Realising The Tactical Effect Of Business Intelligence Tools – An Efficient Algorithm for the Joint Replenishment Problem with Quantity Discounts, Minimum Order Quantity, and Transportation Capacity Constraints
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Realising The Tactical Effect Of Business Intelligence Tools
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Received: 31 December 2022 / Revised: 5 February 2023 / Accepted: 7 February 2023 / Published: 10 February 2023
Over the past few decades, business analytics has been widely used in various business sectors and has been effective in increasing enterprise value. With the advancement of science and technology in the big data era, business analytics techniques have changed and evolved rapidly. Therefore, this paper reviews the latest techniques and applications of business analytics based on existing literature. At the same time, many issues and challenges are inevitable in the advancement of business analytics. Therefore, this review presents current challenges facing business analytics and open research directions that require further consideration. All research papers were obtained from Web of Science and Google Scholar databases and filtered using several selection rules. This paper will help provide important insights to researchers in the field of business analytics, as it presents the latest techniques, various applications, and several directions for future research.
In recent decades, data has been rapidly changing the world. Especially in the era of Big Data, data is cheap and ubiquitous, but what makes data a valuable asset is how it is used to obtain useful information. As there are different types of business objectives, different analytics techniques are required to achieve them. These techniques have many applications in the business area and “business analytics” enable the business application of big data. Since the emergence of the term business analytics, it has been growing by leaps and bounds, reflecting the increasing importance of data in volume, variety, and velocity [1]. Although there is no unified definition of business analytics, existing definitions can be summarized into several dimensions such as a movement, a transformation process, and a capacity set [2].
Interest in analytics and data science is increasing as business organizations widely use business analytics to improve their business value. Business analytics has evolved into an important part of the business decision-making process, using data to inform decision-making and support decision-makers in making strategic, operational, and tactical decisions [3]. In particular, business analytics can help companies harness the value of historical data by leveraging the power of statistical and mathematical models and advanced techniques such as artificial intelligence algorithms. Through these models and algorithms, enterprises can combine disparate data sources for trend forecasting, decision optimization, and more. As business analytics continues to evolve, its applications are widening. It is adapted to certain functional departments and certain non-business areas within the enterprise.
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Judging by the amount of literature in the database, there are many types of literature to study business analytics, including its techniques, impact, applications in certain fields, etc. Among them, several scholars have systematically summarized various aspects of business analytics. However, the techniques and applications of business analytics have changed significantly in recent years as technology has developed rapidly. Therefore, to organize the latest knowledge on business analytics, we present four key research questions:
This article is organized as follows. Section 2 presents the methodology used in this review and conducts a simple bibliographic analysis of the literature. Section 3 concludes with definitions of business analytics in four categories to answer RQ1. Techniques used in business analytics are presented in Section 4 to answer RQ2. Section 5 describes applications of business analytics in several business sectors and industries to answer RQ3. RQ4 is responded to in Section 6 to reveal the challenges facing business analytics. Finally, Section 7 concludes this paper.
To understand research trends in business analytics, we collected relevant academic literature from the Web of Science and Google Scholar databases, as they are widely recognized and contain a large number of high-quality publications in peer-reviewed journals [4]. Then, in Section 2.2 we performed a bibliometric analysis of the existing literature on the number of publications per year and their research directions. Because there was a large amount of material in the research of business analytics, we designed several selection rules to filter the literature for further review. First, publications should contain ‘business analytics’ in their title or abstract. Second, we focused only on English publications. Third, we considered different publication types, including research articles, reviews, and book chapters. What’s more, to consider the novelty and impact of articles, publications before 2020 require at least ten citations, while those after 2020 require at least two citations. Based on selection rules, high impact academic pieces of literature were selected. In addition, it was possible to read all selected papers in full. We read the abstract of each literature to decide whether it was appropriate for the purpose of our subsequent review.
Based on the methodology, we conducted the literature selection process. Figure 1 shows the flowchart of the selection process. We searched Web of Science using the keyword ‘business analytics’ with no other selection criteria in the title or abstract, and the number of results was 821. After filtering by language (English) and publication types (research articles, reviews, and chapters), 365 papers remained. Then, we restricted the number of citations before and after 2020 and excluded 193 results. Finally, we read the abstracts of selected papers to further filter for relevant articles, and 76 papers were ready for in-depth review.
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First, we conducted a quantitative analysis on the business analytics literature based on the annual publication number from 2012 to 2022, which is shown in Figure 2. From 2012 to 2017, the number of publications per year showed a significant upward trend and reached its peak. 2017. After 2017, this number decreased slightly but remained at a higher level than in 2012, which means that research on business analytics is now attracting more scholars.
Second, we performed an analysis of the top ten research directions of the academic literature on business analytics in Figure 3 . It is clear that computer science is the most popular research direction in the published literature on business analytics. Because computer science is an integral part of business analytics and drives the development of business analytics applications. The second most popular research direction is engineering, which indicates the application area of business analytics, while the third is business economics, which shows the value of business analytics in economics. The rest of the research directions reflect the techniques and applications of business analytics, respectively.
Currently, there is still no unified definition of business analytics. Scholars in various fields have defined the term business analytics from various perspectives. Holsapple summarized 18 definitions of analytics in 6 dimensions [2]. Referring to dimensions, this article organizes recent definitions of business analytics into four categories in Table 1.
First, from a techniques perspective, business analytics is considered as an application of any data analytics [5] or data science [6] in business fields, which uses statistical and quantitative tools and techniques to analyze a large collection of data sources to support decisions. For business [7]. More specifically, business analytics can be seen as ‘a broad category of applications, technologies and processes for collecting, storing, accessing and analyzing data to help business users make better decisions’ [8]. With the continuous emergence of new technologies, business analytics can also be viewed as a combination of operations research, artificial intelligence (machine learning), and information systems [1].
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Second, from a process perspective, business analytics
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