Quality Assurance Improvements for Walmart and Sprint
This paper examines inconsistent quality assurance practices at Walmart and Sprint, identifying key deficiencies such as understaffing, poor floor management, and a profit-over-quality orientation. Drawing on the Juran philosophy of total quality management, the paper proposes a data-driven solution: mining consumer reviews and social media sentiment at scale using text analytics and natural language processing. Traditional survey methods—phone interviews, email surveys, and in-person interviews—were considered but rejected in favor of big data approaches for their greater efficiency and modern applicability. The paper concludes that real-time customer feedback gathered online can enable both companies to identify where their quality assurance measures fall short and adjust accordingly.
- The Problem: Quality Assurance Gaps at Walmart and Sprint: Identifies QA deficiencies at Walmart and Sprint
- The Proposed Solution: Customer-Driven Quality Improvement: Proposes social media feedback to improve quality
- Data Collection: Social Media and Consumer Reviews: Big data and text analytics for sentiment analysis
- Options Considered but Rejected: Surveys and interviews rejected for big data
- Conclusion: Data-driven QA approach endorsed for both companies
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What makes this paper effective
- Clearly frames a real-world business problem before proposing a solution, giving the argument a logical cause-and-effect structure.
- Grounds the critique of both companies in a recognized theoretical framework — the Juran philosophy of total quality management — lending academic credibility to the analysis.
- Explicitly addresses and dismisses alternative methodologies, demonstrating critical thinking by showing awareness of other options before defending the chosen approach.
Key academic technique demonstrated
The paper uses a problem-solution structure reinforced by comparative analysis. By evaluating two distinct companies against a shared quality management standard, the author identifies parallel deficiencies and proposes a unified, scalable remedy. The rejection of traditional survey methods in favor of big data analytics is argued on both practical and methodological grounds, illustrating how to justify research design choices within a business context.
Structure breakdown
The paper is organized into four substantive sections: problem identification, proposed solution, data methodology, and alternatives considered. This mirrors a standard applied research or business case format. The references section cites two sources — one academic handbook and one trade publication — appropriate for an undergraduate business or management course. Total length is concise, targeting a focused analytical memo rather than a full research paper.
The Problem: Quality Assurance Gaps at Walmart and Sprint
The problem discussed in this paper is the inconsistent level of quality assurance reflected in the business models and practices of both Walmart and Sprint. For the most part, each of these retailers exhibits a culture in which quality assurance is prioritized. Nonetheless, there are several notable instances in which each of them falls short in their quality assurance measures. Walmart, for example, has neglected important components of the Juran philosophy (Kuei, 1998, p. 21) — the most noticeable of which is a lack of managerial presence on its sales floor. There is a striking similarity between this chain and Sprint: the managerial concerns of the latter appear focused on profit margins rather than quality assurance. Sprint directs customers to 800 numbers instead of directly resolving issues, while Walmart is persistently understaffed and regularly disorganized.
The Proposed Solution: Customer-Driven Quality Improvement
The solution is to reorganize each of these retailers in a way that embeds quality assurance within their business models. The most effective means of doing so is to solicit direct feedback from customers regarding changes they view as beneficial to a quality shopping experience. Perhaps the most efficient way of obtaining this customer input is to gather it online through the various consumer feedback channels currently available. These largely include social media channels such as Twitter and Facebook, though there are a number of additional outlets worth mining for this data. Customer reviews found on these and other sites provide further insight into consumer needs and expectations.
Data Collection: Social Media and Consumer Reviews
The data to be gathered includes consumer reviews and social media sentiment data. The most significant aspect of this data is that it will be gathered and analyzed using techniques designed for big data. Ideally, this data would be collected at scale from various social media platforms and consumer review sites. To accurately gauge sentiment, text analytics solutions will be employed. Such solutions combine various elements of machine learning with natural language processing to interpret the nature of the data analyzed. These solutions can determine whether the text is favorable or unfavorable toward the two companies discussed in this paper. They can also extract concepts and themes from the text that relate to the sentiment expressed about each company. This method leverages some of the most modern techniques for analyzing data, enabling companies to understand what their customers are saying about them. That insight allows these companies to determine how their quality assurance measures are performing, and how they may need to be adjusted to better ensure quality.
Conclusion
By replacing traditional survey methods with scalable big data approaches, Walmart and Sprint can gain real-time, high-volume insight into customer perceptions of their quality assurance practices. Text analytics and natural language processing provide a powerful and efficient framework for identifying recurring themes and sentiment trends in consumer feedback. Organizations that embrace these data-driven methods are better positioned to identify where quality assurance falls short and to make informed adjustments. This approach represents a meaningful step forward for both companies in building a culture of genuine, customer-centered quality assurance.
References
Kuei, C. (1998). Comparing Deming and Juran's philosophies to the formation of total quality leaders' world views. Handbook of Total Quality Management, 21–40. Boston, MA: Springer.
Lamong, J. (2018). Text analytics gains clout to capture insights from the data maze. KMWorld. Retrieved from http://www.kmworld.com/Articles/Editorial/Features/TEXT-ANALYTICS-gains-clout-to-capture-insights-from-the-data-maze-125861.aspx
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