results in a new balance between focus on results and focus on processes. In order to describe this new paradigm of business processes this work refers to Enterprise Knowledge Development (EKD), a comprehensive approach to map and document organizational patterns. In that context, the paper emphasizes the concept of patterns, reviews the main elements of SCRUM and shows how combining SCRUM and EKD provides organizations with a comprehensive framework for managing and improving software and system development

Unison, workplace learning and enhancing learning network theory : a case study

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