SAP Optimization

SD; MM; PP

MD04; VA03

The best way to learn is by doing so. Welcome to the video service that unlocks and reveals the hidden value in your system. Martin here, and today we've got a good one, in this video we're going to explore a material availability date, otherwise known as the MAD date. SAP has such a wide variety of dates which all have specific purposes and lead to a flow of information that drives our supply chain. The material availability date is no exception, as is what drives the required on hand date for MRP, traffic light, stock on hand, and exceptions. It's pretty important. We don't want to miss out on what exactly it is. So, Kristie, why don't you tell us exactly why the material availability date is called the MAD date? Because Martin, it's the date that the customers get mad if we don't have material available, and that might be our external customers, or our sister facilities, or even the manufacturing floor. Okay, before I jump into SAP for this demo, did you at least chuckle? That's it, folks. That's as funny as she gets. Yeah, okay. So what will we see in the demo today? We will explore how the MAD date gets determined. And some very important and often overlooked lead time considerations. How it shows up in the stock requirements list and what the impact is on the MRP run and exception monitoring. Off we go! All right, let's go in and see what this MAD date is all about. So, as we previously said, the MAD date is the date that the customer gets MAD if we don't have the product available. It's the date that the product is needed to be on the shelf so that all the other subsequent activities that are required in order to get it out the door to the customer on time based on when we made and are now trying to keep that promise. So, if you go into a sales order, and I'm going to show you an example of what I would call a flat schedule. I'll explain how this is actually working. You may see this a lot on your sales orders and what I want to do is explain what maybe should be happening instead. So let's just go in and we're going to grab the second item and I'm going to go in and I can see that there's a schedule line. So we ran an availability check. There's a schedule line in place and I can see the first date is the 2nd of December, that's when they're looking to get this product from us. And right now we can see that it was not able to be fully confirmed for the 2nd of December but instead has been confirmed partially for the 2nd and partially for the 4th. So this customer is allowing us to do two shipments. So multiple, partial shipments in this case, it happens to be two. Now, if we go in here, though, to the shipping tab, this is what allows us to get to that mandate, and this is so important because this is what drives the supply chain, right? This is the date that we're transferring over because it's the date we've committed to the customer and we're driving our supply chain to be able to meet this date. And if you look here, we have the delivery date of 12/2 and everything else is sitting flat to that date, right? So there's no additional time that is allotted for any of these additional pieces of the puzzle, and SAP has loads of dates and they're all based on lead time offsets. Lead time becomes very, very important, and the really nice thing about SAP is that it allows us all of these different lead time buckets so we can go through and figure out how much time we realistically need in order to accomplish each of these activities in order to be able to make sure that we get this to the customer on time. And so think about it as, you know, your quality inspection time, or your goods receipt processing, or dock to stocks time on the supply side, your planned delivery time, or in house production time, or the time on your routings. Same thing applies for a customer, so we've got a bunch of different things that we have to do. So we're shipping from a particular shipping point, we may have a route and a route schedule involved. The customer may have a receiving calendar that dictates when they're able to receive goods. Let's say it takes five days to ship to the customer and we're responsible for coordinating that delivery. So if the delivery date was 12/2 and we need five days for it to move and make its way to the customer, probably we're going to have a material availability date that is at least five days, if not longer before that in order to be able to