In this episode, Emma and Ryan explore the real-world impact of inventory inaccuracies for companies using Microsoft Dynamics 365 Business Central. From missed shipments to halted production lines, the conversation highlights how even minor miscounts can create significant disruptions. They dive into the pros and cons of cycle counting versus full physical inventory—when each makes sense, how to balance both, and why structured processes matter more than the method itself. Whether you need a continuous improvement approach or a clean-slate reset, this deep dive will help you find the right inventory strategy for your business.
Website: https://InventoryForDynamics.com
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(04:00):
Welcome to Inside Insight, your quick hit of
(49:15):
tips, tools and trends for manufacturing, Distribution
(01:28:51):
and Dynamics 365 Business Central, brought
(02:02:48):
to you by Insight Works. Ever feel like you're
Ryan (02:53:43):
just drowning in information, you know, trying
(03:33:19):
to sift through everything just to really understand
(04:18:35):
one complicated thing? It's, it's a constant
(04:58:11):
battle these days, isn't it? Needing to get
(05:43:26):
up to speed fast without getting lost in the
(06:34:21):
weeds. Well, that's exactly what we're doing
(07:13:57):
today. We're taking a deep dive into something
(07:59:13):
we really vital, but maybe a bit overlooked
(08:44:28):
sometimes. Manufacturing, production, scheduling.
(09:07:06):
And specifically we're looking at it Inside
(09:46:42):
Microsoft Dynamics 365 Business Central. Our
(10:20:39):
mission basically is to unpack how this key
(11:05:54):
part of operations is. Well, it's really transforming.
(11:51:10):
We're seeing this shift away from just gut
(12:36:26):
feeling and intuition towards intelligent automation.
(13:10:22):
And we want to explore why this is more than
(14:06:57):
just a tech upgrade, why it's strategically
(14:46:33):
crucial for anyone in modern manufacturing.
Emma (15:25:20):
Yeah, that's a great way to put it because
(16:24:10):
for, gosh, for a very long time, production
(17:16:28):
scheduling, especially in ERP systems like
(17:55:41):
Business Central, it really did lean heavily
(18:41:27):
on visual tools and honestly, on human intuition,
(19:33:45):
you'd have schedulers, often really experienced
(20:12:58):
people literally dragging orders around on
(20:52:11):
a screen, on a gaunt chart. They're trying
(21:44:29):
to manually balance all these competing demands
(22:30:15):
and constraints.
Ryan (22:43:20):
Okay, so visually dragging things around, it
(23:25:48):
sounds intuitive, maybe.
Emma (23:44:00):
It feels intuitive, absolutely. Especially
(24:10:45):
if you think back to maybe simpler times, less
(24:58:55):
complex production. But the reality is that
(25:36:22):
approach introduced a lot of risk, a lot of
(26:24:32):
inconsistency, and that's a huge problem in
(27:02:00):
today's environments like high MITs production,
(27:34:06):
multiple manufacturing stages. Complexity is
(28:00:52):
just the name of the game now.
Ryan (28:39:40):
Okay, let's unpack that a bit more. This manual
(29:25:29):
method, the drag and drop thing, it seemed
(30:06:13):
like solid scheduling practice once. It probably
(30:41:52):
made sense when things were stable, predictable,
(31:17:30):
maybe lower volume, like a craftsman managing
(31:53:09):
a familiar workshop. So what fundamentally
(32:23:42):
changed? Why is sticking with that method now
(33:04:26):
not just inefficient, but actually kind of
(33:40:04):
risky?
Emma (33:46:30):
Well, the whole manufacturing landscape just
(34:24:18):
accelerated past what one person can manually
(35:08:26):
optimize effectively. Think about it. We've
(35:46:14):
got much tighter lead times, huge increases
(36:30:22):
in product variety, sometimes down to individual
(37:14:29):
customization, and then there's the constant
(37:52:18):
stream of disruptions, supply chain issues,
(38:30:06):
labor shortages, machine breakdowns. It's relentless,
(39:07:55):
right? So clinging to those old ways, the manual
(40:04:39):
tweaks, relying on someone just seeing the
(40:48:46):
right sequence, it's not discipline anymore.
(41:26:35):
It's really a sign that your scheduling processes
(42:17:00):
haven't caught up with the technology that's
(43:01:07):
available now. If your production's evolved,
(43:38:56):
your scheduling has to evolve too. It's that
(44:29:21):
simple.
