Episode 1: When SIOP Gets Real: Capacity, Analytics, and Better Decisions

Speaker A: M the Manufacturing Edge, where ops leaders break barriers, drive growth, and redefine what's possible. In this episode, the team explores why a modern S IOP process is now essential for navigating today's volatile demand and supply environments, and how manufacturers can use realistic capacity modeling and better analytics to move from constant firefighting to proactive data driven planning that sticks.

Speaker B: Howdy, listeners, and welcome to the Manufacturing Edge. I'm your host, Will Merriman. Today we'll be discussing SIOP planning in the experience TBM has in this arena. If your SIOP process still runs on disconnected spreadsheets, conflicting numbers from different systems, and forecasts that no one fully trusts, it's not really a planning system. It's more of a guessing game that puts your entire operation on its back foot. This is the podcast where we get past those buzzwords and talk about the real levers that move factories forward from frontline scheduling all the way up to the boardroom. When manufacturers operate that way, the result is predictable. Today I'm here with Brian Cromer to talk about how to fix that and how better SIOP discipline, capacity modeling and analytics tools can bring real clarity to operational planning. Brian Cromer is TBM's managing director of Global Supply Chain. Thanks for joining me today, Brian. How's it going?

Speaker C: Thanks, Will. It's going well. Glad to be here.

Speaker B: It's great to have you. I'm excited to get into the discussion today.

Speaker C: It's a good topic. So.

Speaker B: Yeah. And I know something you have a lot of experience with here at TBM in previous roles.

Speaker C: Let's get to it.

Speaker B: Yeah, let's unpack, uh, what's changed in the manufacturing environment and why SIOP has moved from nice to have to something that's absolutely essential, which is a material shift in how it was previously viewed. From your perspective, Brian, what do you think has changed in the manufacturing environment that makes SIOP more important today than maybe five to ten years ago?

Speaker C: Yeah, it's a lot of complexity. So it's really about understanding the demand patterns and what is changing in the market. And what we see is market conditions are changing faster and it's becoming more important to be able to quickly respond. And it was just stats. We see the volatility and the demand shaping up that could be caused by people. The pattern itself, it could be issues with the supply chain and disruptions that we didn't foresee coming. There's also a lot of adjustments with labor forces. So we're seeing that there's a trend in, uh, complexity of the type of skill required, which can impact the, uh, availability of having labor as well. So it's really all about the complexity and the need to be able to quickly adjust and respond.

Speaker B: And is that complexity? Is that something that companies without a siop, a dedicated SIOP function, run into? You know, I'm just trying to get a feel for companies are focusing on this and have a official formalized psyop program versus one that doesn't and where that disconnect might exist.

Speaker C: Great question. So if you think about it, uh, in the past, with less complexity, people were able to quickly respond to changes or to variations, the order patterns that were coming. So being reactive was a little bit easier today with a lot the, the challenges and flexibilities and really complexity in the type of product mix that you have, it's a way more critical to be able to quickly respond and adjust. So being reactive like it was in the past doesn't work well in the current state. Some of the outputs of the app, ah, could lead to poor productivity impacts to your service, to your customer. On time delivery gets extended out and pushed. You could have high inventory levels that would impact your working capital. So being able to have good visibility in the future and strategically plan for it versus being reactive is a critical, uh, necessary requirement today in the manufacturing world.

Speaker B: Yeah, and I was about to say too. Are there any warning signs? Seems like you covered them on what companies might be able to look to and say, okay, this is, um, a red flag that we might need to address with something like a dedicated SIOP function and leadership warning signs. You can, you know, think of any other red flags that may spur this kind of activity?

Speaker C: Yeah, a couple of others. If overtime starts to go up, you see that the energy around firefighting really starts to escalate. Uh, a lot of warning signs come from customer complaints because their, uh, delivery gets impacted. So any of those warning signs could be, uh, a point to break down in the upfront SIOP process and really understanding what the business looks like in the future to be able to plan for it and better accommodate the adjustments.

Speaker B: Yeah, the data plays a big role and is the baseline for all this work.

Speaker C: It is. And there's often a disconnect between the sales organization and the operations. So sales, a lot of times, just as a belief, we'll go get the, the orders and you'll be able to fulfill what I need. And without communication between the two, which is one of the primary purposes of siop, to be able to understand each sides of the equation and make sure. That we're prepared to be able to service, uh, all the customers in a timely manner the most effectively and efficiently as we possibly can.

