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Supply Chain Expert Video Series
GAINS ON is a video series featuring GAINS Co-Founder Bill Benton sharing his supply chain insights based on decades of industry leadership in supply chain planning. GAINS ON allows viewers to learn about the latest advances, processes, and insights that can make your supply chain planning function more impactful and effective. It’s a masterclass for anyone wanting to learn about supply chain planning.
The on-demand video series includes insights on topics including:
- Demand Driven Materials Resource Planning (DDMRP)
- Inventory Optimization
- And more
Check back often for new videos to help you master the art of supply chain planning, or follow GAINS on LinkedIn to see the latest GAINS ON videos.
About Bill Benton:
Bill Benton, widely recognized as a visionary in the supply chain industry, is known for rolling up his sleeves and solving complex supply chain challenges. He is passionate about bringing practical innovation to supply chain planning using ML and AI. Bill has received several awards for his professional achievements and is a frequent speaker at the Gartner Supply Chain Symposium and other industry events.
Optimization as a Service (OASIS)
In this installment of GAINS On: Co-Founder Bill Benton discusses GAINS Supply Chain Optimization as a Service (or OASIS) offering. What it entails, how most companies can benefit from its offerings, and why OASIS is able to offer greater abilities at a lower cost than doing it yourself.
(00:05): Hello, my name's Bill Benton. I'm the Co-Founder here at GAINSystems, and I wanted to talk about a concept we call OASIS.
(00:32): You might have heard the term pass planning as a service or BPaaS business process as a service. This is a flavor of that that's intended to provide maximum results with minimum time and complexity. And this alone can help, optimizing this can help reduce working capital by 15 to 20% and/or reduce expediting and/or increase service results. So, so the first thing here is what is my inventory policy? And that's gonna be in two parts. One is what is the optimal amount of service or safety stock that you should hold by product or SKU or part? And then within that, across each location, and then how much should you replenish? What's your order size or order quantity? The second thing we look at is combined, do you stock, and if so, at what service level. So there we're talking about what is your deployment strategy?
(01:35): Do you have every item, part, or product at every location, or do you say withhold that, and have some I items only at higher levels, right? And not at the end points or point of sale or consumption. And then if you do stock it, what is the optimized service level or availability of that item? So as you probably know, the, as you increase availability or service level, your need for inventory goes up at an increasing rate. At the same time as you increase service level, certain costs go down like expediting or penalties or loss sales or premium purchases from alternative suppliers, all that goes down. There's some point that's optimal. The last two elements that are delivered as part of OASIS, our flow optimization and what we call multi-echelon optimization, these aren't necessarily applicable to everyone. Flow optimization is applicable where you do have the ability to work around tiers in a multi-tier network.
(02:38): What flow optimization does is determines not just, you know, periodically after a design project once every four years, but continuously where you should change flows. Do you want to go direct from the supplier or the plant to the DC bypassing the hub? Do you want to go from the hub to the branch or point of sale or field service location bypassing the DC? When do you want to do that? Maybe it's seasonal, maybe it depends on what your minimums are, what your transportation dynamics are, frequency of delivery. We take all that into account and give you a simple output, simple to implement. Not simple to define, but simple implement, which is what are your sourcing rules and when do they change, right? So this can be simple, which is we just want to do it once a quarter. We don't need seasonal profiles or we wanna really look at it and truly optimize in depth.
(03:35): So the first of five benefits or advantages of OASIS versus a traditional implementation or transformation project is the ability to move at pace. So as I alluded to earlier, we look for very raw data inputs, simple demand transactions, supply history as it relates to work orders or purchase orders or transfer orders. And with these data, we'll mine all of that to create what we need to determine the optimum inventory policies. So this can be done periodically. Usually it can be pulled without IT resource and even allow it to be done through analysts and, and not IT resources. And then secondly, how do we minimize the amount of data going back? As I alluded to earlier, there can be as few as two fields that we're passing back and we can do a Pareto approach by looking at every period that can be monthly or even as infrequent as quarterly.
(04:39): What are the top X percent of items that give you 80% of the benefit so that we're not constantly doing a lot of updating for marginal improvement, but really focusing any effort on the maximum leverage changes. And in that fashion, again, we can defer or even avoid IT involvement given the scarcity of those resources in updating your current planning system. Or planning methods could be Excel, or reorder point, as simple as that, with these optimum values. The second advantage of a process like OASIS is benefits realization. And this comes in sort of three distinct elements. One is work out, right? So if this doesn't work, we know you're not gonna continue it. You're not, you haven't sunk into a long-term commitment for a solution that has to get integrated and work through several stages before you can use it. So we, we know we have to deliver not just output, but results.
