The idea behind customer forecasting is quite simple – inventory planning will be more receptive to changes in demand if you factor in your customer’s change in demand. In other words, taking orders from as many as customers as possible and planning your stock-full to avoid stock-out. Just in time (JIT) environment promises to keep business expenses low by keeping excess inventory out of the warehouse, which in return increases profitability. As far as practicality is concerned this technique can fall apart quickly. For example, forecasting for 10,000 products, out of 20 locations, 10 customers, and 12 months is not just unreasonable but almost impractical and it requires an enormous amount of manpower to do such a task – 24M forecasts to be checked each month. Forecasting for such a vast network of locations and products is almost impossible or will be prohibitive from a cost perspective. If you are a small business with let’s say you have 100 customers that are buying 20 units each month, the customer forecast will be so small that there will be too much noise to aggregate it all and produce a reliable forecast.
A better alternative to customer forecasting is to carry buffer stock. It is also quite possible that you end up with combined buffer stock that is far beyond your intended buffer stock level if you add stock forecast form your inflated customers. Also, always consider the demand from those customers too who don’t provide you with the forecast. Hence, the net forecast does little or no help in inventory planning.
Sometimes the statistical forecast is more convenient which has the capability to answer many questions necessary to know for a successful forecast. For example, are you going to collect forecast spreadsheets from each customer or how are you going to combine all data, etc.
Sorting through this kind of data is easy and quick if you have some sort of ERP system implemented, but sometimes additional help is also required to build a dynamic inventory forecast to use this data in replenishment. Some add on tools are also used to utilize ERP data for accurate forecasting.
You do not need to resort to guessing techniques to forecast inventory replenishment, nor should you need a large dedicated team to be doing that for your business. Deploying the right ERP tools for forecasting and making decisions based on their history with you, is key to optimizing your inventory and ultimately the profitability of your business.