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Royal Furniture is a USA (United States of America) based furniture shop currently facing different problems in terms of sales. Royal Furniture though their sales increased over the years the overall profit of the shop is decreasing. Royal Furniture shop sells its products through online mode throughout different states of the USA. The sales of the shop are highest in Delaware and then New York. But the shop tried to increase its sales in other States. To find the problem faced by the shop the sales data of the company for its products bookcase are analyzed through a data analytics tool to find the potential factor that restricts the overall growth of the shop in every state of the USA. There are four modes of product delivery in the Royal Furniture shop these are First class, Same day, second class, and standard class. The shop delivers most of the product through standard ship mode but the preparation time for this mode of shipment is highest as compared to other modes and from the data set it is also seen that in some regions there are losses. The shop sells furniture in three categories and these are consumers, Corporate, and Home office. From 2013 to 2016 the sales of furniture is increased in three segments but the increment in the consumer segment is lower as compared to the other two segments.
All the issues related to the sales and preparation time of the Royal Furniture shop will be analyzed with the help of data analytics. Real-time database analysis allows firms to observe the history and predict what will happen. This is the power of stream data analysis: knowing what happened (informative), knowing why it occurred (diagnostics), anticipating what could occur (indicative), and, last, deciding how to impact future incidents (prescriptive). Data analytics may boost company organizational outcomes and sectors in a variety of manners. It has the opportunity to assist decision-makers to acquire insight into facilitating strategies that will offer a safe place for investors.
In this report the application of data analytics in the business is discussed and how data analytics help in business is analyzed through various theoretical frameworks from various literature that are available in online journals. And then using the understanding and knowledge of Data analytics the data set of the Royal Furniture Shop is analyzed and critically evaluated to find which factor affects the business process.
Power et al. 2018 state that Business intelligence is indeed a developing idea. There is not any universally accepted, authorized explanation of business intelligence. Considering the many uses of the word in practice, course offerings, and study, that objective appears unrealistic. Since 2012, the aspect of business intelligence has evolved, but more work requires to be performed to fix the issues stated. Business analytics may entail IS, however, it is a fast-evolving pass field of study and application. Data analytics encompasses a wide range of operations and duties (Aydiner et al. 2019). The outcomes of applying business analytics appear to differ depending on the level of utilization and dedication to utilizing data analytics in evaluation. Management must monitor data regarding data analytics programs and evaluate progress. Business intelligence is a methodical way of understanding that employs quantitative, qualitative, and analytical analytic methodologies and tools to analyze the data, obtain information, insights, and assist decision-making. A number of methodologies, such as predictive, diagnostic, prescriptive, and optimization approaches, may be used in any given investigation. Data analysis and business intelligence are both subsets of statistics. Diagnostic, prescriptive, and predictive analytics are sorts of frameworks that are all common subcategories of both data and business analytics.
According to Ajah and Nweke, 2019, Business analytics and big data are two different business developments that are having a good influence. Information developed in modern society is massive and rising at an increasing rate. These comprise organized and unstructured information, both of which inundate enterprises on a regular basis. The bulk of the world's largest digital information is highly unstructured, which includes text files, music, social and online postings, photos, emails, and videos, among other things. The standard relational database management system (RDBMS) could not manage complex data (Delen and Ram, 2018). As a result, data expansion necessitates a reconsideration of methodologies for data acquisition, storage, and analysis. That's the function that data analytics has taken on. As a result, the purpose of this study is to draw the interest of companies and academics to the different benefits and advantages of data analytics (Nam et al. 2019). The article discusses and evaluates the latest developments, potential, and drawbacks of data analytics, and also how it is helped firms to establish effective business plans and stay competitive. Moreover, the paper discusses the numerous business analytics and big data applications, information sources created in such programs, and their main properties (Conboy et al. 2020). Ultimately, the paper not only explains the hurdles for effective data analytics project execution, but it also indicates the present research directions paths in data analytics that need to be explored further.
According to Mikalef et al. 2020, the idea of duration is a key component in business analytics and big data. According to the researchers, intelligence must account for the dynamic intricacies of companies and the individuals that use it. They explore the components that influence the application and acceptability of these technologies in the organizational environment, with an emphasis on the application of business analytics and big data for the purposes of text mining. According to the results, the quality of information impacts aspirations and behavior and information retrieval usage via facilitating conditions of usage, beliefs of external interference, and perceived utility (Lee et al. 2020). The results of this paper also indicate the critical need for senior management commitment in the use of information extraction. Building solid quality of information through key attributes is a fundamental precondition to competitive effectiveness.
