Oregon Chainsaw Case

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Oregon Chain Saw is a company that produces chain saws which are built with either 17-inch or 21-inch chains. The manager of the factory located in Portland, Oregon, Lee Spencer, is trying to determine how many chains they will need to produce during the next year in order to meet market demand. Additionally, Spencer would like an estimation of the number of workers that will be required for the expected level of production so that they can prepare ahead of time. In the forecast, we were asked to take into account both the chains that are packaged for the replacement parts market as well as those packaged for the production of new chain saws. Within the case we were given the demand per month for the last 3 years. The data is organized into 3 categories: chain demand for replacement market, chain demand for production of new products, and total chain demand. The last information we were given that was pertinent to the case was the time it took a worker to produce both the 17-inch chains and the 21-inch chains, as well as the total minutes a worker would be able to work per month. Given all the data, we had to calculate the forecast using a couple different methods in order to determine which method would give us the most accurate forecast. In the end, we found the Linear Regression method to be the best method, as there was a clear trend with no indication of seasonal influences. This is evident by the data found on the Excel sheet as well as the answers to the following questions. 1. For the replacement parts market of the 17-inch chains, based on its demands over the last three years, suggest a method to forecast its monthly demands for the next year. 1). Display graphically the demand pattern of the past three years. Refer to Excel attachment. 2). Determine and defend your method of forecasting. We used the Linear Regression method of forecasting because

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