scholarly journals Justifying Business Intelligence Systems Adoption: A Literature Review on Healthcare Supply Chain Perspective

2021 ◽  
Vol 16 (1) ◽  
pp. 108-114
Author(s):  
Nur Syahidah Wong Abdullah ◽  
Sylvia @ Nabila Azwa Ambad ◽  
Sakka Nordin ◽  
Jasmine Vivienne Andrew ◽  
Karen Esther Tan
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Inventory Management ◽  
Business Intelligence ◽  
Clinical Pathways ◽  
Data Driven ◽  
Accurate Information ◽  
Time Data ◽  
Data Driven Decision Making ◽  
Management Platform

Business Intelligence (BI) systems have played an essential position in facilitating information sharing, strategic cost-cutting, and improvement in business process management through data-driven decision-making analytics. The technological enablers of Industry 4.0 have empowered the clinician to attain accurate information in formulating predictive and data-driven diagnoses based on artificial intelligence-enabled medical devices resulting in an efficient and quality clinical pathway for patients. However, there is a noticeable distinction between the hospital's technological aptitude between clinician and non-clinician. The current technological capability of the hospital information system is to digitize daily business processes that could not offer intelligence reports for predicting, forecasting, and data-driven decision-making support. The compilation of past works of literature is expected to justify the need for the healthcare supply chain to adopt BI solutions that produce near real-time data in making efficient inventory management and procurement to support the clinician in delivering efficient and quality clinical pathways for patients by bringing the supplies at the right moment. Hence, a study of BI solutions in healthcare supply chain operation is achieved through a narrative overview of existing literature from papers published online. The results show that appropriate technological tools, resource competencies, and supplier management platform as the essential dimensions to support the business intelligence adoption effort. The study, therefore, not only identified the critical dimensions in facilitating BI adoption but also offer practical awareness to the healthcare policymakers to better understand the strategic need for BI systems in managing the entire hospital operations to gain a competitive advantage.

scholarly journals Data-Driven Decision Making: Qualitative Study at Digital Startup using Semantic Domain Analysis

I-STATEMENT ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 09-14
Author(s):  
Asri Pertiwi ◽  
Nahlia Roseno
Keyword(s):  
Decision Making ◽  
Qualitative Study ◽  
Business Intelligence ◽  
Domain Analysis ◽  
Data Driven ◽  
Future Research ◽  
Data Driven Decision Making ◽  
Semantic Domain ◽  
Domain Semantics

Business Intelligence (BI) is commonly applied to large companies, but there are a few evidence of BI practice in startups. Although startup founder understand that data and information are very important, but how this used for decision making needs to be further explored. Through interviews with four startup’s founders, the transcript result were analyzed using domain semantics and taxonomy analysis. Several findings are outlined which are followed by suggestions for future research.

scholarly journals Self-Service Business Intelligence success factors that create value for business

2021 ◽  
Author(s):  
Jonida Sinaj ◽  
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Success Factors ◽  
Data Driven ◽  
Competitive Advantages ◽  
Data Driven Decision Making ◽  
Service Business ◽  
The Future ◽  
Self Service

Business Intelligence and Analytics have change the business needs, but the market requires a more data- driven decision-making environment. Self-service Business Intelligence initiatives are providing more competitive advantages currently. The role of the users and freedom of access is one of the essential advantages that SSBI holds. Despite this fact, there is still needed analysis on how business can gain more value from SSBI, based on the technological, operational and organizational aspects. The work in this paper serves to analysis on the SSBI requirements that bring value to business. The paper is organized starting from building knowledge by upon the existing literature and exploring the domain. Data will be collected by interviewing experts of the fields. The main findings will provide future suggestion related to the topic and the results will serve both the companies that have implemented it and the ones that want to see it as a perspective in the future.

scholarly journals A Data-Driven Decision-Making Model for the Third-Party Logistics (3PL) Industry

10.29007/4v3j ◽  
2019 ◽  
Author(s):  
Faith Moyo ◽  
Brenda Scholtz ◽  
Mohammed Alhassan
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Third Party ◽  
Data Driven ◽  
Third Party Logistics ◽  
Data Driven Decision Making ◽  
The Third Party Logistics ◽  
Informed Decisions ◽  
Execution Phase ◽  
Challenges And Opportunities