Ryan (44:35:40):
And it sounds like a big part of this is that
(45:31:47):
reliance on tribal knowledge, right? That one
(46:07:30):
key person who just knows how everything works.
Emma (46:48:20):
Oh, absolutely. That's a classic situation,
(47:21:40):
and it sounds great until you realize how vulnerable
(48:11:42):
it makes you. Manual scheduling basically assumes
(48:50:36):
one person can hold all these variables in
(49:35:04):
their head. Which machines can sub for others,
(50:19:31):
which jobs save setup time if run together.
(51:03:59):
Who's available to work? Are the materials
(51:42:53):
even here yet? I mean, think about juggling
(52:27:21):
all that for hundreds of production orders
(53:06:15):
and business Central. It's. It's immense, impossible,
(53:45:10):
almost, pretty much. And it creates this huge
(54:29:37):
bottleneck, this single point of failure. What
(55:08:32):
if that scheduler is sick or overloaded or
(55:53:00):
worse, what if they leave the company? All
(56:37:27):
that crucial knowledge trigger just walks out
(57:16:22):
the door. And, you know, studies back this
(58:00:49):
up. Research shows manual methods lead directly
(58:39:44):
to more variability, more inconsistency. We're
(59:13:04):
talking potentially 15, 20% higher overtime
(59:46:25):
costs, more scrap. It adds up fast.
Ryan (60:25:20):
Okay, so if intuition and this fragile tribal
(61:12:16):
knowledge just aren't cutting it anymore in
(61:53:20):
this complex world, what's the big shift? What's
(62:40:16):
the answer?
Emma (62:52:00):
The clear direction? The paradigm shift is
(63:41:42):
towards advanced automated scheduling systems.
(64:17:12):
These systems are built precisely to tackle
(65:06:54):
these challenges. The complexity, the variability.
(65:49:30):
They work by taking that tribal knowledge,
(66:39:13):
all those subtle rules, preferences, constraints
(67:21:49):
that only the expert knew, and translating
(68:11:31):
it into configurable logic rules the system
(69:01:13):
understands.
Ryan (69:08:20):
So it's codified.
Emma (69:25:40):
Exactly. Instead of someone guessing the best
(70:04:06):
sequence, the system evaluates every single
(70:37:04):
job based on parameters you define. Things
(71:15:30):
like, how important is this customer? Are these
(71:59:27):
jobs part of the same setup family? Are the
(72:48:52):
material ready? What's the actual real time
(73:27:19):
capacity on that machine? The schedule it produces
(74:11:16):
is consistent, it's responsive, and it's grounded
(74:49:42):
in reality, not just a hunch.
Ryan (75:22:40):
And the key benefit there sounds like repeatability.
Emma (76:14:40):
Absolutely critical. Automation gives you a
(76:44:05):
repeatable system. So when demand suddenly
(77:13:30):
spikes or key people are out or a supplier
(77:57:38):
is late, the underlying logic is still there.
(78:36:52):
The system just adapts to the new inputs. It
(79:21:01):
means the shop floor doesn't just grind to
(80:00:14):
a halt because the main scheduler called in
(80:39:28):
sick. It keeps things flowing.
Ryan (81:05:20):
That makes a lot of sense for consist, for
(81:53:52):
control. But let's be real, a lot of places,
(82:42:24):
maybe even using business central, they still
(83:20:10):
mostly schedule by just the due date. Feels
(84:03:19):
simple, right? Keep the customer happy. But
(84:41:04):
is that really the best way now? Or is it Kind
(85:40:23):
of deceptively simple. This is where it gets
(86:23:32):
really interesting.
Emma (86:34:20):
You nailed it. It's a really common pitfall.
(87:19:43):
Scheduling only by due date often doesn't work
(88:05:07):
well anymore. Think about why. Sales might
(88:44:50):
put in default leads without checking capacity.
(89:24:33):
Or MRP runs might backschedule from that due
(90:09:57):
date. But they don't always consider the real
(90:55:20):
constraints. Machine availability right now.
(91:23:43):
Tooling labor. It often creates schedules that
(92:03:26):
look okay on paper, but just aren't feasible.
Ryan (92:50:10):
Okay, so how do automated systems handle prioritization
(93:32:50):
better?
Emma (93:39:30):
They move beyond just dates to incorporate
(94:21:36):
business defined priority logic. It's about
(94:57:41):
strategy. So for example, you can set rules
(95:45:48):
so rush orders for your top tier customers
(96:33:54):
automatically jump the queue. Or maybe high
(97:16:00):
margin jobs get prioritized to boost profitability.