Speaker B: Do you feel that companies have constraints on data before even considering any type of SIOP activity? Are there constraints that naturally occur within companies that you see?

Speaker C: Absolutely. It really starts back with the demand. And a lot of people believe that if we get our demand figured out, we have good visibility to what we believe the customer requirements will be, then that's all we really need for siop. Where it really starts to break down is the availability of taking that demand information, understanding what your supplier requirements are, understanding what your capacities, potential constraints could be to understand if, uh, we potentially have labor issues or we need to be planning to add additional labor resources and really ultimately do we have enough time and available capacity to satisfy these requirements. What we find, as businesses change and as complexity comes into play, especially when there's multiple site locations that have the capability to produce the same products, merging those data sets together becomes very complicated. So getting a, a good visibility of overall capacity requirements and potential constraints at a global level has become very, very difficult.

Speaker B: It's like the baby step, building up, making sure data is correctly recorded but also consistent across the organization so then it can be analyzed going forward into something like a capacity model to you, Brian, what do you think is the most effective way to make a capacity model, and what does that look like in practice for you?

Speaker C: Yeah, I believe when you start to look at different systems that say you have multiple ERP systems and you have warehouse management systems, so you have data that resides in all these different, uh, locations, to be able to get it centralized is the first and foremost property. So how do we put it all into a single data warehouse and have the structure consistent to where we can start to aggregate effectively across all the different businesses and be able to perform and build out a really strong, robust process for determining what kind of potential capacity constraints we might have.

Speaker B: And once you get all that information together, you know, it can be a long process to synthesize and summarize, but also detail all that data.

Speaker C: Absolutely. It's cleaning, uh, that data is very time consuming and it's not the easiest thing. So to ensure that you get the data at Clinton's properly, you have to talk with the right individuals at each of the different locations, at each of the different functions within an organization to make sure that you understand exactly what the intent of using that data is. So once we have it understood how they're using the data, then we can properly Pull it together and synced across all the different companies.

Speaker B: Uh, a huge first step.

Speaker C: Absolutely.

Speaker B: Are there any surprises once you have that data wrapped, fully analyzed and understood, at least from a analysis standpoint, what surprises have you seen when working with clients? They look at the data and say, okay, we weren't expecting this number here, or we see improvements, but how is that going to affect our actual operations from a process standpoint?

Speaker C: Yeah, it makes it very easy to see potential bottlenecks. A lot of times you have seasonality, so businesses think that they have enough capacity and they won't have any issues producing everything that needs to be produced. But if you look at it based on a month to month or a week to week requirement, then there's bottlenecks that potentially can exist and it becomes easy to see and oftentimes as a surprise to many organizations. The other key component are what assumptions go into the model. So as we think about efficiencies in a manufacturing environment, how we incorporate their OEE and actual production capability versus what standard ADA would say that you could do is very important. So there's often a disconnect in what we set the system up and what the system might be able to predict that you potentially could do. And when you're in the actual assumptions for operations, then we realize that we actually do have a potential capacity constraint. Oftentimes too, you'll see that, uh, there's assets that just aren't being utilized effectively. So you see that we have an excess of capacity on, uh, some of your assets. So you're not properly utilizing the assets that you have.

Speaker B: Asset utilization comes in following the analysis. Is there any main path that you seem to take and want to take following that full understanding of the data or to you, is it more of a custom fix and you'd like to work client by client to see, okay, what's actually happening in our supply chain. What can the data inform us right now? And then what's that big first step? Is there one direction you typically go or is that more of a custom decision for each client?

Speaker C: Yeah, the foundation is often the same across. Ah, but then very quickly you realize that every business is different. So it really becomes a one off, one by one understanding of the current business scenario. What are the potential risks that they have and to be able to go and address each one independent of each other. Just because there's so much variation between the different types of businesses and the different types of clients that we work with. So each industry can be different, the demand patterns could be significantly different. But it all comes down to being able to aggregate the data, understand it and pinpoint where the potential failures could come so we can be proactive and fix them M in the future.

Speaker B: What are some of those initial directions you would take once you get the visibility from that initial data?