The evolution of the supply chain has resulted in a growth in the usage of technology and data generated and distributed within the industry. Third-party logistics (3PL) companies operating within the supply chain industry are not maximising the capabilities of data to make well- informed decisions. The purpose of this paper is to address this gap and to develop a prescriptive, theoretical model for data-driven decision-making (DDDM). To address the gap, a literature review of DDDM in 3PL industry and in other contexts was conducted. The proposed model is built based on the consideration of existing DDDM models and frameworks; data and data analytics principles to collect, store, manage and analyse data; and the Cynefin framework. Existing models and frameworks for DDDM do not provide explicit guidelines on how to apply DDDM in a 3PL and supply chain context. The proposed DDDM model constitutes of three phases, namely: (1) the setup phase, that considers data knowledge and decision-making knowledge; (2) execution phase; and (3) the learning phase. The application of the model in 3PL companies can support the decision- making process in these companies, with a consideration of the challenges and opportunities that exist in the supply chain. The decision-makers in 3PLs can thus make better-informed decisions that positively impact their enterprises and the supply chain.

scholarly journals Improving Decision Quality for Business Users Based on Cloud-based Self-Service Business Intelligence Tools

2020 ◽  
pp. 1-11
Author(s):  
Christian Ploder ◽  
Reinhard Bernsteiner ◽  
Thomas Dilger
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Data Science ◽  
Data Driven ◽  
Decision Quality ◽  
Data Driven Decision Making ◽  
Significant Difference ◽  
Self Service ◽  
Business Informatics ◽  
User Friendly

The ever-growing volume of data promotes data-driven decision-making in more cases and more areas than before. The development of user-friendly self-service BI (SSBI) tools enable business users to autonomously execute tasks in the area of Business Intelligence (BI), statistical analysis, or data science. Cloud computing offers the opportunity to provide SSBI as services as well. This paper focusses on cloud-based SSBI tools and their support for data-driven decision-making by business users. This paper aims to identify the influence of a deeper understanding of business informatics on (a) the handling of the cloud-based SSBI tools and (b) the data-driven decision making performance. An experimental setting was used to collect empirical data. Two groups with equal knowledge in business administration, but different backgrounds in business informatics have been defined. Based on different backgrounds in business informatics, the results show no significant difference in handling the cloud-based SSBI tool but reveal significant differences in decision-making performance.

scholarly journals A business case for a data-driven decision-making tool to support the UNBC research enterprise.

2020 ◽  
Author(s):  
◽  
Mark Barnes
Keyword(s):  
Decision Making ◽  
Data Visualization ◽  
Business Intelligence ◽  
Resource Planning ◽  
Business Case ◽  
Data Driven ◽  
Visualization Tool ◽  
Data Driven Decision Making ◽  
Business Decisions ◽  
Design Document

Business intelligence tools allows for data-driven decision-making within organizations using historical events to predict future trends, which is especially valuable when allocating operational resources. As a research-intensive Canadian university, UNBC has seen a significant increase in activities related to supporting the research enterprise, which requires additional resources (human, capital, financial etc.) in order to effectively and efficiently advance the mission of the research community. As outlined in our Annual University Accountability Report, 2018/19 was an incredibly productive year for research with more than $14 million received in support of research. The University has seen a significant increase in the number and breadth of agencies and organizations funding research at UNBC. The administration of research awards involves both pre-award and post-award processes, which requires responsible allocation of available resources to ensure a sustainable model will be developed to achieve goals outlined by the institution’s strategic priorities and build the foundation to reach our goal of a research enterprise generating $25M in annual research revenue. Therefore, using business intelligence tools to utilize historical data to predict the necessary resourcing needs of the institution will allow UNBC to make strategic investments in research and remain competitive on the provincial, national and international stage. Informed decision-making when investing resources are critical to the success of any business. The goal of my MBA project is to gather critical information to be used in the development a data visualization and forecasting tool that will allow for informed decisions for the allocation of resources necessary to support the research mission at UNBC. The objectives of the MBA project are two-fold, which include the development of the business case for the UNBC data visualization tool (DVT) and also the completion of a design document. The information gathered6 from this project will be used in the future (post-MBA) to develop a data visualization tool that will allow for the on-going monitoring of UNBC’s progress towards putting in place the appropriate resources to reach $25M in annual research revenue. Specifically, the MBA project will consist of completing a comprehensive business case outlining the “business need” and potential solutions. Secondly, the MBA project will consist of developing a “design document” for an eventual tool that will be used to visualize research funding and labor information to inform business decisions for resource planning for the UNBC research enterprise. This design support system will be used by senior leadership within UNBC to effectively and efficiently make decisions to allocate resources.

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