#16: Optimer planlægningen: Strategier for ledelse af forsyningskæder

Søren Hammer Pedersen (00:10):

Hello, everybody. Warm welcome to this S&OP Masterclass from Roima. My name is Søren Hammer Pedersen. I’m the chief commercial officer for PERITO IBP within Roima and has a huge passion for supply chain planning. The purpose of these masterclasses is to dive into trending topics within supply chain planning, give you our perspective on these topics, and hopefully give you something that you can use in your daily planning life. And today is no different. We are talking about planning excellence, and really exciting topic that people sometimes forget when they are changing or transforming their supply chain planning. And, as always, I’m not alone in the studio. Today I brought my very good colleague, Benjamin Obling, who will help me shed a light on this exciting topic. Welcome, Benjamin.

Benjamin Obling (00:57):

Thank you.

Søren Hammer Pedersen (00:58):

Before we get started, as we have done before, people might not know you, so please give us your two cents on who is Benjamin?

Benjamin Obling (01:06):

Yes, my name is Benjamin Obling, overall responsible for onboarding new clients in the PERITO IBP solution and the continuous operation of our clients in the Roima IBP product.

Søren Hammer Pedersen (01:18):

Perfect. Let’s dive into it. When we say planning excellence, what do we mean then?

Benjamin Obling (01:26):

Yeah, so it’s the combination of looking at the business logics and the processes and how do we re-engineer the processes in planning so that we make the planning excellent, so to speak. So that would say, okay, the current processes we have, how do we change that? Which ones do we change? Which ones do we remove, the best processes? No process, as I said, but also how do we make sure that our systems are aligned with the processes and the purpose?

Søren Hammer Pedersen (01:57):

When we look at different topics for this podcast, one thing I always keep at back of my head is why should people care, basically? Why, if you are looking to transform your supply chain plan and improve it, is it something that people should care about?

Benjamin Obling (02:12):

Yeah, because if you want to improve the supply chain, you need to do something, obviously. That could be changing the processes, it could be changing the tools, the competencies, people. But you can say that’s really what the planning excellence is, about looking at how is the current process and the current tools, how do we solve the task, and how can we improve that? So changing the processes, changing the tools and the whole balancing. And the really interesting part of this is as one element is the processes, the way we do things as compared to the tool, what will the tool or a system do for us compared to how do we then handle the tool and the system? How much can we automate, in the sense, can we remove a process that will remove all the possible errors when we can automate it in a strong way? That is the easy way of doing change management. But then we have processes that we need to continue to do obviously, and how do we then make sure that those processes are then done in the most efficient and highest quality way?

Søren Hammer Pedersen (03:16):

It sounds, in all fairness, pretty logical when you say that there. So why do companies forget to do this when they change? What is that you’re seeing there?

Benjamin Obling (03:29):

Yeah, so one thing could be you have installed a new tool, it could be a supply chain software tool for example, or it could even be an ERP system, and then you expect that that will solve all of your problems. Very few expect that. So it’s normally not overlooked in the beginning. But the problem is when you start to implement it, et cetera, do you still remember to change and challenge all the existing processes and say, ”Okay, do we need to do it in the same way, or could we accommodate it in the way that the system does? Could we use the standard approach?”

(04:01):

I think there is a lot of, let’s say, movement towards using more standard functionality, which I think is very healthy and very good. The other way that would be one that you overlook it, one pitfall. The other pitfall is that you start with the process and you don’t use a tool to convey it because I think you say there are different schools in saying that the change here and improvement in supply chain is 10% tools and 90% people or vice versa, depending on whether you’re selling change management, then you’ll say it’s 90% people.

(04:33):

And if you’re selling supply chain tools, you’ll say it’s 90% tools. I think you need to do both actually at the same time because a tool can be a tremendous conveyor and tremendous way of changing what you do because you need to change when you use the tool. And if you then challenge the processes in the same way and make sure that you have the change management so people actually work in a different way, it’s a lot easier to do the change with a tool. Also, because you can work with the process, but if you really need to automate it and remove it, why work with the process instead of just removing it automated?

