Please give your opinion

To decide how much product to stock, a retailer makes different forecasts. Different types of forecasting require different types and quantities of data. One method is to select a forecasting model appropriate for the identified and trending patterns in the data. Trends can tell you about changes in demand for the product over time. Secondly, the retailer can use qualitative forecasting to adjust stock levels based on the previous year's output and sales. This type of forecasting uses expert judgment based on estimation methodology rather than numerical analysis (Vend Retail Blog, 2018). The third method may be quantitative forecasting which applies mathematical modeling. Such methods are objective and can handle large amounts of data to generate the forecast. These methods are generally better than qualitative data, provided that accurate data are available. The fourth methodology is closer to the quantitative approach. Time series analysis uses a model to estimate future values based on previously observed data (Vend Retail Blog, 2018).

Consequences: Over-forecast

If the forecast exceeds the actual, the retailer makes an error of over-forecast. High volumes will result in more staff requirements that may require more work on the restructuring process. They may also end up having an overstock of supplies, which will affect storing costs. It will all cost the retailer considerably.

Consequences: Under-forecast

If the forecast is lower than the actual value, the retailer makes an error of under-forecast. If you estimate the least demand for the product, and suddenly you need a lot of amount to sell, there is no guarantee that the manufacturer will meet the demand. The suppliers need to bring in extra resources, which will be troublesome (Sharp, n.d.).