{"id":7735,"date":"2022-10-27T09:11:04","date_gmt":"2022-10-27T13:11:04","guid":{"rendered":"https:\/\/smartcorp.com\/intermittent\/blanket-orders-copy\/"},"modified":"2022-12-20T14:46:48","modified_gmt":"2022-12-20T18:46:48","slug":"voorraadbeheer-temidden-regime-verandering","status":"publish","type":"post","link":"https:\/\/smartcorp.com\/nl\/inventory-optimization\/managing-inventory-amid-regime-change\/","title":{"rendered":"Voorraad beheren te midden van regimeverandering"},"content":{"rendered":"

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\u200bIf you hear the phrase \u201cregime change\u201d on the news, you immediately think of some fraught geopolitical event. Statisticians use the phrase differently, in a way that has high relevance for demand planning and inventory optimization. This blog is about \u201cregime change\u201d in the statistical sense, meaning a major change in the character of the demand for an inventory item.<\/p>\n

An item\u2019s demand history is the fuel that powers demand planners\u2019 forecasting machines. In general, the more fuel the better, giving us a better fix on the average level, the volatility, the size and frequency of any spikes, the shape of any seasonality pattern, and the size and direction of any trend.<\/p>\n

But there is one big exception to the rule that \u201cmore data is better data.\u201d If there is a major shift in your world and new demand doesn\u2019t look like old demand, then old data become dangerous.<\/p>\n

Modern software can make accurate forecasts of item demand and suggest wise choices for inventory parameters like reorder points and order quantities. But the validity of these calculations depends on the relevance of the data used in their calculation. Old data from an old regime no longer reflect current reality, so including them in calculations creates forecast error for demand planners and either excess stock or unacceptable stockout rates for inventory planners.<\/p>\n

That said, if you were to endure a recent regime change and throw out the obsolete data, you would have a lot less data to work with. This has its own costs, because all the estimates computed from the data would have greater statistical uncertainty even though they would be less biased. In this case, your calculations would have to rely more heavily on a blend of statistical analysis and your own expert judgement.<\/p>\n

At this point, you may ask \u201cHow can I know if and when there has been a regime change?\u201d If you\u2019ve been on the job for a while and are comfortable looking at timeplots of item demand, you will generally recognize regime change when you see it, at least if it\u2019s not too subtle. Figure 1 shows some real-world examples that are obvious.<\/p>\n

\"Figure<\/a>

Figure 1: Four examples of regime change in real-world item demand<\/p><\/div>\n

 <\/p>\n

Unfortunately, less obvious changes can still have significant effects. Moreover, most of our customers are too busy to manually review all the items they manage even once per quarter. When you get beyond, say, 100 items, the task of eyeballing all those time series becomes onerous. Fortunately, software can do a good job of continuously monitoring demand for tens of thousands of items and alerting you to any items that may need your attention. Then too, you can arrange for the software to not only detect regime change but also automatically exclude from its calculations all data collected before the most recent regime change, if any. In other words, you can get both automatic warning of regime change and automatic protection from regime change.<\/p>\n

For more on the basics of regime change, see our previous blog on the topic: https:\/\/smartcorp.com\/blog\/demandplanningregimechange\/<\/a> \u00a0<\/p>\n

 <\/p>\n

An Example with Numbers in It<\/strong><\/p>\n

If you would like to learn more, read on to see a numerical example of how much regime change can alter the calculation of a reorder point for a critical spare part. Here is a scenario to illustrate the point.<\/p>\n

Scenario<\/u><\/p>\n