Leveraging Business Intelligence and Data Analytics in an Integrated Digital Production Platform to Unlock Optimization Potentials

Alsaeedi, Ayesha Ahmed Abdulla Salem; Elabrashy, Manar; Alzeyoudi, Mohamed; Albadi, Mohammed; Soni, Sandeep; Isambertt, Jose; Tripathi, Deepak; Hidalgo, Melvin

doi:10.2118/208209-ms

Cited by 2 publications

(2 citation statements)

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“…(2021) dives deep into self-service BI tools and shows how they are useful in building informative dashboards to support management. There already are plenty of cases of good use of BI tools to monitor efficiently KPIs through some sort of alarming system: a case of success is the one of a big gas manufacturer as reported by Alsaeedi et al. (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…One very recent work by Arnaboldi et al (2021) dives deep into self-service BI tools and shows how they are useful in building informative dashboards to support management. There already are plenty of cases of good use of BI tools to monitor efficiently KPIs through some sort of alarming system: a case of success is the one of a big gas manufacturer as reported by Alsaeedi et al (2021). When carefully engineered, BI tools are able to support decision-making continuously and not only at certain intervals and they are also able to be scaled and applied in many different contexts as discussed by Rezgui and Maaouia (2016).…”

Section: Business Intelligence and Artificial Intelligence In Managem...mentioning

confidence: 99%

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Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations

Bruccoleri

Pietrosi

Scaccianoce

2023

BPMJ

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PurposeThe nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the absence of appropriate control. The purpose of this study is to develop a solution based on Artificial Intelligence technology to avoid data overloading and, at the same time, under-controlling in business process monitoring.Design/methodology/approachThe authors adopted a design science research approach. The authors started by observing a specific problem in a real context (a healthcare organization); then conceptualized, designed and implemented a solution to the problem with the goal to develop knowledge that can be used to design solutions for similar problems. The proposed solution for business process monitoring integrates databases and self-service business intelligence for outlier detection and artificial intelligence for classification analysis.FindingsThe authors found the solution powerful to solve problems related to KPI overload in process monitoring. In the specific case study, the authors found that the combination of Business Intelligence and Artificial Intelligence can provide a significant contribution to the detection of fraud, corruption and/or policy misalignment in public organizations.Originality/valueThe authors provide a big-data-based solution to the problem of data overload in business process monitoring that does not sacrifice any monitored Key Performance Indicators and that also reduces the workload of the business analyst. The authors also developed and implemented this automated solution in a context where data sensitivity and privacy are critical issues.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Business Intelligence and Artificial Intelligence In Managem...mentioning

confidence: 99%

Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations

Bruccoleri

Pietrosi

Scaccianoce

2023

BPMJ

View full text Add to dashboard Cite

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Optimization of Reservoir Monitoring Planning, Reporting, and Shortfall Identifications Using a Centralized Automated Integrated Asset Operation Model System

Alsaeedi

Albadi

Alharthi

et al. 2022

Day 3 Wed, November 02, 2022

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With a vast reservoir with a complex and dynamic system production system containing more than 300 wells, both producers and injectors, keeping track of the field operational activity like Reservoir Monitoring Plan (RMP) jobs can lead to sub-utilization, confusion, lack of efficiency, and loss of time. This paper describes an integrated and collaborated method that supports monitoring and execution of field activity tasks utilizing the integrated production optimization platform and the business intelligence tools. Reservoir monitoring planning (RMP) is one of the critical workflows used to ensure the smooth execution of tasks at the required time. This enables the user to plan future tasks based on the reservoir behavior and have a quick comparison between actual and planned tasks. The process starts with inputting the planned tasks into the integrated system, categorizing the tasks based on types, and assigning the executors. The system sends reminders/notifications of the planned task approaching the task due date to all the stakeholders. It also provides an automated direct summary/bird's eye view utilizing the business intelligence tool. Using an integrated asset operation model (IAOM) solution in a digital platform, this planning and monitoring workflow has enabled the users to establish a standardized and unified central repository for the tasks to ensure the single source of truth. With the help of this advanced workflow, inter-departmental communication gaps have been reduced tremendously, thus enabling better execution, analysis, gaps, or bottleneck identification. The automated summary dashboard contains the comparison of the actual status of tasks versus planned tasks. This helps in optimal facility utilization based on dynamic RMP monitoring. Additionally, the integrated solution for planner, performer & approver enabled the users to prioritize the activity based on bottlenecks faced during the past months and reduce the times used to update the monitoring Excel sheets. This outlining process provides a standardized approach across the assets, leading to improved tracking efficiency, minimizing the time spent on manual monitoring, planning, and receiving automatic reminders to avoid delay in the planned tasks, which assisted the users in focusing on production optimization and solving different bottlenecks. This reservoir monitoring and planning approach aligns with the overall corporate strategy of using an integrated asset operation Model (IAOM) for providing end users with tremendous opportunities related to system optimization. This also supports the users’ drive to switch the approach from individual people oriented to standardized process oriented. This approach supports standardization of the work process across the organization and a minimum of $ 700K value proposition from manpower time saving over five years.

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Leveraging Business Intelligence and Data Analytics in an Integrated Digital Production Platform to Unlock Optimization Potentials

Cited by 2 publications

References 0 publications

Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations

Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations

Optimization of Reservoir Monitoring Planning, Reporting, and Shortfall Identifications Using a Centralized Automated Integrated Asset Operation Model System

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