Production Uncertainty Research Articles

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The notable increase in petroleum demand, together with a decline in discovery rates, has highlighted the desire for efficient production of existing oil wells worldwide. Mainly, the productivity of the existing large oil fields makes us consider the principles of managing reservoirs to make the most of extraction. At the same time, many different uncertainties in the course of the developing oil field, including geological, operational, and economic uncertainties, have a detrimental impact on the reservoir's effective production, which is why dealing with uncertainty is crucial for maximizing output. There is a broad variety of studies on managing oil reservoirs under uncertainty information in the literature. In this study a short review of earlier works has been done on optimization strategies and management of uncertainty in reservoir production.

Exploring the Boundaries of Success: A Literature Review and Research Agenda on Resource, Complementary, and Ecological Boundaries in Digital Platform Business Model Innovation

Digital platform business model innovation is a rapidly evolving field, yet the literature on resource, complementary, and ecological boundaries remains limited, leaving a significant gap in our understanding of the factors that shape the success of these platforms. This paper explores the mechanisms by which digital platforms enable business model innovation, a topic of significant theoretical and practical importance that has yet to be fully examined. Through a review of the existing literature and an examination of the connotations of digital platforms, the design of platform boundaries, and the deployment of boundary resources, the study finds that (1) the uncertainty of complementors and complementary products drives business model innovation in digital platforms; (2) the design of resource, complementary, and ecological system boundaries is crucial to digital platform business models and manages complementor and complementary product uncertainty while promoting value co-creation; and (3) boundary resources establish, manage, and sustain cross-border relationships that impact value creation and capture. Based on these findings, four research propositions are proposed to guide future research on digital platform business model innovation and provide insights for effectively innovating business models and influencing value creation and capture.

Virtual Reality-Based Digital Twins: A Case Study on Pharmaceutical Cannabis

Digital Twins are digital equivalents of real-life objects. They allow producers to act immediately in case of (expected) deviations and to simulate effects of interventions based on real-life data. Digital Twin and eXtended Reality technologies (including Augmented Reality, Mixed Reality and Virtual Reality technologies), when coupled, are promising solutions to address the challenges of highly regulated crop production, namely the complexity of modern production environments for pharmaceutical cannabis, which are growing constantly as a result of legislative changes. Cannabis farms not only have to meet very high quality standards and regulatory requirements but also have to deal with high production and market uncertainties, including energy considerations. Thus, the main contributions of the research include an architecture design for eXtended-Reality-based Digital Twins for pharmaceutical cannabis production and a proof of concept, which was demonstrated at the Wageningen University Digital Twins conference. A convenience sampling method was used to recruit 30 participants who provided feedback on the application. The findings indicate that, despite 70% being unfamiliar with the concept, 80% of the participants were positive regarding the innovation and creativity.

A production capacity investment decision-making tool for the indoor vertical farming industry

Indoor vertical farming (VF) systems are a form of controlled environment agriculture, making use of technology to improve food availability and security while reducing environmental impacts. Commercially, it is a young industry with potential to disrupt the food supply chain status quo but is faced with uncertainty and risk with respect to product demand and production technology efficacy. As a result, VF firms must make decisions to develop production capacity that meets market demand while ensuring long-term profitability. This research presents a method for VF firms to facilitate the allocation of capital resources to new production capacity considering demand and production uncertainty. The method makes use of two mixed integer linear programming models, executed sequentially, to identify a production technology plan and then to identify production capacity plans under a range of different demand scenarios. Profit is used to evaluate performance and the method includes a multi-criteria comparison of how production plans perform using a range of preselected criteria.

ANALISIS GHARAR DALAM OBYEK JUAL BELI ONLINE

Ease of doing business online, bringing up creative business ideas in marketing business products, or selling various business products, including the practice of dropshipping and mystery boxes. This research will discuss the practice of gharar in online buying and selling in the form of dropship and mystery boxes. This research is a type of descriptive qualitative research. which aims to understand the phenomenon or occurrence of buying and selling online in the form of dropship and mystery box in depth and descriptive. The results of this study are that the continuity of the handover of dropship products to buyers still depends on ownership or full authority over the goods being traded. Dropshipp does not interpret aspects of legal ownership of goods. This can cause uncertainty (gharar) in the handover of goods to buyers, the availability of goods and time. Uncertainty in mystery box products can also contribute to greater risk. The equilibrium point in a mystery box product is not the result of the intersection of supply and demand with the same quality. This means that the level of balance that is created is an apparent balance because it brings together demand and supply that differ in quality.

