The current well planning practice is usually done section by section with limited help of some knowledge-based tools. This thesis presents an integrated approach and a software prototype developed for well planning. It considers the geological input, i.e. pore pressure, over burden etc., to generate a step by step interactive drilling plan. The implemented well planning stages include the casing setting depth, casing and hole size determination, casing selection and then drill string design and modeling. The system is integrated with a Computer Aided Design (CAD) system for generating three-dimensional parametric model. The conceptual design and CAD modeling system are integrated in such a way that any changes in the design will be reflected to the CAD model. Such intelligent CAD design practice is new in the drilling industry. An Operational Parameters module is also attached with the system to predict the drilling coefficients by using offset well data and determine the optimum weight on bit, and the drill string rotation that minimizes drilling cost per foot for a single bit run. Based on this approach, an integrated well planning system can be fully developed and it will be very useful for the decision making of drilling companies.

No description available.

Yes / Process safety has drawn increasing attention in recent years and has been investigated from different perspectives, such as quantitative risk analysis, consequence modeling, and regulations. However, rare attempts have been made to focus on inherent safety design assessment, despite being the most cost-effective safety tactic and its vital role in sustainable development and safe operation of process infrastructure. Accordingly, the present research proposed a knowledge-driven model to assess inherent safety in process infrastructure under uncertainty. We first developed a holistic taxonomy of contributing factors into inherent safety design considering chemical, reaction, process, equipment, human factors, and organizational concerns associated with process plants. Then, we used subject matter experts, content validity ratio (CVR), and content validity index (CVI) to validate the taxonomy and data collection tools. We then employed a fuzzy inference system and the Extent Analysis (EA) method for knowledge acquisition under uncertainty. We tested the proposed model on a steam methane-reforming plant that produces hydrogen as renewable energy. The findings revealed the most contributing factors and indicators to improve the inherent safety design in the studied plant and effectively support the decision-making process to assign proper safety countermeasures.

This research project aims to develop a novel A3 thinking approach to support knowledge driven design that aids the generation of decision making within a Lean Product and Process Development (LeanPPD) environment. This research comprises the development of a new A3 template as a technique of problem solving in product design, the adoption of a reflection practice structured in a new A3 template for knowledge capture and sharing, and the generation of the process of using the A3 thinking approach for effective implementation. Providing useful knowledge as a design reference to generate decision making at the initial stages of product development in product design helps the designers to prevent recurrence of the same problem, eliminate design mistakes and enhance design decision. In order to achieve a novel A3 thinking approach, a research methodology consisting of four phases was developed. The first phase synthesises the A3 best practice through literature and documentation reviews. The gap analysis and results from the reviews have identified several problem-solving approaches and learning cycles that have to be considered in the research. The second phase is to evaluate the approaches and their impacts and applications in product design. In order to complete this, several research methods are selected and performed (e.g. focus group and semistructured interview) within the collaborative companies. The third phase is to develop the A3 thinking approach by utilising the LAMDA learning cycle, developing a new A3 template or so-called A3LAMDA, adopting the reflection practice and generating the process of using the new A3 thinking approach. Finally, the validation of the new A3 thinking approach through industrial case studies and expert judgements have been performed. This approach has been implemented in the automotive sector and was applied to four industrial case studies and six A3LAMDA reports were collected. As a result of the findings of this research, the utilisation of the A3 thinking approach aided the generation of knowledge driven design in product design by integrating the knowledge management capabilities; knowledge creation, capture and sharing.

The globalisation of economy, increase of employees’ mobility, the forthcoming shortage of people next 10 years and, hence, war for talented people are some of the most important problems of corporations today. This thesis has a purpose to show systematic picture of the organisation, which will be named as Talented Organisation, with appropriate conditions for having talented people within and developing their talents. Some assumption of the research discussed in this paper are about the talented people themselves as special, difficult to work with, but interesting for any organisation. They can create some problems for leadership and co-workers and at the same time represent the most important source of ideas and to be essential resource. It will be described the strategy for gaining the Talented Organisation which will facilitate the process of hiring talented people as well as will promote the utilisation of inner organisational potential: identifying, breeding, nurturing and retaining of talented people.