Speaker C: There's multiple ways that you get to handle it. You can encourage customers to switch to a different product that be run on a different piece of equipment that's not constrained at that point in time. So you could try to shape the demand. You might be able to look at opportunities to outsource some of the business throughout periods of year to alleviate that constraint, although a longer term decision. Then you also look at do I need to invest in equipment for the long term? So is this going to be a uh, continued constraint year after year? Is it going to get worse or is it a one time event to be able to look at do we need to invest in the business and add additional capacity by getting new equipment?

Speaker B: So the, the actual decisions that come from the data seem to be instantaneous in some cases, but also in some cases need to be looked at from, from multiple angles. And sometimes that can be made difficult by the way that clients are storing their information. I've worked at places in the past where some of the data sets that we kept weren't in any type of visualization tool but kept in spreadsheets, which may be visualized or not in some particular way. But the automation of uh, that visualization I think is something that not only helps the clients but can speed up the SIOP process. I know you've helped many clients at uh, TBM and prior understand and interpret data that may or may not sit in spreadsheets. But what role do analytics platforms that can automatically visualize data play in this process? And how could something like a Power BI step in and be uh, a force multiplier?

Speaker C: Sure. So throughout everybody has migrated to Excel. So we have these powerful ERP systems that MRP functionalities that people believe are going to be the one all end all of being able to show you all your capacity constraints to be able to pinpoint where you have issues. The reality of life is everybody extracts the data, puts it into Excel and as we all know, we found mistakes in Excel spreadsheets. So while they work well, they're not very robust. What a platform like Power BI has been able to do is to ensure consistency and speed of updating a lot of the data. Once you have a core data warehouse set up, you can link Power BI directly to that data Warehouse ensuring that, uh, the source of truth is always consistent and it's always the same. So as we refresh, it becomes very quick to do that, to be able to assess on a day to day, week to week, or month to month how the business looks in the future so that we can be proactive and plan to it versus the traditional reactive when we see something that it's already too late to quickly adjust. And that's what results in the firefighting and the extra expense to be able to make sure that we satisfy the customer. So it's really about speed and being proactive.

Speaker B: Are there any considerations companies should be looking at before having to use Power bi, or is it the same as we talked about before as far as data cleansing and quality?

Speaker C: Yeah, you really assess what is the source of truth, where does it reside and how do we access it. The key is to build out what does it look like so that we get a centralized location of the data. That can then be the connection point to be able to build all the different modelings and produce the visualizations required to help the business. All businesses make the proper decisions.

Speaker B: And once we get that data visualized in a tool like Power BI or something similar, what does that do for an organization? What can that empower internally? And then also working with tbm, I know you've had some direct experience both this year and, uh, very recently with, uh, some clients. And what does that look like from a quality of planning perspective?

Speaker C: Sure. So a couple of examples would be a global business that had multiple plants on different ERP platforms. Each one of the businesses was really siloed, looking at their own capacity constraints, their own capabilities, and they wouldn't react quick enough. So the result of that was having to shift some of the production between the different sites where the capabilities would be the same. But because they didn't plan far enough ahead, it ended up costing a lot of freight expense to have expedited material, uh, moved. And as you can imagine, if your locations are global, then expediting freight can become very expensive and cost the company a lot of money. Where, if we would have known far enough in advance, you could have made a better decision whether we needed to build some inventory, whether we needed to go ahead and plan earlier to have, say, a sister plant assist with some of the production so that we don't incur all those heavy costs and we're able to plan for it. Another is if you have organizations that are disconnected to where we have a, uh, sales group or the demand group that knows of changes that are coming with new products or new introductions and it's not properly communicated to the rest of the organization. So that procurement can ensure that product is available for production. So we can make sure that manufacturing has the available capacity to satisfy the demand. So that's really where SIOP can give that visibility so that discussions can be made early on and be prepared for what we see coming in the future. So by the time you get an order, it's often too late and that's really costly to the business. So SIOP is intended to give a long, long range view so that we can make strategic decisions.

Speaker B: It seems the real measurement that we look at and that clients are considering is the actual impact our service level is going to be improving, our inventories being reduced, our plants using capacity more effectively, and can they plan it more accurately and those things can be translated into tangible operational and financial results following an engagement. Where do you typically see the biggest financial impact from improving this planning visibility, whether it's with recent clients or maybe if you can think back to examples in, uh, your past book of work.

