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STUDY OF PROFIT GENERATION IN A SUPERMARKET: A BI STUDY Table of Contents 1.0 INTRODUCTION 3 1.1 Statement of Problem 3 1.2 Aim and Objectives of the Research 3 1.3 Research Questions and Hypotheses 3 1.4 Rationale 3 2.0 LITERATURE REVIEW 4 3.0 DATA 5 4.0 RESEARCH METHODOLOGY 5 REFERENCES 7 1.0 INTRODUCTION As with every other business firm, a supermarket’s profitability is an outcome of the interaction between internal factors and environmental factors (von Berg, 2020). Statement of Problem Retail store management is, actually, a lifetime of extremely complex dealings, consisting of short-term and long-term tasks by character (Guru and Prabhu, 2020). Kanya (2018) stated that retail managers are perpetually faced with the situation of decision making and action taking for the purpose of meeting the wants and needs of customers and responding to competitors’ actions. The main issues are while the sum of data collected by organizations continues to rise at an appalling pace, examining customer data and data accuracy indicates a distinct need for greater capabilities and analytical skills (Paliszkiewicz, 2020). Waller (2020) claimed that significant chunk of businesses are struggling to introduce data cultures within their organizations. 1.2 Aim and Objectives of the Research The aim of this Business Intelligence (BI) study is to critically analyse the profit generation in a supermarket with the help of a secondary dataset. Below are the research objectives: To demonstrate the effectiveness of BI in businessesTo assess the factors influencing the profit generation in a supermarketto critically analyse the profit generation in a supermarket by analysing the chosen secondary dataset using R 1.3 Research Questions and Hypotheses The key research question is: Does a particular aspect influence the profitability of a supermarket? The null and alternative hypotheses are: H0: A particular aspect influences the profitability of a supermarketH0: The profitability of a supermarket does not get influenced by a particular aspect 1.4 Rationale The research rationale of this study is that most of the past studies focused on the factors include: profit maximization, growth and Profitability Performance of Supermarkets (Alieva, 2017; Hernant, 2009; Farid et al., 2018). However, almost no BI studies analysed analyse the profit generation in a supermarket. The significance of this research resides in analysing the factors resulting the profit generation in a supermarket and these insights may help supermarkets to tweak their business operations accordingly. 2.0 LITERATURE REVIEW One of the first industries to make important investments in integrating customer data and gathering data in data warehouses was the Retail Sector (Tapscott, 2009). Usage of business intelligence systems to examine data to better business efficiency with an emphasis on reducing operational costs, retailers have in general attained an important return on their IT system investments without compromising customer experience (Witcher et al., 2020). Replenishment, space, markdown, price, allocation, assortment and promotion are levers used by a retailer to perfect performance. Davis (2019) claimed that the key to thriving decisions with regard to these levers is Data driven decision making. Economic theory supposes that clear competition prevails when the number of companies marketing a homogeneous product is so high and the market share of each business is so small that no single company is able to control the price of the product by varying the amount of yield it sells (Greenlaw et al., 2017). In terms of profitability, e.g. return on supermarket assets, is defined as the measure to which gross earnings transcends operating costs is connected to the amount funded in the store. Profitability performance in itself is connected to other economic performance characteristics and can be considered as the “ultimate” economic performance (Miller, 2016). As in case of every other business firm, the ancestry of the economic performance of the supermarket can be narrowly classified into environmental factors (competitive conditions and local demand) and to internal elements (von Berg, 2020). Farther, a definite factor can have different effects on diverse performance aspects. For example and all else being equal, a significant reduction in prices is likely to increase sales efficiency, but it is more likely to have damaging effects on operating and gross profits (Woodward and Wherry, 2019). 