The Significant Impact of the COVID-19 Pandemic on Supply Chains

As the COVID-19 pandemic has spread across the world, the existence of disruptions in demand and supply have become more severe, conducted by containment measures taken by countries and affecting different sectors around the world. Although businesses and workplaces are restarting activities in some countries, with containment measures gradually being lifted, overall consumer demand is expected to remain low, also determined by the loss of jobs and income. Therefore, the scale of the impact on supply chains exceeded anything most companies had anticipated. This study aims to understand how companies were affected and identify some lessons learned about their vulnerabilities and the possible ways to address them in the long term. On the other hand, it is intended to reveal some of the impacts of COVID-19 and make some practical suggestions that can help in political and operational decisions to strengthen and build additional resilience in supply chains in the future.

Intellectual support for socially-oriented operational decisions making during hospitalization, taking into account limited resources and human behavior

The research was carried out with the financial support of the Russian Foundation for Basic Research within the framework of scientific project No. 20-04-60054 in terms of support for making socially-oriented decisions and budgetary topic No. 0073-2019-0005 in terms of organizing user interaction with the system.

Rapid and Efficient Waterflood Optimization Using Augmented AI Approach in a Complex Offshore Field

Waterfloods are amongst the most widely implemented methods for oil field development. Despite their vast implementation, operational bottlenecks such as lack of surveillance and optimization tools to guide fast paced decisions render most of these sub-optimal. This paper presents a novel machine-learning, reduced-physics approach to optimize an exceptionally complex off-shore waterflood in the Gulf of Suez. Leveraging a hybrid data-driven and physics approach, the water flooding scheme in Nezzezat reservoir was optimized to improve reservoir voidage replacement, increase oil production, and reduce water production by identifying potential in wells. As a by-product of the study, a better understanding of the complex fault system was also achieved. Including the geological understanding and its uncertainty is one of the key elements that must be preserved. All geological attributes, along with production rates are used to solve for pressure and inter-well communication. This is later supplemented by machine-learning algorithm to solve for the fractional flow of inter-well connections. Combining the inter-well connectivity and fractional flow, an optimization was performed to reach the best possible conditions for oil gains and water-cut reduction. A global optimization is possible thanks to the low computational demand of this approach, as thousands to millions of realizations must be run to reach the best solution while satisfying all constraints. This is all done in a fraction of the time it takes to run a traditional reservoir simulation. For the present case, the paper will present the underlying physics and data-driven algorithms, along with the blind tests performed to validate the results. In addition to the method's inner workings, the paper will focus more on the results to guide operational decisions. This is inclusive of all the complex constraints of an offshore field, as well as the best reservoir management practices, when reaching optimal production and injection rates for each well. An increase in production was achieved with some reduction in water-cut, while honoring well and platform level limitations. While these represent the gains for a particular month, optimization scenarios can be run weekly or monthly to capture the dynamic nature of the problem and any operational limitations that might arise. The ability to update the models and run optimization scenarios effortlessly allows pro-active operational decisions to maximize the value of the asset. The approach followed in this paper solves for the critical physics of the problem and supplements the remaining with machine learning algorithms. This novel and extremely practical approach facilitate the decision making to operate the field optimally.

Conflict and chaos: A case study of Ireland’s COVID-19 outbound travel market

During the COVID-19 pandemic the international outbound travel market from Ireland collapsed, declining at one point by 94%. This case study paper explores the environment which framed the collapse in travel, positioning it as one of conflict and chaos. The main objective is to document and analyse the legal, industry and societal factors which may have contributed to the collapse, identifying the key regulations, decisions, metrics, and societal responses, and exploring their intersection with outbound tourism. Three areas of inquiry are explored, namely: 1) the legal instruments used by government to restrict travel, 2) operational decisions made by industry, and 3) societal and media response to the pandemic. Three findings are presented from the desk research. First, it is suggested that the conflicting agendas of government and public health, the mainstream media and the travel industry would be more effectively dealt with in private as opposed to via news articles, social media arguments, and openly published letters. Second, clarity of communication from all three bodies needs improvement due to its impact on consumer confidence. Finally, the article proposes lessons for government in relation to future crisis management situations regarding outbound travel.

Empiricism and collaboration on grid data analytics

This paper discusses the need for data-driven tools to manage modern electric grids, where planning and operational decisions increasingly require empirical data on various time scales. The advancement of such tools will hinge on deploying instrumentation to collect faster and more localized measurements, capitalizing on state-of-the-art software solutions to facilitate big-data workflows, and enabling open exchange of data and information with research collaborators.

NAJLON MARKET – A TRADITIONAL INSTITUTION OF TRADE AND SOCIAL TRENDS

Traditional trade institutions organised as fairgrounds represent certain market and social trends in the trade of second-hand goods and other products that are characteristic of such places. One of such institutions is the Novi Sad Fairground, better known as the Najlon Market. The paper includes a quantitative and qualitative methodological approach to understand the nature and importance of such markets in the world, with special reference to the Najlon market. The entire trade process is based on the interests of customers and consumers in the form of personal satisfaction. The model of development of this market is based on investment in operational decisions of sellers how to fulfill the interest of consumers and strategically in terms of eliminating deficiencies in terms of infrastructure and management.

