Quality Management Using Data Analytics: An Application to Pharmaceutical Regulation

2015 ◽  
Author(s):  
Vishal Ahuja ◽  
John R. Birge ◽  
Chad Syverson ◽  
Elbert S. Huang ◽  
Min-Woong Sohn
Keyword(s):  
Quality Management ◽  
Data Analytics ◽  
Pharmaceutical Regulation ◽  
Using Data

Diagnosis of quality management systems using data analytics – A case study in the manufacturing sector

2020 ◽  
Vol 115 ◽  
pp. 103183 ◽  
Author(s):  
Rafael Sanchez-Marquez ◽  
José Miguel Albarracín Guillem ◽  
Eduardo Vicens-Salort ◽  
José Jabaloyes Vivas
Keyword(s):  
Quality Management ◽  
Data Analytics ◽  
Manufacturing Sector ◽  
Management Systems ◽  
Quality Management Systems ◽  
Using Data

Energy Prediction Using Data Analytics in Smart Grid

2018 ◽  
Vol 06 (06) ◽  
pp. 110-115
Author(s):  
Panchami Anil ◽  
Anas P V ◽  
Naseef Kuruvakkottil ◽  
Anusha K V ◽  
Balagopal N
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Prediction ◽  
Using Data

Organizing and Managing Data Analytics Teams for Procurement

2020 ◽  
Author(s):  
Benjamin Shao ◽  
Robert D. St. Louis
Keyword(s):  
Best Practices ◽  
Business Strategy ◽  
Data Analytics ◽  
Team Members ◽  
Procurement Process ◽  
Using Data ◽  
And Training ◽  
Different Levels ◽  
The Way

Many companies are forming data analytics teams to put data to work. To enhance procurement practices, chief procurement officers (CPOs) must work effectively with data analytics teams, from hiring and training to managing and utilizing team members. This chapter presents the findings of a study on how CPOs use data analytics teams to support the procurement process. Surveys and interviews indicate companies are exhibiting different levels of maturity in using data analytics, but both the goal of CPOs (i.e., improving performance to support the business strategy) and the way to interact with data analytics teams for achieving that goal are common across companies. However, as data become more reliably available and technologies become more intelligently embedded, the best practices of organizing and managing data analytics teams for procurement will need to be constantly updated.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

2021 ◽  
Keyword(s):  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Reproduction Number ◽  
Healthcare Systems ◽  
Regional Level ◽  
Data Driven ◽  
Effective Response ◽  
Reproduction Numbers ◽  
Exploratory Data ◽  
Using Data

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.

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