Søren Hammer Pedersen (05:09):

Yeah. But I think maybe the key word of what you’re saying there is challenge. That is a thing that people forget, that if you’re implementing the tool then you’re implementing as is in a new tool. So you get the same with a new shiny system, but you can forget to challenge what you do. Why is it that that challenge is so difficult to get into the transformations?

Benjamin Obling (05:33):

I think one element would be, did you check your existing processes and you’ve replicated it into the new tool, is one challenge that you get. ”Okay, we’ve always been doing it like this. Now we have this new tool, how can we bend it so we’re doing the same?” And the other way would be you take the standard completely and then you don’t bend it at all towards what your business needs is, because the thing here is, what is bad habits and what are real business needs? And that’s really the element that is super tricky when you have the workshops working with the processes, with the tool, how do we optimize supply chain?

(06:11):

Really balancing when is this input that you receive from, let’s say, a very experienced employee or planner, etc, or someone at the factory floor, when is that just the grumpy old man in the corner that don’t want it to change and it’s just an old habit, ”We should just reduce it and disregard it and get out of the room,” etc, on when is it actually a super important input because he or she has seen it all and she knows also from previous implementations of similar tools, ”This is where the pitfall is. We need to solve this part of it.”

(06:45):

So I think finding out, mapping what are these different business logics that we possibly have, and then evaluating how many do we need to take into account, how many can we remove by automating, where do we support it with the new tool, with the new system, and where do we support it with a different process, or even a continued process if it’s something was made for a good reason. I think bearing in mind that the grumpy old man or woman in the corner, they have seen a lot. So having the respect for that, that they say it for a reason. Sometimes they need to be pushed a bit, other times, we need to listen in carefully.

Søren Hammer Pedersen (07:23):

Yeah. So the conclusion, of course, we need the tool, we need the process, but we need to remember how to obtain this planning excellence. So maybe to continue down the path of this challenging and get a bit supply chain nerdy here, dive into this re-engineering or what you want to call it, let’s take three main disciplines, demand planning, inventory, supply planning. If you take them one by one, maybe areas of where when we go in and do this re-engineering or challenging the existing within these three areas, could you give some example of where find the golden nugget and where people really need to focus?

Benjamin Obling (08:04):

Yeah, I think one, if we start with the demand planning, one key element here is, is that a decentral or is it a central process? So if you have multiple companies selling the different goods and you have a parent or you have a headquarter, should the forecast be created in the headquarters? Should it be created locally? And then you can have a lot of discussions around what are the advantages of being local, it’s close to the market, etcetera. On the other hand, you’ll have some scale effects, so shouldn’t do it in the headquarter, etcetera.

(08:37):

Then my recommendation would be to first find out how do you want to do the forecasting? Okay, you need to split it into an automated prediction. Having different models. So it could be AI models, as we’ve also discussed in some of the masterclasses here, saying, ”Okay, having that decentral, that doesn’t make any sense. So should you have five different companies that are working in demand AI? No, that doesn’t make any sense. But what about the market intelligence? Okay, that should be then decentral.” So first mapping out how are we going to solve the problem in the best possible way when we know that, ”Okay, where are we then going to solve it and how?” So that’s in demand planning.

Søren Hammer Pedersen (09:18):

Yeah, just one question on demand planning, that’s also challenging the way we actually do the forecast. One thing is the structure of it, but also really, are we good enough at what we do, the way we have structured the data wise? Also, are we on the right levels? Are we using the right data? All those kinds of things, that needs to be challenged as well.

Benjamin Obling (09:41):

Yeah, and that’s also where I think in the business process, re-engineering. If we’re saying that for example, in demand planning, and you’re taking the demand created in our company in Germany for example, if you’re not looking at the system and just looking at the processes, saying, ”Okay, now we are the 90% processes, the system is not super important.” Then you would say, ”Okay, how can we extract the data more easily in Germany?” So using a better extract for example, ”How can we work with the model in Germany?” And so on. Whereas actually if you started saying, ”Okay, how much can the system do for us?” You would say, ”Okay, we can actually remove the process completely in Germany.” And no process is the best process. ”So let’s remove that,” and then say, ”Okay, but what about that input on the German market in the different brands? How is that developing? We need the sales manager input. Certainly a local task.”

