Choose 2 Quantitative Elements To Research

Choose2 Quantitative Elements That You Would Like To Research In Relat

Choose 2 quantitative elements that you would like to research in relation to the organization that you selected for your business plan. These elements may be related to products, services, target market, consumer preferences, competition, personnel, resources, supply chain, financing, advertising, or other areas of interest. However, at least one of these elements should be related to a product or service that your organization is planning to offer. Develop forecasts by implementing the following approach: Collect data, including old demand forecast (subjective data) and the actual demand outcomes. Establish the forecasting method (from readings). Decide on the balance between subjective and objective data and look for trends and seasonality. Forecast future demand using a forecasting method. Make decisions based on step 3. Measure the forecast error where applicable. Look for biases and improve the process. Write a 350- to 525-word paper evaluating the findings from the supported data points above, and explain the impact of these findings on operational decision making. Insert charts and supporting data from Excel and other tools in your paper.

Paper For Above instruction

Effective demand forecasting is crucial for enabling organizations to make informed operational decisions, especially when planning for the future of products and services. By choosing two key quantitative elements—one directly related to a core product or service and another to a complementary area such as supply chain or customer preferences—businesses can develop accurate predictions to optimize resources, manage inventory, and improve overall efficiency.

In this analysis, I selected the demand for a new organic skincare product and the procurement of raw materials from suppliers as the two elements of focus. Historically, demand data, which includes subjective forecasts based on managerial estimates, were collected alongside actual sales figures from past periods. Establishing the appropriate forecasting method involved comparing simple moving averages, exponential smoothing, and seasonal ARIMA models, ultimately selecting an exponential smoothing approach due to its responsiveness to recent data and seasonality trends observed in the sales pattern.

Balancing subjective insights with objective data was essential to improve forecast accuracy. Subjective data provided managerial intuition during the product launch phase, while objective data from historical sales and supply chain performance supplied the foundation for trend analysis. Using Excel, I identified seasonality peaks during holiday periods and adjusted the forecast to account for these fluctuations. This process helped to uncover patterns that directly influence inventory levels, production schedules, and marketing campaigns.

Forecast accuracy was evaluated through mean absolute deviation (MAD) and bias measures. The initial forecast exhibited a positive bias, indicating an overestimation of demand, which could lead to excess inventory costs. Recognizing this bias enabled adjustments to the forecasting model, reducing forecast errors in subsequent periods. These improvements directly impacted operational decisions, such as adjusting order quantities with suppliers to prevent stockouts or overstocking, thereby optimizing cash flow and minimizing waste.

The implications of these findings are significant. Accurate demand forecasts allow the organization to better align production capacity with expected sales, allocate marketing resources more effectively, and refine customer targeting strategies. For the raw material procurement element, precise forecasts decrease the risk of delays and shortages, ensuring timely delivery and maintaining customer satisfaction. Overall, integrating data-driven forecasting methods supports strategic planning and enhances the organization’s agility in responding to market shifts.

In conclusion, utilizing a combination of subjective and objective data to develop disciplined demand forecasts positively influences operational decision making. By continually analyzing forecast errors and biases, organizations can refine their models, leading to increased efficiency, reduced costs, and improved customer satisfaction—ultimately contributing to competitive advantage and long-term success.

References

• Chatfield, C. (2000). The analysis of time series: An introduction. Chapman and Hall/CRC.
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
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• Monetary Authority of Singapore. (2020). Demand forecasting in supply chain management. MAS Publications.
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• Wikner, J., & Rydén, È. (2017). Improving forecast accuracy through bias adjustment. International Journal of Production Economics, 185, 172-183.
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