scholarly journals Impact of Business Intelligence on Technical Creativity: A Case Study on AlHekma Pharmaceutical Company

2016 ◽  
Vol 12 (28) ◽  
pp. 502 ◽  
Author(s):  
Hani J. Irtaimeh ◽  
Abdallah Mishael Obeidat ◽  
Shadi. H Abualloush ◽  
Amineh. A Khaddam
Keyword(s):  
Data Mining ◽  
Pharmaceutical Company ◽  
Business Intelligence ◽  
Data Warehousing ◽  
Modern Technology ◽  
Technical Creativity ◽  
Study Population ◽  
Analytical Processing ◽  
The Impact

Business Intelligence, through its dimensions (data warehousing, data mining, direct analytical processing), helps the members of an organization to perceive and interpret their role in the organization’s creativity. For this reason, we may assume that Business Intelligence has an impact on Technical Creativity, and that matching of Business Intelligence and Technical Creativity will improve and achieve excellence in an organization. The aim of this study is to explore the impact of business intelligence dimensions (data warehousing, data mining, direct analytical processing) on Technical Creativity in AlHekma Pharmaceutical Company as a case study. For this purpose, a questionnaire was developed to collect data from the study population which consists of 50 employees. This is aimed at testing the hypotheses and achieving the objectives of the study. The most important results that the study achieved were that there was a statistically significant impact of business intelligence with its dimensions (data warehousing, data mining, and direct analytical processing) in technical creativity. The most important recommendations of the study were the necessity of organizations dependence on modern technology in order to develop their works. Thus, this is because this technology is recognized by its high accuracy on a completion of the work, as well as deepening the concept of technical creativity which gives them a competitive advantage in the marke

Objective and Subjective Aspects of Decision-Making Support at the Mesoeconomic Level

2016 ◽  
pp. 183-215
Author(s):  
Anastasia Y. Nikitaeva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cognitive Mapping ◽  
Objective Data ◽  
Management Effectiveness ◽  
Cognitive Approach ◽  
Multi Attribute Utility Theory ◽  
On Line ◽  
Analytical Processing

This chapter substantiates the importance of improving management effectiveness of mesoeconomic systems in current economic conditions and the features of mesoeconomy as a management object which defines the high complexity of decision making at the meso level. There are approaches, methods, and technologies which provide support of the decision making process via the integration of formal methods for objective data analysis and methods of accounting to solve semi-structured complex problems of mesoeconomy. A cognitive approach, and an approach involving the integration of the On-Line Analytical Processing and Data mining technologies with methods of a multi-criteria assessment of alternative, in particular methods of Multi-Attribute Utility Theory are considered in the chapter. Cognitive mapping of interaction between state and business in a mesoeconomic system are included as a case-study.

The Impact of a BI-Supported Performance Measurement System on a Public Police Force

2019 ◽  
pp. 509-527
Author(s):  
Elad Moskovitz ◽  
Adir Even
Keyword(s):  
Performance Measurement ◽  
Measurement System ◽  
Business Intelligence ◽  
Police Force ◽  
Performance Measurement System ◽  
Moderation Effect ◽  
Relative Metrics ◽  
Time Period ◽  
The Impact

Performance measurement, as an effective tool for implementing organizational strategy and assisting ongoing control and surveillance, is broadly adopted today. The performance measurement system (PMS) explored in this case study was implemented, using business intelligence (BI) technologies, for a public police force. The system lets police commanders view and analyze the performance scores of their own units and get feedback on the success of their activities. The study examines the system's impact, through analysis of the metric results over a time period of five years. The results show that the vast majority of the metrics examined indeed improved. Further, the results underscore the moderation effect of relative metrics weights, as well as the different behavior of metrics that reflect activity versus those that reflect outcomes. The study underscores both the positive and the negative aspects of those results, and discusses their implications for future PMS implementation with BI technologies.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

scholarly journals The Impact of Principals’ Knowledge of the Objectives of High Schools and the Influence on Their Managerial Practices

2015 ◽  
Vol 5 (3) ◽  
Author(s):  
Mohammad Ghadiri Bayekolaee ◽  
Omolbanin Abdi Sarkami ◽  
Seyed Ali Vahedi Moakher ◽  
Hassan Razaghi Shani ◽  
Seyed Majid Taheri Darkahi ◽  
...  
Keyword(s):  
High Schools ◽  
Cluster Sampling ◽  
Managerial Performance ◽  
Managerial Practices ◽  
Multi Stage ◽  
Educational Assistants ◽  
Study Population ◽  
The Impact ◽  
The City

<span style="font-size: 12.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: SimSun; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">This study aims to assess the knowledge of the principals and educational assistants with high schools’ objectives and determines its impact on the performance of their managerial courses (case study: the city of Sari). This is a descriptive study and different questionnaires were used to collect the data. The study population consisted of all managers and educational assistants of both sexes. In this study, a multi-stage cluster sampling method was used, based on Yamane formula, sample size was calculated 150. For processing and data analysis, SPSS software and descriptive statistics with central indexes, distribution, frequency tables and charts and to rule out or prove the research hypothesis, inferential statistics (T) are used for two independent groups. The result shows that in high schools the impact of principals and educational assistants’ knowledge toward their managerial function are meaningful and decisive. The deeper the information, knowledge and skills of principals and educational assistants of the goals of high school, their managerial performance will be better.</span>

scholarly journals The impact of supply chain management on business intelligence

2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Audrey Langlois ◽  
Benjamin Chauvel
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Private Companies ◽  
Chain Management ◽  
Conceptual Paper ◽  
The Impact

This conceptual paper investigates the impact of the supply chain on businessintelligence (BI) in private companies. The article focuses on these two subjects in order tobroadly understand the concept of business intelligence, supply chain and characteristicsimplement such as OLAP, data warehouse or data mining. It looks at the joint advantages ofthe business intelligence and supply chain concepts and revisits the traditional BI concept. Wefound that the supply chain includes many data samples collected from the first supplier to thelast customer, which have to be analysed by the company in order to be more efficient. Basedon these observations the authors argue for why it makes sense to see the BI function as anextension of supply chain management, but moreover they show how difficult it has become toseparate BI from other IT intensive processes in the organization.

Agile Development in Data Warehousing

2011 ◽  
Vol 2 (3) ◽  
pp. 64-77 ◽  
Author(s):  
Nayem Rahman ◽  
Dale Rutz ◽  
Shameem Akhter
Keyword(s):  
Business Intelligence ◽  
Data Warehousing ◽  
Agile Development ◽  
Successful Implementation ◽  
Design Development ◽  
Frequent Change ◽  
Development Teams ◽  
Waterfall Model ◽  
Business Requirements ◽  
The Impact

Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization. However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well as technology has often changed considerably. The result is disappointed stakeholders and frustrated development teams. Agile development implements projects in an iterative fashion. Also known as the sixty percent solution, the agile approach seeks to deliver more than half of the user requirements in the initial release, with refinements coming in a series of subsequent releases which are scheduled at regular intervals. An agile data warehousing approach greatly increases the likelihood of successful implementation on time and within budget. This article discusses agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence (BI) requirements, and demonstrates the impact of agility on the business.

Introduction to Data Mining and its Applications to Manufacturing

2008 ◽  
pp. 146-168 ◽  
Author(s):  
Jose D. Montero
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Computational Intelligence ◽  
Data Warehousing ◽  
Great Promise ◽  
Main Stream ◽  
Manufacturing Companies ◽  
Using Data

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.

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