The case under discussion relates to the forecasting system being followed at Yankee Fork and Hoe for managing production. The Yankee Fork and Hoe Company manufactures garden tools ranging from wheelbarrows to rakes and trowels. High competition in the market and simple design of the products force Yankee to maintain high quality, yet low prices. Recently, Alan Roberts, the president of the company has been receiving complaints about late delivery from long-time customers. Knowing that maintaining old customers in such a competitive market is crucial for the company’s operations, Roberts hires a consultant, Sharon Place, to look into the matter by focusing on Bow Rakes, a high volume product. Sharon decides to explore how Yankee plans and forecasts the production of Bow Rakes. For this purpose, she interviews Phil Stanton, who oversees production based on sales forecast and the marketing manager, Ron Adams, who prepares the sales forecast. The primary problem that Sharon identifies is the faulty forecast for production and inventory management which then leads to shortages and late shipments.
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The current forecasting method being used by Yankee Fork and Hoe is informal and based on subjective expectations of the managers. First the marketing department prepares the forecast for the next year based on previous year’s shipping data, shortages experienced in the previous year and changes in the economy. Moreover, the marketing department also considers the increase in demand for occasional promotional activities. On the other hand, production department arbitrarily reduces the forecast by 10%, which results in shortages near the end of the year.
There are a number of issues with the forecasting method being used by the company. Firstly, the marketing department has not identified any formal system in place for forecasting demand. Secondly, there is a lack of communication between both the departments in terms of managing inventory and demand. Both, the production department’s concern for keeping inventory low and the marketing department’s concern for having enough to meet the demand on-time are justified. However, there is a lack of communication between marketing and production department which leads to shortages by the end of the year.
Based on the issues identified, we recommend a number of changes to improve the forecasting method in place at Yankee Fork and Hoe. Firstly, the forecast prepared is based on the actual number of products delivered and shortages instead of the actual number of units that were committed to be delivered. The actual number of units promised for delivery depict the actual demand faced by the company in each month while the actual shipment gives a past-looking estimate. Therefore, we have used the data on the actual number of units promised for delivery for each month as a base for our forecast (see exhibit 1 for raw data on demand for the past four years).
Furthermore, the marketing department of Yankee uses data for last year to forecast demand for the coming year. This method relies on the assumption that the demand for products remains constant throughout the year. On the contrary, it is evident from the graph in exhibit 2 that the demand is usually higher at the beginning and end of the year as opposed to that in the middle of the year. This provides evidence for seasonality in demand for Bow Rakes, making the demand overestimated for months in the middle of the year. We have used monthly demand data for the past years to estimate/forecast the demand separately for each month respectively. Finally, it is highly recommended to increase communication and understanding between both the departments for consolidation of production capacity, optimal inventory levels and predicted demand. For instance, the marketing department notifies production about the expected rise in demand just one month in advance which might not be enough for them to meet the target delivery given the capacity of the company.
Based on the above recommendations, we have used the multiplicative seasonality model for forecasting the demand for Bow Rakes into year 5, for Yankee Fork and Hoe.As shown in Exhibit 3, we have first computed the average demand for each year from year 1 through year 4. From the average annual demand, we have calculated the average increase in demand for each year from year 2 to year 4. The average increase in demand each year comes out to be 2,589 units approximately. Hence, we reach the forecasted figure for average demand for year 5 by adding 2,589 to the average demand for year 4, i.e. 45,928.
Now, using the forecasted average demand for year 5, we work back to get the monthly demand figures. As already mentioned, the demand for Yankee Fork and Hoe is seasonal. Therefore, we calculate the seasonal factors for each month by dividing the monthly demand figure by the average demand of the respective year. The seasonal factors have been presented in exhibit 4 below. We have then calculated the average seasonal factors by taking average of the factors for each month of the four different years. Finally, we have obtained the monthly forecasts for year 5 by multiplying the seasonal factors by annual average demand as shown in exhibit 5. Moreover, the forecasted monthly demand for year 5 has been graphically shown in exhibit 6.
In conclusion, the three main problems identified in the forecasting method in use at Yankee Fork and Hoe and the proposed solutions are summarized as follows. Firstly, the marketing department is not using any formal quantitative forecasting method to forecast the demand for the coming year. We recommend the use of a formal quantitative forecasting method for forecasting sales. Also, we have prepared our forecast using multiplicative seasonality method owing to the seasonality in demand and subject to available information. However, if additional information is available, other sophisticated methods such as exponential smoothing can be used. Moreover, the team uses actual shipping data rather than actual promised delivery i.e. demand data for forecasting purpose. We recommend using the actual promised delivery to get a forward looking estimate of demand. Furthermore, there is a communication gap between marketing and production department. We recommend that the production department should have greater understanding of the forecasting method used by marketing department. In addition, the production should be formally scheduled by keeping a proper inventory management system as well as predicted demand in view in order to avoid shortages.
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