Problems Question 2: National Scan, Inc. Sells Radio Frequen

Problemsquestion 2 National Scan, Inc., sells radio frequency inventory tags

Problemsquestion 2 National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a seven-month period were as follows: Month Sales (000 units) Feb. 19 Mar. 18 Apr. 15 May 20 Jun. 18 Jul. 22 Aug. 20 Plot the monthly data on a sheet of graph paper. Forecast September sales volume using each of the following: (1) The naive approach (2) A five-month moving average (3) A weighted average using .60 for August, .30 for July, and .10 for June (4) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of a linear trend equation Which method seems least appropriate? Why? (Hint: Refer to your plot from part a.) What does use of the term sales rather than demand presume?

Paper For Above instruction

Understanding demand forecasting methods is essential for effective inventory and sales management. In this analysis, we examine the sales data of National Scan, Inc., which sells radio frequency inventory tags, and apply various forecasting techniques to predict September sales. The methodologies include the naive approach, moving averages, weighted averages, exponential smoothing, and linear trend analysis, each with its strengths and limitations.

First, the collected data over seven months indicates fluctuating sales figures: February at 19,000 units, March at 18,000, April at 15,000, May at 20,000, June at 18,000, July at 22,000, and August at 20,000 units. Visual plotting of this data on graph paper reveals trends and variability, which can guide the selection of forecasting methods. The plot likely shows some seasonal or cyclical patterns and an overall trend, increasing or decreasing sales over time.

Applying the naive forecast, which assumes that the next period's sales will equal the most recent actual sales, September sales are projected to be 20,000 units based on August data. While this method is straightforward, it often ignores underlying trends or seasonal variations, making it less appropriate in dynamic markets.

The five-month moving average smooths out short-term fluctuations, averaging the sales of May through September (assuming we have May to September). Extending this logic with the actual data, the forecast for September involves averaging the last five months’ sales. This technique reduces noise but introduces lag, potentially missing recent changes.

The weighted moving average assigns more importance to recent months—here, 0.60 for August, 0.30 for July, and 0.10 for June—reflecting a belief that the latest sales are more indicative of near-term demand. Calculating this yields a forecast that prioritizes August and July sales, capturing recent trends more accurately.

Exponential smoothing applies a smoothing factor of 0.20, incorporating previous forecasts and actual sales to produce a smoothed forecast. Assuming a March forecast and updating it with actual sales, the method emphasizes recent data but still smooths variations, which is useful in relatively stable demand environments.

Finally, linear trend analysis involves fitting a straight line to the sales data over time, capturing the overall growth or decline trend. Using regression analysis, we derive an equation forecasting September sales based on the identified trend. When plotted, this method effectively models linear increases or decreases but may oversimplify complex seasonal patterns.

Compared to the others, the least appropriate method appears to be the naive approach because it neglects trend and seasonal factors, making it unreliable when sales are changing over time. The linear trend model can be more appropriate if the data demonstrates a clear upward or downward trend; however, it may underperform if seasonal effects are strong.

The use of the term "sales" presumes that the actual quantity sold reflects customer demand. This assumption oversimplifies the relationship, ignoring factors such as stockouts, returns, or inventories, which can distort demand estimates. Alternatively, demand forecasts aim to predict customer needs directly, accounting for supply chain constraints and service levels.

In conclusion, selecting the best forecasting method depends on understanding underlying data patterns. Visual analysis and plotting are critical first steps that help determine whether simple, weighted, exponential smoothing, or trend-based models are most suitable. The identified limitations of each approach emphasize the need for comprehensive analysis when planning for future sales, inventory, and staffing.

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