Stop Guessing and Start Knowing: Fixing Routing Times in Business Central

Ryan: Imagine this, it’s Monday morning, right?

Emma: Yeah.

Ryan: And your production schedule looks absolutely perfect. I mean perfect on paper. Every minute accounted for, every resource allocated. You’re feeling pretty good about it.

Emma: Yeah.

Ryan: The ideal start, but then maybe noon rolls around and that two hour welding step, it’s already creeping into hour three.

Emma: Oh, yeah, I know that feeling.

Ryan: And suddenly your assembly team, you know, further down the line, they’re just standing around idle, costing money, costing you money by the minute.

Emma: Yeah.

Ryan: And meanwhile, quietly, almost invisibly, these costing variances are just accumulating, eating away at your margins. Does that sound familiar at all?

Emma: Sounds all too familiar for many manufacturers, unfortunately.

Ryan: Yeah. And this isn’t just like a minor scheduling hiccup, is it? This is the really real, often painful and incredibly frustrating experience of bad estimates and inaccurate routing times just cascading through your whole operation.

Emma: Exactly. These inaccuracies, they don’t just cause one problem. They sort of multiply the headaches across everything. You know, from costing, scheduling and ultimately your ability to actually meet delivery promises.

Ryan: Yeah, it’s like a fundamental breakdown of predictability.

Emma: It really is.

Ryan: So our mission for this deep dive, what we want to get into, is understanding how to move from that painful cycle of just guessing to actually knowing in your production process. And this isn’t just tweaking a number. Right. It’s more fundamental.

Emma: It’s about rethinking how the operation learns and adapts, really. Moving from reactive firefighting to something more proactive.

Ryan: Proactive precision. I like that. So we’re going to try and uncover the blueprint for this. And the cornerstone of it all seems to be something called a routing.

Emma: Indeed.

Ryan: Yeah.

Emma: Yeah. In manufacturing, especially if you’re using systems like, say, Business Central, a routing is essentially, think of it like the DNA of your product, your manufacturing recipe. Basically, it’s a detailed step by step list of every single operation needed.

Ryan: So like setup time, set up time.

Emma: The actual runtime for each unit, even the move time between work centers, and, you know, any waiting periods involved.

Ryan: Right.

Emma: And the critical insight here is its foundational role. Routings directly dictate your scheduling. They calculate your labor and machine costs, and they’re absolutely essential for, well, effective capacity planning. If you want to know how it gets made, how much it costs and how long it should take, you look at the routing.

Ryan: Okay, so if these routings are the absolute backbone, the DNA like you Said, how deeply does an inaccuracy. There’s. How does that ripple through the whole operation? Can you paint a picture of those effects?

Emma: Oh, it’s a profound ripple effect. It really is. What’s often overlooked is how just one seemingly small inaccuracy at that routing level, it can just metastasize, you know, create chaos across the whole thing. Accurate routings are paramount because, well, they directly determine the start and finish dates for production orders. They calculate your labor and machine costs with precision. Hopefully. And they genuinely empower planners to balance workloads across all resources.

Ryan: The loaders, the machine.

Emma: Exactly. Your skilled welders, your automated lines, everything. But when those initial numbers are off, you immediately start seeing those pain points. You’ll get late orders because your estimated times were, well, too optimistic.

Ryan: Right. Underestimated.

Emma: You’ll see inflated costs because your actual labor and machine hours blow past what you planned for eating into your margins. And crucially, you end up with inefficient resource use. Imagine a key part for a big customer order, just sitting idle, waiting, waiting in a queue for hours, maybe days, because the step before it consistently takes 30% longer than the routing says. That’s a direct hit.

Ryan: Reputation, bottom line.

Emma: Reputation, bottom line, team morale, all of it. You’re either overalllocating or under allocating people and machines. It leads right back to those idle teams and missed deadlines you mentioned at the start.

