Contextualizing resilience to critical infrastructure maintenance supply networks

As AI technologies increase in capability and ubiquity, AI accidents are becoming more common. Based on normal accident theory, high reliability theory, and open systems theory, we create a framework for understanding the risks associated with AI applications. This framework is designed to direct attention to pertinent system properties without requiring unwieldy amounts of accuracy. In addition, we also use AI safety principles to quantify the unique risks of increased intelligence and human-like qualities in AI. Together, these two fields give a more complete picture of the risks of contemporary AI. By focusing on system properties near accidents instead of seeking a root cause of accidents, we identify where attention should be paid to safety for current generation AI systems.

Purpose Organizations working in high-hazard environments contribute significantly to modern society and the economy, not only for the valuable resources they hold but also for the indispensable products and services they provide, such as power generation, transportation and defense weapons. Therefore, the main purpose of this study is to develop a framework that outlines future research on systems safety and provides a better understanding of how organizations can effectively manage hazard events. Design/methodology/approach In this research, we developed the high hazard theory (HHT) and a theoretical framework based on the grounded theory method (GTM) and the integration of three established theoretical perspectives: normal accident theory (NAT), high reliability theory (HRT) and resilience engineering (RE) theory. Findings We focused on the temporal aspect of accidents to create a timeline showing the progression of hazard events and the factors contributing to safety and hazards in organizations. Given the limitations of the previous theories in providing a coherent explanation of hazard event escalation in high-hazard organizations (HHOs), we argue that the highlighted theories can be more complementary than contradictory regarding their standpoints on disasters and accident prevention. Practical implications A proper appreciation of the hazard nature of organizations can help reduce their susceptibility to failure, prevent outages and breakdowns of systems, identify areas for improvement and develop strategies to enhance performance. Originality/value By developing HHT, we contribute to systems safety research by developing a new, refined theory and enrich the theoretical debate. We also expand the understanding of scholars and practitioners about the characteristics of organizations working in high-hazard environments.

The ability to assess and therefore react to risk exposure in critical infrastructures and supply chains environments greatly contributes to reaching suitable protection levels and response mechanisms. Due to the unavoidable interdependencies among those infrastructures, that allow disruptions to spread from one to another and likely cause a great impact on society’s welfare state, risk management might be seen as a common and shared concern. The Comprehensive Risk Management approach tries to face this process by gathering information from a broad range of disciplines (physical and logical security, safety, environmental threats, etc.) while taking into account interdependencies of critical infrastructures and supply chains at different layers, going from critical infrastructure operators point of view, to sectoral, national and finally supranational levels. Besides, risk assessment and management processes rely on accurate and timely information to assist decision making, but this information (security holes, attacks or even disruptions suffered by an infrastructure or supply chain)—due to its sensitiveness—does not easily flow between involved or interested parties. This paper provides an analysis of this situation and suggest future fields of action, supported by conclusions drawn from the FOCUS project.

Critical technical infrastructures constitute the backbone of society by providing essential services to vital societal functions and the community at large, hence it is of essence that these are resilient. Critical infrastructures, e.g. power systems, telecommunication systems and railway systems, are designed and operated based on different philosophies of where to put the resilience emphasis, robustness, rapidity of recovery or a combination of the two. Here empirical failure data, such as duration and consequence of disruptions, from several critical infrastructures in Sweden are explored and analysed. To facilitate comparisons, a generic resilience assessment approach is also presented and applied. The results give insight to the resilience level of different infrastructures in Sweden and a basis for an exploration of its reasons, e.g. due to difference in regulatory schemes, design or risk cultures. It is concluded that there exist significant differences of infrastructures resilience levels and the factors shaping the resilience. (Less)

This paper examines knowledge creation through transfer of contradictory viewpoints between designs & production engineering (PE) to achieve quick turn activities from design of products to volume productions with significant upgrade of contradictory performances. Through activities of prototyping for design review, the firm finds problems in future volume productions and transfers contradictory viewpoints of designs & PE in the form of suggestions to design team of customers and subsidiary plants of the firm in overseas. The recipients lead incomplete solutions through knowledge sharing in the initial stage and gradually generate bifocal viewpoints through acquisition of the contradictory viewpoints in contexts of suggestions from the firm in the process of application for their own activities. The innovative solutions can be generated by tacit knowledge creation through bifocal thinking to optimize contradictory performances between designs & PE.