make sure that that happens. So if you go into your sales order and you notice that this is really just a flat schedule, think about what kind of time buckets you need in order to be able to set yourself up for success because what you're trying to get to is that material availability date. So the delivery date offset by whatever time is necessary to get that product to the customer, so when do we need to issue those goods in order for it to hit that delivery date. Now for some of us, that delivery date represents the date it's leaving our facility, for others of us that will represent the date it is actually going to be reaching the customer. So you got to know your particular terms with your customer. Based on the date that you want to issue it, when do you need to start pick, packing, and staging for loading? That might be another day offset. If it's export and you have paperwork to do, it may be several days or even a week or two beforehand that's required. All of those things, calculating backwards, the delivery date minus the lead time for your route and transportation time minus the amount of time it takes to pick, pack, and load is what gets you to the material availability date or when that product would be required. And so as you run your ATP check and it's looking to see when inventory can be available, then you're flipping the schedule and scheduling from that material availability date forward for when it actually is ultimately going to get to the customer based on how much time you need to pick, pack, and stage, and load, and when you're going to actually goods issue and then the amount of time it will take in transportation. In addition to that, we have this transportation planning date and this is able to run in parallel, but what it does is it buys us additional time for things like the administrative work of setting up a shipment, going through the process of getting that booked and ready to go so you're able to actually start that process working on that transportation planning, assuming that you're going to hit that material availability date, which again, all has to do with how predictable and stable that supply is and how well aligned the ATP rules are to what it is that you can make and keep a promise against. So again, if you go into your sales order and you go to the schedule line, you look at the shipping tab and you notice that you have a flat schedule here, I really would like to challenge you to think through these different buckets of activities and make sure that you're setting yourself up for success so that customer is less likely to get mad because we will have the correct date in order to allow for all those other activities to occur in this material availability date or the MAD date. That's what's going to drive the supply chain, that's what you're expediting towards, that's what you're working your supply chain to try to achieve, is that material availability date because that's the date that we need to hit in order to make sure that we get the product to the customer on time. Welcome back from the demo, to summarize. The MAD date is the date that the customer gets mad if material is not available. We explored several lead time components that drive the correct date and the importance of getting this right. And lastly, we looked at how the state is driving MRP and exception messages. The date is the entry point for driving the supply chain. It drives all other dates and decisions related to how to best get that supply for the demand. And if we did all the other upfront work on lead time, so long as we meet this date, we have a really good chance of fulfilling our promise to the customer. Good stuff, Kristie. Thank you, once again. If we go to the trouble to really understand how the MAD date is determined, and then work hard to hit that date or manage the client's expectations, we'll be setting ourselves up for success. You know what I've learned today, Kristie? Most of us should not have flat delivery schedules in our sales orders. We really need to think about those lead times. SAP has a lead time bucket for all the different pieces of the process. So getting this right, neither too short nor too long, makes a big difference in efficiency of the flow of material to our customer. Well, I think that's a wrap today. Folks, if you want to learn more about MAD dates please check out our other videos and of course if you have a burning question please submit it below.

