Internet of Things - Enabled visual analytics for linked maintenance and product lifecycle management

When closed loop product lifecycle management was first introduced, much effort focused on establishing ways to communicate data between different lifecycle phase activities. The concept of a smart product, able to communicate its own identity and status, had a key role to play to this end. Such a concept has further matured, benefiting from internet things-enabled product lifecycle management advancements. Product data exchanges can now be brought closer to the point of end use consumption, enabling users to become more proactive actors within the product lifecycle management process. This paper presents a conceptual approach and a pilot implementation of how this can be achieved by superimposing middle of life relevant product information to beginning of life product views, such as a 3D product CAD model. In this way, linked maintenance data and knowledge become visual features of a product design representation, facilitating a user’s understanding of middle-of life concepts, such as occurrence of failure modes. The proposed approach can be particularly useful when dealing with product data streams as a natural visual analytics add-in to closed loop product lifecycle management

Change detection in streaming data analytics: a comparison of Bayesian online and martingale approaches

On line change detection is a key activity in streaming analytics, which aims to determine whether the current observation in a time series marks a change point in some important characteristic of the data, given the sequence of data observed so far. It can be a challenging task when monitoring complex systems, which are generating streaming data of significant volume and velocity. While applicable to diverse problem domains, it is highly relevant to monitoring high value and critical engineering assets. This paper presents an empirical evaluation of two algorithmic approaches for streaming data change detection. These are a modified martingale and a Bayesian online detection algorithm. Results obtained with both synthetic and real world data sets are presented and relevant advantages and limitations are discussed

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

Opportunity to Leverage Information-as-an-Asset in the IoT -- The road ahead

In traditional product companies, creating value meant identifying enduring customer needs and manufacturing well-engineered solutions. Two hundred and fifty years after the start of the Industrial Revolution, this pattern of activity plays out every day, especially in a connected world where products are no longer one-and-done. Making money is not anymore limited to physical product sales and other revenue streams become possible after the initial product sale, which are service-based information and knowledge in today's IoT (including subscriptions and apps, new analytics for cognitive capabilities...). While information and knowledge are the "new oil" of the IoT era, it nonetheless remains challenging to perceive and extract the real value of those assets, as information is not as tangible and concrete as physical assets. In this respect, this paper introduces the major "laws of information" and discusses how these laws can be leveraged to their full extend thanks to the IoT possibilities. Further, the paper discusses the key challenges that remain to be addressed in today's IoT to concretize such laws. Finally, a set of real-life business use cases identified by the Open Platform 3.0TM Forum are presented from the information law perspectives

Understanding New Product Development and Value Creation for the Internet of Things

This thesis investigates IoT development processes and value creation from the perspective of the business. At the onset of the research there is a lack of existing research on how IoT products and services are designed and developed. IoT is distinctive to traditional product as it is the combination of physical components, smart components, and connectivity that allows for continuous value improvement. Consequently, New Product Development (NPD) process of IoT should reflect vital characteristics of networked artefacts and integrate the data science process. To achieve the research aim, the study is based on the literature review and an inductive approach, using a qualitative research methodology. Through a comprehensive literature review covering interdisciplinary subjects from economics, business, engineering, information systems, innovation, and design studies, a theoretical foundation of value creation activities, a NPD process and practice and design roles are developed. An exploratory multiple case study is adopted to gain a primary understanding of IoT design and development. Six cases are selected for the study from various sectors, including healthcare, smart home, drain maintenance, dairy, vertical farming, and tropical farming. Within the case study methodology, semi-structured interview, document reviews and graphic elicitation are adopted to capture each participant’s distinctive experience and design challenges within the context of the given project. Thematic analysis is used for a purely qualitative, rich, detailed yet complex, account of data analysis in IoT development. The transcribed interview script contents and obtained documents for all the cases are carefully analysed through within- and cross-case analysis strategies, using thematic analysis. To enhance the study trustworthiness, triangulation of multiple data sources, member checks, peer reviews and experts’ reviews are drawn upon. Through the discussion, the conceptual model of the IoT NPD process, the Mobius strip model, is developed, reflecting the attributes of complex development practice, challenges and value creation. The Mobius Strip Model implies three infinite loops of value creation and NPD activities each of which are a hardware centred, software centred, and data and algorithms centred IoT NPD. The hardware centred NPD cycle is hardware centred development which has stricter review gates compared to other two software centred and data/algorithms centred development cycles. The software centred NPD cycle is more flexible, efficient, and effective without major modification to the IoT system. The data and algorithms centred IoT NPD is slow and time-consuming, reflecting the challenges of the data science process. The IoT NPD process involves three different types of subject matter, hardware, software, and data/algorithms development. This research confirmed that value of IoT system can be created through a hardware centred, software centred, and data & algorithms centred which was reflected to a conceptual model. Service-Dominant Logic is applied as the fundamental theory that can explain IoT value creation, including delivering service and scaling up, value co-creation, and user-driven development. However, emerging theories, such as the value space framework, and data as critical resource for value creation, complement to comprehend IoT value creation. Design is not utilised to its full extent but limited as styling and a process within IoT development. Design as styling is mainly focused on designing, prototyping, and testing the product or user interface of web and app, and design as a process is utilised to identify user needs and develop solution ideation. This study provides businesses with an integrative understanding of the value creation, development process, and various challenges in IoT development. The proposed conceptual model of IoT NPD, 'The Mobius Strip Model’, contributes to a body of research by combining interdisciplinary knowledge within the process. The model provides a foundation for scholars to construct other knowledge upon, including business models, development risks, innovation, design, and product management studies