MTH216R3 Example Of Visuals Topic 5 Business Scenario ✓ Solved

Mth216r3 Example Of Visualstopic 5 Businessbusiness Scenariotopic 5

Mth216r3 Example Of Visuals Topic 5 - Business Business Scenario Topic 5 Predicting Tires Purchased Scenario 5 Review the yearly data involving tires purchased at a tire shop with declining business. Predict how many tires will need to be in stock to have available for the customers in 2018. Year Tires Purchased Wheels Purchased Lugnuts Purchased

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Sample Paper For Above instruction

In the context of business management and inventory forecasting, accurately predicting future demand is crucial for maintaining optimal stock levels, reducing costs, and enhancing customer satisfaction. Specifically, in a tire shop experiencing declining business, forecasting the number of tires needed for the upcoming year involves analyzing historical purchasing data and identifying patterns that can guide inventory decisions. This paper explores methods for predicting tire stock requirements for 2018 based on previous years’ data involving tires, wheels, and lugnuts purchased, emphasizing the importance of data analysis, statistical modeling, and strategic planning in inventory management.

To forecast the number of tires needed in 2018, the first step involves collecting and analyzing data from previous years to identify trends or patterns. For instance, suppose the tire shop recorded the following data:

  • 2014: 1,200 tires purchased
  • 2015: 1,050 tires purchased
  • 2016: 950 tires purchased
  • 2017: 850 tires purchased

This data shows a clear declining trend in tire purchases over the years. To project the demand for 2018, statistical techniques such as linear regression can be employed to model the decline and estimate future requirements. A simple linear trend analysis could fit a line to the data points, with the equation predicting the number of tires needed based on the year.

Applying linear regression, the general form of the equation is:

Y = a + bX

where Y represents the number of tires purchased, X represents the year, and a and b are constants determined through data fitting. Using the data points, one can calculate the slope (b) and intercept (a), then substitute X = 2018 to estimate the tires needed.

Based on the calculations, assuming the trend continues, the shop might need approximately 750 tires in stock for 2018. However, given the declining nature of sales, the shop may also consider adjusting order quantities to prevent overstocking, which incurs storage costs and ties up capital. Additionally, examining the purchase patterns of associated items like wheels and lugnuts can provide supplementary insights into customer preferences and purchasing cycles.

Furthermore, beyond simple linear models, more advanced forecasting methods such as exponential smoothing or time series analysis could be applied to account for irregularities and seasonal fluctuations. For example, if the data showed seasonal peaks during certain months, the shop could adjust stocks accordingly, improving responsiveness to demand fluctuations.

Inventory management strategies should also incorporate safety stock levels to buffer against unforeseen increases in demand or supply chain disruptions. Combining historical data analysis with strategic safety stock calculations ensures the shop maintains sufficient inventory to satisfy customer needs without excessive overstocking.

In conclusion, predicting the number of tires needed for 2018 in a declining sales environment requires a systematic approach involving data analysis, statistical modeling, and strategic planning. By analyzing past purchasing patterns and applying appropriate forecasting techniques, the tire shop can optimize its inventory, reduce costs, and improve customer service levels in a challenging business landscape.

References

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