Søren Hammer Pedersen (10:36):

Yeah, I need to bring up one area I really like. I think it’s hugely interesting when we talk demand planning and re-engineering. I’ve seen so many times, especially in large organizations, where people use the forecasting where we get hit by human behavior, basically. The key example is when sales use the forecast to get enough goods into the future has nothing to do with what they actually on planning on selling, but they just don’t want to go stock out, even though it costs us a ton of money in working capital. I guess you need to be very aware of human factors as well when you challenge.

Benjamin Obling (11:19):

Yeah, absolutely. Absolutely. And we need to measure it. That’s one of the levers of changing it. But we’ve seen it multiple times in implementations that we can actually improve the forecast accuracy, actually removing the full market intelligence forecast that was earlier there, improving that. Then we have the market intelligence on top, which in sometimes actually reduce the forecast accuracy. And we certainly don’t want to do that. And sometimes the reason for that is the overestimation so that you have the goods because when you are a salesperson, you’re measured by the bonus on the revenue that you generate. You don’t want stock out. So the incentive structures are actually pushing you or notching you towards having a too high forecast. So what you can do there in order to counterbalance that is to look at the accuracy, compare what is the accuracy of the raw automated forecast compared to the market intelligence forecast. And then put it into different categories saying, ”Okay, how many are minor adjustments, major adjustments, how many are increases or decreases,” because when you split that on the different sales organizations or companies for example, you can quite fast see, ”Okay, we have a tendency to overestimate in Germany. It’s mainly the larger areas here, and it’s this key account manager.”

(12:36):

Then you can drill all the way down and say, ”Jurgen, please reduce your overestimation next time you do your forecast, or challenges that your final answer because we’ve seen this overestimation tendency.” And then again, there you could say the business process re-engineering here is then changed from Jurgen in Germany as the example doing the forecasting himself in Excel, spending a lot of time on that, re-engineering that to saying, ”Okay, the automated prediction will be from headquarter. It will be completely automated, no process. He will get the alerts in Germany, so Jurgen, and he will then do the adjustments on top of that.” So now we’ve reduced the time he spent a lot, he’s just spending the time on the adjustments. And we then have a headquarter person in place in order to oversee does he overestimate? When he does, we’ll just have that phone or teams call say, ”Please, Jurgen, are you absolutely sure he’ll reduce it a bit?” He has reduced the time a lot, we’ve increased the accuracy, and we’ve then re-engineered the process using the tool to do that.

Søren Hammer Pedersen (13:43):

Nice. A lot of potential in the demand planning area. And of course, that’s one of the things we always started in the project, but then look at the inventory and planning on the tactical level. Of course, we’re not down on the nitty-gritty daily here, but on the longer scale here, what are the things that you see within that area that we need to challenge?

Benjamin Obling (14:05):

Yeah, we have the same challenge, so to speak here, that normally balancing the inventories is a very manual exercise. It’s done quite rarely. Sometimes we see safety stocks that are not updated for two years, or even more sometimes. So here it’s also again about, ”Okay, how do we re-engineer that process? Let’s see, how much can we automate? Can we use tools to optimize the safety stocks, come with predictions or proposals? Yes, we can. Okay, that’s probably a central exercise.” Then we might need local planners to review the proposals in order to say, ”Okay, is that correct? Is there something we need to change?” But before we set up that manual process that we actually don’t want, instead we have another wave which we call a decision tree, where we try to map out the different business logics that the different planners, the adjustments they would do, what is the underlying reason for doing those adjustments?

(15:04):

So if we are saying, ”We would like a 99% service level, 95, for example, on all of our A or B products,” that could be the starting point. And then they’re saying, ”Yeah, but on these X number of items, we would like to increase it to 200 instead of 100,” as example. And then we would challenge that, ”Okay, but why do we do that?” So instead of just continuing that process of them overriding it, let’s challenge, ”But why do you do it?” ”But this is because it’s hitting a new client that we have, we would like to onboard them with a good exercise. We’ll give them a bit higher service level in the beginning,” for example. ”Okay, but we couldn’t we take that into account automated, in an automated way?” So instead of just override the manual process, let’s re-engineer that into a business logic in, what we call the decision tree, where we automatically investigate, okay, where do we have new customers or new clients coming in?

