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Magazine Chapter 4: Q25OQ (page 482)
Sales data for two years are as follows. Data are aggregated with two months of sales in each 鈥減eriod.鈥�
Months | Sales |
闯补苍耻补谤测鈥揊别产谤耻补谤测 | 109 |
惭补谤肠丑鈥揂辫谤颈濒 | 104 |
惭补测鈥揓耻苍别 | 150 |
闯耻濒测鈥揂耻驳耻蝉迟 | 170 |
厂别辫迟别尘产别谤鈥揙肠迟辞产别谤 | 120 |
狈辞惫别尘产别谤鈥揇别肠别尘产别谤 | 100 |
Months | Sales |
闯补苍耻补谤测鈥揊别产谤耻补谤测 | 115 |
惭补谤肠丑鈥揂辫谤颈濒 | 112 |
惭补测鈥揓耻苍别 | 159 |
闯耻濒测鈥揂耻驳耻蝉迟 | 182 |
厂别辫迟别尘产别谤鈥揙肠迟辞产别谤 | 126 |
狈辞惫别尘产别谤鈥揇别肠别尘产别谤 | 106 |
a. Plot the data.
b. Fit a simple linear regression model to the sales data.
c. In addition to the regression model, determine multiplicative seasonal index factors. A full cycle is assumed to be a full year.
d. Using the results from parts (b) and (c), prepare a forecast for the next year.
Short Answer
Forecasting is the act of predicting past or future demand, supply, and pricing within an industry.
Step by step solution
(a) Plot the data
A tracking signal could be a measurement that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand.
(b)Simple linear regression model to the sales data
Simple Linear regression is employed to estimate the link between two quantitative variables. you'll use simple linear regression once you want to know:
How strong the connection is between two variables (for example, rainfall and soil erosion).
The value of the dependent variable at a specific value of the independent variable (for example, the amount of soil erosion at rainfall).
a = 123.04 and b = 0.9804
(c) Multiplicative seasonal index factors
The Multiplicative seasonal indexmethod is best for data without trends but with seasonality that increases or decreases over time. It ends up in a curved forecast that reproduces the seasonal changes within the data.
Calculating a seasonal index for historical data that doesn't have a trend. The method produces exponentially smoothed values for the extent of the forecast and the seasonal adjustment to the forecast. The allowance is multiplied by the forecasted level, producing the seasonal multiplicative forecast.
(d) Forecast for the next year
Forecast involves investigating the competition, collecting supplier data, and analysing past patterns so as to predict the longer term of an industry. The forecast for the next year is calculated in column (5) of the table shown below:
Forecast including trend and seasonal = 135.79 x 0.865 = 117.45
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