scholarly journals On Enabling GDPR Compliance in Business Processes Through Data-Driven Solutions

2020 ◽  
Vol 1 (4) ◽  
Author(s):  
Rashid Zaman ◽  
Marwan Hassani
Keyword(s):  
Business Processes ◽  
Data Driven

scholarly journals A Review on “Management by Exception” Surveillance for Well Management: To maximize Oil Production

2018 ◽  
Vol 7 (3.25) ◽  
pp. 90
Author(s):  
Azlinda Abdul Malik ◽  
Mohd Hilmi Hasan ◽  
Mazuin Jasamai
Keyword(s):  
Literature Review ◽  
Business Processes ◽  
Oil And Gas ◽  
Production Performance ◽  
Data Driven ◽  
Well Production ◽  
Monitoring Well ◽  
Oil And Gas Operations ◽  
Manual Interaction ◽  
Structured Literature Review

The business processes and decisions of oil and gas operations generate large amounts of data, which causes surveillance engineers to spend more time gathering, and analyzing them. To do this manually is inefficient. Hence, this study is proposed to leverage on data driven surveillance by adopting the principle of management by exception (MBE). The study aims to minimize the manual interaction between data and engineers; hence will focus on monitoring well production performance through pre-determined parameters with set of rules. The outcome of this study is a model that can identify any deviations from the pre-set rules and the model will alert user for deviations that occur. The model will also be able to predict on when the well be offline if the problem keeps on persisting without immediate action from user. The objective of this paper is to present a literature review on the prediction and management by exception for the above mentioned well management. The results presented in this paper will help in the development of the proposed prediction and management model. The literature review was conducted based on structured literature review methodology, and a comparative study among the collected works is analyzed and presented in this paper.  

Product-level profitability

2019 ◽  
Vol 33 (1) ◽  
pp. 214-237
Author(s):  
Hannu Hannila ◽  
Joni Koskinen ◽  
Janne Harkonen ◽  
Harri Haapasalo
Keyword(s):  
Business Processes ◽  
Data Driven ◽  
Product Portfolio ◽  
Data Governance ◽  
Content Type ◽  
Technical Product ◽  
Product Level ◽  
Level Data ◽  
Corporate Level

Purpose The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance. Design/methodology/approach The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size. Findings Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation). Practical implications The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio. Originality/value The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

Object-Aware Business Processes

2011 ◽  
Vol 2 (2) ◽  
pp. 19-46 ◽  
Author(s):  
Vera Künzle ◽  
Barbara Weber ◽  
Manfred Reichert
Keyword(s):  
Process Modeling ◽  
Process Management ◽  
Business Processes ◽  
Data Driven ◽  
Process Support ◽  
Management Technology ◽  
It Support ◽  
Business Objects ◽  
Knowledge Intensive

Despite the increasing maturity of process management technology not all business processes are adequately supported by it. Support for unstructured and knowledge-intensive processes is missing, especially since they cannot be straight-jacketed into predefined activities. A common characteristic of these processes is the role of business objects and data as drivers for process modeling and enactment. This paper elicits fundamental requirements for effectively supporting such object-aware processes; i.e., their modeling, execution, and monitoring. Imperative, declarative, and data-driven process support approaches are evaluated and how well they support object-aware processes are investigated. A tight integration of process and data as major steps towards further maturation of process management technology is considered.

Exploring sensor data for agricultural statistics: The fruit is not hanging as low as we thought

2021 ◽  
pp. 1-14
Author(s):  
Ger Snijkers ◽  
Tim Punt ◽  
Sofie De Broe ◽  
José Gómez Pérez
Keyword(s):  
Business Processes ◽  
Current Data ◽  
Data Driven ◽  
Sensor Data ◽  
Small Scale ◽  
Use Case ◽  
New Methods ◽  
New Business ◽  
Data Source ◽  
Selection Of

