In this Episode of GAINS On, join GAINS Co-founder Bill Benton as he guides supply chain professionals through the shortcomings and pitfalls of Demand Driven Materials Resource Planning (DDMRP).
(00:00): Hello, my name’s Bill Benton. I’m a co-founder here at GAINSystems. Thanks for taking time to join us today. We’ll be talking about DDMRP, that’s an acronym denoting Demand Driven Materials Requirements Planning.
(00:32): So we’re here to talk about a principle that some organizations, including APICS, are promoting for simplified means of improving planning. One is called DDMRP. Some advantages of things like DDMRP as they relate to improving over baseline MRP, which includes usually a fairly simplistic finished goods forecasting process. So sometimes forecasts are a bit optimistic. As well as you know, they often encapsulate simplistic inventory policy measures like certain weeks of supply for certain inventory classes. There are problems that are inherently complex and failing to embrace that complexity and manage it leads to inferior results in terms of lower than feasible inventory terms, more expediting charges, lower service level to your end customers, less accurate predictions for revenue and budgeting. And because of all this, we think that the slight increase in complexity of an advanced system like GAINS is well worth the investment.
(01:44): So the first point worth mentioning here is this idea of multi-horizon planning. So we have the concept of, for example, of frozen slushy and then highly variable liquid period. So in order to solidify transportation plans or solidify sequencing on the floor and manufacturing execution, you might want to freeze your supply plan over a one or two week horizon. Secondly, you might have a slushy period where it varies, plus or minus some amount. And then lastly, you’re gonna have completely liquid. And within DDMRP, it’s presuming that that these things are, are equally variable across time. So we think this is one area where advanced planning can be very helpful. Secondly, there’s the concept of supply sensing. So there is lead time variability and it’s important to account for that.
(02:46): DDMRP doesn’t always do that. Base level core supply variability management, it’s more based on demand variability. But even if that is included, supply variability can be parsed in the segments across time. So, for example GAINS has AI algorithms for supply sensing, and those algorithms can actually reduce variability over the short run and thereby reduce your need for working capital and therefore increase service or reduce inventory of both. So this is quite important element as well that DDMRP doesn’t manage. Third, we have the concept of looking across different available inventory before executing more production or purchasing of the given item that appears to be below what’s needed.
This can include things like redistribution of excess from other locations, where handling and transporting that excess costs less than holding it, or less than expediting into this particular site could look, look at alternate products where they might be slightly higher cost, but available and much lower cost in acquiring new material, rather than using what’s available. These things are very important. And they’re related to the fourth element here, which is potentially looking into alternative supply sources. So you might have other production lines that could produce something, you might have different suppliers. It might be higher cost, but shorter lead times, it could provide fill and demand for purchasing. All of this nuance and opportunity really is not typically available in any kind of DDMRP model at all is to be done supplemental, usually in Excel.