Many of you are looking for a correct, comprehensive safety-stock calculation. My company’s approach is based on more than 15 years of development and testing. As we have learned through extensive experience, optimized safety stock requires more than a formula. We use complex demand–data modeling and calculations. We provide a service, not a spreadsheet. You send us your data. We send you the results.
Our safety-stock model is correct and comprehensive, providing optimal safety stock levels for your service-level and financial-performance targets. We include all the factors that affect safety stock and service level, and apply the proper statistical techniques to them. We do not utilize the usual stockout-event-based metric, but the same quantity-based fill-rate criterion that most companies use to measure actual service-level performance during a month, quarter or year. Our calculations represent the right-skewed and sporadic patterns typical of real demand data. Our model also includes past-due demand and its disruptions, probability of past-due-demand cancellation, lead time, reorder quantity (MOQ, EOQ, etc.), package size and reorder-review frequency. Finally, our results provide a high degree of confidence, typically 95%, of consistently achieving your target service levels without costly expediting.
For more details, see our white papers at www.topdownleansystems.com/white.htm. Page 16 of the “Common Safety Stock Calculations” white paper has examples of our safety-stock analysis. Also, see how you do on our Safety Stock Quiz, at www.topdownleansystems.com/quiz.htm.
To demonstrate the power of our approach, we would be happy to calculate and analyze safety-stock levels for a sample of your inventory items at no charge. Send me data on up to 30 of your items, and we will send you results – safety stock quantity and safety stock days for each item. Our analysis also includes each item’s range of expected actual performance for fill rate; average reorder and on-order quantities; average quantity on-hand, days on-hand and inventory turnover; average daily demand and demand activity percentage.
We require this data for each item: Item identifier, target fill rate, reorder quantity (MOQ, EOQ, batch size, lot size, etc.) or reorder frequency, package size (order multiple), lead time, probability of past-due-demand cancellation, days in actual service-level measurement cycle, and as much historical daily time-series demand as possible (three years is best, two is better, one is good). We perform extensive pre-screening on your input data to identify potential issues and to avoid “garbage in, garbage out.”
Send me your contact information via www.topdownleansystems.com/contact.php. I’ll provide you with a file containing input-data examples. Of course, I’ll be happy to explain our model in more detail and to answer your questions, at your request.
David McPhetrige, TopDown Lean Systems
Here’s a diagram of the effect of lead time and demand uncertainty on supply chain strategy. Pull strategy works when lead times are short and high demand uncertainty makes building to forecast wasteful. Dell is an example of this approach. The number of feature combinations is high and customers are willing to wait a few days to get exactly what they want. Push makes sense when lead times are long and demand is stable. Canned soup is an example of where customers won’t wait for the fresh soup to be made and delivered. Books were examples of the push strategy, but are now moving toward pull as print-on -demand and ebook readers proliferate. When demand is known and lead times are short Continuous Replenishment or rate-based supply make sense. Suppliers get point-of-sale data to release shipments on an agreed upon frequency to maintain inventory targets. Throughout the continuous replenishment supply chain production and distribution operate on pull, and push at the retail outlets. Positioning inventory strategically is complicated when lead times are long and demand is uncertain. Strategic Inventory analysis can help sort out how much stock to carry, and where. The point in the supply chain where risks can be pooled or demand aggregated is usually the place for some statistical safety stock.
