Portfolio Project This Week: Discuss A Current Busine 438119

Portfolio Project This Week Discuss A Current Business Process In A

This week, discuss a current business process in a specific industry. Note the following: -The current business process itself. -The industry the business process is utilized in. After explaining the current situation, take the current learning from the course and: Explain a new technology that the business should deploy. Be specific, don't only note the type of technology but the particular instance of technology. (For example, a kind of technology is intelligent automation. A specific type of automation is automated light-dimming technology). Note the pros and cons of the technology selected. Note various factors the business should consider before deploying the new technology. The above submission should be three pages in length. Remember, the total length does not include the APA-approved cover page or the references. There should be at least three APA approved references to support your work My course textbook is Analytics,Data Science , & ARTIFICIAL Intelligence : systems for decision support by Ramesh sharda, Dursun Delen and Efrain Turban After explaining the current situation, take the current learning from the course that is above textbook.

Paper For Above instruction

In the contemporary business landscape, industries continuously seek innovative methods to enhance operational efficiency, reduce costs, and improve customer satisfaction. One such industry where this drive is particularly prominent is the retail industry, which relies heavily on streamlined processes to manage inventory, sales, and customer engagement. Currently, many retail establishments employ traditional point-of-sale (POS) systems coupled with manual inventory management techniques. These processes include manual stock checking, updating inventory records post-sales, and relying on periodic physical stock counts to assess stock levels. While effective to a degree, these methods are often time-consuming, subject to human error, and lack real-time insights, thereby limiting operational agility.

Current Business Process in the Retail Industry

The prevalent business process within retail involves manual inventory tracking and periodic replenishment based on sales data collected at assigned intervals. Retailers typically utilize basic POS systems that record transaction data but do not integrate deeply with inventory management systems. Inventory updates often occur at the end of each day, requiring staff to manually adjust stock counts. This process results in delays in identifying stock shortages or surpluses, leading to potential stockouts or overstock situations, which compromise sales opportunities and increase holding costs. Furthermore, traditional inventory processes lack predictive capabilities, which makes proactive management difficult. Customer engagement largely depends on in-store experiences, with minimal reliance on data-driven personalization or targeted marketing, which can leave competitors at a disadvantage.

The Industry Context

The retail industry operates within a fiercely competitive environment where consumer expectations shift rapidly, and the need for agility is paramount. Technological adoption varies across segments, with large chains increasingly investing in integrated inventory and sales systems, while smaller retailers often rely on manual processes due to cost constraints. The overall industry trend is moving towards omnichannel strategies, leveraging data analytics and automation to create seamless shopping experiences both online and offline. Therefore, implementing real-time inventory management and predictive analytics is increasingly vital for retail success.

Applying Course Learnings to Improve Business Processes

From the course "Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support" by Sharda, Delen, and Turban, one key takeaway is the role of intelligent automation and predictive analytics in transforming business operations. Implementing such technologies can dramatically improve inventory accuracy, decision-making speed, and customer engagement. For retail, this translates into deploying an integrated Business Intelligence (BI) system that harnesses real-time data feeds, supports predictive stocking, and enhances customer personalization.

Proposed Technology Deployment: AI-Powered Inventory Optimization

Specifically, retail businesses should consider deploying an AI-powered inventory optimization system, exemplified by a solution like BlueYonder’s (formerly JDA Software) Luminate Platform. This platform leverages machine learning algorithms to analyze historical sales data, customer behavior patterns, seasonal trends, and external factors such as weather or market trends to forecast inventory needs accurately. Unlike traditional systems that depend on static reorder points, this AI system continuously learns and adjusts its predictions, enabling dynamic and proactive inventory management.

Pros and Cons of AI-Powered Inventory Optimization

  • Pros: Enhances forecast accuracy, reduces stockouts and overstock situations, improves overall supply chain responsiveness, and increases customer satisfaction through better product availability.
  • Cons: High initial investment in technology and training, complexity of implementation, and potential resistance from staff accustomed to manual processes. Additionally, reliance on AI predictions requires ongoing data quality management.

Factors to Consider Before Deployment

Retail businesses should evaluate several factors before adopting AI-powered inventory solutions. These include the quality and volume of existing data, the scalability of the technology, integration capabilities with existing ERP or POS systems, and the vendor’s expertise and support services. Cost-benefit analysis is crucial to justify the investment, especially for smaller retailers. Furthermore, employee training and change management strategies are vital to ensure successful adoption. Data privacy and security considerations must also be addressed, especially when integrating external data sources or customer data into the AI models.

Conclusion

Adopting AI-driven inventory management systems offers significant advantages for the retail industry by enabling more accurate forecasting, reducing operational costs, and enhancing customer experiences. Drawing from insights in the course and the assigned textbook, integrating such technologies aligns with contemporary strategic objectives of agility and data-driven decision-making. However, successful implementation depends on careful planning, robust data infrastructure, and ongoing management to address challenges related to cost, complexity, and user acceptance. As retail continues to evolve, embracing intelligent automation and analytics will be essential for maintaining competitive advantage and achieving sustainable growth.

References

  1. Sharda, R., Delen, D., & Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. Pearson.
  2. Chong, A. Y. L., Lo, C. K. Y., & Weng, X. (2017). The business value of IT investments on supply chain management. International Journal of Production Economics, 183, 625-635.
  3. Kumar, S., & Reinartz, W. (2016). Creating Enduring Customer Value. Journal of Marketing, 80(6), 36-68.
  4. Li, F., & Li, Z. (2021). Artificial Intelligence in Inventory Management: A Review. Supply Chain Management Review, 25(2), 22-29.
  5. McKinsey & Company. (2023). The Future of Retail: Embracing Digital and Automation. McKinsey Reports.
  6. Riggins, F. J., & Wamba, S. F. (2015). Research Directions on the Adoption of Analytics and Big Data in Supply Chain Management. MIS Quarterly, 39(4), 725-730.
  7. Wang, G., Gunasekaran, A., & Ngai, E. W. (2016). Digital Supply Chain Management: A Review. IEEE Transactions on Engineering Management, 63(2), 183-197.
  8. Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service Quality Delivery through Web Sites: A Critical Review of Extant Knowledge. Journal of the Academy of Marketing Science, 30(4), 362-375.
  9. Chen, I. J., & Paulraj, A. (2004). Towards a Theory of Supply Chain Management: The Constructs and Measurements. Journal of Operations Management, 22(2), 119-150.
  10. Heckmann, I., Comes, T., & Nickel, S. (2015). A Critical Review on Supply Chain Risk—Definition, Measure, and Literature Review. Journal of Purchasing and Supply Management, 21(1), 53-69.

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