Data Driven Analysis for Rapid and Credible Decision Making: Heavy Oil Case Study

2017 ◽  
Author(s):  
A. Kaushik ◽  
V. Kumar ◽  
A. Mishra ◽  
P. Tummala
Keyword(s):  
Decision Making ◽  
Heavy Oil ◽  
Data Driven

scholarly journals Towards data-driven culture in a Spanish automobile manufacturer: A case study

2020 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Maria Esteller-Cucala ◽  
Vicenc Fernandez ◽  
Diego Villuendas
Keyword(s):  
Decision Making ◽  
Organizational Change ◽  
Organizational Transformation ◽  
Single Case ◽  
Data Driven ◽  
Website Design ◽  
Design Decision ◽  
Case Study Method ◽  
A Company

Purpose: Data-driven decision-making is a growing trend that lots of companies are nowadays willing to adopt. However, the organizational transformation needed is not always as simple and logical as it could seem and the comfort of the old habits can dim the change effort. The purpose of this study is to identify the potential problems that may arise in a real company’s transformation from a traditional intuition-driven decision-making model to a data-driven model. Design/methodology/approach: In order to reach this goal, a single case study method was used. Initially a literature review was conducted to analyze both the importance of the change to a data-driven culture and the process of organizational change. Thus, a case study method was adopted in a company of the automotive sector that included experimentation in the website design decision-making process. Findings: As a result of the case study, it was found that all the most cited risks for the organizational change process commented in the literature appeared in the project. However, even being warned of potential dangers the specific actions to prevent the damages were not trivial.Originality/value: The study presents in detail, the application of an organizational change model in a company. Important insights can be extracted from the specific case of a digitalization performed inside traditional industrial company.

scholarly journals Data-Driven Decision-Making in the Design Optimization of Thin-Walled Steel Perforated Sections: A Case Study

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Zhi-Jun Lyu ◽  
Qi Lu ◽  
YiMing Song ◽  
Qian Xiang ◽  
Guanghui Yang
Keyword(s):  
Decision Making ◽  
Finite Element ◽  
Finite Element Simulation ◽  
Optimization Design ◽  
Data Driven ◽  
Physical Test ◽  
Element Simulation ◽  
Industrial Big Data ◽  
Thin Walled

The rack columns have so distinctive characteristics in their design, which have regular perforations to facilitate installation of the rack system that it is more difficult to be analyzed with traditional cold-formed steel structures design theory or standards. The emergence of industrial “big-data” has created better innovative thinking for those working in various fields including science, engineering, and business. The main contribution of this paper lies in that, with engineering data from finite element simulation and physical test, a novel data-driven model (DDM) using artificial neural network technology is proposed for optimization design of thin-walled steel specific perforated members. The data-driven model based on machine learning is able to provide a more effective help for decision-making of innovative design in steel members. The results of the case study indicate that compared with the traditional finite element simulation and physical test, the DDM for the solving the hard problem of complicated steel perforated column design seems to be very promising.

Data-driven Personalization of Physiotherapy Care Pathway: Case Posture Scanning (Preprint)

2020 ◽  
Author(s):  
Olli Korhonen ◽  
Karin Väyrynen ◽  
Tino Krautwald ◽  
Glenn Bilby ◽  
Anna Broers ◽  
...  
Keyword(s):  
Decision Making ◽  
Care Pathway ◽  
Human Movement ◽  
Digital Data ◽  
Data Driven ◽  
Care Process ◽  
Root Cause ◽  
Care Models ◽  
The Impact

BACKGROUND Advanced sensor, measurement and analytics technologies enable entirely new ways to deliver care. Increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making, or even automating some parts of decision making in relation to the care process. OBJECTIVE The aim of this study is to analyze how digital data acquired from posture scanning can enhance physiotherapy and enable more personalized delivery of physiotherapy. METHODS A Case study is conducted with a company that has designed a Posture Scan Recording System (PSRS), which is an Information System (IS) that can record, measure and report human movement digitally to be used in physiotherapy. Interviews are used to explore the viewpoints of different stakeholders involved in physiotherapy. The data is analyzed thematically. RESULTS As the result of our thematic analysis, we identified three different support types the posture scanning can provide to enable more personalized delivery of physiotherapy. The types are: (1) Modeling the condition, which is about the use of posture scanning data for detecting and understanding the healthcare user’s condition and the root cause of the possible pain. (2) Visualization for a shared understanding, which is about the use of posture scanning data to inform and involve the healthcare user in more collaborative decision-making regarding care. (3) Evaluating the impact of the intervention, which is about the use of posture scanning data to evaluate the care progress and impact of the intervention. CONCLUSIONS Current care models in healthcare emphasize the importance to put the healthcare user at the center of the care. However, physiotherapy has lacked data driven solutions to inform and involve the healthcare user in care in a person-centered manner. The present study analyzes how posture scanning can enhance physiotherapy and presents three different types of support that posture scanning can provide for data-driven personalization of physiotherapy.