The source of uncertainty and optimal monetary policy

We study optimal monetary policy in response to the cost-push uncertainty shock, which is a second-moment shock, in a textbook New Keynesian model. Following a cost-push uncertainty shock, optimal monetary policy faces a trade-off between output gap and inflation stabilization. This is because, even in the absence of first-moment cost-push shocks, cost-push uncertainty generates a time-varying gap between natural output and efficient output. These results contrast with those under a conventional productivity uncertainty shock, which leads to complete stabilization of the output gap and inflation.

Assessing the identification uncertainty in plant products traded as traditional Asian medicines

Biodiversity is threatened by multiple factors including habitat loss, climate change, and over-exploitation. The illegal wildlife trade is one of the key threats to species survival and its regulation and monitoring are dependent on accurate identification. Plants are particularly difficult to identify due to their look-alike properties, which are further aggravated when they are processed eventually in their finished product. Identification of species is critical to monitoring, detecting, and regulating the wildlife trade. In this study, we quantified species misidentification using a match-mismatch experiment adapted from psychology, taking examples of medicinal plant products used in Traditional Asian Medicines. Participants compared 210 pairs of images of plant products, indicating if the paired images were the same (species), different (species), or (they) did not know. We found that the matched pairs (paired images of the same species) had a lower level of error than the unmatched pairs (paired images of different species). Similarly, 1.4% of the image pairs had errors over 75%, three of them as high as 83%. Such errors in species identification can be used by traders to deceive enforcement actions through laundering as less threatened or regulated species. These results suggest that future interventions around identification training should prioritize species with high errors and should consider that product processing may have a significant impact on identification. Further, initiatives related to species identification could benefit from using existing standard methodologies from psychology to inform training needs and measure their impacts which in turn will benefit conservation efforts.

Assessing the Accuracy of PRISMA Standard Reflectance Products in Globally Distributed Aquatic Sites

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications.

Systematic Approach for Investigating Temporal Variability in Production Systems to Improve Production Planning and Control

Including the inherent temporal variability in a production system in planning and control processes can ensure the fulfillment of the production schedule and increase key performance indicators. This benefits the sustainable and efficient use of the system. The current lack of consideration of this inherent temporal variability in production planning leads to optimistic estimates and calculations of planned values that cannot be met. To complete this information, the inherent temporal variability in a production system is investigated using a systematic approach. This approach detects, identifies, and quantifies inherent temporal variability and is applied to a data base created via an automated, event-driven procedure. The approach is tested in a smart factory laboratory. The results to date on improving production planning and control are promising as key performance indicators have been increased. There is still a need for action to ensure the fulfillment of the production schedule. Concluding, work on this topic has just begun, as can be seen from the discussion section. More data need to be collected and aggregated for future research. This publication is intended to motivate researchers to address this issue and better manage the existing uncertainty in production through the use of data.

Oil supply expectations and corporate social responsibility

This paper examines how changes in oil supply expectations affect the social responsibility scores of Chinese listed companies and attempts to identify the source of this expectations in terms of uncertainty and orderliness of OPEC production. We document strong evidence that supply news shocks have a statistically and economically significant impact on corporate social responsibility. Supply expectations strongly impact social responsibility performance in the energy sector, with negative news leading to higher oil prices and increased economic uncertainty. Based on real options theory and precautionary storage effects, firms increase oil inventories when OPEC announcements are disclosed. This leads to increased tension in firms' cash flows, which provides evidence of the expected supply channel operation.

Biomaterial Hierarchy Selection Framework Under Uncertainty for More Reliable Sustainable Green Products

Biomaterial Hierarchy Selection Framework Under Uncertainty for More Reliable Sustainable Green Products

Perceived transaction cost and its antecedents associated with fintech users' intention: Evidence from Pakistan

Researches depict a considerable degree of acknowledgment of financial technology improvement in Pakistan. However, the costs blocking clients' intention to utilize financial technology remain dubious. Building upon Transaction Cost Economics and Innovation diffusion theory, this paper hypothesizes that consumers' transaction cost of fintech is affected by nine antecedents: perceived asset specificity, complexity, product uncertainty, behavioral uncertainty, transaction frequency, dependability, limitations, convenience, and economic utility. Transaction cost has a negative relationship with consumers' intentions to use fintech for online buying or availing services. We tested the model using data gathered from the individuals. The results show that factors that are positively related to consumers' perceived transaction cost, among them product uncertainty (β = 0.231) is the greatest of factors, followed by behavior uncertainty (β = 0.209) and asset specificity (β = 0.17), those that are negatively associated are dependability (β = 0.11), and convenience (β = 0.224). The study is limited in scope, focusing primarily on cost factors. Future research may analyze additional cost-related elements and the actual use of financial technology by using samples from different countries.

