Questions
for analysis
Question
1:
Comment
on the forecasting system being used by Yankee. Suggest changes or improvements
that you believe are justified.
Answer:
There
are several weaknesses of current forecasting system: Using only Qualitative
analysis., forecasting figures are based on the meetings with managers, no
mathematical technique is involved, benefits: quick forecast & advantage of
experience of each manager. Demerits are : forecast tend to be over inflated .
Suggestion:
Implementation
of quantitative method like seasonality technique with linear trend equation2.
Using
actual shipment figure, instead of actual demand figures.
Marketing
forecast is based on actual shipment data.
Trying
to adjust for shortages in actual shipment data by anticipated promotions and
environmental and economical changes.
Suggestion:
Focus
on past demand to project future demand.
Forecasting
based on actual demand will help production department to schedule the
production line more effectively.
Provide
a clearer picture to project realistic volume.
Create
more sales and revenue for the company when anticipating the upward trend of
demand.
Prevent
losses when anticipated downward trend in the market.3.
Lack of
communication between Production and Marketing department
Both do
not have accurate forecasting system and have different perception for the
same.
Production
department think that marketing department over inflates forecast.
Marketing
generate unfaithful forecasts by adjusting past shipment and not predicting
future demands.
To
maintain low-cost production, the long-term purchasing agreement is needed in
order to keep the price low for the raw material from suppliers but having it
just there is the price to pay for the company.
Suggestions
Marketing
should develop a forecasting system that reflects both past shortages and
future expected demands.
Meeting
between the two should conduct at the end of each month.
Both
departments should adjust the anticipated demand monthly to avoid unexpected
changes in the economy and shortage of the raw material.4.
Marketing
division may not be optimistic.
Delay
delivery problem was caused due to low productivity of production department.
Current
production is not sufficient to serve customer needs as it is based
on
“adjusted forecast”.
Production
capacity seemed not to be a problem as rake head & bow could be produced
7,000 & 5,000 units per day respectively, compared to the highest sales
record in the last 4 years (month 11 year 1) at 83,269 units.
Inappropriate
inventory management was the major cause of unproductive production.
Question
2:
Develop
your own forecast for bow rakes for each month of the next year (year 5).
Justify your forecast and the method you used.1.
Answer:
Naïve
method
Naïve
forecasts are the most cost-effective and efficient objective forecasting model,
and provide a benchmark against which more sophisticated models can be compared.
For stable time series data, this approach says that the forecast for any period
equals the previous period's actual value.2.
Moving
average method
Moving
average techniques forecast demand by calculating an average of
actual demands from a specified number of prior periods. Each new forecast
drops the demand in the oldest period and replaces it with the demand in
the most recent period; thus, the data in the calculation "moves"
over time
Weighted
moving average method
When
using a moving average method described before, each of the observations used
to compute the forecasted value is weighted equally. In certain cases, it might
be beneficial to put more weight on the observations that are closer to the
time
period being forecast. When this is done, this is known as a weighted moving average
technique. The weights in a weighted MA must sum to 1.4.
Conclusion
The
table gives us the Four-Year Demand History for the Bow Rake and the
demand figures are the number of units promised for delivery each month. Hence
we could not forecast using the exponential smoothing and the trend adjusted
exponential smoothing. There was no
linear increasing or decreasing trend that was evident hence trend analysis for
linear trends had to be avoided. By analyzing the data provided we could observe
a parabolic trend and season variations with demand increasing during the
first 4 months and last 4 months. Using all the different techniques for
forecasting and taking into considerations the error associated with each we
could conclude that the multiplicative model was the best forecasting technique.
