Forecasting inventory requirements is a specialized variant of forecasting that focuses on the high end of the range of possible future demand.

For simplicity, consider the problem of forecasting inventory requirements for just one period ahead, say one day ahead. Usually, the forecasting job is to estimate the most likely or average level of product demand. However, if available inventory equals the average demand, there is about a 50% chance that demand will exceed inventory and result in lost sales and/or lost good will. Setting the inventory level at, say, ten times the average demand will probably eliminate the problem of stockouts, but will just as surely result in bloated inventory costs.

The trick of inventory optimization is to find a satisfactory balance between having enough inventory to meet most demand without tying up too many resources in the process. Usually, the solution is a blend of business judgment and statistics. The judgmental part is to define an acceptable inventory service level, such as meeting 95% of demand immediately from stock. The statistical part is to estimate the 95th percentile of demand.

When not dealing with intermittent demand, you can often estimate the required inventory level by assuming a bell-shaped (Normal) curve of demand, estimating both the middle and the width of the bell curve, then using a standard statistical formula to estimate the desired percentile. The difference between the desired inventory level and the average level of demand is called the “safety stock” because it protects against the possibility of stockouts.

When dealing with intermittent demand, the bell-shaped curve is a very poor approximation to the statistical distribution of demand. In this special case, Smart leverages patented technology for intermittent demand that is designed to accurately forecast the ranges and produce a better estimate of the safety stock needed to achieve the required inventory service level.

Reveal how forecasts are used with these 4 questions.

Often companies will insist that they “don’t use forecasts” to plan inventory.  They often use reorder point methods and are struggling to improve on-time delivery, inventory turns, and other KPIs. While they don’t think of what they are doing as explicitly forecasting, they certainly use estimates of future demand to develop reorder points such as min/max.

Regardless of what it is called, everyone tries to estimate future demand in some way and uses this estimate to set stocking policies and drive orders. To improve inventory planning and make sure you aren’t over/under ordering and creating large stockouts and inventory bloat, it is important to understand exactly how your organization uses forecasts. Once this is understood, you can assess whether the quality of the forecasts can be improved.

Try getting answers to the following questions. It will reveal how forecasts are being used in your business – even if you don’t think you use forecasts.

1.  Is your forecast a period-by-period estimate over time that is used to predict what on-hand inventory will be in the future and triggers order suggestions in your ERP system?

2. Or is your forecast used to derive a reorder point but not explicitly used as a per-period driver to trigger orders? Here, I may predict we’ll sell 10 per week based on the history, but we are not loading 10, 10, 10, 10, etc., into the ERP. Instead, I derive a reorder point or Min that covers the two-period lead time + some amount of buffer to help protect against stock out. In this case, I’ll order more when on hand gets to 25.

3. Is your forecast used as a guide for the planner to help subjectively determine when they should order more?  Here, I predict 10 per week, and I assess the on-hand inventory periodically, review the expected lead time, and I decide, given the 40 units I have on hand today, that I have “enough.” So, I do nothing now but will check back again in a week.

4. Is it used to set up blanket orders with suppliers? Here, I predict 10 per week and agree to a blanket purchase order with the supplier of 520 per year. The orders are then placed in advance to arrive in quantities of 10 once per week until the blanket order is consumed.

Once you get the answers, you can then ask how the estimates of demand are created.  Is it an average? Is it deriving demand over lead time from a sales forecast?  Is there a statistical forecast generated somewhere?  What methods are considered? It will also be important to assess how safety stocks are used to protect against demand and supply variability.  More on all of this in a future article.