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Got asked what would happen to inventory when the number of stocking locations change. I thought for a minute and remembered a quick estimate. The Square Root Law states that total safety stock can be approximated by multiplying the total inventory by the square root of the number of future warehouse locations divided by the current number.
X2 = (X1) * √ (n2/n1) n1 = number of existing facilities n2 = number of future facilities X1 = existing inventory X2 = future inventory
Here’s an example: Current inventory is 4000 units, 2 facilities grow to 8. Using the square root law the future inventory = (4000) * √ (8/2) = 8000 units.
Where do you want to work? If you’ve ever been to Disney or Epcot you may remember the experience. You park your car or get off the tram and you don’t need to be able to read a map, or the signs, you just follow the walkways and it just seems obvious where to go and how to get there. A ton of science goes into making the experience at the Magic Kingdom completely different from that of the traveling carnival that shows up at the edge of town in the empty lot next to the volunteer fire house. At the carnival you can’t get from here to there, if you even can figure out where there is. Kids may have a bunch of fun at either, but the parental stress level is completely different between the two amusement parks.
Which of the two do you want your office or shop floor to be like, Disney or the carnival? If Disney is your destination, how do you get from here to there?
Making the abnormal viable, finding where the time is piling up isn’t easy, but that’s the mission of the visual office; making the piles of time visible, and then once we can see the inventory of time we just might get uncomfortable and creative and go do something to reduce the inventory, and speed up the flow.
Here are a couple references …
 Simulation is the act of imitating or mimicking the behavior of some situation or some process by means of something suitably analogous. The imitation of a process can be used for debugging, and validating process design changes or use to communicate or train associates.
Simulations can be used for:
Sometimes the simulation is role playing theater, other times the ‘game’ has logic and is reproducible, with known inputs and expected outputs. The photo here is of a recent workshop where we studied how the warehouse dock floor would look after changing the pick waving rules and packaging. Here outbound goods will be switching from trailer loose stack to returnable shipping containers. Would we need more floor space? Do we have enough pickers and loaders? How do we pick and load multiple deliveries nose-to-tail? While computer modeling is certainly a consideration the use of table-top simulation has many benefits:
Here’s the process we used to build our ‘war game’:
Once the model ‘behaved’ like the current process the team began introducing rule changes which uncovered obstacles. One of the first changes was reducing the wave batch size from 90 minutes to 30. Next came changes to packaging and trailer loading. By the end of the workshop new procedures were debugged and ready for full scale dry runs leading to a live implementation next month. Stay tuned … (From David McPhetrige, founder of TopDown Lean Systems, LLC, providing correct, comprehensive, multi-attribute safety-stock analysis, http://topdownleansystems.com.)
Surveys indicate that even world-class companies have average forecast accuracy in only the high 70%’s, especially at the SKU or component level that makes or breaks service levels and financial performance. Contributors to forecast inaccuracy include factors that can be addressed through best practices, and factors that will always be unpredictable:
The reality is that forecasting can be only so accurate, in part because it predicts the timing and magnitude of only three types of variation:
A forecast can predict the timing and magnitude of these variations only so well. Realistically, then, many businesses – and even world-class companies – are left with a significant forecast-accuracy gap that, if not bridged, compromises target fill rates, inventory performance and financial goals. So first, how do you bridge this gap today? Obviously, you strive continuously to improve your forecasting accuracy, and you always will. That said, though, here are some bridging “techniques” that I’ve observed and even been part of, and likely you can think of some that I’ve missed:
Some of these techniques can and do help achieve service-level targets. But at a minimum, they rarely help, and may in fact hurt, financial performance. Fortunately, a financially-beneficial service-level bridge from forecast to reality is right there in your data! How so? Well, there is still variation that’s left over after bias, unforeseeable special causes and the three variations with predictable timing: Common-cause random variation in demand (or usage, for components) and replenishment lead time. The good news is that it is possible to quantify the magnitude of random variation by analyzing historical demand and/or lead-time data. Of course, the timing of random variation is unpredictable (that’s what makes it random), and this means that common-cause random variation must be addressed with properly-determined safety stock. At this point, you may be saying, “I’m already doing that. I use a statistical technique to determine my safety stock levels.” Forgive the blunt and clichéd reply, but – how’s that working for you? Honestly, is your technique consistently achieving your fill-rate targets on an item-by-item basis? Or does it often seem to put too much inventory in place? And in many cases, does it put too little inventory in place, and you have to subjectively override or increase the calculated safety-stock level? The fact that your safety-stock calculation is unreliable does not mean that there is no statistical solution. What it does mean, however, is that you must
We aren’t talking about a spreadsheet, or perhaps an unused ERP feature. We’re talking about outside expertise in safety-stock analysis. A recent CSCO Insights executive brief (from Supply Chain Digest and Cognizant) entitled “Five Strategies for Improving Inventory Management Across Complex Supply Chain Networks” recommends this as the first of five strategies: “Get Much More Granular with Safety Stock Management.” (http://www.scdigest.com/assets/reps/exec_brief_network_inventories.pdf.) This brief advises using “many more attributes associated with each SKU.” The result of this expanded effort is compelling: “The greater the [safety-stock] precision a company will have.” The CSCO Insights authors advise that to do this “obviously requires a lot more work,” and that this increased analysis offers “rich dividends.” The brief concludes that pursuing the strategy of a “higher level of safety stock management” may require a “relatively uncommon skill set” that may best be provided by outside expertise. 
Euclides A. Coimbra and his associates at the Kaizen Institute have created a wonderful and detailed work on the application of continuous improvement to supply chains. Here is a full exploration and application of lean from end to end of the extended value stream. Two thumbs up! Some of the graphics look to once have been powerpoint and when reproduced are to small and grainy to be able to read. There isn’t an index so finding topics is limited to the table of contents. The book is hard bound, and printed on good paper. Some of the vocabulary is odd; “border of line” might be better said as” interface” or “borderline”. Economic Order Quantity, or as referred to in this book, Wilson’s Formula, is treated in a refreshing way.
For a more in depth review check out Jon Miller’s posting on Gemba Panta Rei, |
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