(16:00):

And those materials, can we increase that automatically, increase the service level automatically? Then we don’t need the manual process. Another example could be critical components. So if you are a spare part producer, for example, if you have critical components and you have noncritical, let’s automatically set the critical ones to a higher inventory. Also, again, then we remove a manual process by mapping the business logics. And that would be the grumpy old man again that knows it all because he knows that it’s a critical component. We cannot have a dry run on this one. Okay, but let’s automate it, and let’s make sure that we do it in a consistent way without human interference.

Søren Hammer Pedersen (16:40):

Yeah. So we actually make it smarter, faster in making it automated, but at the same time, less people dependent. So we have the system now that, on the inventory side, does the right thing every time, basically, and no gut feelings on that.

Benjamin Obling (16:56):

Yeah, yeah, yeah. and this could equally be in purchasing as well, for example, so now we talked inventory, it could also be in purchasing saying, ”Okay, we have the MRP,” so the purchase proposals for example, but the different planners, they know that, ”Ah, but when I’m purchasing from China from this vendor, I need to have 1000 units when I place an order,” for example, could be a case. But now, I just have a proposal of 25 on this one, I have another, so I need to make a mix of products, could be a case. I need to populate or fill up a container. I have a minimum MOQ. Okay, how can we automate and populate that? We do that in the order optimizer, again, with business logic or decision tree saying yeah, we don’t just have the, let’s say, the stupid MRP proposal of 25, we actually group that order and now we have a combined order using that logic that was otherwise in the head of all of the different planners.

Søren Hammer Pedersen (17:52):

Yeah. Also maybe a bit on the same path there, stocking strategy is something that people forget in this, very much so. If you just take what you have always done, you may be done an ABC for like 15 years ago, it’s still in there somewhere, that’s a huge area as well.

Benjamin Obling (18:13):

Yeah, it is, basically finding out what should be on stock and whatnot. And you can say if you separate that we have optimization logic, so that could be using Monte Carlo simulation, using deep learning networks for demand planning, using linear programming, for example, for purchase proposals. So these are optimization logics. So that’s one thing. But the other thing is you need to apply some business logic to that optimization. And one business logic is, you can say optimization wise, it cannot tell you should you have this on stock, yes, no. Well, it depends. You need to make a decision. So if it’s a new product, you would maybe want to have it on stock even though it has a very few number of sales observations, for example.

(18:56):

Okay, so then you need to tell the algorithm that, and you can do that by using business logics. Another way it could be, okay, now we have seen a decreasing demand, we have X number of observations, our gross profit is below a certain threshold. Okay, then we suggest that this should go from a make to stock to a make to order, so we don’t want to stock it anymore. So having this dynamic stocking strategy which is based on rules and business logics, instead of based on gut feelings or changing a master data field manually in the ERP system, which is actually, I don’t know, 90% of companies work in that way instead of using business logics.

Søren Hammer Pedersen (19:39):

Yeah. I think many of the things that you have mentioned here, I’m not saying they’re easy, but you need to remember to challenge them, and they are quite logic. One of the things that I have seen as a big thing to challenge that might be necessary is also on the supply side in terms of the MRP. When we transform these supply chain processes, we want to simulate the MRP runs. We want to make sure we look into the future, can see the bottlenecks, all those kind of things. But I have just noticed that in many case companies, they have developed their MIPs over the years for a long period. At some point, it was a very good idea to do some adjustment in the way we does it, and all those adjustments are still in there. And then it hits a simulation. Do you see the same thing? That is a really important thing to be aware of in this, and how to challenge that?

Benjamin Obling (20:35):

Yeah, absolutely. It’s a high focus for us and also for many of our clients at this point, because you could say you’re going to have a better forecast, you’ll have a better safety stocks, that will improve the MRP run, so the planning of proposals in production or purchasing. But what we can just see is exactly as you mentioned there, that there are a lot of different logics that you can use in the MRP run, and all of them separately makes sense that you do that. But the problem is that the end result, and especially if you have many points in the chains, many echelons, you could say, you have a local warehouse, you have a regional warehouse, a central warehouse, you have a producing site, a raw material, etc, you’ll have these bullwhip effects throughout the chain where we can just see when we start to lock the MRP result daily.

