scholarly journals Organization Cybernetics for Railway Supplier Selection

2021 ◽  
Vol 6 (1) ◽  
pp. 33
Author(s):  
Mailasan Jayakrishnan ◽  
Abdul Karim Mohamad ◽  
Mokhtar Mohd Yusof
Keyword(s):  
Data Analytics ◽  
Supplier Selection ◽  
Information Technologies ◽  
Organizational Practice ◽  
Body Of Knowledge ◽  
Research And Teaching ◽  
Performance Reporting ◽  
Organizational Excellence ◽  
Practical Design ◽  
Using Data

The comprehensive stimulation for this research arises from the necessity to continually understand and investigate the Information System (IS) discipline body of knowledge from organizational practice. Specifically, in this study, we focus on comparing a few available excellence frameworks, data analytics, and cybernetics approaches. Such knowledge and skill practice in the IS field is predominant for both IS research and teaching. On the other hand, to propose a relevant performance reporting model using data analytics and cybernetics that entail a body of knowledge and skill is crucial for the development and transformation of organizational excellence. Yet, it helps to design an online real-time organizational dashboard that produces knowledge for its application and decision-making within an organizational practice. IS discipline in an organization is comparatively young and its specification in academia as well as in practice is rapidly changing, we focus on the practical design, and IS structure for organizational excellence through employing information technologies.

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

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

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 Knowledge Transfer in a Project-Based Organization Through Microlearning on Cost-Efficiency

2021 ◽  
pp. 002188632110330
Author(s):  
Teresa Beste
Keyword(s):  
Knowledge Transfer ◽  
Cost Efficiency ◽  
Construction Projects ◽  
Research Study ◽  
Participation Rate ◽  
Project Teams ◽  
Organizational Practice ◽  
Public Sector Organization ◽  
Body Of Knowledge

This paper investigates the role of microlearning on cost-efficiency on knowledge transfer in a project-based organization. As part of an action research study in a Norwegian public sector organization working with construction projects, a microlearning series was initiated to increase knowledge transfer on cost-efficiency. Seven microlearning lessons were distributed to 334 employees, including short questionnaires after the first and last lesson. The study reflects on the design process of the lessons, on the participation rate, and on how it contributes to an increase of knowledge. Microlearning was perceived as relevant by the participants. It makes knowledge transfer less arbitrary by providing a common body of knowledge to all project teams. For the organizational practice, this implies that microlearning also has potential for knowledge sharing on other topics in the project-based organization. Updating the microlearning series with further examples and new lessons is expected to contribute to continuous learning on cost-efficiency.

Effects of the CMS’ Public Reporting Program for Inpatient Psychiatric Facilities on Targeted and Nontargeted Safety: Differences Between For-Profits and Nonprofits

2021 ◽  
pp. 107755872199892
Author(s):  
Morgan C. Shields
Keyword(s):  
Spillover Effect ◽  
Public Reporting ◽  
Unintended Consequences ◽  
Psychiatric Facility ◽  
Quality Reporting ◽  
Performance Reporting ◽  
Reporting Programs ◽  
Using Data ◽  
Inpatient Psychiatric Facility ◽  
Psychiatric Facilities

The Centers for Medicare and Medicaid Services implemented the Inpatient Psychiatric Facility Quality Reporting Program in 2012, which publicly reports facilities’ performance on restraint and seclusion (R-S) measures. Using data from Massachusetts, we examined whether nonprofits and for-profits responded differently to the program on targeted indicators, and if the program had a differential spillover effect on nontargeted indicators of quality by ownership. Episodes of R-S (targeted), complaints (nontargeted), and discharges were obtained for 2008-2017 through public records requests to the Commonwealth of Massachusetts. Using difference-in-differences estimators, we found no differential changes in R-S between for-profits and nonprofits. However, for-profits had larger increases in overall complaints, safety-related complaints, abuse-related complaints, and R-S-related complaints compared with nonprofits. This is the first study to examine the effects of a national public reporting program among psychiatric facilities on nontargeted measures. Researchers and policymakers should further scrutinize intended and unintended consequences of performance-reporting programs.

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

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