Estimating Formation Tops while Drilling Using Rate of Penetration ROP and Mechanical Specific Energy MSE

Accurate determination of formation tops while drilling is a critical part of exploration geology workflow. Operational decisions on coring, wireline logging, casing, and final well depth largely depend on it. One of the commonly used methods for picking formation tops while drilling is to correlate the rate of penetration (ROP) of the new well to wireline logs from offset wells where there is no logging while drilling (LWD) data. Picking formation tops based on only ROP from a new well can result in picking the wrong formation tops. To improve the workflow and outcome, this paper proposes the combination of ROP and Mechanical Specific Energy (MSE) for estimating formation tops while drilling. MSE is a measure of the energy required to crush or drill through a unit volume of rock. Because MSE is related to rock strength, it can be correlated to changes in lithofacies and formation tops. There are three key steps necessary for utilizing mechanical specific energy to estimate formation tops. First, select the input drilling data relevant to the applicable MSE equation. There are several empirical equations in the literature which can be used for estimating MSE. Input data are ROP, Weight on Bit (WOB), Bit Size (BS), Rotation Per Minute (RPM), and Torque (TORQ) from both the offset wells and the new well. Second, utilize a predetermined empirical equation to estimate MSE. Third, correlate MSE and ROP from the new well to both MSE, ROP, and wireline logs from offset wells (where available) to determine formation tops in the new well. Application of the proposed workflow to two wells show 1) distinct bed boundaries, which agree with formation tops picked using wireline logs; (2) that including MSE increases confidence and reliability of the data and makes it easy to identify the different formation boundaries based on the observed features of both MSE and ROP in the new well; and (3) that MSE variations are sensitive to formation strength, which may indicate rock mechanical changes and formation heterogeneity. This paper presents an alternative method of picking formation tops using MSE and ROP while drilling. The preliminary results based on the two test wells showed over 95% match with those picked using wireline logs of the same new well. As a result, this workflow enhances the ability of geoscientists to correlate subsurface geological features, reduces the uncertainty associated with picking formation tops, casing, and coring depths. Furthermore, it improves the confidence in the result, enhances the quality of operational decisions, and reduces the non-productive time (NPT) and well-cost.

Earnings management by health insurance companies in Brazil

PurposeThis study examines whether Brazilian health insurance companies (HICs) engage in earnings management through discretionary accruals or operational decisions by refraining from reporting a low indicator of sustainability in the market (IDSM).Design/methodology/approachThe study used the Jones and Modified Jones models to identify earnings management through discretionary accruals and used the model described by Roychowdhury to estimate the abnormal behaviors of operational decisions. Data covering 2012 to 2018 were collected from the ANS website.FindingsThe results show that HICs engaged in earnings management to avoid reporting a low IDSM. The findings should help health insurance clients make decisions regarding the purchase or change of health insurance. The findings should also encourage regulators to improve their evaluation of the economic and financial risks around HICs.Originality/valueThe National Agency of Supplementary Health (ANS) established a qualification program for HICs, monitoring them based on a set of indicators. Managers may have an incentive to use earnings management to obtain indices that meet the requirements of the ANS qualification program in order to avoid showing signs of abnormality.

Digital Workflow to Enhance Reservoir Management Strategies for A Complex Oil Field Through Real Time and Advanced Engineering Monitoring Solution

Increase recovery from mature oil reservoirs requires the definition of enhanced reservoir management strategies, involving the implementation of advanced methodologies and technologies in the field's operation. This paper presents a digital workflow enabling the integration of commonly isolated elements such as: gauges, flowmeters, inflow control devices; analysis methods and data, used to improve scientific understanding of subsurface flow dynamics and determine improved operational decisions that support field's reservoir management strategy. It also supports evaluation of reservoir extent, hydraulic communication, artificial lift impact in the near-wellbore zone and reservoir response to injected fluids and coning phenomenon. This latest is used as an example to demonstrate the applicability of this workflow to improve and support operational decisions, minimizing water and gas production due to coning, that usually results in increasing production operation costs and it has a direct impact decreasing reservoir energy in mature saturated oil reservoirs. This innovative workflow consists on the continuous interpretation of data from downhole gauges, referred in this paper as data-driven; as well as analytical and numerical simulation methodologies using real-time raw data as an input, referred in this paper as model-driven, not commonly used to analyze near wellbore subsurface phenomena like coning and its impact in surface operation. The resulting analyses are displayed through an extensive visualization tool that provides instant insight to reservoir characterization and productivity groups, improving well and reservoir performance prediction capabilities for complex reservoirs such as mature saturated reservoirs with an associated aquifer, where undesired water and gas production is a continuous challenge that incorporates unexpected operational expenses.

Integrating BPMN and DMN: Modeling and Analysis

The operational backbone of modern organizations is the target of business process management, where business process models are produced to describe how the organization should react to events and coordinate the execution of activities so as to satisfy its business goals. At the same time, operational decisions are made by considering internal and external contextual factors, according to decision models that are typically based on declarative, rule-based specifications that describe how input configurations correspond to output results. The increasing importance and maturity of these two intertwined dimensions, those of processes and decisions, have led to a wide range of data-aware models and associated methodologies, such as BPMN for processes and DMN for operational decisions. While it is important to analyze these two aspects independently, it has been pointed out by several authors that it is also crucial to analyze them in combination. In this paper, we provide a native, formal definition of DBPMN models, namely data-aware and decision-aware processes that build on BPMN and DMN S-FEEL, illustrating their use and giving their formal execution semantics via an encoding into Data Petri nets (DPNs). By exploiting this encoding, we then build on previous work in which we lifted the classical notion of soundness of processes to this richer, data-aware setting, and show how the abstraction and verification techniques that were devised for DPNs can be directly used for DBPMN models. This paves the way towards even richer forms of analysis, beyond that of assessing soundness, that are based on the same technique.