Forecasting Estimated Potentials

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I need to understand the case study below and define the central issue, select forecasting and estimating tools for consideration, analyze considerations and determine their relative importance; recommend and defend the best technique to use; Estimate potentials and forecast sales for the organization featured in the case below:

Two engineers, Bill Parks and Anne Smith,
founded Parker Computer in 1983. The company
specialized in the manufacture of high-end
personal computers and low-priced workstations for
product design and other business applications. Bill
was chairman of the board, and Anne was director of
research and development. For the first 10 years of its
life, Parker enjoyed steady growth in sales and profits.

Parker's success was based on providing customers
with superior computer performance at prices slightly
above average. However, in 1997 aggressive price cutting by large competitors began to erode sales growth. Parker's revenue peaked in 1998 at $75 million.

SOLVING PARKER'S PROBLEMS
Although customers were willing to pay for high-quality
computers in the 1980s, this strategy did not attract
many buyers in the cost-conscious 1990s. Bill Parks
realized that the company had to do a better job of both marketing and cost reduction. The company currently employed a small sales force but relied primarily on a network of local dealers to sell its computers to the business market. Bill knew that the company needed a stronger customer focus, so he hired a CEO with a marketing background. As a result, the company started to pay more attention to marketing activities and began to prepare detailed marketing plans for each product line.

Jane Austin, a recent business graduate, was hired as a
marketing assistant to help with the planning.
Part of Jane's responsibility was to estimate sales
for the PC220 and PC440 computers for the next year.
In the past, these forecasts had been developed using
judgmental procedures. Jane knew that the CEO
expected a more thorough analysis of sales trends for
the 2000 marketing plan. When she was in school,
Jane had become familiar with the use of computers to
predict future sales. This seemed to be a good time to
make use of her computer expertise.

ENTERING THE DATA
Jane entered the quarterly sales data for PC220 from
Exhibit 1 in the first 12 spaces on her spread sheet.
Sales for 1997 were entered as observations 1 through
4, sales for 1998 were entered as observations 5
through 8, and sales for 1999 were entered as observations 9 through 12. Sales figures for PC440 were
entered in a separate column. As a first step in analyzing the data, Jane thought she would plot the sales figures to see what trends were evident. Sales were placed on the Y axis and time was plotted on the X axis. Next she decided to calculate some quarterly seasonal indexes for the two products to see if seasonal adjustments were needed.

SELECTING FORECASTING METHODS
The first method Jane tried with the computer sales
data was the naive approach. Sales in quarter 1 were
used to predict sales in quarter 2, then sales in quarter 2 were used to predict sales in quarter 3 and so on until all the periods had been predicted. Once she had forecasts for 11 periods, she could calculate the average

Quarter PC220 1997 PC440 1997 PC220 1998 PC440 1998
1 1950 770 3150 545
2 2920 620 2600 450
3 2560 623 3002 400
4 3330 830 4250 639

Quarter PC220 1999 PC440 1999
1 2924 350
2 3380 420
3 2554 310
4 2800 775