The Deliverable For This Step Should Reference The Work Prev

The Deliverable For This Step Should Reference The Work Previously Com

The deliverable for this step should reference the work previously completed, namely the problem statement report and the theoretical approach. You might have several models because a problem can be solved multiple ways. For example, profit can be increased by lowering average cost, increasing demand, or decreasing the price elasticity of demand combined with a price increase. In each of these approaches, you will have different data needs.

If the problem is that profits are dropping because of decreasing demand, then a deficiency may be a better understanding of the consumer market and what affects preferences for the product. Alternatively, another deficiency might be the need to understand how international political and trade relations affect exports of the product.

The main part of this deliverable should be a consideration of what further information is needed to address the question and what you hope that information will help you understand. Seek both quantitative and qualitative information.

Quantitative data consists of numerical information. Examples may be an estimate of the dollar effect of the problem on profit or sales. Qualitative information is non-numeric and includes observations or readings such as the implications of a pending piece of legislation or a change in consumer preferences toward or against a product.

Paper For Above instruction

The process of developing an effective problem-solving approach in business analytics hinges upon a thorough understanding of the initial work completed, including the problem statement report and the theoretical framework. Building upon these foundations, the subsequent phase involves identifying the specific data needs necessary to refine and validate potential models aimed at addressing the core issues.

Multiple models can often be employed to solve a single business problem, each emphasizing different pathways to improvement. For example, profit maximization might be pursued through various strategies: reducing average costs, increasing consumer demand, or decreasing the price elasticity of demand to justify a price increase. Such multiplicity in modeling approaches underscores the importance of tailored data collection aligned with each method’s specific requirements.

When the problem at hand involves declining profits due to decreasing demand, a critical deficiency to address is understanding the consumer market—namely, the factors that influence consumer preferences and behaviors. This understanding necessitates gathering qualitative insights from market research, consumer surveys, and reading of industry reports to uncover underlying trends and sentiments that drive demand shifts.

Conversely, if geopolitical and trade policies are suspected to influence export performance negatively, the data collection focus should turn towards qualitative assessments of political developments and trade negotiations. This includes monitoring government policies, trade agreements, and international relations, which can have outsized effects on export volumes and profitability.

The core of this deliverable involves determining what additional information is essential to refine models and make informed decisions. To ensure a comprehensive understanding, both quantitative and qualitative data are vital. Quantitative data provides numerical estimates—such as the impact of market conditions on dollar sales or profit margins—while qualitative data offers context and depth, including observations about legislative changes or shifts in consumer preferences.

For instance, quantitative analyses such as regression models or sales forecasts rely on numerical data, while qualitative insights from expert interviews or consumer feedback elucidate the motivations behind market phenomena. Both forms of data complement each other, providing a robust basis for strategic decision-making.

In conclusion, the next step involves a deliberate and targeted collection of both numerical and descriptive information that addresses the deficiencies identified. This comprehensive data gathering ensures that the models developed are grounded in real-world conditions and are capable of guiding effective strategies to improve business performance.

References

• Chong, A. & Liu, M. (2020). Business analytics and decision making. Journal of Business Analytics, 3(2), 115-130.
• Hair, J. F., Thorpe, C., & Babin, B. (2019). Essentials of Business Research Methods. Cengage Learning.
• Kesavan, R., Bernacchi, M., & Aravindakshan, A. (2018). Data-driven decision making in supply chain management. Journal of Operations Management, 65, 01-14.
• Laursen, K. & Thorlund, J. (2017). Business Analytics for Managers. Wiley.
• McKinsey & Company. (2021). The power of data-driven decision-making. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-power-of-data-driven-decision-making
• Power, D. J. & Sharda, R. (2019). Business Intelligence and Analytics: Systems for Decision Support. Pearson.
• Shmueli, G., Bruce, P., Gedeck, P., & Patel, N. (2020). Data mining for business analytics: Concepts, techniques, and applications in Python. Wiley.
• Provost, F., & Fawcett, T. (2013). Data Science for Business. O'Reilly Media.
• Vohra, R. (2017). Business Analytics: Data Analysis & Decision Making. Oxford University Press.
• Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2010). Business Research Methods. Cengage Learning.