Ryan: So we’re stuck in this cycle of guessing where the plan just drifts away from reality. But, okay, what if there was a way to sort of lift the veil, to peer into the shop floor and see the truth, not just the plan on paper. What’s the key to unlocking that visibility, bridging that gap.

Emma: Right. And that’s where we pivot, you know, from just identifying the problem to actually enacting a solution. It begs the question, how do you stop making these critical decisions on stale estimates?

Ryan: Yeah.

Emma: And instead start planning with live action, accurate data. This is where a dedicated analysis tool comes in. Something like the Routing Analysis app, which integrates nicely with systems like Business Central.

Ryan: Okay.

Emma: Its core purpose is exactly that. Bridge the gap between planned and actual performance. It does this by pulling runtime data directly from completed production orders.

Ryan: So real data.

Emma: Real data. And then it does a few key things. It meticulously compares actual times versus expected times for each step, gives you that.

Ryan: Side by side view, reality versus the blueprint.

Emma: Exactly. Highlights those persistent bottlenecks, the steps that consistently slow things down. And importantly, it measures variability with a standard deviation score.

Ryan: Standard deviation yeah, yeah. What does that tell you?

Emma: It gives you immediate insight into whether your results are predictable and consistent, or if they’re, like you said, all over the map. Perhaps its most powerful feature though, is the ability to update those routings right there on the spot. No waiting for the next big review, no manual number crunching. You see the insight, you act on it.

Ryan: That sounds incredibly powerful. Not just seeing the problem, but actually having the immediate power to fix it. But this isn’t just about the tool itself, is it? It sounds like it’s part of a bigger process.

Emma: Absolutely.

Ryan: Like a continuous thing. What are the practical steps to really make this shift from assumptions to data driven action?

Emma: You’ve hit on it. Precisely. This moves beyond just simple reporting. It’s about creating a living, breathing feedback loop. Loop. A systematic approach.

Ryan: Okay.

Emma: We call this the per loop.

Ryan: P, E A R. Yeah.

Emma: Plan, execute, analyze, adjust and repeat.

Ryan: Plan, execute, analyze, adjust, repeat.

Emma: Got it. It’s basically the engine of continuous improvement for your production process. It ensures your operation is constantly refining itself, adapting to reality. It’s the practical cycle you follow to stop guessing and start operating on facts.

Ryan: Okay, so PEAR is the framework lets dive into that first crucial step, then the execute part. It sounds like the foundation is just getting reliable real world data from the ground floor. What does that first step, capturing actual times really involve?

Emma: That’s absolutely the starting point and honestly it’s non negotiable for this to work. Step one is indeed execute capturing actual times foundational. All the analysis later hinges on accurate real time data. Makes sense on the shop floor. This means operators are diligently logging their setup times, their run times for each operation. And you need to think beyond, you know, the old clipboard.

Ryan: Or guessing at the end of the shift.

Emma: Exactly. Or the end of shift guesswork. For genuine accuracy. This really needs to be done through an automated or semi automated system. The source specifically mentions things like a.

Ryan: Shop floor app like Shop Floor Insight you mentioned.

Emma: Yeah, something like Shop Floor Insight, which is designed for that precise real time capture. The common pitfall here is really underestimating how important this is. If you’re still relying on manual best guess entries where an operator just jots down an approximate time or guesses later.

Ryan: Then the whole analysis is flawed.

Emma: You’re building your analysis on a flawed foundation. It’s the classic garbage in, garbage out scenario. We’ve seen it. Companies invest in analytics, but the insights are unreliable because the input data was just suspect.

Ryan: So accuracy here is paramount. Bedrock of the per loop.

Emma: Absolutely, it’s the bedrock.

Ryan: Okay. So once that precise real world data is pouring in, raw, unfiltered, what’s the.

Emma: Next stage in the loop? How do we start making sense of all those numbers, turning them into insights.