Speaker C: Yeah, so it really spans across all the different areas that we discussed. So first and foremost you typically will see, uh, a drop in inventory because we're able to plant better, set the right inventory levels, ensure that we're servicing the customer, but we're able to do it with less inventories. So more working capital gets improved simultaneously by managing the right level of inventory on time, delivery and customer satisfaction increases and improves. Then it's really all about operational cost. So being able to plan and optimize production to satisfy the requirements, our total cost goes down. And that could be labor cost, that could be material cost. It could also be overtime reduction. So it's really all about the operational, uh, costs and improved efficiencies that you get by planning ahead.

Speaker B: Would you say there's an important first step any type of operations or business development leaders should be taking before any of this occurs?

Speaker C: Sure. The first thing is to assess do they have a process and if we do have a PSYOP process, how effective is it? So are we holding people accountable? Are we tracking the right metrics to ensure that we're having a successful process? Does it lead to being proactive in planning or are we still just being in a reactive mode? So assessing what you currently have and how strong it is would be, uh, a first step to understand what do we need to do. The second piece is to, uh, ask the individuals within the organization what's missing to make your job effective. Do you have the right information to make decisions. Are you able to be strategic forward thinking and get the input from the different functional areas to see where the disconnects and the gaps are.

Speaker B: So really a truly collaborative experience. Not just one sided us coming in and telling how it should be done, but understanding what their needs are and some maybe limiting factors are within the organization.

Speaker C: That is correct. And ah, we do a lot of assessments of SIOP processes. So we look at each of the different uh, sub processes, whether it's demand planning, supply planning, how do we incorporate the two, how do we determine what kind of capacity risks there are, what kind of risks do we have in our supply chain for being able to have product availability? So we look at each of these in great detail to understand where the biggest areas of opportunity to improve are. And as we go through that it helps to pinpoint where there are disconnects between the uh, different areas. In an organization where information isn't properly communicated across and people are working in silos.

Speaker B: Are there any common mistakes that you see, common pitfalls when organizing and analyzing that data from an upfront perspective? You know when you're, when you're truly putting it together and uh, analyzing before you even get into any of the modeling and maybe inventory reduction decisions?

Speaker C: Sure. The one that uh, is very common. People don't understand how the data is being used. So there's assumptions on how they believe that what data they're looking at is being used throughout the organization and by making those incorrect assumptions creates a lot of chaos. Once the next process or functional area does their piece of the work. What's man supplied on the front end isn't uh, what they really needed?

Speaker B: Limiting chaos wherever possible.

Speaker C: Limiting chaos, absolutely.

Speaker B: And Brian, unfortunately we're reaching the end of our conversation. Here are scheduled time slate. So I would like to ask one more thing and I think it would grant a good perspective on any leaders listening to this conversation now and a first step they could take. So if you could provide one recommendation for a potential PSYOP leader or operational leader that's listening right now, what's that first step that you would recommend they take to really get this process in motion but also make sure uh, it's optimized going forward.

Speaker C: Yeah, the very first thing is to assess your current state, identify where the gaps are, where the improvement opportunities are and uh, as you go through that, put a process in place to review it on a regular basis so that as businesses change you can be proactive in changing your processes and really having the right information so that businesses can be successful.

Speaker B: It's a great first step and a great segue into any conversations with tbm. So we'd welcome any comments that listeners have about what Brian has just said. I think there's a lot of great stuff to touch on, not just in the existing content, but if you have questions going forward, we love to hear from you. Brian, I just want to say thank you for joining the Manufacturing Edge today. It was great having you as a podcast guest.

Speaker C: Thank you, Will. Glad to be here.

Speaker B: And just in summary for all of our listeners, we did talk today about the risk of not having a dedicated SIOP function, the power of data discipline, and how really having true, accurate, but also currently updated data makes all the difference. Uh, if you are listening and are seeing any of your own experiences in this conversation, just know that you're not alone and TBM is here to help. Thank you for tuning in. This has been another episode of the Manufacturing Edge. My name is Will Merriman, and as always, stay curious, stay consistent, and keep pushing that manufacturing edge.