Ab initio study of tungsten-based alloys under fusion power-plant conditions

Tungsten (W) is considered a leading candidate for structural and functional materials in future fusion energy devices. The most attractive properties of tungsten for magnetic and inertial fusion energy reactors are its high melting point, high thermal conductivity, low sputtering yield, and low long-term disposal radioactive footprint. However, tungsten also presents a very low fracture toughness, primarily associated with inter-granular failure and bulk plasticity, limiting its applications. In recent years, several families of tungsten-based alloys have been explored to overcome the aforementioned limitations of pure tungsten. These include tungsten-based high-entropy alloys (W-HEAs) and tungsten-based Self-passivating Metal Alloys with Reduced Thermo-oxidation or “SMART alloys” (W-SAs). Given their proximity to the plasma, it is crucial to understand how the exposure of these candidate plasma-facing materials (PFMs) to the neutron fluxes expected in fusion reactors impacts their material behavior over time. In this work, we present a computational approach that combines inventory codes and first-principles DFT electronic structure calculations to understand the behavior of transmuting tungsten-based PFMs. In particular, we calculate the changes in the chemical composition, production uncertainties, the elastic and ductility properties, and the density of states for five tungsten-based PFMs when exposed to EU-DEMO fusion first wall conditions for ten years.

Toward a better understanding of uncertainty for satellite precipitation products over complex terrain with sparse rain gauge data

Satellite precipitation product quality is critical for various meteorological and hydrological applications. Products are subjected to more uncertainties in sparsely-gauged regions than in densely-gauged regions due to insufficient error correction. However, ground-truth-based validation cannot accurately quantify the spatially distributed uncertainty of satellite precipitation products over complex terrain with sparse rain gauge data. In this study, we explored a gauge-independent method, called the Three-Cornered Hat (TCH), to overcome the quantification issue and better understand the uncertainty of satellite precipitation products in such situations. Firstly, the reliability of the TCH method was explored over a topographically complex area. Then, the performance of the Soil Moisture TO RAIN-Advanced SCATterometer (SM2RAIN-ASCAT) and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) in the Chuan-Yu region was evaluated using TCH, with the help of the China Merged Precipitation Analysis (CMPA) precipitation product as the third input. We found that: (1) The high consistency between error standard deviation (ESD) estimated by TCH and root mean square error (RMSE) estimated by ground-truth-based validation demonstrated that TCH is reliable for evaluating precipitation datasets over complex terrain; (2) The ESDs of SM2RAIN-ASCAT and IMERG had different spatial distributions, and the quality of IMERG was overall higher than that of SM2RAIN-ASCAT over a mountainous area; (3) IMERG performed better than SM2RAIN-ASCAT both at low (under 3000 m) and high elevations (over 3000 m) in most years, as well as for most main land cover types (woody savannas, mixed forests, and grasslands) in the study area. This method and these findings provide a new insight for better use of satellite precipitation products over complex terrain.

Dynamic emergent leaf area in tidal wetlands: Implications for satellite-derived regional and global blue carbon estimates

The IPCC Special Report on the Ocean and Cryosphere in a Changing Climate highlights the importance of blue carbon in tidal wetlands in combating climate change. In this study, we highlight the uncertainty associated with leaf area index (LAI) estimations in tidal wetlands, specifically salt marshes, a key vegetation parameter for productivity models and Earth System Models (ESM). LAI, derived from satellite reflectance data, is linked to atmospheric carbon exchange and gross primary production (GPP) across vegetative ecosystems. However, estimating salt marsh LAI is challenging because canopy height and density vary across short distances, and tidal flooding alters the atmosphere-exposed leaf area, hereafter called emergent leaf area index (ELAI), at short time scales. Further, in tidal wetlands dominated by species such as Spartina alterniflora, canopy height and density vary across short distances. We present a novel approach for measuring spatiotemporal dynamics in tidal wetland ELAI. We modeled ELAI from vertical LAI profiles and created spatial estimates across tidal periods. We then linked ELAI with eddy covariance carbon (C) fluxes through footprint modeling and revealed correlations between emergent leaf area and C fluxes. Next, we demonstrated that ELAI can be readily estimated across 10-m spatial scales using Sentinel-2 satellite data, even during high tides (R2 = 0.89; NRMSE = 10%). Finally, we showed a common product, MODIS MYD15A2H, underestimated (20%) LAI during dry conditions but overestimated (7–93%) during high flooding. Dynamic ELAI could reduce uncertainties in satellite-derived global GPP products when developing blue carbon budgets for ecosystems threatened by accelerated sea level rise.