(97:58:06):
Maybe jobs that are late but feed critical
(98:46:13):
downstream assemblies get pushed forward. These 150 99:22:18,857 --> 100:04:24,857 Emma
Welcome to Inside Insight, your quick hit of
(49:15):
tips, tools and trends for manufacturing, Distribution
(01:28:51):
and Dynamics 365 Business Central, brought
(02:02:48):
to you by Insight Works. Ever feel like you're
Ryan (02:53:43):
just drowning in information, you know, trying
(03:33:19):
to sift through everything just to really understand
(04:18:35):
one complicated thing? It's, it's a constant
(04:58:11):
battle these days, isn't it? Needing to get
(05:43:26):
up to speed fast without getting lost in the
(06:34:21):
weeds. Well, that's exactly what we're doing
(07:13:57):
today. We're taking a deep dive into something
(07:59:13):
we really vital, but maybe a bit overlooked
(08:44:28):
sometimes. Manufacturing, production, scheduling.
(09:07:06):
And specifically we're looking at it Inside
(09:46:42):
Microsoft Dynamics 365 Business Central. Our
(10:20:39):
mission basically is to unpack how this key
(11:05:54):
part of operations is. Well, it's really transforming.
(11:51:10):
We're seeing this shift away from just gut
(12:36:26):
feeling and intuition towards intelligent automation.
(13:10:22):
And we want to explore why this is more than
(14:06:57):
just a tech upgrade, why it's strategically
(14:46:33):
crucial for anyone in modern manufacturing.
Emma (15:25:20):
Yeah, that's a great way to put it because
(16:24:10):
for, gosh, for a very long time, production
(17:16:28):
scheduling, especially in ERP systems like
(17:55:41):
Business Central, it really did lean heavily
(18:41:27):
on visual tools and honestly, on human intuition,
(19:33:45):
you'd have schedulers, often really experienced
(20:12:58):
people literally dragging orders around on
(20:52:11):
a screen, on a gaunt chart. They're trying
(21:44:29):
to manually balance all these competing demands
(22:30:15):
and constraints.
Ryan (22:43:20):
Okay, so visually dragging things around, it
(23:25:48):
sounds intuitive, maybe.
Emma (23:44:00):
It feels intuitive, absolutely. Especially
(24:10:45):
if you think back to maybe simpler times, less
(24:58:55):
complex production. But the reality is that
(25:36:22):
approach introduced a lot of risk, a lot of
(26:24:32):
inconsistency, and that's a huge problem in
(27:02:00):
today's environments like high MITs production,
(27:34:06):
multiple manufacturing stages. Complexity is
(28:00:52):
just the name of the game now.
Ryan (28:39:40):
Okay, let's unpack that a bit more. This manual
(29:25:29):
method, the drag and drop thing, it seemed
(30:06:13):
like solid scheduling practice once. It probably
(30:41:52):
made sense when things were stable, predictable,
(31:17:30):
maybe lower volume, like a craftsman managing
(31:53:09):
a familiar workshop. So what fundamentally
(32:23:42):
changed? Why is sticking with that method now
(33:04:26):
not just inefficient, but actually kind of
(33:40:04):
risky?
Emma (33:46:30):
Well, the whole manufacturing landscape just
(34:24:18):
accelerated past what one person can manually
(35:08:26):
optimize effectively. Think about it. We've
(35:46:14):
got much tighter lead times, huge increases
(36:30:22):
in product variety, sometimes down to individual
(37:14:29):
customization, and then there's the constant
(37:52:18):
stream of disruptions, supply chain issues,
(38:30:06):
labor shortages, machine breakdowns. It's relentless,
(39:07:55):
right? So clinging to those old ways, the manual
(40:04:39):
tweaks, relying on someone just seeing the
(40:48:46):
right sequence, it's not discipline anymore.
(41:26:35):
It's really a sign that your scheduling processes
(42:17:00):
haven't caught up with the technology that's
(43:01:07):
available now. If your production's evolved,
(43:38:56):
your scheduling has to evolve too. It's that
(44:29:21):
simple.
Ryan (44:35:40):
And it sounds like a big part of this is that
(45:31:47):
reliance on tribal knowledge, right? That one
(46:07:30):
key person who just knows how everything works.
