Logistics Assignment For Students And Department Faculty

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Extracted instructions: Select one of the case studies at the end of either chapter 7 or chapter 8. After reading the case study, respond to the questions in a 3-4 page paper. Write between 750-1,000 words (approximately 3-4 pages) using Microsoft Word in APA style. Include a cover page and reference page. At least 80% of your paper must be original content. Use at least three references from outside course material, one from EBSCOhost. Cite all references in APA style.

Paper For Above instruction

Forecasting demand is a critical component in logistics and supply chain management, enabling organizations to optimize inventory levels, reduce stockouts, and improve customer satisfaction. In the context of Tires for You, Inc. (TFY), an automotive tire replacement shop, the application of various forecasting methods can significantly impact operational efficiency. This paper examines three forecasting techniques—simple three-month moving average, weighted moving average, and exponential smoothing—applied to the tire sales data of TFY, evaluates their accuracy, and discusses how these methods can be refined to improve demand predictions.

Understanding TFY’s demand patterns begins with analyzing historical sales data. The provided monthly tire sales for 2014 serve as the primary dataset for initial forecasting, while sales from 2016 are used to assess forecast accuracy. The first method employed is the simple three-month moving average, which calculates the average of the most recent three months to predict the next month's demand. This method is straightforward but tends to lag behind sudden changes in demand. For example, calculating the forecast for January 2015 using the sales of October, November, and December 2014 involves averaging these three months. Applying this approach to all periods yields a series of forecasts, which can then be compared to actual sales for accuracy evaluation.

Next, the weighted moving average assigns different weights to the most recent observations, emphasizing recent trends. Using weights of 0.60, 0.30, and 0.10 for the most recent, second most recent, and third most recent periods respectively, provides a more responsive forecast, especially in volatile markets. Calculating forecasts with this method involves multiplying each of the previous three months' sales by their respective weights and summing the results. This approach balances historical data with recent sales trends, often resulting in more accurate predictions during periods of demand fluctuation.

The third technique, exponential smoothing, applies a smoothing constant (alpha) to weigh recent observations exponentially more heavily than older data. With an initial forecast of 9,500 and alpha set at 0.40, subsequent forecasts are calculated using the formula: Forecast for period t+1 = alpha actual demand in period t + (1 - alpha) forecast for period t. This method adapts quickly to changes in demand, making it suitable for environments with fluctuating sales patterns. Calculating the forecasts over the dataset provides a series of predictions that can be assessed against actual sales data from 2016 to determine which method best models the demand.

Assessing forecast accuracy involves calculating error measures such as Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). These metrics quantify the average deviation of forecasts from actual sales, providing a basis to compare the effectiveness of each method. Lower error values indicate more accurate forecasts, guiding managers in selecting the best predictive approach.

In this case, the exponential smoothing method generally outperforms the moving average techniques due to its adaptability to recent demand changes. Its responsiveness to demand fluctuations minimizes forecast errors, making it a superior choice for TFY. However, forecasts can be further refined by incorporating additional data, such as seasonality patterns or external factors like market trends and economic indicators, which influence tire sales.

To improve forecasting accuracy further, TFY can employ advanced methods such as seasonal decomposition or ARIMA models, which capture complex patterns within the data. Regular recalibration of the smoothing parameters and integrating real-time sales data can also enhance prediction precision. Ultimately, combining statistical techniques with managerial insights and external market analysis yields the most reliable demand forecasts, enabling TFY to optimize inventory and improve customer service.

References

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