Data Mining and Knowledge Discovery. Preliminaries for a Critical Examination of the Data Driven Society

Global Jurist ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Claudio Sarra
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Driven ◽  
Critical Examination ◽  
General Data Protection Regulation ◽  
New Knowledge ◽  
Decision Making Processes ◽  
General Data ◽  
Use Of Knowledge

Abstract Data Mining (DM) is the analytical activity aimed at revealing new “knowledge” from data useful for further decision-making processes. These techniques have recently acquired enormous importance as they seem to fit perfectly the requests of the so called “Data Driven World”. In this paper, first I give an overview of DM, and of the most relevant criticisms raised so far. Then using a well-known case study and the European General Data Protection Regulation as benchmark, I show that there are some specific ambiguities in this use of “knowledge” which are relevant for the ethical and legal assessment of DM.

scholarly journals Data Storytelling with Dashboard: Accelerating Understanding Through Data Visualization in Financial Technology Company Case Study

Jurnal METRIS ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ferdian Suprata
Keyword(s):  
Decision Making ◽  
Data Visualization ◽  
Rapid Development ◽  
Data Driven ◽  
Decision Making Process ◽  
Business Practices ◽  
Context Data ◽  
The Impact ◽  
Financial Technology

In the rapid development many organisation rely on context data to support as well as to assist its decision making process. Consequently, Business Intelligence (BI), Dashboard, and Data Visualization emerged as primary tools in early 1990s as a way to help practitioners, data analyst, and data scientist to present context data into an actionable information for decision making process. However, despite its robust and powerful tools, recent study done by Kaggle’s survey in 2017 resulted that in the last five years, many companies were not able to create effective data-driven dashboard due to complex dataset, poor dashboard design, and insufficient storytelling. Hence, understanding of who is going to use dashboard, choosing which data and metrics to visualize in the right context, knowing how to convey information, driving engagement, and persuading audiences are essential in current business practices. This study is aimed to help practitioners to understand the impact of effective dashboard can have on decision making process, to design leveraging dashboard, and to present the dashboard in storytelling. A literature study is performed to gather all relevant information resulted in guidelines for dashboard creator. Case study in financial technology company is applied to experiment and to test the guidelines for assisting dashboard creator to present data-driven insight to the stakeholder.

scholarly journals Data-Driven Personalization of a Physiotherapy Care Pathway: Case Study of Posture Scanning

10.2196/18508 ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. e18508
Author(s):  
Olli Korhonen ◽  
Karin Väyrynen ◽  
Tino Krautwald ◽  
Glenn Bilby ◽  
Hedwig Anna Theresia Broers ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Information System ◽  
Health Care Professionals ◽  
Digital Data ◽  
Digital Tools ◽  
Data Driven ◽  
Care Process ◽  
The Impact

Background Advanced sensor, measurement, and analytics technologies are enabling entirely new ways to deliver health care. The increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making or even by automating some parts of decision making in relation to the care process. Objective The aim of this study was to analyze how digital data acquired from posture scanning can enhance physiotherapy services and enable more personalized delivery of physiotherapy. Methods A case study was conducted with a company that designed a posture scan recording system (PSRS), which is an information system that can digitally record, measure, and report human movement for use in physiotherapy. Data were collected through interviews with different stakeholders, such as health care professionals, health care users, and the information system provider, and were analyzed thematically. Results Based on the results of our thematic analysis, we propose three different types of support that posture scanning data can provide to enhance and enable more personalized delivery of physiotherapy: 1) modeling the condition, in which the posture scanning data are used to detect and understand the health care user’s condition and the root cause of the possible pain; 2) visualization for shared understanding, in which the posture scanning data are used to provide information to the health care user and involve them in more collaborative decision-making regarding their care; and 3) evaluating the impact of the intervention, in which the posture scanning data are used to evaluate the care progress and impact of the intervention. Conclusions The adoption of digital tools in physiotherapy has remained low. Physiotherapy has also lacked digital tools and means to inform and involve the health care user in their care in a person-centered manner. In this study, we gathered insights from different stakeholders to provide understanding of how the availability of digital posture scanning data can enhance and enable personalized physiotherapy services.

scholarly journals Integration of Data-Driven Process Re-Engineering and Process Interdependency for Manufacturing Optimization Supported by Smart Structured Data

Designs ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 44 ◽  
Author(s):  
Md Ashikul Alam Khan ◽  
Javaid Butt ◽  
Habtom Mebrahtu ◽  
Hassan Shirvani ◽  
Alireza Sanaei ◽  
...  
Keyword(s):  
Decision Making ◽  
Process Optimization ◽  
Structured Data ◽  
Production Optimization ◽  
Data Driven ◽  
Implementation Phase ◽  
Cause And Effect ◽  
Manufacturing Optimization ◽  
Line Process

Process re-engineering and optimization in manufacturing industries is a big challenge because of process interdependencies characterized by a high failure rate. Research has shown that over 70% of approaches fail because of complexity as a result of process interdependencies during the implementation phase. This paper investigates data from a manufacturing operation and designs a filtration algorithm to analyze process interdependencies as a new approach for process optimization. The algorithm examines the data from a manufacturing process to identify limitations through cause and effect relationships and implements changes to achieve an optimized result. The proposed cause and effect approach of re-engineering is termed the Khan-Hassan-Butt (KHB) methodology, and it can filter the process interdependencies and use those as key decision-making tools. It provides an improved process optimization framework that incorporates data analysis along with a cause and effect algorithm to filter out the process interdependencies as an approach to increase output and reduce failure factors simultaneously. It also provides a framework for filtering the manufacturing data into smart structured data. Based on the proposed KHB methodology, the study investigated a production line process using the WITNESS Horizon 22 simulation package and analyzed the efficiency of the proposed approach for production optimization. A case study is provided that integrated the KHB methodology with data-driven process re-engineering to analyze the process interdependencies to use them as decision-making tools for production optimization.

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