PP; PTM

MCP7

The best way to learn is by doing. Welcome to the video service that unlocks and reveals the hidden value in your SAP system. Hi, my name is Martin and in this video we are going to focus on how to take the advantage of SAP work center analysis. When used correctly, work center analysis can help organizations gain insight to how well we're able to run the schedule on the floor and identify where the bottlenecks might lay. It's a valuable way to improve performance and uncover opportunities for improved throughput. So Eacliffe, tell us a little bit more about work center analysis. Sure Martin. Work center analysis is a powerful feature when used correctly, how well a work center is performing and keeping its commitment to its schedule. In this demonstration I'm going to focus on three things. Provide an understanding of what insight this report provides from a work center perspective. How it goes about providing this insight on work center performance. And how to evaluate each work center performance. The intent here is the use transaction MCP7 to perform work center analysis. In this report the data is primarily captured by plant, work center, and month. So let's get into this transaction, and what I'm going to do is, because it's a test system, I'm going to run it for a couple of years. So let me execute this, I'm going to bring up all work centers within this plant that has information. Okay and here we can see that we got information currently sitting at the plant level. So basically we specified the amount of historical information we want to take a look at hence the amount of history was driven by that date range. Ideally we should have zero variances and when I mean zero variances just looking at my screen here, what we can see is we have target lead time, we have actual lead time. So based on our master data, this is how much late time we expect versus based on the production confirmation. The variance is then reflected in this column. In terms of execution time I don't have a variance, but we could see what the target is versus actual. If we want to see what the difference is we can do the quick calculation or you can select this column, come here to comparison to key figures, going to compare the target execution time, I'm going to compare that to the actual execution time. Okay, and here we can see the difference. So we'd spent just over 39 days difference between the two. So the question is, hey, is this something I need to take a look at? Okay. And then even queue time again, we have target queue time, actual queue time. This is the amount of wait we expected based on our master data, we're expecting only one day of queue time, we ended up with 23 days of queue time, so deviation of 24 days. So again, what's going on? And this is sitting at the plant level. So what I'm going to do is do a switch drill down, and I'm going to bring it down to a work center. Let's see what this information looks like. So we have the totals still sitting like before on top, but now we can see who's contributing to the variance perspective, so let's look at this the deviation. So I'm going to sort this. I don't see any negatives. So let's do this, we could see the biggest contributor is coming from this particular work center where we said, yeah, it should take us 9 days when in fact it took us only 1.4 days to fulfill that particular operation for that work center. So this is great, but recognize that, look any kind of deviation, positive or negative that could have a significant risk to our operation. if we are running too fast, like this is implying we may not have other components in a timely manner resulting in a shutdown vice versa, if we are not completing orders in time without operation in time we also run risk to the business. So ideally, our goal is to really bring these lead times into alignment. The other thing I'm going to call out is, notice we see these big numbers here, it's like, wow, this is a big deviation, I mean, the difference is 144 days. So how can this only be 14.4 days at the total level? And we have to recognize that the system is actually averaging these numbers at a total level, so because we are dealing with time we just can't simply add it up, so what SAP has opted to do is to take these number of days and just average them by the number of entries or in this case work centers that we have here. So this can be a bit misleading looking at it, and hence it's definitely good to come down to this work center view and actually look at the information at the work center level. And then just to take this one level further here we can see we had a big deviation the question is, okay, when did this happen? I can pick this single line item, I can then do what is called drill down by, which is this icon here, and we'll dive into that specific work center. I'm going to pick months and we could see we have 4 months listed here and for the most part, things were looking pretty good until we came into 2023. So in this case because there's just one entry we will try and get an answer for what's going on, but it definitely looks like an anomoly and for that reason there's a high probability we don't need to take any action, but still, we don't want to second guess this, we want to determinethe root cause of this. You know, was it a matter of something posted incorrectly, in this case did this order linger around for a couple of years, for example given the number of days, et cetera. So at the end of the day, yes we use this transaction, we focus on columns like lead time deviation, we can compare processing time between the two, like what's going on, actual queue time, and of course we can also take further information to consideration like operation data and so forth. Okay, so this is the type of insight that you can gain from doing a work center analysis to help determine which data set you should be going to, to improve the quality of your master data. So in summary we have covered how work center analysis allows you to. Appreciate the feedback that this report provides by work center. Identify which key figures to focus on in this report. And evaluate each work center performance. Thanks Eacliffe. Using this feature allows real-time information on work center utilization and performance allowing the business to improve production planning, optimize resource utilization, and enhance cost control. If you want to learn more about this topic and others in your SAP features and functions please feel free to check out our video catalog and if you have any specific questions feel free to submit them below.