According to Ashrafi et al. 2019, many businesses invest substantial amounts in building Business Analytics (BA) skills in order to enhance their productivity. BA may have a wide range of effects on efficiency. This article investigates how BA competencies influence business flexibility via quality of information and inventive potential. It also investigates the stabilizing influence of environmental instability, both technical and market-related (Wang et al. 2020). The suggested approach was evaluated using statistical information across 154 enterprises, with every firm having two responders (CIO and CEO). The statistical analyses were performed utilizing the Partial Least Squares (PLS)/Structured Equation Modeling (SEM) method (SEM). The findings show that BA skills have a significant influence on a company's flexibility by increasing the quality of the information and inventive ability. They argue that technological and market volatility both decrease the impact of business flexibility on company success. Though BA talents are vital in enhancing company effectiveness, experimental evidence for clarifying in what way and how this occurs is limited (Chiang et al. 2018). The findings reveal that there are substantial relationships between BA and quality of information, as well as between quality of information and company adaptability. This research demonstrates that only meaningful and relatively high-quality data assists enterprises in adapting to changing market conditions. Explained how digital alternatives help IT enhance flexibility and quality of data (IT-enabled abilities). It is stated that businesses depend on information management to produce data with high quality. BA integrates new understanding across company units and provides new skills and information to management in allowing for them to adapt to market developments. Given this fact, management should recognize the importance of adequate data handling in discovering new information and insights.
This data is collected from Kegel.com as secondary data is required. It is the technique of collecting and combining data from several sources. Datasets are secondary which refers to data is collected from third parties.
Data preparation, modeling, and analysis
This includes data analysis and processing, the development of relevant methodologies for quantization utilizing statistical techniques, and the methodological approach to measuring the business value and objectives of the research. The Key Performance Indicators (KPIs) are listed here.
The provided evidence was collected to be semi-structured, which means it has significant organization but does not correspond to a paradigm and contains lacking characteristics. As a result, utilizing the table function, the data was transformed into an Excel table.
After the analysis of the dataset of the Royal Furniture shop, it is found that there is a different factor that affects the sales and business growth of the shop. All the analyses are shown through data visualization of the different graphs below (Chawla et al. 2018).
The above figure (Figure 2) shows the sales of bookcases of Royal Furniture shop in different states of the USA. The shop was sold the highest amount in Delaware and the second highest in New York. In other states, the sales of the shop are comparatively very low. The shop needs to focus on other states in order to increase its sales and it will influence its overall profit.
The above figure (Figure 3) shows the sales of bookcases of Royal Furniture shop in a different region. The shop sales highest in the West region and lowest in the South region. After the west region the sales in more in the East region and then in Central region.
The above figure (Figure 4) shows the sales of bookcases of Royal Furniture shop in different shipment modes. The shop sold the highest amount through Standard Class ship mode and the lowest through the Same day ship mode. The shop needs to focus on sales through the Same-day shipment mode because this will give a competitive advantage to the shop and this will also increase its customer satisfaction.
The above figure (Figure 5) shows the sales of bookcases of Royal Furniture shop in different Segments as the shop sales in three segments consumer, corporate, and home office. From 2013 to 2016 the sales in all three segments is increased but the increment is greater in the corporate segment as compared to the other two. In 2013 and 2024, the sales in consumer and corporate segments are the same but it decreases in the home office segment.
The above figure (Figure 6) shows the sales of bookcases of Royal Furniture shop in different years from 2013 to 2016. The sales of the shop are increased over the years but in 2014 the sales of the shop is decreased and then the sales have increased.
The above figure (Figure 7) shows the average profit of bookcases of Royal Furniture shop in different years. Figure 6 shows that sales of the shop increased over the year from 2013 to 2016 but the average profit of the shop increased impressively as compared to the sales. The average p[rofit is highest in 2015 in these four years and the next year in 2016 the average profit of the company is decreased.
The above figure (Figure 8) shows the average preparation time of shipment of bookcases of Royal Furniture shop in different ship modes. The average delivery preparation time is highest for Standard Class ship mode which is about 5 days and lowest for same-day ship mode. Then the second class is about 3 days, and then the First-class about 2 days. Figure 4 represents that the shop sales are highest through Standards class and the preparation time of this mode is also highest. The shop can increase its sales by decreasing the preparation time.
In every particular inquiry, a variety of strategies, such as predictive, diagnostic, prescriptive, and optimization approaches, may be applied. Statistics include both data analysis and business intelligence (Golfarelli and Rizzi, 2020). Firms may use real-time database analysis to look at the past and forecast what will happen in the future. This is the power of stream data analysis: understanding what happened (informative), why it happened (diagnostics), forecasting what could happen (indicative), and choosing how to affect future episodes (prescriptive). Business analytics may include IS, but it is a rapidly expanding subject of research and application. Data analytics covers a wide variety of activities and responsibilities.
Royal Furniture Shop in USA encounters inconsistency in economic growth, which has an impact on customer service and profitability. To address the prospective issue confronting the organization, a dataset of stores from prior years is studied using data analytics to identify the probable issue affecting the business. Data analytics has the ability to improve more than simply company operational performance. There is also great potential for economic development and enhancing the existing socioeconomic standard of life. Data analytics covers a wide variety of activities and responsibilities. The effects of using business analytics tend to vary depending on the amount of use and commitment to using data analytics in evaluation. The vast majority of the world's digital information is extremely unstructured, and includes, among other things, text files, music, social and online postings, images, emails, and videos. According to the findings, the quality of information influences ambitions and behavior, as well as information retrieval utilization, through encouraging usage conditions, thoughts about external interference, and perceived value. The findings show that there are significant correlations between BA and information quality, as well as between information quality and company flexibility. By using data analytics to extract precise customer information from these numbers, the company may get deeper insights into consumer behavior and provide more tailored experiences.
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