Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data. By conducting a systematic literature review, this paper aims to present state-of-the-art research efforts into the use of AI for throughput bottleneck analysis. To make the work of the academic AI solutions more accessible to practitioners, the research efforts are classified into four categories: (1) identify, (2) diagnose, (3) predict and (4) prescribe. This was inspired by real-world throughput bottleneck management practice. The categories, identify and diagnose focus on analysing historical throughput bottlenecks, whereas predict and prescribe focus on analysing future throughput bottlenecks. This paper also provides future research topics and practical recommendations which may help to further push the boundaries of the theoretical and practical use of AI in throughput bottleneck analysis.     

Small satellite remote sensing and applications – history,current and future

The small satellite renaissance began in the 1980s and is changing the economics of space. Technological trends have supported the advancement of small satellites in the 1–500 kg range. The number of countries actively participating has grown substantially during the past years. Satellite constellations (groups of satellites working in concert) are emerging as a powerful and effective application. In this paper, we focus on the small satellites than can perform remote sensing or Earth observation tasks. An overview is presented of the small satellite literature on Earth observation. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application for Earth observation. Small satellite programmes are classified according to the geographic regions. The small satellite industry and small satellite systems are addressed. In terms of applications, small satellite constellations are discussed in more detail. Finally, future developments are put forward. Telegeoprocessing combined with grid computing will provide the infrastructure and technologies for the development of Processing on Demand for Small Satellite Constellation systems.     

Cobol: a historic past, a vital future?

One of the strangest stories in the software world centers around the programming language Cobol. Academics have reviled it for decades; its demise has been predicted since the 1960s; industry gurus have suggested that programmers who know only Cobol are committing career suicide. You would think, based on all this bad press, that Cobol was not long for this software world. Yet the giant lumbers on. In poll after poll, it is either the most-used or the second-most-used programming language in industry. In fact, in one industry poll, each year those contacted predict that Cobol's use will diminish by the next poll, and each year the findings are that Cobol is the only language whose use has actually increased. The devil refuses to die. What's with Cobol? I give my Loyal Opposition answer to that question and then give some input on the subject from a Cobol expert who is tracking Cobol's future directions     

The autonomous industrial plant – future of process engineering,operations and maintenance

Almost every day we read about new advances in self-driving cars and artificial intelligence. For autonomous driving, there already exist established standards with six levels describing the steps from driving without assistance to fully automated, i.e., autonomous driving. While many people and industries – in particular the automotive industry – believe that self-driving cars are feasible in the near future, one may ask how feasible full autonomy is in the process industry? Is it realistic to aim towards a fully autonomous chemical plant, and what does full autonomy even mean for the process industries? This paper introduces a definition of autonomy and corresponding levels of autonomy for the process industry. It starts from an abstract definition of six autonomy levels that are applicable to various industrial domains, also illustrated by recent examples from different industrial applications such as mining and marine. It also discusses how to achieve an autonomous system in general, based on cognitive capabilities and AI technologies potentially implementing them. Then, it discusses the applicability of this definition for process industry in a first step. Finally, the taxonomy is further refined by studying some key autonomous features of two process lifecycle phases: operations and engineering, and examples for the meaning of each autonomy level per feature are given.     

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Herein, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.     

Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios

Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration relies on standards-compliant and interoperable interfaces between different manufacturing stages and systems. In this paper, we present a comprehensive review of the current landscape of manufacturing automation standards, with a focus on end-to-end integrated manufacturing processes and systems towards mass personalization and responsive factory automation. First, we present an authentic vision of smart manufacturing and the unique needs for next-generation manufacturing automation. A comprehensive review of existing standards for enabling manufacturing process automation and manufacturing system automation is presented. Subsequently, focusing on meeting changing demands of efficient production of highly personalized products, we detail several future-proofing manufacturing automation scenarios via integrating various existing standards. We believe that existing automation standards have provided a solid foundation for developing smart manufacturing solutions. Faster, broader and deeper implementation of smart manufacturing automation can be anticipated via the dissemination, adoption, and improvement of relevant standards in a need-driven approach.     