<p>The globalisation of economy, increase of employees’ mobility, the forthcoming shortage of people next 10 years and, hence, war for talented people are some of the most important problems of corporations today. This thesis has a purpose to show systematic picture of the organisation, which will be named as Talented Organisation, with appropriate conditions for having talented people within and developing their talents.</p><p>Some assumption of the research discussed in this paper are about the talented people themselves as special, difficult to work with, but interesting for any organisation. They can create some problems for leadership and co-workers and at the same time represent the most important source of ideas and to be essential resource.</p><p>It will be described the strategy for gaining the Talented Organisation which will facilitate the process of hiring talented people as well as will promote the utilisation of inner organisational potential: identifying, breeding, nurturing and retaining of talented people.</p>

Recent developments and hype around artificial intelligence (AI) have arisen as result of two main factors: increase in computational power and data. Although marketing is considered as one of the main business applications within AI today, there is a lack of literature combining the disciplines. Marketing management tools, which utilise AI in supporting decision making are referred to as knowledge-driven marketing management support systems (MMSS). These systems provide besides quantitative analysis, further qualitative facets into marketing management. Despite the willingness of many SMEs to engage with the technology that may foster competitive advantage, many adoption processes fail. The purpose of this thesis is to explore the factors influencing adoption of knowledge-driven MMSS in SMEs in Finland and Sweden. Qualitative primary data was collected from nine company representatives at top management level in Finnish and Swedish firms. Companies were classified in three categories, providers, adopters and non-adopters of knowledge-driven MMSS.   The findings show that there are several factors influencing adoption of knowledge-driven MMSS. The factors were grouped into technological, organizational and environmental factors, based on the TOE framework. Even though SMEs suffer from a lack of resources compared to large companies, this research suggests that they are at the forefront of adopting AI for marketing purposes. Additionally, it was found that the factors affecting adoption are dependent on whether the knowledge-driven MMSS is built in-house or outsourced.   This study has contributed to the identified gaps in literature by combining the disciplines of AI, marketing and SMEs, and by exploring the factors behind adoption of knowledge-driven MMSS. The authors of this thesis have the aspiration that the developed post-empirical framework will serve as a guiding tool for top management and marketing managers in SMEs looking to adopt knowledge-driven MMSS into their organizations.

Debris-slides are one of the most frequently occurring geological hazards in metasedimentary rocks of the Anakeesta ridge in Great Smoky Mountains National Park (GRSM), which often depends on the influence of multiple causing factors or geo-factors such as geological structures, slope, topographic elevation, land use, soil type etc. or a combination of these factors. The main objective of the study was to understand the control of geo-factors in initiating debris-slides using different knowledge and data-driven methods in GIS platform. The study was performed in three steps: (1) Evaluation of geometrical relationship between geological discontinuity and topographic orientation in initiation of debris-slides, (2) Preparation of knowledge-driven debris-slide susceptibility model, and (3) Preparation of data-driven debris-slide susceptibility models and compare their efficacy. Performance of the models were evaluated mostly using area under Receiver Operating Characteristic (ROC) curve, which revealed that the models were statistically significant.

Ontology-based technologies like Semantic Web and Knowledge Graphs are promising for knowledge management in manufacturing industries. In the literature there are abundant of publications related to using ontologies to represent and capture knowledge in manufacturing. Many of them cover the use of ontologies for managing knowledge in different aspects of Product Lifecycle Management (PLM). Nevertheless, very few of them cover how ontologies can be used with virtual factory models, data and information as well as the knowledge generated from using these models and their corresponding engineering activities. An “extension” of existing ontologies is badly needed as digital, virtual models in terms of simulation and digital twins have become more popular in the industry. Without such an extended knowledge management process and system, it is difficult to re-use the artefacts and knowledge generated from the expensive and valuable virtual engineering activities. Relying on the cutting-edge graph database technologies and what they can offer regarding knowledge management, and also recent developments in the domain ontology field, an extended knowledge management implementation, specifically designed for virtual engineering has been done. Moreover, a clear roadmap for establishment of knowledge bases around production systems armed with Virtual Factory(VF) and Multi-Objective Optimization (MOO) processes has been provided. This, includes defining key elements of manufacturing procedures, constructing an ontology, defining data structure in preferably a graph database, and accessing valuable historical (provenance) data regarding different engineering entities and/or activities.