(21:26):

And then we compare it, and that’s a very interesting exercise I would encourage people to do. Sounds very nerdy, but when you start to look at and put that on top of each other, you can just see that sometimes the MRP result, the production proposals or purchase proposal are all over the place. And it’s changing. Even within a few days, it’s saying, ”Ah, now you should produce this, you should postpone it, etcetera, postpone, advance alert, etc.” And it’s really because of all these nitty-gritty rules that maybe each of them makes some sense, but at the end, the uncertainty around the MRP result is so high that you might even be better off, if we’re talking about a supplier forecast, sending them your raw directly derived forecast, for example, because you know it’s going to be around a hundred, there will be some seasonality, etcetera. Maybe you have different factories, so it’s not trivial to calculate.

(22:21):

But you could say having that stable forecast or at least showing that to the vendors, and then showing, ”Okay, this is the base load that we are going to give you with this seasonality. We know we have this much on stock, so we probably won’t raise any purchases right now, but we are going to do it with this MOQ in X period of time out, but we’re not certain.” So this purchase, that will move back and forward and be larger and smaller. So finding a way to balance that and really also reducing the number of logics in the MRP is quite important. And then having the business logic, you can say, afterwards, the right business logics in order to group the purchase or production proposals is of course relevant.

Søren Hammer Pedersen (23:11):

Yeah. Yeah. Nice. So of course demand inventory is supply. Do we also need to challenge the master data sometimes?

Benjamin Obling (23:17):

Yeah. Yeah, absolutely. So master data, we’ve also touched upon it in other masterclasses that it’s also always a pain point and everybody are not satisfied with their master data. So in our approach here is really to predict the master data to make alerts on the master data, so as much of it as we can quantify as possible. And the good part is that most of it can be quantified. So that’s things like lead time or lead time uncertainty, MOQs, could be consumption rules and so on. So many of those can actually be calculated, predicted. So for example, an MOQ, just take a super simple, you have 50 as an MOQ in your ERP system, but you actually consistently purchase 100, and you’ve done that for the last 10 times. Let’s change that to 50, we’re pretty sure you can actually just do that automated, just change it, or we can make alerts.

(24:18):

And the important part of the alerts is okay, we can see that you sometimes have a hundred, sometimes 75, you have 50 in the master data. So which one should you use? We’ll make a proposal, 75, for example, and then we will rank the proposals, the alerts based on when are you going to use it. So it’s something you’ll use now. So it has impact, it’s important, it’s way out there. And now it has a huge financial impact because you’ll have this tail of master data that is super crappy, but just why start there? If it’s something you’re going to purchase in two years or it’s a special customized bomb that you rarely use, et cetera, let’s forget about it because fixing everything now will never happen.

Søren Hammer Pedersen (25:04):

Yeah. It sounds like there’s a lot of components and a lot of areas that could be challenged here. And if you then want to go into this, you want this transformation of your supply chain planning, maybe you are looking into either acquiring one or updating your systems to do the planning, but you want to focus on this planning excellence as well. Do we have a structured approach how you go about this? Where to start, where to end, and where is it in the whole transformation process, this re-engineering?

Benjamin Obling (25:42):

Yeah, so it’s fairly early in the process when you’re implementing a new tool, a supply chain planning tool. So setting up the design of that, but it’s in the phase where you’re mapping what are the assets and processes that you have and the tools that you have. Okay, how does that fit into the new system? So how would we solve that in the new system? Can we use the standards? Okay, are we ready to do that? Where do we have these business logics? Listening to the grumpy old man or woman, where do we actually need to pay attention? Let’s make those business logics and test them, and then we start to set it up. So I would say in the first 30% of the project is where you re-engineer the processes and support it with the tool. The very important part is then in the validation, that’s the change management. So the validation of the business logics, because, a bit like with the MRP setting up a lot of different rules, here we’re also talking about setting up business rules.