The biological transformation of industrial manufacturing – Technologies,status and scenarios for a sustainable future of the German manufacturing industry

The German manufacturing industry is forced to evolve its processes, techniques, and organizations due to increasing global competition and progressive sustainability requirements. In this context, the soaring possibilities of bio- and information technology have recently let few authors develop the vision of a biological transformation of manufacturing, a concept that to date has been barely concrete to politicians, scientists, and managers. In this paper, we present results of the first systematic assessment of the biological transformation of the German manufacturing industry. We chose a combination of the Delphi method and scenario planning in order to assess key technologies, determine the status quo of Germany and provide a forecast of potential developments. Thereupon, we identify ten fields of action for setting the course for a sustainable industrial value creation. We conclude with a summary and recommendations for decision makers in politics, industries and research.     

A systematic review on security in Process-Aware Information Systems – Constitution,challenges, and future directions

ContextSecurity in Process-Aware Information Systems (PAIS) has gained increased attention in current research and practice. However, a common understanding and agreement on security is still missing. In addition, the proliferation of literature makes it cumbersome to overlook and determine state of the art and further to identify research challenges and gaps. In summary, a comprehensive and systematic overview of state of the art in research and practice in the area of security in PAIS is missing.ObjectiveThis paper investigates research on security in PAIS and aims at establishing a common understanding of terminology in this context. Further it investigates which security controls are currently applied in PAIS.MethodA systematic literature review is conducted in order to classify and define security and security controls in PAIS. From initially 424 papers, we selected in total 275 publications that related to security and PAIS between 1993 and 2012. Furthermore, we analyzed and categorized the papers using a systematic mapping approach which resulted into 5 categories and 12 security controls.ResultsIn literature, security in PAIS often centers on specific (security) aspects such as security policies, security requirements, authorization and access control mechanisms, or inter-organizational scenarios. In addition, we identified 12 security controls in the area of security concepts, authorization and access control, applications, verification, and failure handling in PAIS. Based on the results, open research challenges and gaps are identified and discussed with respect to possible solutions.ConclusionThis survey provides a comprehensive review of current security practice in PAIS and shows that security in PAIS is a challenging interdisciplinary research field that assembles research methods and principles from security and PAIS. We show that state of the art provides a rich set of methods such as access control models but still several open research challenges remain.     

Human–automation interaction for multiple robot control: the effect of varying automation assistance and individual differences on operator performance

In a human–automation interaction study, automation assistance level (AL) was investigated for its effects on operator performance in a dynamic, multi-tasking environment. Participants supervised a convoy of manned and unmanned vehicles traversing a simulated environment in three AL conditions, while maintaining situation awareness and identifying targets. Operators’ situation awareness, target detection performance, workload and individual differences were evaluated. Results show increasing AL generally improved task performance and decreased perceived workload, however, differential effects due to operator spatial ability and perceived attentional control were found. Eye-tracking measures were useful in parsing out individual differences that subjective measures did not detect. At the highest AL, participants demonstrated potentially complacent behaviour, indicating task disengagement.

Practitioner Summary: The effect of varying automation assistance level (AL) on operator performance on multiple tasks were examined in a within-subjects experiment. Findings indicated a moderate AL improved performance, while higher levels encouraged complacent behaviour. Effects due to individual differences suggest that effective AL depends on the underlying characteristics of the operator.     

IT Challenges for the New Millennium – Research, Development, and Deployment Issues

Introduction

IT Challenges for the New Millennium – Research, Development, and Deployment Issues     

Past and future of software architectural decisions – A systematic mapping study

ContextThe software architecture of a system is the result of a set of architectural decisions. The topic of architectural decisions in software engineering has received significant attention in recent years. However, no systematic overview exists on the state of research on architectural decisions.ObjectiveThe goal of this study is to provide a systematic overview of the state of research on architectural decisions. Such an overview helps researchers reflect on previous research and plan future research. Furthermore, such an overview helps practitioners understand the state of research, and how research results can help practitioners in their architectural decision-making.MethodWe conducted a systematic mapping study, covering studies published between January 2002 and January 2012. We defined six research questions. We queried six reference databases and obtained an initial result set of 28,895 papers. We followed a search and filtering process that resulted in 144 relevant papers.ResultsAfter classifying the 144 relevant papers for each research question, we found that current research focuses on documenting architectural decisions. We found that only several studies describe architectural decisions from the industry. We identified potential future research topics: domain-specific architectural decisions (such as mobile), achieving specific quality attributes (such as reliability or scalability), uncertainty in decision-making, and group architectural decisions. Regarding empirical evaluations of the papers, around half of the papers use systematic empirical evaluation approaches (such as surveys, or case studies). Still, few papers on architectural decisions use experiments.ConclusionOur study confirms the increasing interest in the topic of architectural decisions. This study helps the community reflect on the past ten years of research on architectural decisions. Researchers are offered a number of promising future research directions, while practitioners learn what existing papers offer.     

