What really causes a bad forecast? What is the impact to the business when you have a poor or unreliable forecast? I have found that the financial impact can be quite severe when forecasts are not reliable? How about you? I want to use this post to share an example of the impact an unreliable forecast had on one of my clients.
Here is a brief synopsis of the situation. The client is a large manufacturer in the wireless industry. They manufacture their equipment offshore and also warehouse a large portion of their inventory offshore. But, here is where the problem comes in. Because of unreliable sales forecasts, they also had to maintain a large inventory presence onshore. Their poor forecasting not only forced them to warehouse millions of dollars of inventory onshore, but also negatively impacted their supply chain management, causing them to over manufacture, just in case.
“What does 75% closed really mean?”
I was reviewing the forecasting process with one of the client’s sales managers and noticed that every stage of their pipeline forecast had a % associated with it. This is a fairly common practice, but here is where I ran into the real root cause of their forecasting challenge. I selected a stage that was marked 75% and asked, “What does 75% closed really mean?” He couldn’t tell me. There was no clear definition for the 75% stage, nor for any of the other stages as well. Without a clear definition of each stage in the pipeline, how could they have a reliable forecast? How can they tell if an opportunity was truly advancing? How many of you have seen a similar situation?
So what to do? We began by stepping back and looking at their entire sales process model in their CRM system. While they had defined some characteristics of each stage, the definitions were not very clear and left a lot of room for interpretation. We reexamined how they were selling their solutions, how the customer was buying the solutions, and came up with key buying milestones that we could turn into measurable criteria to advance an opportunity through the pipeline. We linked the definition of each stage of the sales process to a stage in the customer’s buying process. We made a clear definition of the criteria necessary for advancing an opportunity to the next stage. With advancement criteria clearly defined, we were ready to improve forecasting accuracy.
We started by reassessing the existing pipeline and more effectively tracking all opportunities. We applied the new criteria to all of the opportunities in the pipeline. As you might expect, there was a lot of movement – most of it to the earlier stages of the sales process. Once the projections were adjusted to match the criteria, the forecast became much more reliable. The monthly and quarterly forecast reliability increased significantly. Communications with the supply chain management organization also improved. And, within one quarter, they were able to reduce the level of on-hand inventory required in the US by approximately 15%, saving millions in inventory and manufacturing costs.
I would like to hear from the followers on experiences they have had and how they assessed the situation and improved their forecasting.