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and add the sales numbers to chronicle the past twenty four months. Clearly

product one was the best-selling apparatus, and product three, four and five

where sales laggards.

Entering the information into spreadsheet form was also necessary to

present the eight products in graphical form. Of the following graph types that

where at my disposal (line, bar, circle) to clearly illustrate the upward and

downward trend of each of the eight product I chose the line graph method. A

circle graph is good percentage comparisons or comparison of market share. Bar

graphs can illustrate a snapshot in time but can distort trend data.

At this point our class gathered into groups to discuss which product to

discontinue. Obviously product one was not going to be of the discontinued

products, since it was our volume leader. Based on the sales figure for the

past twenty-four months my group decided to eliminate products three, four and

five. Also, products three, four and five had the highest minimum direct costs

as well. Since these products where expensive to manufacture and where our

lowest selling products a group decision was made to discontinue these products.

The discontinued product was then rolled over into a new product, now

referred to product nine. Unfortunately, we where unable to decide by the

information given if any of the discontinued products was a high margin product,

low volume product (IE 50? big screen color Trinitron tube with oak cabinet and

stereo sound).

Moving right into our next step we began to analyze our bar charts to

make our starting forecast. We viewed sales from each product to see if they

fall under one the following situations: Base (Base + Trend) (Base + Trend) *

Seasonality

When a product is base the sales alter little each sales period or change

erratically with external market signals. An example of a product that would

fall under the base model would be sand bags. Sand bags sell at the same level

month after month. If a retailer sells a hundred bags in March the will sell a

hundred bags in October. But, in a flood plain after terrantiel downpour, the

sales of sandbags increase exponentially. This is because many people purchase

the sandbags to hold back the rising flood waters. Another example of a product

that would emulate the base model is insulin. There is a limited number of

people with insulin dependant diabetes. The people with insulin dependant

diabetes unfortunately die off, but are replaced with other people who fall ill

to the disease. There is very little movement up or down in the sale of insulin.

The base plus trend model illustrates that a product has a trend of

upward or downward groth in sales. Products at the begining or ending of their

respective product cycles will display this type of performance. Sales of a new

product such as Microsoft Windows95? disk operating system will fall into this

category. The sales of May are expected to be larger than April, the sales of

April will be larger than March and so on. While the sales may decline (or

increase) during a particular time frame, a trend of upward or downward growth

will be apparent.

Lastly, the base plus trend times seasonality attempts to forecast the

swings in demand that are caused by seasonal changes that can be expected to

repeat themselves during a single or consecutive time period. For example,

florists experience a predictable increases in demand each year, both occur at

similiar (or exact) times during the year; Mothers Day and St. Valentines Day.

Florists must forecast demand for roses and other flowers so they can meet this

predictable demand. If I where to construct a ten year historical graph for a

neighborhood florist, there would be clear increase in demand every February and

May, in every one of those years. A caveat to the previous example would be

that in most lines a business forecasting is never this easy. If it was there

would not be a production management class or operations management science!

Some other methods used to forecast demand are: delphi method,

historical analogy, simple moving average, box Jenkins type, exponential

smoothing and regression analysis. Forecasting falls into four basic types:

qualitative, time series analysis, casual relationships and simulation. All of

the proceeding have pluses, minuses and degrees of accuraccy. I often depends

on the precision of previous data. Also, as is often stated in financial

prospectuses ?past performance does not guarantee future results?.

For product one I used base plus trend. The sales started of at 1246

units and gradually increased to 2146 at the end of twenty four months. There

was a slight dip in sales between month nine-teen and month twenty three. This

drop can from internal or external variables.

Product two was little more tricky. The swing where eratic and showed

no detectable trend. I may have been able to use (Base + Trend) * Seasonality

if there was not a decrease in sales from month eight and an increase in sales

in month sixteen. For this I had to employ the base or simle method.

While I find it hard to comprehend how television sales can be seasonal,

products three, five and six fall under (Base + Trend) * Seasonality models. I

was able to replicate the wave in demand with my forecast. Perhaps consumers

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