A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Domains such as utilities, power generation, manufacturing and transport are increasingly turning to data-driven tools for management and maintenance of key assets. Whole ecosystems of sensors and analytical tools can provide complex, predictive views of network asset performance. Much research in this area has looked at the technology to provide both sensing and analysis tools. The reality in the field, however, is that the deployment of these technologies can be problematic due to user issues, such as interpretation of data or embedding within processes, and organisational issues, such as business change to gain value from asset analysis. 13 experts from the field of remote condition monitoring, asset management and predictive analytics across multiple sectors were interviewed to ascertain their experience of supplying data-driven applications. The results of these interviews are summarised as a framework based on a predictive maintenance project lifecycle covering project motivations and conception, design and development, and operation. These results identified critical themes for success around having a target or decision-led, rather than data-led, approach to design; long-term resourcing of the deployment; the complexity of supply chains to provide data-driven solutions and the need to maintain knowledge across the supply chain; the importance of fostering technical competency in end-user organisations; and the importance of a maintenance-driven strategy in the deployment of data-driven asset management. Emerging from these themes are recommendations related to culture, delivery process, resourcing, supply chain collaboration and industry-wide cooperation

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

An agile based integrated framework for software development.

Doctor of Philosophy in Management. University of KwaZulu-Natal. Durban, 2018.Software development practice has been guided by practitioners and academics along an evolutionary path that extends from a Waterfall approach, characterised as highly prescriptive, to an approach that is agile, embracing the dynamic context in which software is developed. Agile Methodology is informed by a set of generic principles and agile methods that are customised by practitioners to meet the requirements of the environment in which it is used. Insight into the customisation of agile methods is pivotal to uphold the evolutionary trajectory of software development methodology. The study adopted a ‘socio-technical’ orientation to enhance the implementation of Agile Methodology. The social component of the study was aligned to the role played by organisational culture in the adoption of software development methodology. The amorphous concept of organisational culture has been operationalised by implementing the Competing Values Framework to develop a model that aligns organisational culture to an optimal methodology for software development. The technical component of the study has a software engineering focus. The study leveraged experiential knowledge of software development by South African software practitioners to develop a customised version of a prominent agile software development method. The model has been developed so that it is compatible with a variant of organisational culture that is aligned with agile methodology. The study implemented a sequential research design strategy consisting of two phases. The first phase was qualitative consisting of a phenomenological approach to develop the study’s main models. The second phase was quantitative, underpinned by technology acceptance theory, consisting of a survey based approach to determine South African software practitioners’ acceptance of the agile-oriented technical model that was developed in the study. The results from the survey indicated an 80% acceptance of the model proposed in study. Structural Equation Modelling was used to demonstrate that the inclusion of organisational culture as an independent construct improved the predictive capacity of technology acceptance theory in the context of software development methodology adoption. The study’s overall theoretical contribution was to highlight the significance of organisational culture in the implementation of agile methodology and to extend the evolutionary path of software development methodology by proposing an agile oriented model that scales the software process to an organisational infrastructure level

Mapping Cloud-Edge-IoT opportunities and challenges in Europe

While current data processing predominantly occurs in centralized facilities, with a minor portion handled by smart objects, a shift is anticipated, with a surge in data originating from smart devices. This evolution necessitates reconfiguring the infrastructure, emphasising computing capabilities at the cloud's "edge" closer to data sources. This change symbolises the merging of cloud, edge, and IoT technologies into a unified network infrastructure - a Computing Continuum - poised to redefine tech interactions, offering novel prospects across diverse sectors. The computing continuum is emerging as a cornerstone of tech advancement in the contemporary digital era. This paper provides an in-depth exploration of the computing continuum, highlighting its potential, practical implications, and the adjustments required to tackle existing challenges. It emphasises the continuum's real-world applications, market trends, and its significance in shaping Europe's tech future

The Organization of New Service Development in the USA and UK

We understand a great deal about the organization and management of new product development in the manufacturing sector, but we know relatively little about how applicable this research and practice is to the service sector. In this paper we introduce and test a framework for managing new product development in services. This framework is derived and tested by analyzing 108 service firms in a combined US and UK dataset, and then each national sub-sample separately. Our results generally support the predictive capability of the framework, and suggest that the development strategy, processes, organization and tools derived from manufacturing, specially those of concurrent engineering, are applicable to services. However, the framework better fits the US than UK data, which may question the notion of a 'best practice' applicable to different contexts.product development, services, concurrent engineering, simultaneous development

Data-driven maintenance of military systems:Potential and challenges

The success of military missions is largely dependent on the reliability and availability of the systems that are used. In modern warfare, data is considered as an important weapon, both in offence and defence. However, collection and analysis of the proper data can also play a crucial role in reducing the number of system failures, and thus increase the system availability and military performance considerably. In this chapter, the concept of data-driven maintenance will be introduced. First, the various maturity levels, ranging from detection of failures and automated diagnostics to advanced condition monitoring and predictive maintenance are introduced. Then, the different types of data and associated decisions are discussed. And finally, six practical cases from the Dutch MoD will be used to demonstrate the benefits of this concept and discuss the challenges that are encountered in applying this in military practice

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications