Innovation Leading Organizations

Innovation is one of the most likely factors to boost effectiveness, efficiency, and sustainability of an organization, regardless of its sector of activity, as for instance, the armed forces. The absence of innovation can affect the organizations in different ways, from suboptimal operational effectiveness and improvement of organization processes (administrative, operational, or logistics), bringing with it negative impacts on human resources motivation – the main keepers of knowledge, institutional culture, and organizational values. Innovative organizations also generate a stronger sense of belonging across their ranks and structures and fosters effectiveness in fulfilling organizations' missions. Hence, fostering innovation across any typology of organization is crucial and requires a proper approach to promote the desirable involvement of the entire workforce. This text, based on a review of some relevant literature, exposes critical enabling factors. Based on a cause-and-effect analysis, it proposes some recommendations for the practitioner as well as the academic.

The Relationship between Best Practices of Human Resource Management and the Operational Effectiveness of MSMEs

All businesses have the main objective, namely to gain profit and be sustainable. To achieve this, the role of good human resources (HR) practices is significant. How are the relationships and dependencies of the two variables? We have collected many international publications that discuss MSMEs and HR best practice governance issues in many contexts. We get data from an electronic search for the Google Scholar application. Next, we continue the analysis process by starting with a coding system, in-depth interpretation, critical evaluation, and final summary as the findings data on the analysis questions are valid and consistent. Based on existing data, we conclude a very close relationship between governance and HR best practices towards achieving the operational effectiveness of MSMEs to achieve profit and sustainability.

An operational conceptualization of human resource analytics: implications for in human resource development

Purpose The purpose of this paper is to examine the evolution of the concept of human resource analytics (HRA) and propose an operational framework demonstrating the sources generating data for HRA, as well as the impact of HRA on multiple levels in the organization. Design/methodology/approach A review of literature was conducted to present the existing body of knowledge and build upon for the development of an operational framework for successful implementation of HRA as a human resources (HR) process. Findings Building upon the existing literature, this paper presents an operational HRA framework, positioning HRA as an analytical process through integrating advanced statistical methodology. HRA presents a tool to obtain evidence-based analytical results for improving people-related performance, operational effectiveness, and ultimately the impact of the business strategy. By using HR big data, HRA impacts multiple organizational levels, from individual employees to HR functions and the organizational strategy. Practical implications While research on data analytics has recently flourished across various management fields, this has not been the case for the broader field of HR. This is especially a growing concern as the lack of understanding of the basics and fundamentals of people analytics in the field of HR may delay the effective implementation and operationalization of HRA and present additional barriers impacting on-going HR activities, as well as HR’s role as a strategic business partner. HR practice may greatly benefit from gaining an understanding of HRA and the multi-levels of impact it may have on the organization. Originality/value This paper explores various concepts related to HRA by examining terms such as “HR metrics” vs “HR” and “HR big data” vs “big data.” Furthermore, the comprehensive HRA operational framework presented in this paper provides HR professionals and researchers with a better understanding of HRA in the age of data analytics and artificial intelligence.

INTRODUCING OPERATIONAL INFORMATION INTO EARLY STAGE SHIP DESIGN USING QUEUEING NETWORKS

During the early stages of ship design a set of requirements needs to be identified, accounting for financial and technical feasibility, and operational effectiveness. This process of requirements elucidation creates a need for information regarding various design alternatives and their effect on the feasibility and effectiveness of the design requirements. When one considers internal layout and process driven ships, ships where the arrangement of spaces has a strong influence on the effectiveness of the ship's operational processes, a gap in available methods has been identified. This paper proposes a method based on queueing networks that allows a naval architect to study the effects of different arrangements on the execution of various sets of operational processes. Using this model a better understanding of the interaction between the ship's arrangement and its operational processes can be obtained. This understanding can improve the requirements elucidation process and can lead to the development of better design requirements.

