Forecasting Case Analysis MGT 3332 Fall 2020 Important Notic ✓ Solved

Forecasting Case Analysismgt 3332fall 2020important Notice All Team M

Forecasting Case Analysis MGT 3332 Fall 2020 Important Notice: All team members must do all parts of the project and compare results. DO NOT divide up parts since many are related. Teams submit a Word and Excel file only after all members agree. Only one submission is allowed. The case involves predicting demand for Fresh, a liquid detergent. Data for 48 monthly sales periods includes demand, price, industry average price (AIP), advertising expenditure (ADV), and the price difference (DIFF). The assignment involves creating time series plots, regression analyses, correlation matrices, moving averages, exponential smoothing, seasonal indices, trend and regression analysis, and forecasting for early 2020 months, with detailed interpretation and conclusions to be provided.

Sample Paper For Above instruction

The forecasting of product demand plays a crucial role in managing inventory, setting strategic marketing initiatives, and ensuring effective production planning. In the context of Fresh, a liquid detergent brand produced by Enterprise Industries, accurately predicting demand facilitates efficient resource allocation and minimizes costs associated with overstocking or stockouts. This paper explores comprehensive forecasting methods, including time series analysis, regression models, and seasonal adjustments, to develop a robust demand prediction framework for Fresh based on the dataset provided for 48 months.

Introduction

Forecasting demand involves analyzing historical data to predict future consumption patterns. Since demand dynamics are influenced by numerous variables such as pricing, advertising, industry trends, and seasonality, a multifaceted approach is necessary. The data for Fresh encapsulate demand, pricing strategies, industry discounts, advertising investments, and demand over 48 months. By examining these relationships through various analytical techniques, we can determine the most accurate forecasting method and understand the underlying demand drivers.

Time Series Plotting and Initial Observations

The initial step involves creating scatter plots for all five variables over time, with fitted trend lines, equations, and R-squared values. These visualizations reveal the nature of trends, seasonality, and potential relationships. For instance, demand may show an increasing trend or seasonal fluctuations aligned with certain months. The trend lines help quantify the general movement of variables and assess the strength of their linear relationships. Such insights guide subsequent modeling efforts.

Demand Versus Influencing Variables

Constructing scatter plots of demand against DIFF, ADV, AIP, and Price (with demand on the y-axis) allows for examining direct correlations. Fitted lines provide equations and R-squared, indicating how well these variables explain demand variation. Typically, a strong negative correlation between demand and Price suggests that lower prices boost sales. Conversely, advertising expenditure (ADV) may show a positive relationship. Analyzing these graphs facilitates understanding which factors are most influential, guiding regression modeling.

Correlation Analysis

Correlation matrices quantify relationships among all six variables. A correlation coefficient (r) above 0.50 with demand indicates a strong association. For example, demand might strongly correlate negatively with Price and positively with ADV and AIP, reflecting typical market behaviors. Variables with high correlation coefficients are key predictors, and understanding these assists in variable selection for regression models.

Moving Averages for Short-term Forecasting

Using 3-month and 6-month moving averages to forecast demand for January 2020 involves computing averages of demand over the respective periods. The Mean Absolute Deviation (MAD) quantifies forecast accuracy. Comparing MAD allows identifying whether short-term or slightly longer-term averages better predict demand. Generally, smaller MAD indicates a more precise forecast, but the suitability of moving averages depends on the data's trend and seasonality characteristics.

Exponential Smoothing Forecasts

Applying exponential smoothing with various alpha values (0.1 to 0.9) provides smoothed demand forecasts. Calculating MAD for each alpha identifies the optimal smoothing factor. A lower MAD indicates a better fit, commonly resulting from alpha values aligning with the data's inherent volatility. Exponential smoothing can adapt to trend and seasonal components if appropriately configured.

Seasonality and De-seasonalization

Calculating seasonal indices via the Simple Average (SA) method involves averaging demand for each month over multiple years and normalizing these to obtain indices. Dividing actual demand by seasonal indices yields de-seasonalized demand, which isolates the underlying trend. This process enhances the accuracy of subsequent trend and regression analyses by removing seasonal effects.

Trend Analysis on De-seasonalized Data

Performing regression analysis on de-seasonalized demand values assesses trend patterns. The regression outputs include the trend equation, goodness-of-fit metrics (r, R-squared), and significance tests. A significant regression with high R-squared signifies that the trend component effectively explains demand fluctuations over time. If trend analysis is suitable, demand exhibits a clear upward or downward trend post-de-seasonalization.

Seasonally Adjusted Forecasts

Forecasting demand for January through March 2020 involves combining trend predictions with seasonal indices. These seasonally adjusted trend forecasts incorporate both temporal patterns and seasonal fluctuations, producing more accurate short-term estimates critical for inventory and supply chain decisions.

Regression-Based Demand Predictions

Using simple linear regression models with ADV and DIFF as independent variables—applied on de-seasonalized data—yields explicit demand equations. The MAD evaluates model precision. The regression output, including coefficients, p-values, F-statistics, and R-squared, indicates the strength and significance of predictors. The most significant predictor is identified based on statistical significance and contribution to explaining demand variation.

Multiple Regression Modeling

Constructing multiple linear regression models with multiple predictors (Period, AIP, DIFF, ADV) enables comprehensive demand estimation. Model significance is assessed via the F-test, R-squared, and p-values. Rank ordering of variables based on standardized coefficients or p-values reveals dominant demand drivers. These models facilitate refined demand forecasting and strategic planning.

Forecasting for Future Months

Using the optimal regression model, demand forecasts for upcoming months are generated. These forecasts are adjusted seasonally to reflect periodic fluctuations. Accurate forecasting informs inventory management, production scheduling, and marketing efforts, reducing costs and improving service levels.

Conclusion

Effective forecasting combines multiple analytical techniques to reveal demand patterns and key drivers. In the case of Fresh, regression analysis, seasonal adjustments, and smoothing methods provide a robust forecast framework. The selected models offer insights into price sensitivity, advertising impact, and seasonal effects, informing strategic decision-making. Overall, an integrated forecasting approach enhances inventory control and supports organizational goals.

References

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