This paper presents a propositional framework for an updated understanding of the relationship between the internal social networks of a multinational enterprise (MNE), harnessing of tacit knowledge within these networks, the role that culture plays in tacit knowledge exchange, and the ultimate creation of innovation. Thus, we examine theories of tacit knowledge creation and exchange, social networking theory, cultural influences and propose an integrated understanding of MNE innovation creation through internal social networks. The contribution of this paper is in the formulation of propositions that encapsulate the functional relationships between internal social networks, tacit knowledge, cultural considerations and innovation creation in MNEs. Managerial and research implications are then drawn.

The design, construction and maintenance of Critical Infrastructures (CI) is commonly based on standards that are rigorous, so as to withstand any climate or weather-linked pressures. However, due to climate change, climate characteristics may shift, resulting in increased frequency/magnitude of potential failures, or exposure to new unknown risks. As vital components for the normal functioning of modern societies, the resilience of CIs under climate stressors encompasses their structural integrity, their operational elements, and their capacity to maximize business output. In this work, we propose an integrated and participatory methodological approach to enhance the resilience of interconnected CIs to urban flooding under climate change, by assessing the risk and introducing adaptation measures.The main objectives of the proposed methodology and approach are: (i) to provide scientific evidence for better understanding of how future climate regimes might affect normal operation of interconnected CI in urban areas during their lifespan; (ii) to assess the cost-effectiveness of different adaptation measures; (iii) to involve local stakeholders and operators in the co-design of the approach, as well as the assessment and the evaluation of adaptation measures; (iv) to combine computational modelling with advanced 3D visualisation techniques for effectively engaging stakeholders in decision making; (v) to include risk assessment and damage functions co-designed by end-users and local stakeholders; (vi) to integrate all of the aforementioned components in a specifically designed cloud platform as a Decision Support System for end-users, (vii) to validate the DSS by the end users and local stakeholders.The paper presents the computational background and tools. Additionally, it describes a Case Study in Torbay, UK, where the full methodology and the proposed participatory approach have been applied, with all the specifics, i.e., the scenarios of extreme flooding, the numerical and visualisation results, the response of the stakeholders and the evaluation of selected adaptation measures.

Critical Infrastructures are infrastructures that provide vital or critical services for the population such as supply of electricity, drinking water, food, and health care. Interruptions of these services may have a direct or indirect adverse impact on the population's welfare. I.e. failures of critical services can lead to injuries or even losses of life, property damages, social and economic disruptions and thus drastically reduce the overall security of supply. We propose a new resilience measure that can be applied together with other known graph-related measures in order to achieve resilient, efficient, and economic feasible critical supply networks. In contrast to pure graph measures, the resilience measure we propose, takes spatial information of infrastructures and their criticalities w.r.t. to the availability of e.g. power or water into account. To enable a comprehensive evaluation of network topologies, a composite resilience metric is introduced, which uses these aforementioned measures as weighted building blocks. For evaluating critical network designs, the proposed methodology applies this composite resilience metric and Multi-Criteria Decision Analysis. Based on this, a genetic algorithm is proposed for identifying disaster resilient water supply networks.

Although mentoring is believed to improve the craft of beginning principal, the context of collective engagement between mentor and mentee principals that optimize their leadership learning are rarely investigated. This study explored co-creating dispositional values, knowledge conversion modes, and enabling conditions as contexts that favor the creation and transmission of explicit and tacit knowledge for leadership learning for the mentee principal.

