Accurate forecasts are critical because each extra-percent of forecast error comes with a steep price, literally. Indeed, when the costs associated to forecast errors are usually supra-linear, or put more simply, the costs associated to forecast errors increase much faster than the error itself.
As a simple example, a greater forecast error increases the need for safety stocks and thus working capital requirements. But if the working capital goes too high, bank interests start to rise, leading to even more expensive safety stocks.
But there are also more subtle negative consequences: past forecast errors may lower future accuracy. Indeed, historical demand itself is rarely known, instead, we usually rely on the historical sales data as an efficient approximation of the demand. Yet, this approximation is not perfect. For example, a stock-out prevents any sale to be made for a particular product. Yet, in case of a stock-out, zero sale does not equate zero demand.
For statistical forecasting algorithms, that relies on time-series analysis, it can be quite hard, using the sole sales data, to distinguish a zero sale caused by a stock-out from a zero demand. As a result, a lot of stock-outs (as they lead to lower sales) can be statistically interpreted as a lower demand; which, eventually, generates even more stock-outs.
Increasing your forecast accuracy now is one of the key to increase the forecast accuracy tomorrow. Accurate forecasting is not the destination but the journey.