Executive summary and one paragraph conclusion total is 1 and half paragraph

Best Homes, Inc.: Forecasting

Background

Best Homes is a new home construction company based in Kansas City, Missouri. They build only residential and new homes throughout the United States. They have expanded the Midwest and the West coast and to the south, starting in 1945 the East coast. They build all types of new residential housing, from low-end to high-end housing in the market. Best Homes was a private company until 1958 when the initial public offering began. The company started small but expanded to one of the largest home builders in the United States. The case presents monthly sales data from 2011 to 2015. This data is representative of home builders since we estimated the sales of Best Homes based on a 4% market share of the total sales of new homes in the U.S. from the U.S. Census web site. Thus, the trend and seasonality are in line with U.S. home sales in total. The case explains the problem facing Best Homes in terms of annual planning and the S&OP process. Forecasting is put in the context of how the forecast will be used. Also, sales projections are being gathered from the field, and the case asks students to reconcile those with the forecasts based on historical data. Best Homes competes on the basis of their outstanding brand reputation. Their reputation is gained by building quality houses at competitive prices. The cost per square foot of the house is comparable to that of its competitors, but its design and interior finish are excellent. This provides an advantage that competitors can't find. Show after completion of the building, including the installation of inner walls, floors, windows, siding, cabinets and timber works, to provide a beautiful home. Use part-time or contract for other parts that are not marked upon completion. However, workers who perform foundations, rough walls, roofs, wiring and piping. In each of these areas, however, they employ more than 60 percent of full-time workers. All new employees, part-time or contract employees are assigned full-time employees and receive quality control training for their work during the first six months.

Objective

Financing uses this to forecast the company's overall revenue and to prepare estimates of revenue and balance sheet forecasts along with quarterly income estimates. Marketing uses monthly forecasts to plan sales forecasts, employment plans, sales incentives and sales targets. Operations and supply chains use forecasts for sales and operational planning (S & OP) planning processes. S & OP is performed for annual forecasts and updated monthly to coordinate sales forecasts and resulting employment plans for new, contracted and part-time employees. Along with the expected dismissal. Each month, the S & OP process starts with an updated rolling forecast every 12 months. The employment plan and the start of housing construction are then set up next month and planned for the next three months. The plan also includes a purchase plan for materials used in construction. Housing. Monthly updates may require adjusting both the capacity and inventory of new homes. All features, including finance, marketing, sales, operations, and HR, participate in the S & OP process. The first part of the planning process is to predict the demand for new homes every month. Shows the number of detached houses built by Best Homes every month. This data is a forward forecast for all of 2016. Predicting average monthly demand alone in the future is not enough. Actual demand may be significantly higher or lower than average. As a result, you should also predict standard or average absolute deviations. The monthly production level of the new house is set to average demand. In addition, if demand exceeds the average, secure a safe inventory of new homes. With three months of lead time to build a new house, all inventory and production levels should expect three months of lead time. This shows an important prediction for both predictions.

1. What forecasting methods should the company consider?  Please justify.

· The company should use time series method

· It suitable for not much data and based on seasonal patterns

· According to the calculation the 12 months moving average.

· The advantages of this method are easy to understand, and the moving average can smooth the estimate that makes the company see the trend.

· The moving average can be calculated by using the previous sale and calculate the average.

2. Use the classical decomposition method to forecast average demand for 2016 by month. What is your forecast of monthly average demand for 2016?

· As a result, the blue trend is a sale forecast and the orange trend is calculated sale with the 12 months moving average

· The linear function from the sale itself is y = 11.758x+1003.4 where r-square is 0.5888

· The linear function from 12 months moving average is 12.442x+925.28 where r-square is 0.9504

· R-square can describe How accurate of the function which has value between 0-100%

· When we compare the 12 months average r-square with the sale r-square the 12 months average r-square is more accurate

· The company should use 12 months moving average function y=12.442x+925.28 where x is a month order start from January 2011 to forecast monthly average demand for 2016

3.      Best Homes is also collecting sales projections from each of its regions for 2016? What role should these additional sales projections play, along with the forecast from question 2 in determining the final national forecast?

· The sale growth rate has dropped since 2012. If the company focus only number of sales which is growth overtime will start to lose market competitive.

· This comparison of growth rate will lead to discussion of developing the company’s marketing, operation, finance, and human resource.

· However, the sale projection from all region will help assuming the overall demand forecast it help to manage the reasonable inventory which can prevent over operation cost and inventory cost.

Moving Average 12 Preceding Months

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 840 880 1120 1200 1120 1120 1080 1000 960 1000 920 960 920 1200 1360 1360 1400 1360 1320 1240 1200 1160 1120 1120 1280 1440 1640 1720 1600 1720 1320 1240 1240 1440 1280 1240 1320 1400 1560 1560 1720 1520 1400 1440 1480 1520 1240 1400 1560 1800 1840 1920 1880 1760 1720 1640 1400 1560 1440 1520 MOV. AVG.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 1016.6666666666666 1023.3333333333334 1050 1070 1083.3333333333333 1106.6666666666667 1126.6666666666667 1146.6666666666667 1166.6666666666667 1186.6666666666667 1200 1216.6666666666667 1230 1260 1280 1303.3333333333333 1333.3333333333333 1350 1380 1380 1380 1383.3333333333333 1406.6666666666667 1420 1430 1433.3333333333333 1430 1423.3333333333333 1410 1420 1403.3333333333333 1410 1426.6666666666667 1446.6666666666667 1453.3333333333333 1450 1463.3333333333333 1483.3333333333333 1516.6666666666667 1540 1570 1583.3333333333333 1603.3333333333333 1630 1646.6666666666667 1640 1643.3333333333333 1660 1670

MONTH

SALES