(26:44):

So it’s really important that those rules are then doing what we want them to do. And that is really the validation with the business users. So when we’re talking new customers, as we discussed, should have a higher service level, so the items that they purchase, is it actually doing that? So let’s look at all of the item lists where we have singled out, this is the business rule, it will do that. Is that correct, yes, no, let’s validate with the planners. Then we know, okay, it’s going to do that continuously, so then we can feel safe. But in the validation phase, so that’s the later part when we start to just before we go live and after go live during the hypercare, it’s really important to validate those rules because when we automate and we remove processes, that’s super cool. We can do something very consistently, but we can do very wrong things very consistently. We don’t want to do that. So that’s really where we want the validation and the hypercare.

Søren Hammer Pedersen (27:40):

Yeah. So you’re basically saying it’s the whole process, you have to remember this. It’s not just the two workshops in the beginning, quick fix.

Benjamin Obling (27:47):

I guess that was what I said.

Søren Hammer Pedersen (27:50):

Yeah. But it sounds reasonable, and something that I can recognize. But of course, another question that our listeners definitely are wondering, again, back to the why should I care, what do you get out of the business value of this? If you have this element in and obtain this planning excellence, what do you get out of it as a company?

Benjamin Obling (28:12):

Yeah, so one element is of course you don’t stick to the old habits just because they’re old. So you don’t force your new system into having that old process. So let’s try to adapt to a new system, for example, because that will give you a stronger process. And you can say outcome wise, the purpose of it all is of course a higher accuracy in the planning, that would be in the forecast, in master data, in inventory, in supply, in purchase orders, etc.

(28:40):

So that’s the business outcome of it, that you can serve your clients better at a lower cost, so lower working capital, etc, and then you can also reduce the manual work that you’re doing, which is normally often directed towards something that creates more business value because the people who are doing these business logics and are adjusting for all the wrong things in the system, all the missing business logics that are in the existing or in the old system, all the people who do that, they are normally pretty good and skilled people who knows a lot because they need to do that in order to make these corrections, and they can be utilized so much better in other places.

(29:24):

And then also, you can ensure a higher level of consistency in your planning. So if we say that you have a higher service level for critical components, for example, okay, so you have these super planners who will make sure that when they do an adjustment, they change it from 100 to 200 because it’s a critical component. But will they always do that? No, they won’t. They will forget it sometimes, or they will overlook things. So when we set up business logics, decision trees rules doing that, it will do the same every time. That means we improve it, and then we find some flaws in that. We’ll improve it a bit again, but we’ll remember everything we had before. So in that case, we’ll get at a better and a better stage because we don’t forget things, as humans does.

Søren Hammer Pedersen (30:11):

Yeah. Yeah. So a lot to be had if you get this right and remember the challenge. I would still bet you I can go into a supply chain conference anywhere in the world and find people who have carried out transformation projects, acquired a system, did what they thought were the right things in terms of planning excellence, and never harvested the benefits from this. How do you make sure, if you have done things right and have challenge, you actually get what you want out of this?

Benjamin Obling (30:50):

Yeah, it’s really about following up. So making sure that when we continue operations or in the daily sales operation or sales execution, that we make sure that we follow up what do we actually do? So when we have the new better forecast, when we have new better safety stocks, new better purchase proposals that are grouped and so on, taking all of these things into account, are we then actually using those proposals, or are we still doing something else? There is a very strong tendency to still look at the old exec spreadsheets and say, ”Ah, but I normally purchase when we are at 200 here.” Yeah, okay, but now you’re going into a lower season. We have a safety stock where we actually said we want to reduce the inventories, et cetera, so you should actually wait until, let’s say a hundred. ”So you should wait dear planner, so please don’t send that purchase proposal yet.”

(31:40):

Having a structured approach on how do we actually perform is super crucial with different techniques of doing that inventory controlling where you follow up on how does the actual planning perform, or look at supplier performance, et cetera. So there are different techniques looking at the accuracy on, you could say the automated forecast as compared to the market intelligence. That was our German, Jurgen, who had the overestimation we talked about.

Søren Hammer Pedersen (32:09):

Poor guy.

