- Submitted by: leonthompson
- Views: 158
- Category: Other
- Date Submitted: 02/10/2013 10:50 PM
- Pages: 2
Forecasting inventory is a vital function in most businesses. Businesses can forecast the demand for their product or services using many tools such as on historical data. With proper forecasting of needs such as supplies, revenue, and product required, a business can anticipate the demands of the consumer. Good forecasting will meet customer demand and decrease the cost of unsold inventory. Historical data will also show the increases and decreases in demand of seasonal products such as snow boots.
I will analyze historical data of an organization over a period of four years. Based on this analysis, inventory will be forecast for year five.
I forecasted all four periods, but focused primarily on years three and four because of the huge decline in revenue growth. I used year three as the base year. I used the simple price index formula: (Current Year Price/Base Year Price) x100 (Triplett, Jack E). The base year from year three provided me with an index value of 100 from my calculation’s. I could determine that the yearly average index has shown a consistent yearly increase for the past four years: Year one average was 38,113, and Year Two average was 40,586, which showed a 6.49% increase over the year one. Year Three’s 44,802 average had a 10.39% increase over year two. Finally, year four average (45,896) held a percentage increase of 2.4% over Year three (44,802).
The source of data I used to compile my information came from University of Phoenix student portal. The winter historical inventory data covered a 12 month timeframe over a four year period. I could obtain the average of each year by adding the actual demand of each month and dividing the number by 12. This gave me the median average from year three’s base, which I increased by 4.8% for each month that gave me my year five forecast for each of the 12 months. I used 4.8% because it was double (2.4% increase) from the last two years of historical data. The moving average over the last...