DM

MM02; MD04

Hey folks, Martin here. Are you ready to tackle uncertainty and challenge? Are you comfortable with confronting the level of risk and uncertainty in your forecast head on? Well, today's the day. Today we're talking about forecast error and bias, and how to put the consumption horizon to work for you in managing your way through the risk that is inherent in your forecast. If this is a challenge for your business, you're in good company. Predicting customer behavior is a challenge for most organizations, and it's a topic that we're going to continue to build upon over time on this channel. In fact, if you search, you'll find other videos on monitoring forecast performance, working with consumption modes, and choosing a planning strategy that addresses different kinds of variability, volatility, and risk tolerance. Check them out. But specifically for this topic, we're going to be talking about forecast bias. To help us today on this topic of forecast bias, we have Kristie. Kristie, I know this is something that you love tremendously. This is something you deal with all the time. You may get even excited about this. So take us away. Yes, it's true. I do love a good demand planning puzzle. And while we may hit temporary plateaus in improving the quality of our forecast on some of our individual materials or products and in some of our segments. What we can do is get really great at managing the risk. And that is what I want to chat with you about today. I remember exactly when the shift in perspective hit me. I was in an IBP meeting that was well on its way to becoming a post mortem on forecast quality, and I remember hurting for my team as they tried to explain all the things that they were doing to try to get the forecast "right". And all the blame that was coming their way for our failures as an organization to deliver to the customer. Our cost to serve is ridiculous and our suppliers are tired of it. Forecast. The shipment was late and the customer is upset again. Forecast. Precious time, materials, and capacity gone because. Forecast. Now I'm a manufacturing gal at heart that also happens to love demand planning. So you know what? I know that SAP and supply chains salute all too well. It looks like this. And it's not helpful. So let's stop doing that. Baby steps are a good place to start. So let's focus the conversation. Supply chains are made up of quantity and time. So today, we're going to focus on time as an ally in dealing with the volatility in quantities. We'll also address our bias. Are we dealing with a bull or a bear? And then we're going to talk about the importance of differentiating where it matters and setting the appropriate rules in place as we consider our plan for every part. One of the tools that we have that can really help us is to understand the bias in our forecast and that is if we are consistently under or over predicting. What the demand will be for a particular item, and this is for those of us who are working on the supply side. We look at this at the material, the plant and potentially even the MRP area level. So it's very granular in terms of how we are observing that forecast. There are a ton of videos to help us to understand and unpack the different tools. I want to bring a couple of them together, though, today in the context of bias. And I'm going to talk specifically about consumption and the way that we can manage our consumption parameters to help protect us against some of the risks that's inherent in our forecast process. Here are a couple of other tools, though, before we go there. The first is we can take our average daily consumption. So that is what we have been using over the last X number of periods and compare it to our projections, our average daily requirement where those are wildly different, that gives us a great way to have a conversation with our counterparts. In demand planning and they can help us to understand the reasons for why that may be different. We want to make sure that we do respect the demand plan, just like when we say that we can't get production done by a particular date or we can't get supply in by a particular date the demand planning team the customer experience team has to trust that we are doing everything in our power to get it there when we see the demand plan and we have the conversation we ask the question at some point we have to say we've done everything in our power to get the best prediction that we can on this particular item. And it's good to ask the questions and certainly if you see something to say something. But at some point I do want to emphasize it is important that we start to work the process and commit. What we're talking about today can help us to manage through the inherent variability and volatility that we're going to experience with demand over time. One of the other things that we can get a quick line of sight on is how our forecast that is in the now is performing. So here's a good example. This is our remaining balance open to sell. It is December right now. We have nothing left and we have requirements for 45 units. Looks like that is a pretty typical demand. You can see November has 48 pieces remaining open. Looks like we might have had a timing issue there. The demand came in in a different time bucket than what we were expecting and we have 36 pieces projected for January. Looking like that's a little less than what we are seeing in the months that follow. So this is where we start to say, okay, what's going on? Are we over under forecasting? Is there some predictability to that? And if so, how can we set our consumption rules in place to help set us up for success? So, let's go in there and take a look. I'm going to go into the material master. This all lives on the MRP3 tab. Now