Business Systems Analysis in the Fashion Retail Sector

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Introduction

Technology has been one of the most significant tools of the 21st century and as such, most businesses have benefited from it. This paper focuses on a small brick-and-mortar enterprise operating in the fashion sector with a product catalog that includes apparel, shoes, and handbags. This business sells products for both genders and offers discounted designer outfits and accessories. Currently, the proprietor is looking to implement new technologies to solve various problems such as allowing the customers to purchase products at the ease of the business’ website. Moreover, the owner intends to integrate brick-and-mortar operations with an online-based process considering that there will be some cybersecurity issues to emerge.

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Problem Statement

In particular, the proprietor is seeking technologies that will help the business integrate its online shopping, inventory, and shopping management, and provide order status. In addition, the selected skill and equipment should be able to process online payments, enable both internal and external communication with the employees and customers, and consumer contacts management. Lastly, they should allow the enterprise to manage its sales, payments, and other brick-and-mortar procedures (Sumbal et al., 2017). Currently, the organization finds it challenging to manage consumer information and does not have a proper reporting instrument to assist its executive committee makes informed decisions. Therefore, its long-term objective is for these technologies to help the business establish and improve its online presence to increase customer satisfaction.

Technologies

The selected two technologies adopted for this paper are big data and knowledge management and business analytics and business intelligence solutions. They will play a vital role in helping the small business integrate its in-store processes with an online-based operation. Today, one of the most significant products of technology is big data. It refers to the technologies that utilize various software developed to examine, process, and extract information from complex and large sets of data (Ekambaram et al., 2018). Today, this technology has become widespread in the fashion retail segment as it is being used for trend forecasting and analyzing consumer behavior (Kotouza et al., 2020). The fashion sector’s customer base is considered sensitive to the changing aspects and it is gradually transitioning towards personalization. Purchasers wish to be given multiple options that satisfy their style preferences, contexts, and cultural and musical influences (Pillai et al., 2020). As the demand for customization upsurges, so does mass custom-building of apparel, in an attempt to limit the increase of unwanted stock.

Therefore, big data plays a vital role in matching consumer expectations as most manufacturers are using internet data, especially ones collected from sales, social media feedback, market research, and social media. Moreover, big data is also improving the management of inventories by increasing operational efficiency, accelerating order fulfillment, preventing overselling, and boosting customer satisfaction (Ekambaram et al., 2018). Therefore, the business owner will benefit from using this technology to improve the operations of its fashion brand.

In contrast, knowledge management (KM) technologies are used to collect, analyze and report organizational data for informed decision-making processes. It is supported by artificial intelligence and other technologies used for the acquisition of knowledge, case-based reasoning, databases, and computer-based simulations (Sumbal et al., 2017). Fashion retailing is a complex sector that involves various processes, particularly in the acquisition of raw materials to support the purchasing process. Therefore, KM technologies have the potential to organize the existing data in companies and add value to their products and procedures (Appelbaum et al., 2017). Therefore, the business owner will address most of the organization’s challenges by implementing these technologies in the business’ operations.

Business analytics allow organizations to use statistical techniques to examine data in order to understand and make informed decisions for the strategic benefit of an organization. These technologies exist in three categories such as predictive, descriptive, and prescriptive analytics. The fashion retail segment is currently using these technologies to examine trends which helps them to gain insight into the market developments by using data-driven solutions on social networking sites and other platforms (Ratia et al., 2019). Moreover, predictive analytics enable them to improve online shopping by understanding consumer habits and preferences. Prognostic search capabilities allow businesses with an online presence to examine their past click-through patterns, product preferences and purchasing history in real time (Ratia et al., 2019). Therefore, this technology will help the business owner, especially in inventory management by optimizing processes. In contrast, business intelligence solutions are also similar to analytics since both of them help in examining information and delivering information that is essential for senior management to make decisions (Appelbaum et al., 2017). It integrates business analytics, data visualization, and mining to enable inform companies of the best choices to opt for.