Ryan: Right. That brings us to step two. Analyze. In routing analysis, once that rich, accurate data is captured, you bring it into the analysis tool.

Emma: Okay.

Ryan: In our context, that means opening up the routing analysis app, say within Business Central. And this isn’t just looking at a list of numbers. It’s where you start sculpting the data. You know, sculpting it how you set your filters. Maybe you want to look at a specific date range for recent trends, or focus on a particular routing number for a tricky product. Or just find the biggest problems.

Emma: Exactly. You can apply variance thresholds to immediately pinpoint the biggest discrepancies that need attention. Then, couple of clicks, you generate your results.

Ryan: And that’s where the raw data becomes meaningful.

Emma: That’s where those hundreds of individual data points start to aggregate into meaningful high level insights that tell a story about your actual performance.

Ryan: Okay, and once you’ve generated those results, what are you specifically looking for? How do you tell the difference between just normal variation and a real problem?

Emma: That’s step three. Analyze. Spot the issues. This is where the analysis tool really shines. The app presents those crucial side by side comparisons. Planned times versus actual times.

Ryan: A visual comparison.

Emma: That immediate visual tells you where reality deviates from the blueprint. But it goes further, providing that critical variability data. Specifically, the standard deviation score.

Ryan: Right, the consistency measure.

Emma: Think of standard deviation as that measure of consistency. Low standard deviation means your process is predictable, like a dart thrower hitting close to the bullseye consistently, even if the average is slightly off.

Ryan: Okay.

Emma: A high standard deviation, though, tells you that even if the average time looks okay, the process itself itself is highly unpredictable. Times are all over the map.

Ryan: One hour, then four hours.

Emma: Exactly. So you’re looking for two significant variances where actual times consistently diverge from planned or a high standard deviation, or both. Or ideally, you spot both. That combination is a clear indicator. You’ve found a problem area, a bottleneck, an unpredictable process that needs attention fast.

Ryan: That’s like forensics for the production line.

Emma: It’s a bit like that, yeah. Digging into the numbers to find the hidden truths. I’ve seen companies realize they were basing plans on data from five years ago, totally unaware of today’s real bottlenecks.

Ryan: Wow. Okay, so this goes beyond just reporting. It’s identifying the root cause. Once we’ve spotted these issues, these inconsistencies, what’s the very next step. How do we turn that insight into actual change?

Emma: That’s where we move into the adjust phase of par. Step four is adjust take action. The real power of an integrated tool like this is you can update the routing times directly from that analysis screen right there. No switching modules, no jumping around, no searching for the routing, no manual recalculations. Once you’ve identified a discrepancy, and crucially, you understand why it’s happening. Maybe a new machine is faster or a process takes longer. With a new material, you just fix it. You immediately adjust the plan setup or run times. Right there in the routing, you’re literally fixing the blueprint. And then this is vital, you certify that change.

Ryan: Certify? What does that do?

Emma: Certification ensures that all new production orders created from that moment on will accurately reflect these improved data driven times.

Ryan: Ah, so it prevents the problem from just happening again on the next batch.

Emma: Exactly. It prevents the problem recurring. It’s a direct surgical fix that stops small errors becoming systemic.

Ryan: Okay. And finally, step five. You said it’s a loop, so this isn’t a one time fix, right? What does that final repeat stage mean for long term improvement?

Emma: You’re absolutely right. Step five is repeat, monitor and refine. This is definitely not a set it and forget it solution. It has to be cyclical to build that consistency and predictability over time.

Ryan: So run the analysis regularly.

Emma: You run this analysis regularly? Maybe weekly, monthly, quarterly. Depends on your production volume and complexity. The goal is to keep your routings continuously synced with shop floor reality. Adapting as things change, as processes evolve, new equipment comes online, new efficiencies are found. You adapt. It’s that continuous improvement loop in action, constantly tuning the manufacturing recipe. And over time, over time, as you consistently capture data, analyze spot issues, adjust and then repeat. You’ll see your variances shrink dramatically. Your schedules will stabilize. You’re not just improving, you’re creating a responsive, self correcting operation.

