Supply chain intelligence: benefits, techniques and future trends

Supply Chain Management is a philosophy to manage logistical processes in complex systems, that are very difficult to integrate and analyze. Such systems can be effectively analysed by the use of Business Intelligence applications. The capability to make the right decision at the right time in collaboration with the right partners is the definition of the successful use of BI. This paper explains the need for Supply Chain Business Intelligence and introduces the driving forces for it’s implementation. New technologies such as data mining, and their role in BI systems are also discussed. Finally, key BI trends and technologies that will influence future systems are described.supply chain, business intelligence, data mining

Machine Learning and AI in Business Intelligence: Trends and Opportunities

The integration of machine learning and artificial intelligence (AI) in business intelligence has brought forth a plethora of trends and opportunities. These cutting-edge technologies have revolutionized how businesses analyze data, gain insights, and make informed decisions. One prominent trend is the rise of predictive analytics. Machine learning algorithms can sift through vast amounts of historical data to identify patterns and trends, enabling businesses to make accurate predictions about future outcomes. This empowers organizations to optimize operations, anticipate customer needs, and mitigate risks.  By leveraging business intelligence, companies can uncover hidden patterns, identify opportunities for growth and improvement, optimize business processes, and ultimately make informed decisions that drive their success. Another trend is the adoption of AI-powered chatbots and virtual assistants. The opportunities presented by machine learning and AI in business intelligence are extensive. From automated data analysis and anomaly detection to demand forecasting and dynamic pricing, these technologies empower businesses to optimize processes, reduce costs, and identify new revenue streams. In conclusion, the integration of machine learning and AI in business intelligence offers promising trends and abundant opportunities. By leveraging these technologies, businesses can gain a competitive edge, drive innovation, and unlock new levels of success in the digital era

A review and future direction of agile, business intelligence, analytics and data science

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions

The GIS and data solution for advanced business analysis

The GIS Business Analyst is a suite of Geographic Information System (GIS)-enabled tools, wizards, and data that provides business professionals with a complete solution for site evaluation, selective customer profiling, and trade area market analysis. Running simple reports, mapping the results, and performing complex probability models are among the capabilities The GIS Business Analyst offers in one affordable desktop analysis solution. Data and analyses produced by The GIS Business Analyst can be shared across departments, reducing redundant research and marketing efforts, speeding analysis of results, and increasing employee efficiency. The GIS Business Analyst is the first suite of tools for unlocking the intelligence of geography, demographic, consumer lifestyle, and business data. It is a valuable asset for business decision making such as analyzing market share and competition, determining new site expansions or reductions, and targeting new customers. The ability to analyze and visualize the geographic component of business data reveals trends, patterns, and opportunities hidden in tabular data. By combining information, such as sales data of the organization, customer information, and competitor locations, with geographic data, such as demographics, territories, or store locations, the GIS Business Analyst helps the user better understand organization market, organization customers, and organization competition. The business intelligence systems bring geographic information systems, marketing analysis tools, and demographic data products together to offer the user powerful ways to compete in today's business strategies.Geographical Informatic Systems, business analysis

Data Mining to Support Engineering Design Decision

The design and maintenance of an aero-engine generates a significant amount of documentation. When designing new engines, engineers must obtain knowledge gained from maintenance of existing engines to identify possible areas of concern. Firstly, this paper investigate the use of advanced business intelligence tenchniques to solve the problem of knowledge transfer from maintenance to design of aeroengines. Based on data availability and quality, various models were deployed. An association model was used to uncover hidden trends among parts involved in maintenance events. Classification techniques comprising of various algorithms was employed to determine severity of events. Causes of high severity events that lead to major financial loss was traced with the help of summarization techniques. Secondly this paper compares and evaluates the business intelligence approach to solve the problem of knowledge transfer with solutions available from the Semantic Web. The results obtained provide a compelling need to have data mining support on RDF/OWL-based warehoused data

New Trends in Cloud Based Business Intelligence Development in the Management of Business Organizations

The paper is based on the authors research conducted among 400 Polish enterprises as well as external research, the most important trends in the development of Cloud BI systems were identified, which include: the emergence of intelligent Cloud BI systems (through the application of Artificial Intelligence, Machine Learning and Robotic Process Automation solutions) as well as developments in the areas of multimedia, process, intuitiveness and mobility. In addition, in terms of trends in the development of Cloud BI systems, deeper integration, the rise in popularity of systems offered as Open Source, and the reduction of development time for this class of systems were mentioned

Developing new business models from a controller perspective

The Fourth Industrial Revolution and its effects, the pandemic and the new market trends that are emerging as a result, pose significant challenges for companies. Flexibility is no longer enough, adapting to digitalization, developing a new business model using an integrated work environment and self-service business intelligence are needed. The controller plays a key role in this process, as a business partner is actively involved in building the models. The controller has several new or refocused tools at their disposal, such as multidimensional decision-making procedures, digital reports, scenario analysis, specialized KPIs, and more. This study reviews how these tools can help shape a new business model, and how this digitalization can help

Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal

The COVID 19 black swan event has disrupted every aspect of life in unprecedented ways, causing organizations to scramble to effectively sense and respond to the tumultuous business environment. Business intelligence and analytics (BI&A) capability has gained attention as a key weapon in the arsenal needed to combat turbulent times and to adjust to the post-pandemic new normal. Post-pandemic BI&A trends point to changes in organizational priorities for BI&A infrastructure that influence the traditional view of BI&A architecture and its role within an organization. As a result, new challenges and opportunities are emerging. This paper identifies and examines twelve key post-pandemic BI&A trends from industry practice and six major research themes. It also proposes an initial set of research questions that could inspire future research in BI&A in the post-pandemic new normal

Implementing a Business Intelligence System for small and medium-sized enterprises

Over the years, Business Intelligence (BI) systems have become critically important to organizations due to the increasing fast-paced competition, the vast amount of daily generated data and the complexity of how to manage collected data. Business intelligence systems empower organizations to gain insights and to understand a clearer view of their vast data, business and customers, which help to make better decisions and hence produce better results and increase profit. BI refers to a collection of an organization’s resources such as tools, technologies, applications, systems and databases which enable organizations to manage insights of their business data, activities and performance in order to make better decision. However the majority of existing BI systems, target and support large organizations, and the small and medium-sized organizations (SMEs) are mostly overlooked due to lack of substantial finance. The paper elaborates the considerations for implementing BI systems for SMEs. Some new trends such as cloud BI solutions, open BI sources solutions are reviewed. The paper finally provides for the implementation of Business Intelligence system for a SME, the purpose and constraints of the system are detailed