New business processes are increasingly data driven as sensors have become ubiquitous. Sensor data could be a valuable new data source for official statistics. To study this presumption Statistics Netherlands conducted a small-scale use case in the area of agricultural statistics in collaboration with an innovative farmer. A selection of his sensor data was explored for overlap with current data demands in surveys. The aim of the study was to obtain insights in the available agricultural data, their data structure and quality, and explore new methods of data collection for agricultural statistics. The conclusion is that these data are valuable for replacing or pre-filling (parts of) certain agricultural surveys. However, many more challenges surfaced than we expected, to which the title of this paper refers. These challenges will be discussed in this paper.

scholarly journals The data-driving management in oil and gas industry: examples and opportunities

2021 ◽  
Vol 6 (4) ◽  
pp. 137-146
Author(s):  
Andrey S. Bochkov ◽  
Mariia G. Dymochkina
Keyword(s):  
Decision Making ◽  
Best Practices ◽  
Business Processes ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Data Driven ◽  
Management Approach ◽  
Gas Industry ◽  
Data Driven Approach ◽  
Using Data

Background. Decision-making process in the oil and gas industry, traditionally extremely expensive, should be based on the point of maximizing the business value. Forecasting the effectiveness of investments of any business unit in oil and gas should be based on a data-driven management approach. The purpose of this article — to study methods and best practices of applying a data — driven approach to decision-making and analyze the possibility of scaling methods of best practices in the processes in oil and gas company. Materials and methods. Research a various case with data-driven management shows that using data-driven approach allows solving several tasks at once: to make a fast and quality decisions based on data that can always be checked, and the result can be analyzed; to reduce the costs by eliminating inefficient steps and increase the flexibility of the process; to form the correct attitude to data (data culture) and prepare for the implementation of the technologies of Industry 4.0. Analyze cases revealed two common and important things: engineering of business processes from the key performance indicators and the technological development. Results. In article discusses the topic of applying a data-driven decision-making approach in oil and gas companies using several examples of Gazprom Neft. These examples shows that better effect from the using of data-driven management is achieved by consistently modeling business processes for achieving maximum values; highlighting and fixing key business performance indicators and creating a digital monitoring of these indicators, which allows you to the achievement of goals. Conclusions. In the conclusion of the article there are recommendation about using data-driven management approach for various processes of an oil and gas company.

scholarly journals Discovering generative models from event logs: data-driven simulation vs deep learning

2021 ◽  
Vol 7 ◽  
pp. e577
Author(s):  
Manuel Camargo ◽  
Marlon Dumas ◽  
Oscar González-Rojas
Keyword(s):  
Deep Learning ◽  
Business Process ◽  
Business Processes ◽  
Simulation Models ◽  
Generative Models ◽  
Generative Model ◽  
Process Models ◽  
Data Driven ◽  
Learning Approaches ◽  
Business Process Models

A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.

Are They Ready for the Big Thing?

2021 ◽  
pp. 200-210
Author(s):  
Matthias Lederer ◽  
Juluis Lederer
Keyword(s):  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Business Processes ◽  
Future Trend ◽  
Data Driven ◽  
Big Data Applications ◽  
All Solutions ◽  
Management Approaches ◽  
And Control

Data-driven business processes management (BPM) is regarded as a central future trend because automation often makes huge amounts of data (big data) available for the optimisation and control of workflows. Software manufacturers also use this trend and call their solutions big data applications, even if some features are reminiscent of traditional data management approaches. This chapter derives from the basic definitions of big data including 13 central requirements that a big data BPM solution must meet in order to be described as such. One hundred twenty-one process management solutions are evaluated on the basis of these to determine whether they are real big data applications. As a result, less than 5% of all solutions analysed meet all requirements.

scholarly journals How do top- and bottom-performing companies differ in using business analytics?

2017 ◽  
Vol 30 (6) ◽  
pp. 874-892 ◽  
Author(s):  
Guangming Cao ◽  
Yanqing Duan
Keyword(s):  
Organizational Performance ◽  
Business Processes ◽  
Academic Research ◽  
Data Driven ◽  
Business Analytics ◽  
Analytical Strategy ◽  
Content Type ◽  
And Performance ◽  
Data Driven Decisions ◽  
Effective Use

Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA. Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies. Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment. Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities. Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance.

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