Software Process Improvement (SPI) is an approach to systematic and continuous improvement of a software producing organization's ability to produce and deliver quality software within time and budget constraints. SPI initiatives often experience difficulties and problems when the improvements are going to be implemented and institutionalized in the organization's daily practices. In this paper we discuss the learning processes that take place in SPI based on an analysis of three industrial SPI projects. A finding of the study is that an SPI project needs to pay attention to the creation of both tacit and explicit knowledge in order to improve practices. A second finding suggests that implementing and institutionalizing improvements should take its starting point at the group level and involve the development projects. A third finding suggests that sustained SPI processes require ongoing interactions between different learning processes.

A comprehensive framework to efficiently plan short and long-term investments in water supply and sewer networks

INTRODUCTION Value creation strategies in the 21st century have shifted from product differentiation and operational efficiency to knowledge creation and relationship building across the value network. Knowledge management (KM) and relationship management (RM) are two sides of the same coin in gaining competitive advantages in the knowledge-based economy. The focus of relationship management has been on customer relationship management (CRM), which has helped companies to acquire and retain customers and increase market share and profitability. For businesses that have a high percentage of sales through indirect channels, such as the hightech industry (Tanoury & O'Leary, 2000), partner relationship management (PRM) has become an important business strategy in the sell-side relationship management. As procurement cost and suppliers' commitment to meet production requirements can drastically impact a firm's performance (Aberdeen, 2001; Lang, Paravicini, Pigneur, & Revaz, 2002; Carey, 2005), the upstream supplier relationship management (SRM) is equally important as the downstream CRM and PRM. Enterprise relationship management (ERM) is a business strategy that optimizes the relationships between a firm and its customers, channel partners, suppliers and alliance partners in order to maximize opportunities. Knowledge management is a value creation strategy that exploits the knowledge assets of an enterprise to achieve business goals. It is concerned with the creation, sharing and utilization of knowledge in an organization. The internal focus of knowledge management to improve product innovation and operational efficiency has shifted to an external focus of enhancing business processes across the extended enterprise. In particular, the application of KM to CRM business processes and the management of customer knowledge have received increasing attention both in academia and in business. The merging of KM and ERM yields two perspectives: the ERM-oriented KM (EKM) and the KM-oriented ERM (KERM). The focus of prior research in this area has been on the integration of KM and CRM. While different models have been proposed for customer knowledge management (CKM) and knowledge-enabled CRM (KCRM), there are no standardized definitions of these terms. A commonly accepted concept is that CKM deals with the processes of managing customer knowledge, whereas KCRM deals with the utilization of knowledge to improve CRM processes. Gibbert, Leibold and Probst (2002) described CKM as the management of knowledge from customers and emphasized CKM as the strategic process by which customers are empowered as knowledge partners. It proposed five styles of CKM that can be used by companies depending on the nature of their customers. Paquette (2006) described CKM as the processes that a firm employs to manage the identification, acquisition and internal utilization of customer knowledge. Su, Chen and She (2006) proposed a conceptual framework for an E-CKM model by incorporating IT into the CKM model. Eppler, Siefried and Popnack (1999) classified business processes by knowledge intensity and process complexity. High CRM process complexity implies a high degree of knowledge to pursue the process goals. KCRM is the enablement of CRM processes by knowledge across the operations of marketing, sales and service. Gebert, Geib, Rolbe, and Piernpp (2002) and Gebert, Geil, Kobe, and Brenner (2003) described a CKM model based on a research approach in business engineering developed at the University of St. Gallen. The term CKM used by the St. Gallen authors refers to both the CKM and the KCRM aspects in the context of this paper. The St. Gallen approach derives from the reflections about CRM (Derliyski & Frohlich, 2004; Zanjani, Rouzbehani, & Dabbagh, 2008), in which customer knowledge is applied to support CRM processes (Gebert et al., 2002; Bueren, Schierholtz, Kolbe, & Brenner, 2004). The St. Gallen concept of the CKM model describes the basic elements for successful knowledge management in customer-oriented processes (Gebert et al. …