Business Requirements

Objectives

The primary purpose of using technology in management is to help businesses reduce their operational cost while protecting their financial data on the internet. Moreover, it also plays a vital role in safeguarding other proprietary information that creates a competitive advantage for the business over its rivals. Therefore, this project will help the fashion brand eliminate the challenge it faces in trying to reach its target market in a better way than its competitors. In addition, the project will also provide a criterion for helping the brand’s managerial committee make informed decisions for its strategic existence. In the long run, the fashion company will be able to establish a long-standing relationship with its customers thereby improving its sales.

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Project Description

There are several steps that the business owner will have to incorporate to make the most of these technologies. For example, the brand will have to start by developing its website and ensure that users can easily navigate through it. Moreover, the owner should also ensure that it is appealing to the consumers. Since there is increased competition from numerous corporations already using exceptional web pages, the business will have to differentiate it to attract and maintain customers. In addition, incorporating cybersecurity protocols into the site will be essential for the confidentiality and security of customer credit and debit cards (Ratia et al., 2019). Another significant factor to consider will be bandwidth management to handle user traffic as a result of more consumers visiting the webpage (Appelbaum et al., 2017). Lastly, the owner will have to find a way to formulate a timeline for meeting the requirements identified in the introduction.

Technology Requirements

The two technologies identified above are required to help the case organization achieve various objectives. For example, meeting inventory management goals will be vital for the business as it will boost its sales. These technologies should also enable the business to establish an online shopping platform accompanied by a system that supports digital payment processing. Additionally, business analytics should facilitate order status checks thereby improving their fashion retailer’s brick-and-mortar operations such as customer contact, inventory, payments, and sales. Lastly, the technologies are expected to facilitate collaboration both internally and externally with the employees, customers, and other stakeholders

Competitors and Technology

Business Analytics and Business Intelligence Solutions

Macy’s is an American company offering a wide range of apparel for both genders and also includes other items such as home textiles and décor, cosmetics, and accessories. The business has its headquarters located in New York and it was founded by Rowland Hussey Macy in 1958 (Tokosh, 2019). Its primary market is based in the United States, but it has a presence in over 91 countries having amassed approximately 17.3 billion in sales in 2020 (Tokosh, 2019). The company has extensively utilized the principles of business analytics and intelligence solutions to manage its operations. For example, it has invested in predictive analytic technology to gain insight into consumer purchasing patterns and enhance email and webpage marketing initiatives (Tokosh, 2019). As such, based on the buyer’s habits, the software allows Macy’s to target consumers thereby augmenting their expenditure.

Big Data and Knowledge Management

Kohl’s is fashion retail whose headquarters are located in Wisconsin, United States. In 1927, the company was established by Maxwell Kohl, a Polish immigrant who launched a grocery store in Milwaukee (He et al., 2017). It currently has a massive chain of department stores with at least 730 stores situated in 41states in the US (He et al., 2017). Its main consumer segments are middle-income individuals who tend to purchase products for their families. Kohl’s ensures that its products are low priced through its cost-leadership approach, transformational management information systems, limited staffing, and centralized purchasing, distribution, and advertising (He et al., 2017). The company has benefited from investing in modern technologies to improve its operations.

Kohl’s is an archetype that has leveraged the power of big data and knowledge management. Its personalized marketing plans are directed by big data technologies and suggest how brick-and-mortar fashion retailers should use them (He et al., 2017). In particular, the company utilizes these technologies to create personalized marketing initiatives. It also opts for data science to help in merchandising allocation while utilizing external information such as macro-economic conditions and social data, which will determine the items to be stocked (He et al., 2017). Moreover, its practical analytics program is an asset to its store’s existing dashboard, which indicates sales data and other statistics.

Benefits of the Technologies

Macy’s has massively benefited from the use of business analytics and intelligence solutions. For example, the company has observed an increase in productivity when transitioning from the traditional techniques to incorporating these modernized technologies. Moreover, there has been a 12% rise in online sales which has been achieved through directed emails to registered consumers as well as other visitors who opt for using the company’s website (Tokosh, 2019). Additionally, the use of these technologies has enabled the American brand to save costs of up to $500,000 annually (Tokosh, 2019). As such, it can maximize time efforts to offer value-added insights to product items, content, and promotion that have a tailored and exceptional experience for the consumers. In essence, business analytics and intelligence solutions have allowed Macy’s to successfully comprehend buyer behavior and the effect of its marketing strategies on profitability.

