Thanks for the comment, and for the very practical questions! (And sorry for the delayed reply!)

You asked about my solution. I have partnered with Right Sized Inventory, http://www.rightsizedinventory.com, (RSI) to provide a cloud-based SaaS inventory analysis that correctly addresses all 6 real-world issues I mentioned in the blog above. All you need is a browser.

RSI is an objective, statistically-sound, comprehensive Monte-Carlo simulation of inventory behavior for every one of your inventory items, in every one of your locations.

RSI’s result is the optimal inventory level for each item-location: The minimum inventory level that consistently achieves your target service level. The RSI results are also expressed as parameters that you may download and import to your MRP or other replenishment-planning system, such as ROP, safety stock (MRP), Min-Max, kanban or PAR level.

RSI is data-driven, so you must provide data as described on the RSI website. Once you have developed the data queries you need (and RSI can help you), updating your RSI analysis is easy. Just a query extract and an upload.

RSI offers a free trial. Many RSI users are able to be up and running on the free trial within a day, using only the templates and instructions on the RSI website. Others have used assistance from RSI’s customer support at no extra charge.

RSI also offers most compelling subscription pricing. Our goal is to make your ROI so obvious and appealing that you won’t need a long, drawn-out sign-off and approval cycle. We think you’ll agree!

]]>Thanks for your interest! The methodology and resulting analytical capability are fully embedded in a SaaS solution called Right Sized Inventory: http://www.rightsizedinventory.com. As you would expect, it’s proprietary intellectual property. However, the RSI Web site contains a number of the differentiating details. I’ll summarize them here:

Standard academic techniques and formulaic approaches are usually insufficient to produce quality inventory analysis for the following reasons:

• Demand is rarely normally-distributed, so normal-based standard-deviation calculations ignore reality.

• Lead time (LT) is also rarely normally-distributed, so LT variation needs a more sophisticated approach than what you find in typical safety-stock (SS) calculations.

• The common z factor represents a stockout event – more accurately, the probability of no stockout event – but businesses almost universally measure their actual SL performance as a quantity-based fill rate: either unit quantities or order lines filled on time. Of its own accord, the event-based z factor drives an unnecessarily-large multiplier.

• Common sense says that the larger the MOQ, especially when MOQ lasts longer than LT, the less incremental SS is needed. Yet, few formulas even attempt to address this, and the ones that do so aren’t correct. Want proof? A very large MOQ may not require any incremental SS, yet try finding a formula that will come up with an answer of zero.

• Likewise, experience tells us that smaller MOQs drive short replenishment intervals (RIs) and frequent receipts. In turn, these frequent receipts, with multiple receipts inside of a LT cycle, provide a measure of de facto SS. Again, any formula that tries this doesn’t do it optimally.

•A typical deterministic SS formula would provide only 50% confidence. Effective SS analysis needs to enable simple analysis of service-level performance with higher confidence levels.

• Does your unfulfilled demand become disruptive past-due backlog? If so, you know past-due backlog needs more SS. I haven’t found a formula that even tries this one. But you can estimate the probability of demand not fulfilled on time being canceled vs. becoming past-due backlog.

• Each item has a replenishment / reorder interval, or RI. It may be determined by the MOQ, EOQ, lot or package size, etc. Or perhaps your business has a set frequency, replenishing once per week or once per month. RI is not the same as LT. But it is equally critical to the analytical outcome.

• An item’s replenishment method affects its inventory requirements. Many techniques simply use standard formulas to calculate SS or Kanban. But today’s supply chains often embrace demand-driven techniques like Reorder Point or Min-Max. We support 6 different replenishment methods and enable our users to simulate inventory levels with different replenishment methods to find the one that best fits their business.

• A standard formula provides only a single value as an answer. Our analysis uses Monte Carlo simulation to provide a more robust answer and a range of outcomes around that answer. In addition, we provide simple, effective, graphical tools to understand the answer and avoid the “black box” skepticism often associated with optimization answers. Finally, we provide easy-to-use tools to run scenarios and explore the impact that various changes in your supply chain would have on your required inventory.

]]>Thanks so much for participating in this safety-stock quiz!

Yes, you’re right, XYZ’s historical average daily demand = 100, and its historical average lead time = 9.1 days.

I have some questions for you. Please don’t view my questions as argumentative or critical. I’m simply interested in fostering an informative discussion on optimal safety stock and inventory position.

My questions:

1. Why do you say that average inventory quantity on hand should be 1183, which is avg daily demand * avg lead time + safety stock? XYZ’s reorder quantity, or MOQ, is 2000. Theoretically (although theory doesn’t always apply), XYZ’s average QOH should be half of its MOQ + safety stock. This would be 1000 of cycle stock plus your 273 of safety stock, or 1273.

2. What is your logic for saying that safety stock should be 30% of avg daily demand * avg lead time? How do you know that this level of safety stock will achieve XYZ’s target service level of 98% for 11 months out of 12?

3. Why do you say that the minimum QOH would be 700, or minimum lead time * avg daily demand? The minimum QOH will be the quantity on hand just prior to the next receipt. My analysis says that the 2nd-percentile expected QOH will be 180.

4. Why do you say that the maximum QOH would be 1400, or maximum lead time * avg daily demand? The maximum QOH will be the quantity on hand just after a receipt, and the reorder quantity is 2000. My analysis says that the 98th-percentile expected QOH will be about 2600.

I’m eager to know your thoughts on this. Thanks again for joining the discussion!

David McPhetrige

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