Ryan: So what does this whole per loop mean for the listener, for their bottom line, their customers? It really sounds like it transforms production from, well, chaos to predictable control. What are the actual tangible benefits?

Emma: Yeah. When you truly embrace this pear loop plan, execute, analyze, adjust, repeat. The tangible benefits are pretty significant and they spread across the whole operation. Firstly, and maybe most importantly, you get improved cost accuracy.

Ryan: Right? Realistic costing.

Emma: By aligning actual run times with planned costs, your product costing stops being theoretical. It reflects true production expenses. Big impact on profitability.

Ryan: Okay, I’m asking.

Emma: Secondly, it leads to better scheduling. You stop underestimating Times, which causes those painful delays that ripple through everything. Predictability means more reliable delivery dates for.

Ryan: Customers, fewer angry phone calls, hopefully.

Emma: And this naturally leads to bottleneck removal. You identify and proactively fix those slow steps before they snowball into major blockages.

Ryan: And it sounds like you also get more consistency, which is just huge in manufacturing. Taking the guesswork out.

Emma: Absolutely. You achieve significantly reduced variability by standardizing processes and basing estimates on real data. Your results become much, much more predictable.

Ryan: Which must help with resources.

Emma: Exactly. That predictability allows for optimized resource use. You can truly match people and machines to real workloads. Cutting costly idle time or preventing resources from being overstressed makes sense. And ultimately it all boils down to data driven planning. You’re making strategic operational changes based on actual facts, not outdated assumptions or just gut feelings.

Ryan: It’s a complete shift then from reactive.

Emma: Firefighting to proactive, informed management. It leads to a much smoother, more efficient, and frankly more productive, profitable operation.

Ryan: This sounds incredibly powerful, a real transformation, but you mentioned it earlier and I think it bears repeating because it’s so fundamental. All this analysis, all these benefits, they really hinge on one critical factor, don’t they?

Emma: It does. And it raises that crucial question again. Your analysis is only as good as the data you feed it. As the old saying goes, garbage in, garbage out, right? This powerful analysis from a tool like routing analysis depends entirely on gain good, consistent, accurate shop floor data capture. If you’re still relying on those best guess manual entries, the whole thing falls apart. Then even the most sophisticated analysis will be flawed right from the start. The insights won’t be actionable because they’re not built on reality. So it is absolutely crucial to pair this analysis tool with a reliable, accurate time tracking method on your shop floor.

Ryan: That data integrity is non negotiable, non negotiable for success.

Emma: It’s the bedrock of the whole pear process.

Ryan: What a deep dive today. It really brings home hell. Something as I guess seemingly small as routing times. That product blueprint can have such a massive cascading impact. Inaccurate times don’t just mess with your schedule. They erode profits, hike up costs, and ultimately they can chip away at customer trust.

Emma: That’s exactly right. And the per approach we’ve talked through today using tools like routing analysis, it gives you that precise, precise visibility and the actionable steps to tackle these issues.

Ryan: Proactively before they hit the bottom line.

Emma: Before you hit the bottom line. It’s about empowering you to really take control of your production process.

Ryan: So maybe consider this.

Emma: Yeah.

Ryan: Stop letting your routings tell tall tales. Embrace that per loop. Get the real facts from your shop floor, analyze them, make the updates to your manufacturing blueprints, and then just repeat that process consistently. Watch things run smoother, more predictably, and, yes, significantly more profitably.

Emma: And maybe just one final thought. As you think about the power of PER for routings, consider what other areas of your business might still be relying on those tall tales instead of real, actionable data.

Ryan: Good question.

Emma: The principles of plan, execute, analyze, adjust, and repeat. They apply far beyond just the shop floor. Where else can you use this systematic approach to find hidden truths and drive improvement?