Expert Advisory System for Production Surveillance and Optimization Assisted by Artificial Intelligence

Petroleum Engineers are usually responsible for 50-200 wells. The wells in highly instrumented fields generate 10-20 measurements every few seconds. This makes it difficult to be on top of every well, every day. This challenge carries a significant opportunity cost, therefore the surveillance process requires automation by implementing surveillance-by-exception. Faster identification of problems is great, but not enough unless the required activities are executed in a timely manner. The ability to execute quickly and safely requires a well-structured coordination effort between the different disciplines involved in field operations. In line with ADNOC Digital Transformation strategy, the solution described in this paper intends to couple surveillance by exception (a Petroleum Engineering workflow) with field operations execution (a multi-disciplinary set of workflows in the field). The integration is achieved by creating a simple yet robust action tracking system, and feeding it automatically with new opportunities, so that it is kept up to date. Automatic diagnosis becomes opportunities. Opportunities become activities. Activities are assigned, executed and closed. All activities are tracked on a high level, which provides insights and visibility to all parties on who is doing what, when and how to close the opportunity. The surveillance by exception engine consumes real time measurements from the historian. It then runs a set of soft sensors using full physics, reduced order models, proxy and data driven machine learning models, which utilize most of the measurements. The measured and calculated values are then fed to an expert system, which automatically diagnoses the wells and creates tickets with recommendations to the production engineer. The engineer reviews the ticket and forwards to field operations for execution. The log of activities enables a direct measure of operational effectiveness. This paper describes the philosophy of the system, how it works, lessons learned and the results of implementation across 6 oilfields and 600+ wells in Abu Dhabi.

Bank Risk Capital and Its Effectiveness in Selected Euro Area Banking Sectors

Risk capital or capital at risk (CaR) refers to the amount of capital set aside and maintained by banks to cover different types of risk. For banks, it is used as a buffer against claims or expenses in the event that ordinary capital is not enough to cover them. Thereby, risk capital can also be recognized as risk-bearing capital or surplus funds. Risk capital may generate very high costs, but on the other hand it protects against insolvency. That’s why a bank needs to find the ‘Gold mean’—the optimal value of risk capital that will not lower its efficiency, but still ensure financial security. The main objective of the study is identification of interdependencies between bank risk capital and effectiveness of the aggregated Eurozone banking sector and selected national banking sectors of the euro area. The paper tries to answer the research question whether the risk capital supports or lowers banks’ operational effectiveness. The adopted research hypothesis stated that there is a positive correlation between profitability and size of bank risk capital. To verify the hypothesis regression models were used. The results indicate that the size and structure of bank capital impact on the credit institutions’ effectiveness in the analyzed banking sectors, however with different intensity. Thereby, the article fulfils a research gap in the field of research studies that take into account how capital at risk and specific capital adequacy regulations may impact on a bank’s efficiency.

Criticality Analysis Based on Reliability and Failure Propagation Effect for a Complex Wastewater Treatment Plant

Wastewater treatment is a critical and necessary task every human settlement is obligated to address. If not, the consequences might be catastrophic, not just for humans but for the ecosystems as well, pushing research into finding new ways to improve wastewater treatment processes to make them safer and more efficient. Hence, there is a need to address matters, such as reliability and maintainability of Wastewater Treatment Plants (WWTP), when analyzing the availability and operational conditions. These should be addressed by analyzing the plant operational effectiveness impact (P-OEI), and in this article specifically, a WWTP study case to identify design flaws or improvement opportunities. A vital aspect of a complex system is to determine the contribution to resilience, reliability, and availability of every element embedded in the system. This is performed by adapting and applying the P-OEI methodology and real data of a WWTP located in Chile. This methodology breaks down the system into several levels of disaggregation similar to RBD methodology, analyzing the upstream for availability and the downstream for the P-OEI analysis from the system itself to the individual elements within subsystems. The potential impact on the overall system’s lack of efficiency is also quantified by an Expected Operational Impact (EOI) index, which is also calculated by the methodology. The P-OEI and EOI analyses performed in this study are powerful tools to assess the design and performance of complex systems and WWTP in particular.