Total quality management: principles and implementation

Ensuring Quality Ensurance Measures
The Problem
The problem discussed within this document is the inconsistent levels of quality assurance reflected in the business models and practices of both Wal-Mart and Sprint. For the most part, each of these retailers evinces a culture in which quality assurance is prioritized. Nonetheless, there are a couple of salient instances in which each of them is remiss in their quality assurance measures. Wal-Mart, for example, has neglected some important components of the Juran philosophy (Kuei, 1998, p. 21)—the most noticeable of which is a dearth of managerial presence on its sales floor. There is a poignant similitude between this chain and Sprint’s chain; the managerial concerns of the latter are seemingly focused on profit margins as opposed to quality assurance. Sprint directs customers to 800 numbers instead of directly assuaging any issues; Wal-Mart is perpetually understaffed and regularly disorganized.
The Solution
The solution is to organize each of these retailers in a way in which quality assurance is ingrained within their business models. The most efficacious means of doing so is to solicit direct feedback from customers regarding changes they view as advantageous for facilitating a quality shopping experience from these vendors. Perhaps the most efficient means of obtaining this customer input is to solicit it online via the various means of consumer feedback prevalent. These largely include social media channels. Examples of these channels include sources such as Twitter and Facebook, although there are a number of additional outlets worth mining for this data. Moreover, customer reviews found on these sites and others provide additional intelligence into consumer needs.
Data That Would Be Gathered
This data includes consumer reviews and social media sentiment data. The most significant fact about this data is it will be gathered and analyzed using techniques designed for big data. Ideally, this data would be gathered at scale from various social media sites and those which facilitate consumer reviews. However, to accurately gauge the sentiment data, text analytics solutions will be used. Such solutions combine various facets of machine learning with natural language processing to understand the nature of data analyzed. These solutions can determine whether the text is favorable or unfavorable to the two companies mentioned in this document. It can also extract concepts and themes from the text which relate to the sentiment conveyed about the companies. This method leverages some of the most modern techniques for analyzing data, and enabling companies to understand what their customers are saying about them. That insight allows these companies to determine how their quality assurance measures are working, or how they need to adjust them for better assurance of quality.
Options that Were Considered But Rejected
The options that were considered but rejected involved using some of the traditional means of gathering data for empirical evidence based research studies. These include measures such as leveraging survey instruments. Specifically, the author considered determining a population sample and randomly calling people on the phone to solicit feedback about their experience with either Wal-Mart or Sprint. A similar approach was considered in which the survey instrument would be issued via email. The author even discussed the possibility of conducting interviews with various customers in person. All of these methods were ultimately rejected because of the abundance of information available online on social media and customer feedback websites. However, the final solution of mining big data for social media sentiment with text analytics was selected because of both the modernity and efficacy of this approach. Several organizations are relying on text analytics for these use cases, and data-driven processes in general to inform their decision-making.
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 cloud to capture insights from the data maze. www.KMWorld.com Retrieved from http://www.kmworld.com/Articles/Editorial/Features/TEXT-ANALYTICS-gains-clout-to-capture-insights-from-the-data-maze-125861.aspx