Why We Struggle (continued)
We Seek to Balance Value and Cost
Supply chain and inventory management are used to maximize the value of inventory and minimize its cost. One of the primary methods of controlling inventory and trigger responses from suppliers and manufacturing is to use a continuous forecasting process (typically on a monthly cycle). The forecast is updated each cycle and predicts how much is left to be sold in the current time period and what will be sold in future time periods. Netting against the inventories already available, supplier and manufacturing orders are placed based on their respective lead-times. Customers draw from current inventories while more product arrives in advance of their future orders.
Finding Balance Has an Inherent Obstacle
The laws of forecasts dictate that the more detailed the forecast, the more it will be wrong. For the purposes of keeping the correct amount of inventory, we need to forecast a quantity for each SKU for every order cycle. This is an enormous amount of detail to try and get right. When both positive and negative differences are measured, error rates of 30-50% for each cycle are common. and error rates increase as lead-times lengthen. We try to use more data or special software algorithms, but in the end, a forecast is still a guess based on incomplete information. Error rates may temporarily abate, but stubbornly return.
It Makes Inventories Slow to Respond
Because the forecast will have errors in both directions, we are in a constant state of ordering more than needed for many SKUs and too few for others. In fact, it is very rare to order the right amount for a SKU. Many believe that the normal inventory re-order process automatically nets out the mistakes in the future cycles. Those SKUs that have too much inventory will result in a smaller or no re-order. Those that have too little inventory will produce a larger order. While this theory seems good, it ignores the time it takes to correct. At the very minimum, it takes the length of the order cycle plus the lead-time of the product. For many companies this could be months. And in the case of demand being significantly less than forecasted, it could take years to sell the excess.