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.     

Information systems strategy: Past,present, future?

The purpose of this paper is to contribute to the current discourse in the Strategic Information Systems (SIS) domain about the future and identity of SIS. We draw on Nelson’s theorisation of the co-evolution of Physical and Social Technologies to redefine the SIS domain as a Complex Adaptive System (CAS) for the co-evolution of ICT and organisational capabilities and business models to create social and economic value. We conduct a meta-analysis of the domain based on a longitudinal review of SIS research over 33 years, and contrary to contemporaneous SIS literature which suggests that a paradigm shift may be necessary to address the increased turbulence, uncertainty and dynamism in the emerging competitive landscape, we find that the SIS research domain has the requisite adaptive capacity to evolve gracefully to address the challenges of the emerging networked competitive landscape. Drawing on complexity science and network theory we identify four priorities for the development of the domain for the future: conceptualisation of the SIS Domain as a CAS for the co-evolution of Physical and Social Technologies; the adoption of the network paradigm; access to a science of networks; and adoption of Complexity Science as an articulation device within SIS and across disciplines.     

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 Electronic Signatures and eIDAS – Analysis of the Concept

We discuss the model of electronic signatures as described by the European eIDAS Regulation from the perspective of common understanding of electronic signatures in the cryptographic community. We show that these two perspectives do not present the same picture. The discrepancies between them may become opportunities as well as barriers for rapid deployment of electronic signatures and seals in business and administration.We focus particularly on validation of advanced electronic signatures and its interplay with the data protection requirements of GDPR. We show that by tweaking the existing technical standards one can reduce the number of problems and achieve compliance with both GDPR and eIDAS.Among others, we wish to bring attention to the evolving regulatory framework that without any doubt will have a substantial impact on the ecosystem of electronic signatures.     

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.     

Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction?

The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation.     

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.     

Predictive use error analysis – Development of AEA,SHERPA and PHEA to better predict,identify and present use errors

In health care, the use of technical equipment plays an integral part. To achieve a high level of patient safety, it is important to avoid use errors when handling equipment. Use errors can be mitigated by performing analyses of potential use errors during the design process. One proactive analytical method for use error analysis is Predictive Use Error Analysis (PUEA), which is a further development of the methods Action Error Analysis (AEA), Systematic Human Error Reduction and Prediction Approach (SHERPA) and Predictive Human Error Analysis (PHEA). PUEA employs a detailed process for breaking down the user's tasks into steps and then identifying and investigating potential errors of use for each step. Compared with other methods, it is significant in its use of two question levels, greater inclusion of human cognition theory and that the results of the analysis are presented in matrixes.     

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.     

Input data management in simulation – Industrial practices and future trends

Discrete Event Simulation has been acknowledged as a strategically important tool in the development and improvement of production systems. However, it appears that companies are failing to reap full benefits of this powerful technology as the maintenance of simulation models has become very time-consuming, particularly due to vast amounts of data to be handled. Hence, an increased level of automation of input data handling is highly desirable. This paper presents the current practices relating to input data management and identifies further research and development required to achieve high levels of automation. A survey of simulation users shows that there has been a progress in the use of automated solutions compared to a similar study presented by Robertson and Perera in 2002. The results, however, reveal that around 80% of the users still rely on highly manual work procedures in input data management.     

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