3.0 DATA The dataset used for the analysis contains the historical sales information of three branches off a multi-chain supermarket company, collected over a period of Three months. The information presented in the dataset (Kaggle, 2019) is: Receipt id: PC created sales slip receipt unique id numberBranch: Part of supercenter (3 branches are recognized by A, B and C).City: Area of supermarketClient type: Kind of clients, recorded by Individuals for clients utilizing card and Norrmal for without card.gender: gender of the billed clientProducts: General classification categories— Electronic extras, Design adornments, Food and drinks, Wellbeing and magnificence, Home and way of life, Sports and travelUnit value: Cost of every item in $Amount: Number of items bought by clientDuty: 5% charge for client purchaseTotal: All inclusive cost including DutyDate: Date of procurement (Recorded from January 2019 to March 2019)Time: time of purchase (10am to 9pm)Installment: Installment utilized by client for procurement (3 techniques are accessible — Money, Credit and Ewallet)Cost of Goods Sold: Cost of merchandise soldGMP: Gross Margin RateGM: Absolute Gross Income from the purchase (Target Variable)Rating: Client satisfaction rating on their general shopping experience (On a scale of 1 to 10) 4.0 RESEARCH METHODOLOGY The aim of this section is to discuss the research methodologies that will be adopted to analyse the chosen dataset. In an effort to identify drivers for profitability, and propose suggestions and arrive at insights, numerous scientific methods can be employed. As a part of the dissertation, statistical and data visualization methods relevant and applicable to the dataset will be used. Stacked Bar Charts for Comparison, candlestick charts to track changes, Boxplot for identifying outliers and estimating distribution, can be used as data visualization techniques; whereas Regression, Correlation, Chi-Square Test and Other hypothesis testing techniques can be used to identify the significance of the relationship between the independent and dependent variables. REFERENCES Alieva J, (2017). Retail Management. Published by Umea School of Business and Economics, UK. Davis J, (2019). Retailers Catch Up with Technology Investments [Online]. Available at https://www.informationweek.com/strategic-cio/it-strategy/retailers-catch-up-with-technology-investments/d/d-id/1333608? Farid S, et al., (2018). Direct and Associated Factors Influencing the Growth in Supermarket Activity in Bangladesh. Published by Asian Research Journal of Arts & Social Sciences, Volume 5, Issue 1, Pages 1-12. Greenlaw SA, et al., (2017). Principles of Economics 2e. Published by https://www.google.co.in/books/edition/Principles_of_Economics_2e/-ZvAswEACAAJ?hl=en Hernant M, (2009). Profitability Performance of Supermarkets. Published by EFI and the author, UK. Kaggle, (2019). Supermarket sales [Online]. Available at https://www.kaggle.com/aungpyaeap/supermarket-sales Kanya (2018). 7 Major Challenges in the Retail Industry & How to Overcome Them [Online]. Available at https://www.hashmicro.com/blog/7-major-challenges-in-retail-industry/ Miller A, (2016). The Unified Theory of Profitability. Published by Business Expert Press, UK. Paliszkiewicz J, (2020). Management in the Era of Big Data. Published by CRC Press, UK. Prabhu TL, ‎Guru M, (2020). Retail Management. Published by Nestfame Creations Pvt. Ltd., UK. Tapscott D, (2009). Business Intelligence for the Retail Industry[Online]. Available at https://dsimg.ubm-us.net/envelope/114572/333072/1269962312562_44.Business_Intelligence_Actionable_Insights_for_Business_Decision_Makers.pdf von Berg S, (2020). The business model cycle. Published by Cuvillier Verlag, UK. Waller D, (2020). 10 Steps to Creating a Data-Driven Culture [Online]. Available at https://hbr.org/2020/02/10-steps-to-creating-a-data-driven-culture Wherry FF, Woodward I, (2019). The Oxford Handbook of Consumption. Published by Oxford University Press, UK. Witcher B, et al., (2020). The Top Retail Technology Investments In 2020[Online]. Available at https://www.forrester.com/report/The+Top+Retail+Technology+Investments+In+2020/-/E-RES158037

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