EVALUATION OF BUSINESS EFFECTS OF MACHINE-TO-MACHINE SYSTEM

The tightening competition and pressure in the project schedules often leave no time or space for the assessment of business impacts of different investments and projects. In addition, in many cases the assessment may be challenging and there is no experience available to undertake it. Therefore, companies often commit to different projects and investments without careful planning and vision of the costs it may cause. The goal in this thesis is to present and clarify the possible applications for the designed platform. The different benefits and its scope of use are also evaluated. Its potential market size is also assessed and its payback period calculated. Moreover, the investment eligibility from customer point of view is evaluated using several investment decision methods. In order to enable the practical business impact assessment, the designed platform is applied to fleet management business. In order to facilitate and increase the assessment of business impacts, a decision support system is also created. It is built on the understanding gained from the cost-benefit analysis conducted in the fleet management case and three other cases from the machine-to-machine business. As a background for the thesis, an overview of the existing solutions is presented and few well-known service models are described. Also an introduction to three sales forecasting methods is given. In order to build a basis for the decision support system, few investment decision methods are presented. As a result, a good understanding of different applications of the platform was gained. It was found to be suitable for any business in which vehicles are involved as they share several common properties such as location information, fuel consumption, speed, and status information. Its potential market size was assessed very promising despite low market share assumption. The payback period was found as very appealing and the investment strongly eligible. The created decision support system was found to be successful. It can be seen as a reliable tool as it consists of several investment decision methods. However, experience from the business area is still needed because any system cannot provide thorough means to identify all the crucial cost factors involved in an investment.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

INCORPORATING PREDICTIVE MAINTENANCE BEST PRACTICES INTO MARINE CORPS TRAINING AND OPERATIONS

The Marine Corps currently utilizes a traditional time-based strategy for ground equipment maintenance, conducting preventative maintenance at specified time intervals and corrective maintenance when failure occurs. In 2020, the Marine Corps initiated the transition from this maintenance strategy to a Condition Based Maintenance Plus (CBM+) strategy, which detects subcomponent anomalies in advance through data analytics so maintenance can be conducted before failure occurs. Hypothetically, CBM+ will generate increased cost-savings, reduce man-hour requirements, and improve operational availability for Marine Corps ground systems. Using a case study methodology, this project highlights best practices within the commercial mining, railroad, and heavy equipment industries by interviewing maintenance professionals and supplementing these discussions with existing literature. We then used a thematic analysis across five themes: organizational structure, asset classification, information technology (IT) infrastructure, data management, and maintenance decision making. By highlighting commonalities across the cases and evaluating best practices, we drew three key conclusions. First, some Marine Corps ground systems are not CBM+ compatible. Second, significant upgrades to existing maintenance infrastructure are necessary. Finally, CBM+ should be used as a decision-making framework to maximize cost-savings and combat readiness.Captain, United States Marine CorpsMajor, United States Marine CorpsApproved for public release. Distribution is unlimited

Diagnóstico de fallos y optimización de la planificación en un marco de e-mantenimiento.

324 p.El objetivo principal es demostrar el potencial de mejora que las técnicas y metodologías relacionadas con la analítica prescriptiva, pueden proporcionar en aplicaciones de mantenimiento industrial. Las tecnologías desarrolladas se pueden agrupar en tres ámbitos: - El e-mantenimiento, relacionado fundamentalmente con el desarrollo de plataformas colaborativas e inteligentes que permiten la integración de nuevos sensores, sistemas de comunicaciones, estándares y protocolos, conceptos, métodos de almacenamiento y análisis etc. que entran continuamente en nuestro abanico de posibilidades y nos ofrecen la posibilidad de seguir una tendencia de mejora en la optimización de activos y procesos, y en la interoperabilidad entre sistemas.- Las Redes Bayesianas (Bayesian Networks ¿ BNs) junto con otras metodologías de recogida de información utilizadas en ingeniería nos ofrecen la posibilidad de automatizar la tarea de diagnóstico y predicción de fallos.- La optimización de las estrategias de mantenimiento, mediante simulaciones de fallos y análisis coste-efectividad, que ayudan a la toma de decisiones a la hora de seleccionar una estrategia de mantenimiento adecuada para el activo. Además, mediante el uso de algoritmos de optimización logramos mejorar la planificación del mantenimiento, reduciendo los tiempos y costes para realizar las tareas en un parque de activos

Digital twins: a survey on enabling technologies, challenges, trends and future prospects