Similarly, Kohl’s has also enjoyed the benefits of using big data and knowledge management. These technologies have enabled the company to improve its store performance by providing managers with real-time customer information which has ultimately enabled it to boost sales (He et al., 2017). Moreover, store personnel at Kohl’s are able to anticipate how specific inventory might sell based on consumer purchasing history and searches (He et al., 2017). Based on client data, store personnel is also able to get staffing suggestions from the dashboard, especially around how many individuals are on the sales floor against the people in the store during a particular hour and the departments that required additional staff depending on how items are in that category are selling.

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While the company was testing the practicality of its pragmatic analytics program, it realized its potential. Therefore, after expanding its presence to 50 locations, it embarked on an initiative to incorporate the technology into three hundred of its 1,158 store fleet (He et al., 2017). As such, the corporation has been retrenching some of its stores, and a bid to implement techniques such as mobile and self-checkout, buy online pick-up in store storerooms and radio frequency identification (RFID) enabled inventory (He et al., 2017). Therefore, the business in the case scenario should incorporate both business analytics and intelligence solutions and big data and knowledge management to improve its operations.

Technology Solution

Big Data and Knowledge Management

Big data and knowledge management meet all the requirements needed to help the case organization improve its brick-and-mortar operations. For example, these technologies can be imperative for an online shopping platform they can assist by giving insights into buyer tendencies ad demographics which is imperative in building tailored experiences (Ekambaram et al., 2018). Therefore, the fashion retailer can utilize big data to send an email with personalized discounts and exclusive offers to re-engage consumers. Big data can also play a critical role in improving inventory management by creating a central warehouse for stock, thereby minimizing the possibility of overselling by considering available stock levels in real time (Ekambaram et al., 2018). Moreover, the technology can also help the case organization in providing order status checks since it has software applications that can be used to track inventories and orders in real time.

Big data and knowledge management are the tools the case organization requires to process online payments. As such, these techniques help in securing online payment processing since it has the ability to incorporate various payment functions into a unified platform, thereby simplifying the process of examining trends (Sumbal et al., 2017). Lastly, this technology meets the requirement of facilitating collaboration between staff workers and customers. For example, the technologies incorporate systems for a collaborative network where decision-making is decentralized.

Business Analytics and Intelligence Solutions

These technologies will supplement big data and knowledge management to meet the requirements by examining data in detail to provide solutions that will the case organization. Business analytics and related technologies can be adopted by the case organization to improve its online shopping experience by utilizing customer preferences (Appelbaum et al., 2017). Under inventory management, business analytics can be used to make improvements in areas that minimize operational costs. Collaboration between employees and buyers can also be achieved by this technology since it facilitates monitoring and examining where staff workers are having challenges interacting with the customers (Appelbaum et al., 2017). Additionally, analytics can enable the case organization to manage customer contacts through data collection and reporting. Therefore, customer information records can be created to maintain preferences, patterns, and addresses and possibly suggest ways in which case organization can improve its security processes. Moreover, the technology can also meet the case organization’s requirement of reporting since it presents and organizes data in a way that managers can visualize (Appelbaum et al., 2017). As such, the business will be able to gain insight into shopping trends, availability of inventory, consumer inclinations, and many more.

Recommendations

Technology Solution

Generally, the two technologies identified above may solve most of the challenges facing the case organization. While big data collects information and processes it into meaningful information, business analysts use this content to make strategic decisions. When the aforementioned technologies are used together, businesses can get more favorable results, especially when integrating online shopping for improved customer experience. Therefore, the business owner should consider implementing business analytics and intelligence solutions due to their proactive and predictive approaches to managing organizational aspects.

