Yankee Fork and Hoe Company Case Study

 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.