Business Finance Management Homework

 

 

Business Finance Management Homework

 

 

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Business Finance Management Homework

1) Deming, Juran, and Crosby have distinct philosophies in the field of quality management. Deming in his account put more emphasis on the systemic approach to quality by advocating for progressive improvement, and focusing on processes and statistical methods instead of asserting blame on others (Speegle, 2019). Juran on the other hand, stresses the significance of quality planning, top management involvement, and setting measurable quality objectives in order to meet client needs. Meanwhile, Crosby supported the “zero defect” concept and maintained that quality problems can be solved through prevention and not detection (Speegle, 2019). Even though, they both stressed quality, their strategies differed extensively based on focus and strategies. For example, When Deming and Juan emphasize a holistic and organization-wide view, Crosby mainly focuses on eliminating weaknesses at the source.

2)  Even though quality control (QC) and quality assurance (QA) are elements of quality management, their roles or functions varies in specific degrees. Firstly, QA refers to a systematic and pre-emptive approach that focuses mainly on preventing potential defects by creating processes, guidelines, and standards (Klesta & Bartz, 2016). Its primary goal is to ensure quality is established in the product or processes from the start of production to eliminate any potential defects and ensure consistency throughout the operations. Meanwhile, QC involves the process of inspecting the products and processes before and after the production process to ascertain and remedy potential deficiencies. The main objective is to ensure the end result meets the required quality standards.

3)  Dimensions and metrics are two important concepts used in data analysis and measurement in the context of analytics and performance assessment. A dimension is a qualitative feature that allows data categorization such as product categories. Meanwhile, metrics are quantitative attributes utilized in the analysis of performance such as website traffic (Mammola, 2019). However, these two concepts are interrelated in a specific way such that dimension can be used to categorize data for analysis while metrics offer numerical value that can be analyzed within those dimensions.

4) It is important to assign weights in various decision-making processes, especially in multi-criteria data and decision analysis. The weights indicate the relative significance of every dimension in relation to the general objective under assessment. Therefore, by assigning weights, decision-makers are able to express their priorities, stressing specific dimensions over others in terms of their strategic goals.  The process significantly aids in ensuring accurate analysis that reflects the priorities of the decision-maker. This enables more informed and objective decision-making in intricate circumstances where several dimensions are involved.

5) A weighted and a raw dimension scores differ in a number of ways. For instance, a weighted dimension score considers the assigned significance of each dimension while a raw dimension score does not involve incorporation of any weighting. Calculating a weighted dimension score involves multiplying each element within the dimension by its corresponding weight and then summing, which provides a compound score reflecting the relative significance of the dimension. Meanwhile, a raw dimension score involves simple aggregation of the component values within the dimension without any consideration for their relative importance. Finally, a weighted score provides a more balanced evaluation enabling greater emphasis on critical aspects, while raw scores treat all elements in equal measure during analysis.

6) The validity and reliability of a survey questionnaire are two different aspects of its quality. Validity means the extent to which the questionnaire measures what is intended to be measured. In other words, it examines the extent to which the questionnaire accurately captures the concept under investigation. Meanwhile, reliability refers to the stability and consistency of the results from the questionnaire over time across different respondents (Sürücü & Maslakci, 2020). In short, it examines whether the questionnaire provides consistent results when administered to a similar group of respondents. In summary, validity mainly addresses the hypothesis of whether the right thing is being measured, whereas reliability is concerned with the consistent nature of measurement.

7) An affinity diagram plays a significant role as a tool in the content analysis as it aids in categorizing and organizing massive volumes of qualitative data such as ideas or texts into more meaningful themes. Since content analysis usually encompasses sifting through unsorted and diverse data, an affinity diagram enables researchers to group related content items in terms of patterns or common themes that resurface during the process of analysis. Therefore, clustering content components together makes the process of identifying recurring themes and trends within the data simple, and further makes it efficient to draw important conclusions and lessons from the content analyzed.

8)  The "excellent product quality" would not be a strength for my firm if it is solely equivalent to the quality of competing products in the same market. While maintaining differences in quality with competitors is very important to stay competitive in the market, "excellent product quality” may not offer a unique competitive advantage (Chan et al., 2018).  For example, strengths usually entail elements that set the firm apart in the market.  In order for a strength to be recognized, "excellent product quality" must surpass or differentiate itself meaningfully from competitors, attracting clients and potentially demanding higher prices or market share.

 

 

References

Chan, Y. C., Fung, K. Y., & Ng, K. M. (2018). Product design: A pricing framework accounting for product quality and consumer awareness. AIChE Journal, 64(7), 2462-2471.

Klesta, E. J., & Bartz, J. K. (2016). Quality assurance and quality control. Methods of Soil Analysis: Part 3 Chemical Methods, 5, 19-48.

Mammola, S. (2019). Assessing the similarity of n‐dimensional hypervolumes: Which metric to use? Journal of Biogeography, 46(9), 2012-2023.

Speegle, M. (2009). Quality concepts for the process industry. Cengage Learning.

Sürücü, L., & Maslakci, A. (2020). Validity and reliability in quantitative research. Business & Management Studies: An International Journal, 8(3), 2694-2726.