Yankee Fork and Hoe CompanyCase Study

Yankee Fork and Hoe Company Case Study Case Facts •

Yankee Fork and Hoe Company is a leading producer of garden tools.

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Long time customers are experiencing frequent late shipments because of manufacturing issues which cannot fulfil customer demand.

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Phil Stanton, is in charge of inventory and is concerned about high costs and keeping the inventory low.

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Ron Adams, the marketing manager, is concerned about having enough rakes on hand for timely shipments.

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They both have bias views on how the forecasts should be run.

Questions for analysis Question 1: Comment on the forecasting system being used by Yankee. Suggest changes or improvements that you believe are justified. •

There are several weaknesses of current forecasting system: 1. 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: forecast tend to be over inflated Suggestion: • Implementation of quantitative method like seasonality technique with linear trend equation 2. 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 more clear 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 overinflates 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 reflect 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. 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 3. Weighted moving average method When using a moving average method described before, each of the bservations 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. Additional seasonality model 5.

Multiplicative seasonality model

Conclusion •

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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 seasonal 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.