Many times the responses to a blog post are more valuable than the original post itself, especially when the original post poses a question. In the case of “How accurate does the forecast need to be?” that was certainly the case. The following are some “nuggets” from the responses received to the original post that are worth sharing. Stephen Mills (who responded to the LinkedIN version of the post) talked about using what we know about past relationships and other key variables that may be in the future to determine what sales, demand and production should be. It’s the “shocks” to the forecast that can’t be built into the model. How you respond to the shocks will determine the impact of an inaccurate forecast. Running scenarios to determine the impact of “future shocks” to your replenishment times, inventory policies and customer relationships, etc. all play a factor on how accurate the forecast needs to be. Stephen also supplied the best quote related to forecast accuracy,
“Forecasts are either wrong or lucky.”
Stephen points out that a robust end to end supply chain will ensure that an inaccurate forecast doesn’t mean bad luck for the business. It’s only one piece of the puzzle. Another respondent also pointed out that forecast accuracy was only one piece of the equation. This response also talked about forecast communication. Communication between functional partners on everything from market trends, process improvements and “shocks” are discussed in a timely manner so adjustments can be made in time to improve the business. Some relevant examples included the case where demand for a product may be unexpectedly soft, so marketing may shift promotions to help on the sales and supply chain side. Finance would also be in the loop so they could adjust their balance sheets. I believe overall respondents agreed that the need for the forecast to be accurate is a function of such factors as the cumulative lead time, safety stock policies and flex capacity. Continuous improvement activities around lead times and quality will take some of the burden off those responsible for developing the forecast. Operational excellence and the ability to respond to “shocks” are a competitive advantage when unexpected demand opportunities present themselves. One response pointed out that this introduces an element of "time" to this issue of forecast accuracy. How good the forecast needs to be will be dependent on the range of the forecast and service level policies, especially on critical lead time items. As pointed out, forecast should not be left on its own but accompanied by all the background and risk information so demand plans can be set, supply rationalized and plans easily re-evaluated if the “shocks” hit. This was only a small sample of some great insights from the blog responses. Thanks to all those who participate in these discussions. It really is worth it!