Emma (46:48:20):
Oh, absolutely. That's a classic situation,
(47:21:40):
and it sounds great until you realize how vulnerable
(48:11:42):
it makes you. Manual scheduling basically assumes
(48:50:36):
one person can hold all these variables in
(49:35:04):
their head. Which machines can sub for others,
(50:19:31):
which jobs save setup time if run together.
(51:03:59):
Who's available to work? Are the materials
(51:42:53):
even here yet? I mean, think about juggling
(52:27:21):
all that for hundreds of production orders
(53:06:15):
and business Central. It's. It's immense, impossible,
(53:45:10):
almost, pretty much. And it creates this huge
(54:29:37):
bottleneck, this single point of failure. What
(55:08:32):
if that scheduler is sick or overloaded or
(55:53:00):
worse, what if they leave the company? All
(56:37:27):
that crucial knowledge trigger just walks out
(57:16:22):
the door. And, you know, studies back this
(58:00:49):
up. Research shows manual methods lead directly
(58:39:44):
to more variability, more inconsistency. We're
(59:13:04):
talking potentially 15, 20% higher overtime
(59:46:25):
costs, more scrap. It adds up fast.
Ryan (60:25:20):
Okay, so if intuition and this fragile tribal
(61:12:16):
knowledge just aren't cutting it anymore in
(61:53:20):
this complex world, what's the big shift? What's
(62:40:16):
the answer?
Emma (62:52:00):
The clear direction? The paradigm shift is
(63:41:42):
towards advanced automated scheduling systems.
(64:17:12):
These systems are built precisely to tackle
(65:06:54):
these challenges. The complexity, the variability.
(65:49:30):
They work by taking that tribal knowledge,
(66:39:13):
all those subtle rules, preferences, constraints
(67:21:49):
that only the expert knew, and translating
(68:11:31):
it into configurable logic rules the system
(69:01:13):
understands.
Ryan (69:08:20):
So it's codified.
Emma (69:25:40):
Exactly. Instead of someone guessing the best
(70:04:06):
sequence, the system evaluates every single
(70:37:04):
job based on parameters you define. Things
(71:15:30):
like, how important is this customer? Are these
(71:59:27):
jobs part of the same setup family? Are the
(72:48:52):
material ready? What's the actual real time
(73:27:19):
capacity on that machine? The schedule it produces
(74:11:16):
is consistent, it's responsive, and it's grounded
(74:49:42):
in reality, not just a hunch.
Ryan (75:22:40):
And the key benefit there sounds like repeatability.
Emma (76:14:40):
Absolutely critical. Automation gives you a
(76:44:05):
repeatable system. So when demand suddenly
(77:13:30):
spikes or key people are out or a supplier
(77:57:38):
is late, the underlying logic is still there.
(78:36:52):
The system just adapts to the new inputs. It
(79:21:01):
means the shop floor doesn't just grind to
(80:00:14):
a halt because the main scheduler called in
(80:39:28):
sick. It keeps things flowing.
Ryan (81:05:20):
That makes a lot of sense for consist, for
(81:53:52):
control. But let's be real, a lot of places,
(82:42:24):
maybe even using business central, they still
(83:20:10):
mostly schedule by just the due date. Feels
(84:03:19):
simple, right? Keep the customer happy. But
(84:41:04):
is that really the best way now? Or is it Kind
(85:40:23):
of deceptively simple. This is where it gets
(86:23:32):
really interesting.
Emma (86:34:20):
You nailed it. It's a really common pitfall.
(87:19:43):
Scheduling only by due date often doesn't work
(88:05:07):
well anymore. Think about why. Sales might
(88:44:50):
put in default leads without checking capacity.
(89:24:33):
Or MRP runs might backschedule from that due
(90:09:57):
date. But they don't always consider the real
(90:55:20):
constraints. Machine availability right now.
(91:23:43):
Tooling labor. It often creates schedules that
(92:03:26):
look okay on paper, but just aren't feasible.
Ryan (92:50:10):
Okay, so how do automated systems handle prioritization
(93:32:50):
better?
Emma (93:39:30):
They move beyond just dates to incorporate
(94:21:36):
business defined priority logic. It's about
(94:57:41):
strategy. So for example, you can set rules
(95:45:48):
so rush orders for your top tier customers
(96:33:54):
automatically jump the queue. Or maybe high
(97:16:00):
margin jobs get prioritized to boost profitability.
(97:58:06):
Maybe jobs that are late but feed critical
(98:46:13):
downstream assemblies get pushed forward. These 150 99:22:18,857 --> 100:04:24,857 Emma
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