Knowledge translation (KT) was traditionally framed as a problem of moving research results into policy and practice. The impetus for the flow of knowledge originated with researchers constructing new knowledge and seeing its utility, or with policymakers and administrators seeing problems in practice and looking to researchers for solutions. In the 1970s, a shift in focus away from knowledge use was exemplified by Caplan’s (1979) two-communities theory, which posits that researchers and policymakers comprise two different communities with two different languages (Jacobson, Butterill, & Goering, 2003). A shift back to knowledge use with a new focus on user-centered design is evident in more recent KT models that provide frameworks for researcher and user interaction in order to build better understanding between diverse groups. The flow of knowledge from its construction in one context to its use in another context has been variously termed knowledge translation, knowledge exchange, knowledge transfer, research transfer, technology transfer, knowledge transformation, knowledge dissemination, knowledge mobilization, knowledge utilization, and research utilization. The terms are often used synonymously, but a specific term is sometimes used because it highlights a particular component of the knowledge flow process. For example, knowledge exchange implies a sharing of information between partners of equal value and focuses on the movement of knowledge between them, whereas research utilization implies the transformation of research results into usable knowledge and focuses on embedding the usable knowledge in practice. Information technologies have the potential to support knowledge translation in powerful ways. Key processes in the translation of knowledge include: (1) knowledge creation, management, and dissemination; (2) recognition of links between existing knowledge and its potential application to problems or practice; (3) translation into usable knowledge in practice; and (4) change in practice. Information technologies are a natural solution for these knowledge translation processes. For example, group and social software such as blogs and wikis support collaborative construction and sharing of knowledge; knowledge management systems support capture, storage, accessibility, and maintenance of constructed knowledge; and most Internet-based technologies support dissemination of information. Well-designed virtual communities provide online environments for the kinds of human interaction that enable collaborative exploration of ideas, that foster recognition of potential links between existing knowledge and its application to solve problems or change practice, and that inspire people to transform their practice. Data mining and artificial intelligence techniques can be used to enhance identification of potential links between knowledge in one context and problems in another context.

Considerations of critical infrastructure are now a major concern in Australia as in many other countries. The concern stems from a variety of causes, including the state of development, condition and level of use of existing infrastructure systems, especially transport networks; difficulties associated with public sector provision of new infrastructure; publicprivate partnership arrangements for infrastructure provision; and perceptions of risks and threats to infrastructure from both natural disasters (e.g. floods, fire or earthquake) and from human malevolence such as acts of sabotage, war or terrorism. The Australian Federal Government has defined critical infrastructure as ‘that infrastructure which if destroyed, degraded or rendered unavailable for an extended period, will significantly impact on social or economic well-being or affect national security or defence’ (Attorney-General’s Department, 2003). A pertinent question is then how to identify critical locations in an infrastructure network. For example, the road transport network is large, wide and diverse in nature. Are there particular locations or facilities in that network where loss or degradation of certain road sections (links) will have significant impacts? How should such impacts be assessed?

Adaptive planning of water infrastructure systems is crucial to bolster urban resilience in the face of climate change while meeting the needs of rapidly changing urban metabolisms. Urban water systems maintain intricate interconnections with other critical infrastructure domains (CIDs). Multi-sector dependencies and joint management of different CIDs have gained interest in recent research to mitigate undesired cascading effects across domains. Yet, combined modeling and joint simulation of multiple CIDs needs to overcome the limitations of tools and software often siloed to individual infrastructure domains. In this paper, we contribute a systematic review of 24 recent peer-reviewed publications on coupled simulation of urban water systems (water supply and drainage networks) and other CIDs, including energy grids, mobility networks, and IT infrastructure systems, extracted from a larger set of 222 publications. First, we identify trends, modeling frameworks, and simulation software enabling the combined simulation of interlinked CIDs. Then, we define an agenda of priorities for future research. Acknowledging the opportunities provided by open-source tools, data, and standardized evaluation schemes, future research fostering coupled simulation across CIDs should prioritize knowledge transfer, address differences in spatial and temporal dependencies, scale up simulations to a network level, and explore multi-sector interconnections beyond bilateral dependencies.