A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset

The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002–2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.

Determination of broadband atmospheric turbidity from global irradiance or photovoltaic power data using deep neural nets

Photovoltaic technologies provide significant capacity to electric grids, however, resource variability and production uncertainty complicate power balancing and reserve management. A crucial step in predicting solar generation is determining clear-sky irradiance. Clear-sky attenuation can be modeled using broadband atmospheric turbidity factors, but model accuracy is dependent on the measurements used to determine the current and future state of aerosol loading and water vapor content, which requires close proximity measurements, in time and space, to account for turbidity variability. Such measurements, though, are only available in near real-time at a limited, and decreasing, number of sites. This paper proposes a new method for estimating time-varying local turbidity conditions from more readily available pyranometer or PV output data. The method employs a long short-term memory recurrent neural network to distill the turbidity-driven signal from global irradiance (or global irradiance driven) observations, despite an inherent dampening issue. The method is developed to operate in near real-time for solar forecasting applications. Validation examines the ability of the method to (1) reproduce turbidity estimates derived from historical measurements of beam irradiance under clear-sky conditions; and (2) provide input for clear-sky models in the form of persistence forecasts generated from daily mean values.

Architectural framework of digital twin-based cyber-physical production system for resilient rechargeable battery production

AbstractRechargeable battery production should yield highly diversified batteries, overcoming performance degradation caused by the complexity of production processes, dynamic disturbances, and uncertainties. Resilience must be achieved to overcome these limitations while satisfying the core technical requirements. This study developed an architectural framework for a cyber-physical production system (CPPS) using a digital twin (DT) to achieve resilience. Activities for resilience, operational characteristics, and CPPS were analysed to determine the core requirements. This analysis presents a novel model of activities for resilience. Moreover, the DT-based CPPS architecture, service composition procedures, and the asset description for providing inputs to the elements in the CPPS were designed according to these requirements. The proposed architectural framework applies the asset administration shell principles for efficient interoperability. The service composition procedures are classified into the type and instance phases to ensure static and dynamic technical functionalities. Moreover, the asset description is suitable to indicate the required information elements of rechargeable battery production. The DT-based CPPS was applied in a rechargeable battery production for an industrial case study to verify and validate the proposed method. The average accuracy of the DT application was 95.24%, indicating that it can provide technical functions with high accuracy. As a result, these technical functions can be executed within a sufficient action time, and the high simulation accuracy prevents performance degradation during production. Additionally, the DT is suitable for event diagnosis and provides a dynamic response. Furthermore, the proposed method can eliminate the data, analysis, and decision latencies.

Solving the product uncertainty hurdle in social commerce: The mediating role of seller uncertainty

Social commerce, which combines the transactional side of e-commerce with social interactions, has grown in popularity throughout the last years. Despite the fact that the issue of uncertainty in e-commerce has been extensively discussed, little is known about the mechanisms that customers of social commerce employ to lessen their perceptions of uncertainty. The role of various factors, including return policy leniency, information quality, seller popularity, seller reputation, and number of positive comments, in product uncertainty in social commerce is still little understood. Additionally, the social commerce literature indicates that more research is required on the mediating role of seller uncertainty. According to the empirical evidence from 471 customers, return policy leniency, information quality, seller popularity, seller reputation, and number of positive review comments all have significant negative effects on seller uncertainty. In addition, the results demonstrate that seller uncertainty partially mediates the relationships between these factors and customers’ perceptions of product uncertainty in social commerce. These findings reveal important implications for both practice and theory.

Uncertainty of multi-source vegetation products on regional climate simulation in China

Uncertainty of multi-source vegetation products on regional climate simulation in China