my colleague and friend Patrick has put out a couple of great videos around consumption mode and forward consumption period and backward consumption periods. He's gone through and he's demoed as you change those settings what happens. So I will let him speak with you about that. What I want to address is the consumption based on bias. So how do you think about that depending on if you tend to over or under forecast? Now it's important to note that your consumption mode and the way you're consuming your forecast and what's eligible for consuming your forecast does tie back to your planning strategy. So there is a tight connection there and that is a big topic to explore. But when we're talking about consumption mode, think about it like this. So your sales orders, for example, are coming in and they're eating away at the forecast that is out there, the demand plan that's in the system. I think about them like Pac Man. It makes me less angry when things are wrong. So I think about it like Pac Man. We are coming in, that sales order is eating away at the demand plan. Now sometimes, that Pac Man gets too full and it just stops eating and then we end up with extra forecasts out there that's just hanging out like that November forecast we just saw. Sometimes, in a particular period, it may overeat. So, for example, the December time period that we saw that was completely consumed and now we're moving into January. When we know that we are maybe not right in terms of timing, but we are roughly right in terms of quantity, that is where the consumption mode can really help us. And really that's what it's saying. This is how much or how far out I am allowed to consume that forecast. So at some point, if I tend to under forecast, my demand plan is not high enough. I may want to allow those additional sales orders to sit on top of the forecast that we've put in. So it's going to stop eating away, it is additional incremental demand on top of the forecast. If I tend to under forecast, backward consumption and then controlling or not allowing, or controlling the horizon of forward consumption becomes my friend. So I don't continue to add to the problem. I'm not in a position where I allow it to continue to consume forward to January or February when I know I'm already over my forecast in December. I don't allow that problem to continue because I restrict how far forward I'm allowed to consume that forecast. If I am, over forecasting, so I am in a position where I am planning too much, this is where I really want to lean into that backwards and then that forwards consumption and I might allow myself to go a little bit further back and a little bit further forward in order to smooth that out because that might mean that I am a little bit off in terms of when that forecast is hitting. But if I'm roughly right and I'm confident that I'm going to consume it within the next couple of periods, then I might allow those days to go further out. Your consumption periods are in work days, they are subject to your factory calendar. So make sure that you're aware of that. A lot of times people come in, they put 30 days, they assume it's a month. Depending on your factory calendar, that may not be the case. So that's something really important to be aware of as you're going in and you're adjusting those dates, so you really want to think about whether you tend to under or over predict that demand and then use that to help you to choose the correct consumption mode and the period that you need for being able to smooth out that forecast. So look at your risk buckets and figure out what those bands look like and then adjust the timing so that you're getting the smoothest demand signal to your supply partners. Very, very helpful to be able to come in and fine tune this and make sure that we have the right rules in place so that we don't compile or add on or complicate the situation by allowing that forecast consumption to go too far out and allowing those sales orders to overeat into future periods when we really want to restrict that in if we do tend to under forecast. So whether you're overly optimistic or if you're pessimistic with your forecast, there is help for you here and it really surrounds the consumption mode and the consumption periods and how far out you allow that Pac Man or those sales orders to eat that forecast. You know what all good demand planners have in common? Radical candor, excellent storytelling, and intense curiosity. They live in a world where the good jobs are rare and the criticism is high. So to get better at all this, the first step is to know thyself as a person. As a collective that builds a consensus plan and as products, product families, customer and customer groups, whatever is the right level for you to get to a roughly right picture of demand. We have to be champions of risk and attack it heads on. If we can acknowledge and address where we're most likely to be wrong and historically how wrong without outliers and in which direction we tend to be wrong in, we can evaluate what we need to borrow from and how much time we need. Most importantly, the bias doesn't go away if we ignore it. So we need to work with it, rather than against it, and have SAP help us make it work. We are supply chain stewards, and good ones make it work with the cards that we have, while we are working on getting to a better hand. Much more to come on this particular topic. Okay, wow, Kristie. I mean, you were off to the races on that one. I can't imagine where this is going to go next. Hey folks, I'm sure there'll be plenty more videos to come if you're looking for those other videos we mentioned earlier use the chatbot, it will recommend them for you. If you have a specific question for us, please submit it below.