Digital Twin (DT) is an emerging technology surrounded by many promises, and potentials to reshape the future of industries and society overall. A DT is a system-of-systems which goes far beyond the traditional computer-based simulations and analysis. It is a replication of all the elements, processes, dynamics, and firmware of a physical system into a digital counterpart. The two systems (physical and digital) exist side by side, sharing all the inputs and operations using real-time data communications and information transfer. With the incorporation of Internet of Things (IoT), Artificial Intelligence (AI), 3D models, next generation mobile communications (5G/6G), Augmented Reality (AR), Virtual Reality (VR), distributed computing, Transfer Learning (TL), and electronic sensors, the digital/virtual counterpart of the real-world system is able to provide seamless monitoring, analysis, evaluation and predictions. The DT offers a platform for the testing and analysing of complex systems, which would be impossible in traditional simulations and modular evaluations. However, the development of this technology faces many challenges including the complexities in effective communication and data accumulation, data unavailability to train Machine Learning (ML) models, lack of processing power to support high fidelity twins, the high need for interdisciplinary collaboration, and the absence of standardized development methodologies and validation measures. Being in the early stages of development, DTs lack sufficient documentation. In this context, this survey paper aims to cover the important aspects in realization of the technology. The key enabling technologies, challenges and prospects of DTs are highlighted. The paper provides a deep insight into the technology, lists design goals and objectives, highlights design challenges and limitations across industries, discusses research and commercial developments, provides its applications and use cases, offers case studies in industry, infrastructure and healthcare, lists main service providers and stakeholders, and covers developments to date, as well as viable research dimensions for future developments in DTs

Condition-Based Maintenance Implementation and Potential in USMC Ground Transport

NPS NRP Technical ReportCondition-Based Maintenance (CBM) has been successfully implemented in private-sector operations to reduce maintenance costs and asset downtime. USMC is currently transitioning to a CBM+ approach to maintenance, addressing its unique organizational and operating environment. This project identifies private-sector best practices and lessons learned most applicable to USMC as well as important hurdles for USMC adoption. It identifies maintenance and readiness metrics changes that may be relevant in future USMC and joint sustainment operations, taking into account both CBM and maintenance in an expeditionary environment.HQMC Installations and Logistics (I&L)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Teknoekonominen toteutettavuusanalyysi etäylläpidon liitettävyydestä tehtaissa

Maintenance activities play a major role in factory operations, as they prevent breakdowns and extend machine life. With the advances in sensor, computing and communications technology, sensor data can be increasingly exploited for real-time supervision of machine condition. However, the acquisition of the data is challenging due to proprietary technologies and interfaces applied in Industrial Networks. Therefore, sensor data is rarely utilized in other processes than automation. As the industry is heading towards a new industrial era, also referred to as Industrial Internet or Industrie 4.0, there is growing need to improve data availability for applications that can realize its potential value. In this research, the focus is on the feasibility of remote maintenance deployment in factories. The topic is approached from the connectivity viewpoint. The research is conducted by reviewing the literature, and by interviewing numerous industry experts regarding the connectivity and data exploitation in factories. These form the basis for the value network analysis, in which Value Network Configuration (VNC) method is applied, to analyze the value distribution among different actors in alternative remote connection cases. As a result of the VNC analysis, three alternative value network configurations are formed. They provide a high-level technical architecture of the remote connection implementation and discuss the accumulated value of each actor concerning remote maintenance service. The insights gained from the VNCs and literature are then employed to propose a future technical architecture for remote maintenance connectivity in factories.Huoltotoimet ovat suuressa roolissa tehtaan toiminnassa, sillä ne ehkäisevät konerikkoja ja pidentävät koneen käyttöikää. Sensori-, laskenta- ja tietoliikenneteknologian kehittymisen johdosta sensoridataa voidaan hyödyntää yhä enemmän koneen kunnon reaaliaikaiseen valvontaan. Datan saanti on kuitenkin haastavaa teollisissa verkoissa käytettyjen sovelluskohtaisten teknologioiden ja liitäntöjen takia. Sen vuoksi sensoridataa hyödynnetään harvoin muissa prosesseissa kuin automaatiossa. Teollisuuden suunnatessa kohti uutta teollista aikakautta, joka tunnetaan myös nimillä Teollinen Internet ja Teollisuus 4.0, on datan saatavuutta parannettava sovelluskohteille, jotka voivat realisoida sen potentiaalisen arvon. Tämä tutkimus tarkastelee etäylläpidon käyttöönoton toteutettavuutta tehtaissa. Aihetta lähestytään liitettävyyden näkökulmasta. Tutkimus suoritetaan tarkastelemalla kirjallisuutta sekä haastattelemalla lukuisia teollisuuden asiantuntijoita koskien liitettävyyttä ja datan hyödyntämistä tehtaissa. Nämä muodostavat perustan arvoverkkoanalyysille, jossa sovelletaan arvoverkkokonfiguraatio-menetelmää, jolla analysoidaan arvon jakautumista eri toimijoiden kesken vaihtoehtoisissa etäyhteystapauksissa. Arvoverkkokonfiguraatioanalyysin tuloksena muodostetaan kolme vaihtoehtoista arvoverkkokonfiguraatiota. Ne tarjoavat korkean tason teknisen arkkitehtuurin etäyhteyden implementaatiosta ja tarkastelevat toimijoiden kerryttämää arvoa etäylläpitopalvelun osalta. Arvoverkkokonfiguraatioista ja kirjallisuudesta saatujen näkemysten pohjalta esitellään lisäksi tulevaisuuden tekninen arkkitehtuuri etäylläpidon liitettävyydelle tehtaissa

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (3/4)

Technical report about sustainable urban freight solutions, part 3 of