Operators׳ adaptation to imperfect automation – Impact of miss-prone alarm systems on attention allocation and performance

Operators in complex environments are often supported by alarm systems that indicate when to shift attention to certain tasks. As alarms are not perfectly reliable, operators have to select appropriate strategies of attention allocation to compensate for unreliability and to maintain overall performance. This study explores how humans adapt to differing alarm reliabilities. Within a multi-task simulation consisting of a monitoring task and two other concurrent tasks, participants were assigned to one of five groups. In the manual control group none of the tasks was supported by an alarm system, whereas the four experimental groups were supported in the monitoring task by a miss-prone alarm system differing in reliability, i.e. 68.75%, 75%, 87.5%, 93.75%. Compared to the manual control group, all experimental groups benefited from the support by alarms, with best performance for the highest reliability condition. However, for the lowest reliability group the benefit was associated with an increased attentional effort, a more demanding attention allocation strategy, and a declined relative performance in a concurrent task. Results are discussed in the context of recent automation research.     

How much can temporally stationary factors explain cellular automata-based simulations of past and future urban growth?

Driving factors are usually assumed temporally stationary in cellular automata (CA) based land use modeling, hence the persistence of their relationships. Therefore, major questions as to how much do the temporally stationary factors explain the past and future urban growth, and how long can these factors justify the projection of urban scenarios in the future, are worth further study. We selected seven explanatory driving factors to calibrate a DE-CA (differential evolution-based CA) model to simulate urban growth in Ningbo of China during 2000–2015 and project nine scenarios of urban growth from 2015 to 2060. We evaluated the effects of factors on urban growth using generalized additive models (GAM) based on fitting statistics such as accumulative deviance explained (ADE). Our results show remarkably temporal change in factor effects on the future urban growth – the ADE peaks with 34.7% in 2045 for the total projected urban growth since 2015 while that for every five years decreases continuously from 26.5% during 2000–2005 to 1.9% during 2050–2055, but slightly increase to 3.0% during 2055–2060. These indicate that the stationary factors have less strong explanatory power to the new urban areas that are farther away from the existing built-up areas. The results suggest that a 30-year period in the future is most suitable to project the urban growth scenarios, where the new urban area approximates the initial urban area. The specific best period for scenario projection elsewhere can then be identified using the method presented in this study.     

Situation awareness acquired from monitoring process plants – the Process Overview concept and measure

We introduce Process Overview, a situation awareness characterisation of the knowledge derived from monitoring process plants. Process Overview is based on observational studies of process control work in the literature. The characterisation is applied to develop a query-based measure called the Process Overview Measure. The goal of the measure is to improve coupling between situation and awareness according to process plant properties and operator cognitive work. A companion article presents the empirical evaluation of the Process Overview Measure in a realistic process control setting. The Process Overview Measure demonstrated sensitivity and validity by revealing significant effects of experimental manipulations that corroborated with other empirical results. The measure also demonstrated adequate inter-rater reliability and practicality for measuring SA based on data collected by process experts.

Practitioner Summary: The Process Overview Measure is a query-based measure for assessing operator situation awareness from monitoring process plants in representative settings.     

REMOTE: desk-top Virtual Reality for future command and control?

This paper discusses a study in supporting collaborative military planning in which groupware, video-conferencing and a desktop Collaborative Virtual Environment (CVE) were used. It discusses the design and implementation of the CVE and the setup and execution of the study using questionnaires and observation. The results of the study questionnaires showed that the CVE was not seen by users as the best of the ways offered to support collaborative planning; these results are discussed and their implication for the design of such a CVE are assessed.

QSIA – a Web-based environment for learning,assessing and knowledge sharing in communities

This paper describes a Web-based and distributed system named QSIA that serves as an environment for learning, assessing and knowledge sharing. QSIA – Questions Sharing and Interactive Assignments – offers a unified infrastructure for developing, collecting, managing and sharing of knowledge items. QSIA enhances collaboration in authoring via online recommendations and generates communities of teachers and learners. At the same time, QSIA fosters individual learning and might promote high-order thinking skills among its users. QSIA's community, conceptual architecture, structure overview and implementations are discussed.