P2P

ME5A; MD04; ME53N

Hey, welcome back fellow SAP explorers, Martin here. And today we're going to be looking and exploring a feature in SAP that has a strong value proposition, but is often overlooked. What we're chatting about today is the importance of the release date in driving the procurement process. What drives your PO placement today? Do you run off the release date or the delivery date? So today, Kristie is with us, and I know you love the process cadence, so have at it. Tell us more about the value of release date in procurement. Cadence keeps the chaos at bay, Martin and yes, the release date is one of the many dates in the procurement process. And it is one that is often overlooked. But it really represents a critical milestone. It is what helps ensure we're setting our suppliers and ourselves up for success by smoothly running through key process steps with the right amount of time to get them done. Today I want to show you how the release date is calculated and where we can find it. Let's go in and take a look. I love making a Reveal TV video on something that I have done wrong in the past and have found so much value in once I learned what it was for. And I remember in the early days of setting all of this up not knowing exactly when I need to get a purchase order to my supplier and being really worried that I could be past you and passing that ball to them and then not set them up for success and not get what we need when we needed it. So enter math on the part of SAP and enter this lovely field called the start or the release date. The start date if it's production, it is the release date if it is purchase orders or purchase requisitions that need to be converted into purchase orders. It is the starting line for the procurement process. It lets us know when we need to start moving that purchase requisition onto the next stage in order to be able to get that purchase order delivered on time based on all the master data that we have maintained in the system. So if you cannot see this column right now in your stock requirements list, it is hiding from you. And there are a number of columns here that are sometimes missing. Sometimes you'll be missing opening date. Sometimes you'll miss start and release date, and sometimes you'll miss rescheduling date. It's fiddly, but you just have to hover over the fields until you can see you'll see actually a double line arrow appear and then you have to drag that out in order to be able to get theparticular column exposed But this is a good one. And so it lets us know when we need to release. So in order to have this purchase order here on time, we have to start the process or get that purchase requisition converted into a purchase order no later than 08/27/2024 in order for it to get here on September 23rd. Okay, and if I double click in here I can even get a little bit more information without even having to leave my stock requirements list. So I can see the goods receipt processing time for this is 3 days, so the date that it is planned to be available. So the material availability date is the 23rd of September. That means we have to receive it from the supplier so that it can go through all of its stock to stock activities, receiving, quality inspection, etc. We have to have it by the 18th of September, okay? So that means that we have a weekend in there because those are our working days, subject to our factory calendar, and in order to make all of that magic happen so that the supplier can be set up to deliver on time, in order to start our process and get through it, get the purchase order out the door and over to them on time, we have to release this by the 27th of August. And if we go into the purchase requisition, we can further look at those details and see the planned delivery time. Okay, so all of that math is happening for us, we don't have to look at a calendar, it's right here and then all along the way it's letting us know if we have any exception messages. So you can see this is some old housekeeping that needs to be taken care of because not only is my start date in the past, but also my finish date is in the past too. So we really missed the boat on that. So how do you make sure that that doesn't happen? Well, you go to List Display of Purchase Requisition. So you might be using any of the ME57, ME58, ME59 transactions to move through your procurement process. You may be working in ME21N and pulling a list of requisitions. This is another great place to look. This is ME5A, you can see right down here. And when I was coming in here previous life, I would run this based on delivery dates and then try to estimate my lead time offset. Don't need to do that. Come in here, put in the release date. This is everything that you would want to go and work on. So your release date up to whatever the date is that you're working with. So you know, today, tomorrow, if you're about to be out of the office for the holiday break, you might reach out a little bit further than that, but it should be very, very near term. And then you would go in and pull a list of purchase requisitions that were standing out there that needed to go through, be released, and converted into a purchase order. This should not be reaching far out into the future. When we release things to our suppliers early, we can no longer get a good read on their performance or their ability to deliver on time and in full. Because we've released it to them early, we're giving them more lead time than what they asked for. And we also are limiting our flexibility. So the one thing we know about demand is that it changes. And so if we have trouble being correct in terms of time or quantity, we want to make sure that we maintain that flexibility for as long as possible. If you're struggling with that and you're trying to give your supplier more visibility, so maybe you're releasing really early, like this case, this is way out into the future. We don't want to do that. We want to have our dates be nice and tight to what we should be working on today, tomorrow, this week. If you find that you're needing to do that, then chances are you need to explore other options in sourcing such as scheduling agreements or other ways to get a good forecast to your supplier. So make sure you check out some of those other Reveal TV videos and they'll help guide you through that. But this release date is here and it's present in many of our purchase requisition related transactions. Extremely helpful for helping us to produce a list of purchase requisitions that we need to go through and work and get out to our suppliers in purchase orders. So, release date. It's a very, very helpful field available to you in SAP. Welcome back from the demo. As we highlighted today, Release dates represent the date we need to act to give our suppliers the time they need to successfully deliver to us. They can be a leading indicator of process adherence, improvement, or challenge. We can work with them in variants and we can use them to select our requisitions and convert them into POs. And we no longer have to do the math around lead time to determine if it's time to cut that PO or not. And I totally used to do this. I had a calendar at my desk and I was figuring out if it was 63 or 91 days of lead time and what date I needed to release it. Now we even have Google and other tools to help us get better, but why use those when SAP is already doing this work for us? Time marches on Kristie, thank you so much. The release date sounds like an asset to the process that gets us the right signal at the right time. Win win. Thanks again. Hey folks, if you want to learn more about other particular topics related to procurement, we have a whole section on procurement that you can look into. And if you're struggling to find a video, feel free to use the AI chatbot.