Övergången från CATIA V4 till CATIA V5 innebär nya möjligheter för konstruktörerna på Scania att arbeta med Knowledge Based Engineering, KBE, för att effektivisera och kvalitets-säkra sitt arbete. Då CATIA V5 är en ny plattform som innehåller verktyg med samlingsnamnet knowledgware, för att bygga in kunskap i modeller ville Scania undersöka potentialen i att arbeta med KBE, och hur detta skulle kunna ske på Scania. Vid traditionell produktutveckling tas en helt ny artikel fram vid behov och ofta innebär detta att arbete som tidigare utförts, görs om igen. Syftet med arbetet är därför att undersöka huruvida KBE i CATIA V5 kan erbjuda möjligheter att återanvända kunskap från tidigare arbete och samtidigt kvalitetssäkra denna, samt utreda vilka knowledgewarelicenser som i så fall kan vara lämpliga för Scania. För att göra detta har en litteraturstudie genomförts för att undersöka vad som har gjorts inom området, och även en intervjustudie har utförts inom R&amp;D på Scania. Vidare har sakkunniga på Linköpings Universitet och Tekniska Högskolan i Jönköping intervjuats. Detta material har sedan sammanställts och analyserats för att sedan resultera i slutsats och rekommendationer. Arbetet har resulterat i en demonstrationsmodell för Scania internt, som baserar sig på den information som framkommit under litteraturstudier och intervjuer. Att arbeta med KBE har både fördelar och nackdelar där den största svårigheten ligger i att bedöma om en artikel lämpar sig för KBE-modellering. Vinsterna med KBE är att stora tidsvinster kan göras och kvalitet kan säkerställas. De mest användbara licenserna för Scanias vidkommande är KWA och PKT, med vilka exempelvis kontroller av standarder och återanvändning av geometrier kan göras. Den slutliga rekommendationen baserat på teori och resultat är att Scania bör överväga att införa KBE som arbetssätt, och därför tillsätta en grupp som fungerar som expertis inom KBE. Denna bör då fungera som support och en resurs vid skapande av KBE-modeller och ansvara för att dessa är korrekta och underhålls. Vidare bör arbete med att definiera fysiska gränssnitt mellan artiklar startas och lämpligtvis då av GEO- eller Layoutgrupperna. / The transition from CATIA V4 to CATIA V5 opens up new possibilities for designers at Scania to work with Knowledge Based Engineering, KBE, in order to increase efficiency and assure quality. As CATIA V5 is a new platform complete with tools, referred to as knowledgeware, for infusing knowledge into models, Scania wanted to investigate the potential of working with KBE, and how this could be used at Scania. In traditional product development a completely new model is produced when needed, and this often entails performing tasks already undertaken and completed. Therefore, the purpose of this thesis is to ascertain whether or not KBE in CATIA V5 can offer the possibility to reuse knowledge from previous work and assuring the quality of this, and if so, determine which knowledgeware licenses would be appropriate for Scania. In order to do this, a literature study was conducted in order to look into what had been done in this field. Also, an interview study was carried out within Scanias R&amp;D department. In addition to this, interviews were held with expertise at Linköping University and Jönköping University. The material was then compiled and analyzed, resulting in conclusions and recommendations. The thesis resulted in a demonstration model for Scania internally, based on the information gathered from literature and interviews. Working with KBE has its pros and cons, the biggest difficulty being to determine whether or not an article is suitable for KBE-modelling. The profits of KBE include quality assurance and sizeable reductions in design time. The most useful knowledgeware licenses for Scania are KWA and PKT, which for example enables users to implement checks for standards and to easily reuse geometry. The final recommendations of this thesis, based on theory and results, is that Scania should consider introducing KBE, and should therefore appoint a group to function as an authority on KBE. This group would provide support and act as a resource in the creation of KBE-models, and also be responsible for the validity and maintenance on these. Furthermore work should begin with defining physical interfaces between articles, preferably by GEO- and Layout groups.