Benjamin Obling (32:10):

Yes, exactly. Okay, how do we make sure that you remove that overestimation. ”And dear purchaser, you should not purchase it yet. You need to have eyes and stomach. You need to wait until the proposal is there.” And on the other hand, also making sure that that feedback loop is also open to say, okay, maybe the planner actually purchased it earlier, and that was good because maybe we missed something in the model. There was a business logic that we didn’t have. So if we didn’t have this critical component element, for example, yet, and the purchaser was just purchasing it earlier because, ”Ah, but this is a critical component, so I cannot wait,” okay, let’s incorporate that into the rule and explain to the planner and show the planner, make that into action with the planner so the planner knows next time, ”I can trust the system here,” because it is taking critical components into account already.

Søren Hammer Pedersen (33:01):

Yeah. One thing what you just said made me think is you mentioned, of course, importance of following up and actually changing planning behavior. But one topic in my head is always lack of focus or that the focus goes away. I think I see projects when I talk to people where there’s a huge focus up until, and maybe a bit after go live from a transformation project, meaning that is everything working? Are we on track? Are we keeping deadlines? But if you meet the same company six months down the road, is the steering committee still meeting every month looking at the results? How is the focus in the organization? Are we actually harvesting benefits? I see a pitfall there. How do you experience that in your work?

Benjamin Obling (33:51):

Absolutely, it is. In the process, re-engineering it, follow up and having consistent follow up, and have someone centrally making sure that that follow up actually happens and does it in a consistent way. Get it into Outlook in your calendar as a repeated task. Basic things like that, but making sure that also that the tool supports you. So you don’t need preparation. You shouldn’t spend a lot of time doing a forecast accuracy analysis. You open the tool and you have that. And that’s an important part in setting up the process in the beginning, that the tools are following up, they are there. You don’t need a lot of focus or a lot of time going into preparing a follow-up on the inventories. You spent the time on the follow-up, not on the preparation. So automating that as well.

(34:38):

And then of course, again, automating as much as possible. The more business logics we can put into it, then you don’t need that attention in the same way because you already know that all the critical components, they do have a higher service level, so they will automatically get a proposal earlier, because you could say many of these things, if you want to improve forecast accuracy, you want to balance the inventories, et cetera, if you put all the good brains of the company together and you made the best forecast you could possibly do, put all the efforts in it, every company could do that. They can make a super forecast for the next year. The problem is they can’t do it on a consistent basis automated without spending a ton of time, and no one has a ton of time to automate.

Søren Hammer Pedersen (35:25):

It’s super interesting, and I think we could talk for hours about this topic, but maybe final area just to touch upon is, of course, we strongly believe that you need to have this focus. That is a given in your transformation. But if you’re sitting out there watching, listening to this and want to get into starting here, how do you start with this transformation? What is your best recommendation there?

Benjamin Obling (35:50):

Yeah, so you’re mapping the different objectives. What is it you want to do with this change process, of course, or change management? So what are the business objectives you want to obtain? You want to better supply chain, for example. Okay, where do you see the main pain points in that? Is that in demand planning? Is it across the end-to-end? And then down to, okay, so how well are we system supported? Can we work with the existing tool? Can we improve that, or do we need to change it? And then doing the tool and process together. If the conclusion is, we need a new tool, then have it working with the tool and the process, re-engineering together. Instead of arguing whether it’s 90 to 10 on processes versus tool, use the tool to push the change in the processes. And then map out again, automate as much as you possibly can because that’s the best way of knowing that the process will be consistent.

Søren Hammer Pedersen (36:48):

Yeah. And maybe final words, remember to challenge and automate. That is the key, key thing...

Benjamin Obling (36:54):

Absolutely.

Søren Hammer Pedersen (36:55):

... In this. I think we’re out of time. Benjamin.

Benjamin Obling (36:58):

Yes.

Søren Hammer Pedersen (36:58):

Thank you so much for your great, great input as always on this.

Benjamin Obling (37:02):

Thank you for having me.

Søren Hammer Pedersen (37:03):

And thank you all out there watching, listening. Really appreciate you dialing in with us here today in these masterclasses. We think this is a really, really critical area, and would of course love to discuss more with some of you out there. So if this isn’t something, planning excellence, that has your interest, go into the Roima website, check things out, or reach out to Benjamin and I online, we’ll be happy to discuss with you. Other than that, thank you for your time, and we hope to see you in our next masterclass.