Over Benefit

Business analytics and intelligence solutions are essential for evaluating data related to consumer inclinations and also help in determining the kind of items to store and the ones to discard from the inventory catalog. Moreover, this pair will also significantly improve the fashion retailer’s efficiency because it will access information on products (Appelbaum et al., 2017). The manager will find it easy to identify products that are not popular among the customers.

Basic Security Consideration

Security Features

Security is a critical factor that should be considered before implementing the aforementioned technologies. Therefore, it is of significance that appropriate safety measures should be incorporated to ensure that consumer personal details, payment information, and confidential data are secured (Appelbaum et al., 2017). For example, data encryption can be used to secure business intelligence tools, especially in communication platforms. Therefore, data on the server should be enciphered and protected by two-factor authentication (Asghar et al., 2019). The business owner should also consider setting permissions and hierarchies to limit accessibility. In addition, firewalls can also be used to prevent possible instances of data breaches (Asghar et al., 2019). Moreover, it is also recommended that the proprietor should acquire a security sockets layer (SSL) certificate to improve the security of the case organization’s website.

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Third Party Vendors

It is important for third-party vendors the case organization is collaborating with to have the same principles and understand the confidentiality of data and information. Therefore, the case organization should comprehensively research numerous vendors to select a distinguished and reputable merchant (Asghar et al., 2019). In particular, the business will have to review the vendor’s profile, reputability ratings, and customer reviews and feedback. Therefore, a decision matrix will be constructed to aid the owner selected the most reputable vendor.

Internal Safeguards

Internal safeguards are important because they help prevent unauthorized personnel from gaining access to significant organizational assets. First, the owner will ensure that accessibility is limited to protect valuable information, and each employee with be permitted to access systems based on their ranking. For example, executive managers will be allowed to access all information without limited access. Other employees such as the cashiers will be allowed to access specific parts of the system to allow them to complete transactions (Asghar et al., 2019). Lastly, the case organization will install software application programs to detect various cases of malware attacks and other intruders.

Conclusion

Big data and knowledge management have played a vital role in addressing the needs of most fashion retailers. Moreover, business analytics and intelligence solutions have also ensured that most business processes are streamlined. This paper has discussed the aforementioned technologies and how they have helped two companies, Macy’s department store and Kohl’s, a fashion retailer operating in the United States. The case organization has access to information required to implement either of the technologies to improve its online presence, inventory management, online payments, and handling of consumer contacts. As such, the fashion retailer should implement business analytics while also implementing the safety standards and internal safeguards proposed above.

References

Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29–44. Web.

Asghar, M. R., Hu, Q., & Zeadally, S. (2019). Cybersecurity in industrial control systems: Issues, technologies, and challenges. Computer Networks, 165, 106946. Web.

Ekambaram, A., Sørensen, A. Ø., Bull-Berg, H., & Olsson, N. O. (2018). The role of big data and knowledge management in improving projects and project-based organizations. Procedia Computer Science, 138, 851–858. Web.

He, W., Wang, F.-K., & Akula, V. (2017). Managing extracted knowledge from big social media data for business decision making. Journal of Knowledge Management, 21(2), 275-294. Web.

Kotouza, M. T., Tsarouchis, S.-F., Kyprianidis, A.-C., Chrysopoulos, A. C., & Mitkas, P. A. (2020). Towards fashion recommendation: An AI system for clothing data retrieval and analysis. IFIP International Conference on Artificial Intelligence Applications and Innovations, 433–444. Web.

Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57, 102207. Web.

Ratia, M., Myllärniemi, J., & Helander, N. (2019). The potential beyond IC 4.0: The evolution of business intelligence towards advanced business analytics. Measuring Business Excellence, 23(4), 396-410. Web.

Sumbal, M. S., Tsui, E., & See-to, E. W. (2017). Interrelationship between big data and knowledge management: An exploratory study in the oil and gas sector. Journal of Knowledge Management, 21(1), 180-196. Web.

Tokosh, J. (2019). Is the Macy’s in my mall going to close? Uncovering the factors associated with the closures of Macy’s, Sears, and JC Penney stores. Growth and Change, 50(1), 403–423. Web.

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