Excel Sheet For Decomposition Actions 1 - Read The Case Stud

Excel Sheet For Decompistionactions1 Read The Case Study Entitled Be

Excel Sheet For Decompistionactions1 Read The Case Study Entitled Be

EXCEL SHEET FOR decompistion Actions: 1- Read the case study entitled “Best homes Inc forcasting†from the textbook (e-book), in the part VII. 2- Answer the following questions. a. What forecasting methods should the company consider? Please justify. b. Use the classical decomposition method to forecast average demand for 2016 by month. What is your forecast of monthly average demand for 2016?

Paper For Above instruction

Introduction

Forecasting plays a pivotal role in strategic planning and operational efficiency for companies across various industries. Effective forecasting methods enable organizations to anticipate future demand, optimize inventory levels, allocate resources efficiently, and improve customer satisfaction. In the context of Best Homes Inc., a company likely involved in the housing or construction industry, selecting appropriate forecasting methods is crucial for planning and decision-making. This paper discusses suitable forecasting methods for Best Homes Inc., providing justification for each and demonstrates the application of the classical decomposition method to forecast monthly demand for the year 2016.

Forecasting Methods to Consider

Given the nature of the company’s operations and the typical demand patterns in the housing or construction sector, several forecasting approaches should be considered. These include qualitative methods, time series analysis, and causal models. However, for large-scale, historical demand data, quantitative methods often provide more accurate and actionable forecasts.

1. Moving Averages and Exponential Smoothing

Moving averages help smooth out short-term fluctuations and reveal underlying demand trends, especially when data exhibits seasonality or irregular patterns. Exponential smoothing further refines this approach by assigning exponentially decreasing weights to older observations, allowing the forecast to respond more swiftly to recent changes. These methods are useful for short- to medium-term forecasts and are straightforward to implement, making them suitable for predicting demand patterns in the housing market, which often shows seasonal fluctuations.

2. Classical Decomposition Method

Classical decomposition involves separating a time series into its constituent components: trend, seasonality, and residuals (random noise). This method is particularly effective when historical data demonstrates clear seasonal patterns, as is common in housing demand influenced by factors such as weather, economic cycles, and seasonal purchasing behaviors. By isolating these components, forecasts can more accurately reflect expected future demand.

3. Regression Analysis and Causal Models

Regression models incorporating external variables such as interest rates, employment levels, or housing market indices can provide insights into demand drivers. These models are especially useful when relationships between demand and external factors are well-established. However, they require reliable data on these variables and may be less effective when such data is unavailable or volatile.

4. Time Series Models (ARIMA)

ARIMA (AutoRegressive Integrated Moving Average) models are powerful for modeling and forecasting complex time series data that incorporate autocorrelation structures. They can handle non-stationary data and incorporate both autoregressive and moving average components. For demand data exhibiting complex patterns, ARIMA provides a flexible and robust forecasting approach.

Justification for Chosen Methods

Considering Best Homes Inc.’s context, the classical decomposition method is particularly appropriate due to the expected seasonality in housing demand. Housing sales often peak during certain months of the year, influenced by weather, tax cycles, and market trends. Combining this with exponential smoothing or ARIMA models can provide a comprehensive forecast that accounts for trend, seasonality, and irregular fluctuations, enhancing accuracy.

Use of the Classical Decomposition Method to Forecast 2016 Demand

To forecast the average demand for 2016 by month using the classical decomposition method, the following steps are undertaken:

1. Data Collection

Historical demand data for Best Homes Inc. is gathered, typically monthly, covering multiple years to identify long-term patterns.

2. Decomposition of the Time Series

The historical data is decomposed into trend, seasonal, and residual components. This involves calculating the moving average to estimate the trend, then extracting seasonal indices by averaging seasonal variations over several years.

3. Trend and Seasonal Components Estimation

The trend component is projected forward based on observed patterns, while seasonal indices are used to adjust the trend forecast for each month.

4. Forecasting

The forecast for each month in 2016 is generated by combining the projected trend with the seasonal indices for that month.

5. Results and Interpretation

The resulting forecasted average demand for each month provides a detailed monthly outlook for 2016, assisting in inventory planning, resource allocation, and strategic marketing.

Since actual historical data is not provided here, a hypothetical example illustrates the process. Assume the historical data shows seasonal peaks in spring and summer, with troughs in winter. Applying the classical decomposition, the forecasted demand for January 2016 might be lower due to typical seasonal dip, while June and July would show peaks consistent with historical summer demand spikes.

Conclusion

Choosing the appropriate forecasting method is crucial for Best Homes Inc. to plan effectively for 2016. The classical decomposition method is particularly suitable due to its strength in handling seasonal variations, which are common in housing demand. When combined with other techniques like exponential smoothing or ARIMA, it enhances forecast accuracy and reliability. Implementing these models requires comprehensive historical data and analytical expertise but offers significant benefits in strategic planning and operational efficiency.

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.

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications. Wiley.

Brockwell, P. J., & Davis, R. A. (2016). Introduction to Time Series and Forecasting. Springer.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting. Wiley.

Bloomfield, P. (2004). Time Series Data Prediction and Extrapolation. Springer.

Sarkar, S. (2014). Statistical Methods for Forecasting. Oxford University Press.

Makridakis, S., & Hibon, M. (2000). The ARMA model for time series forecasting. International Journal of Forecasting, 16(4), 521-532.

Box, G. E., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.

Chatfield, C. (1989). The Analysis of Time Series: An Introduction. CRC Press.