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(Forecasting and ERP) - Case Study Example

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As one can see from the table, the Company can use the given data for both, time-series as well as causal forecasting method. As data is available for 17 months, it offers the using of causal forecasting for comparison from March 2007 to July 2007 with corresponding figures…
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Case Study (Forecasting and ERP)
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Download file to see previous pages As taken from the case study, the reason of intermittent demand design is the variation in the customer demand because of the internet. Customers are well aware about the arrival in the market of new models of air-conditioners. COOL Air-Cond. is not financially strong enough to keep a stock of all latest models of the air-conditioners. For measuring the changing customer demands, the Company should approximate the customer requests by the co-efficient of variation of the demand of customers seeking new models. Customer requests for different new models of wide range air-conditioners are the root cause of erratic demand. For this, it is preferable to employ a totally time series approach, which is normally connected to a demand distribution so that stock levels could attain increased percentages of target customers.
C. For approximating forecast error rates, Bill should use Croston’s method, which fulfils the intermittent demand needs. It is a relatively correct way in terms of mean square errors when the average demand interval crosses 1.25 review times. It is noticed that forecast perfection in totality is strong to the selection of break-points above 1.25 but very less to values below 1.25 (Boylan & Syntetos, 2008).
D. In such a scenario when forecasts can be regularly intermittent, high or low, in the absence of any early pointer, demand distribution measure can be the most suitable alternative. It is because it can help in setting stock levels for achieving high percentages in accurate forecasting. It is done by making distributional assumptions of demand by classifying demand as ABC. For instance, A and B product types could be normally distributed as their demand is forecasted accurately but C product types could be Poisson. In actuality some product types have relatively erratic demand than Poisson, which is scattered. By using the Poisson dispersion index, which is the ratio of ...Download file to see next pagesRead More
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