(05:40): Secondly, it's extremely streamlined by virtue of the fact that we know how to do this. We've done this hundreds of times and know how to deliver quickly and focus, more importantly, focus on those things that are really gonna add value rather than, say, merging it all together in one large transformation. And then lastly, depth. So I mentioned multi-echelon, I mentioned optimizing service level. These, these are things that probably you simply aren't capable of quickly, but something that we could do faster, right? So that still might be a phase two of OASIS, but phase two might come in half the time of a phase one of a traditional deployment. Third element is skill sustainment or supplementation. So the skills required to optimize inventory, optimize flows, et cetera. These are fairly rare. They are sometimes hard to retain. And for medium and even larger organizations, it's hard to have a team that stays interested and does this month by month in the same context.
(06:59): So we at GAINSystems have a large and growing team of engineers and decision scientists that revel in this and, and have a great variety of tasks across industries and within industry, across companies. So we have a dependable team that has the skills that we have sustained that's ready to deploy rapidly. So this blends with some of the earlier benefits we discussed, but in terms of complexity, by preventing a long system acquisition and selection process, myriad approvals, by looking at this simple consultative service we can, in fact, streamline the point to making a decision and getting results started fast. On another complexity note, we minimize the IT integration and the amount of data because we can do a lot of data fill and we can estimate and we can use parameters and heuristics to avoid having to burden your IT organization or your planning organization with dozens of decisions before you get your first output.
(08:09): So with our immersed team, lastly, we'll talk about fifth and final benefit that we think is very significant and that's minimized disruption. So rather than the team having to do their day job in an old system and then implement a new system and coordinate across different groups, including IT and others, we bring that to the table. So this is not an additional job, in addition to your day job, think of us as supplemental teammates that are here to help you do your job better. Additionally, by improving inventory balance as a planning organization, you're dealing with less expediting, which reduces your effort significantly, and reduced disposal of surplus and excess. So we believe not only are we implementing a tool to get better financial and operational results, we're a net reduction in effort because we're gonna reduce your expediting and excess disposition. In summary, OASIS provides means to significant results, very quick fashion, with a very streamlined approach, both in terms of data and effort, and we believe is the fastest way to a quick win and a nice lead-in to subsequent advanced planning capabilities. Thank you for joining. Again, I’m Bill Benton, Co-Founder of GAINSystems. We look forward to talking to you about this and opportunities in the near future. Thank you.
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.
(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
(00:33): Particularly in seasonal environments, but even just with general variability across time. There's gonna be times where you need to pre-build to avoid having less than needed to capacity in the future. One of GAINS' practitioners, for example, has over 50% of their demand in 10% of the year between Thanksgiving and Christmas, they’re a jewelry manufacturer. This type of pre-building, pre-buying, etc., really isn't feasible in the DDMRP construct. Some demands are large project based demands. These are things that are known in advance, are large increments, which will increase variability unless they're recognized and filtered as one-off events that are scheduled in advance. And secondly, there are things like outliers, one-time exports selling off excess, you know, at a low price. These are things where you don't want to plan more inventory just because you have these one-off events that you can see as outliers and/or filter as incremental scheduled demands. So that's the first.
(01:39): Secondly there are simple things that cause variance that can be managed like seasonality. So in a DDMRP environment, fluctuations across the year are seen as variability, no different from gaining and losing customers in many cases, new product launches, right? So that can be seen as not a step function upward because you've now adding a new way of new products that might use the same componentry as existing products, but seen as another false source of variability. And then of course, things like promotions. All of these things can be managed, recognized, and filtered out so that you understand that these aren't random variability that require costly management methods or working capital investments, but things that can be dissected and managed. Now, that said, you do need to manage around that core variability that can't be managed and discerning between the two is essential.
(02:37): Prioritizing given scarcity. So if you're trying to fulfill as much demand is feasible where you can't fulfill all the demands and you have to make decisions about which components cure or produce or which items to expedite for distribution or sales order. Understanding the interdependencies between items that go into a same bill material or interdependencies between items on the same sort of perspective sales order isn't at all managed by DDMRP. So again, that's, that would be a large manual effort to say, I have these shortages. How do I combine shortages of these items across other items in a BOM or bill of distribution or bill of sale into a complete perfect order. I want to thank you for your time today and would love to chat further about all of these topics. Thank you.