Raj Govindarajan asked the following question:
Your blog on Safety Stock Formula was very fascinating. I work in a Retail company and I am trying to apply the safety stock formula to the retail environment. Is it fair to consider “Replenishment Intervals” as “Lead Time”? In other words, for example, I have a lead time of 7 days, but I order every day to the store; so should I consider demand variability for 7 days or 1 day?
As you may recall, I am advocating wariness in applying the formula. If you are ordering every day for delivery 7 days later, you are not using the reorder point logic the safety stock formula is based on. With a reorder point, you are only placing an order when your stock crosses a threshold, and the stock on hand at that time is supposed to carry you until the order is delivered.
The question you are faced with, for each item, is “How much do I need to order today to make sure I don’t run out 7 days from now?” The elements you have to make that decision are as follow:
- The quantity on hand you have today.
- The already ordered quantities that will be delivered in the next six days.
- Your sales forecast, with confidence interval, for the next seven days.
The tricky part is the sales forecast. The safety stock formula assumes a consumption rate that fluctuates around a constant mean. This may not fit your products. To check it out, you need to analyze sales history. Cell phones and artichokes are both retail products, but with different demand structures.
For your products, you need to know whether they are on a trend that is long-term compared to 7 days, and which kind of trend. In addition, is there a weekly pattern in sales? Do your products sell more, or less, on week-ends? Data mining on your sales history can give you the minimum on hand you can expect at the end of six days and the quantity you need to receive on the seventh to avoid running out.
And you have to keep in mind that these calculations are only valid in the absence of earthquakes, hurricanes, stock market crashes, wars breaking out, new product introductions, or any other event that can severs the connection between historical data and the near future.