Key takeaways:
“Just one more system” often increases total cost by adding integration work, risk, and slowdowns you don’t see on the purchase order.
The biggest hidden costs show up in data consistency, cybersecurity exposure, and the people time required to keep a growing tech stack running.
A clear target architecture, funded integration and data governance, and a defined scale plan prevent incremental investments from turning into a “Frankenstack.”
Food manufacturers rarely set out to build a messy digital environment. It usually happens one “reasonable” decision at a time: a new quality tool here, a scheduling add‑on there, a dashboard to connect them, and another point solution to solve a plant-specific pain.
Before long, you’re running a “Frankenstack,” a stitched-together set of systems that technically works, but quietly adds operational drag. It can lead to slower decisions, more rework, higher risk, and longer time-to-value.
The “Frankenstack” problem
Incremental technology investments feel safe because they’re smaller, easier to approve, and often delivered quickly. The trap is that each “small” choice can create long-lived complexity.
Food manufacturing leaders tend to see the visible line items:
Software license
Hardware
Implementation
Training
But the real cost curve often shows up later in:
Integration work that never ends
Data that doesn’t match across plants
Extra cybersecurity exposure with each new connection
Teams spending time “keeping systems talking” instead of improving performance
Pilots that look great but never scale beyond one site
Where the hidden costs show up
Integration and data harmonization
Every new system introduces new connections and new definitions.
Common “slow leaks” in food manufacturing include:
Manual workarounds: Exports, spreadsheets, re-keying, and reconciling numbers.
Conflicting definitions: Yield, downtime, scrap, rework, and even “completed batch” mean different things across plants and systems.
Reporting delays: Leadership dashboards look clean, but only after hours (or days) of massaging data.
Brittle integrations: One vendor update breaks the data flow, and your team becomes the permanent fix-it crew.
The cost isn’t just IT work. It’s production time, quality assurance rework, and slower decisions on the floor.
Here’s a simple test: If you need meetings to decide which number is “the real number,” you’re paying the data harmonization tax.
Cybersecurity exposure
Each added system can expand your cyberattack surface, especially when it touches plant operations.
This matters more now because:
More systems need access to production data
Vendors need remote access to support tools
More devices connect to your network
Employees use more apps, logins, and workflows
Even if your plant systems aren’t directly exposed to the internet, incremental tools often introduce extra user accounts and permissions, more third-party access paths, and more systems to patch, monitor, and maintain.
Hidden cost: the ongoing effort to secure and govern a larger, more connected environment, plus the potential impact when something goes wrong.
Talent burden
A Frankenstack can create a complex staffing model:
A specialist for System A
A different specialist for System B
Integration knowledge that lives in one or two people’s heads
Vendor management overhead that grows every quarter
In food manufacturing, this hits hard because your best people are already needed for:
Food safety and compliance demands
Uptime and throughput goals
Workforce turnover and training
Continuous improvement
When your talent is spread thin across too many systems, your organization not only pays in direct labor and vendor costs, but in opportunity cost (what your teams didn’t improve because they were busy “keeping the stack alive”).
Change fatigue across sites
For many operational leaders, this is where incremental tech investments really break down.
Each new system introduces:
Training cycles
New standard work
New escalation paths
Changes to quality assurance signoffs
New maintenance routines
If Plant A, Plant B, and Plant C each get different tools at different times, you end up with:
Inconsistent workflows
Inconsistent metrics
“This is how we do it here” resistance
Slow cross-site learning
Change fatigue isn’t a soft issue. It creates hard results:
Lower adoption
Incomplete data
Workarounds
Stalled rollouts
Slower innovation transfer from lab to network
If you lead innovation, you’ve probably lived this pattern:
The pilot works in a controlled environment
The first plant implementation limps along
Scaling to more lines or sites becomes a brand-new project
Incremental tools can accidentally make this worse by creating one-off environments:
Pilots built on data that isn’t available everywhere
Solutions that only fit one plant’s workflow
Integrations that don’t transfer cleanly to another site
Manufacturing companies investing in tech often face implementation challenges tied to transformation complexity and workforce readiness. That’s exactly what kills the pathway from experimentation to full business impact when each site is running a different patchwork.
Deciding when incremental is smart and when it’s a trap
Not all incremental investments are bad. The goal is to separate bridge investments from platform investments and to stop treating platform needs like a series of small one-offs.
Bridge investment
A bridge investment is a short-lifecycle move that:
Solves a specific problem fast
Delivers clear value quickly
Has minimal long-term lock-in
Won’t block the future architecture
Examples in food manufacturing might include:
A targeted upgrade to reduce labeling errors in one area
A temporary connectivity layer while you standardize
A focused tool to close a compliance gap with a defined end date
Bridge investments are fine as long as they’re designed to be temporary.
Platform investment
A platform investment is meant to become shared capability:
Standard ways to capture production data
Standard ways to connect systems
Shared data definitions across plants
Reusable workflows and reporting
This usually includes core systems and foundations like:
Enterprise resource planning (ERP) systems
Manufacturing execution system (MES) layers
Integration standards and data governance
Security and identity foundations
You don’t have to buy one mega-system to do platform work. But you do need to decide what you’re standardizing and how new tools will connect.
6 questions to prioritize technology investments
Use these questions to pressure-test whether “one more system” is a smart bridge or the next piece of your Frankenstack.
What is the value pathway? Can we clearly explain (step by step) how this improves margin, reduces risk, or increases speed?
Will this be reusable across plants? If the answer is “only here,” treat it like a bridge with a sunset plan.
Does it fit our data standards? Will it use the same definitions for batches, downtime, yield, and quality events? If you don’t have standards, this is your signal to create them.
What is the vendor and lock-in risk? If we switch vendors later, how hard is it to extract data and retire the tool?
What is the adoption burden? How many roles change? How much training is required? What happens on night shift and weekends?
What is the security posture? What new connections, accounts, remote access, or devices does this introduce. And who owns securing them?
If you’re struggling to answer these questions simply, it’s a sign the “small” investment may have a large hidden tail.
What to do instead
Define the target architecture
Leaders often ask, “What’s the right vendor?” A better starting point is: What is our target architecture? Meaning: a simple map of how work and data should flow across plants, regardless of vendor.
A practical target architecture for food manufacturing usually clarifies:
What systems are “core” vs. “site-specific”
Where production data is captured
How quality assurance data connects to production context
How enterprise reporting is built from consistent definitions
This reduces vendor-led decision making and keeps investments aligned.
Fund integration and data governance as first-class work
Integration and data governance aren’t “nice-to-have.” They are the difference between scaling fast and rebuilding every time you add a plant.
Data governance means:
Agreed definitions for key metrics
Clear data ownership (who fixes what)
Standards for naming, batch structure, and event tracking
A process for handling changes so Plant A doesn’t drift away from Plant B
If you don’t fund this explicitly, you still pay for it, just later while putting out fires.
Build a scale plan before the pilot
Many pilots fail not because the technology is bad, but because scale wasn’t designed in.
Before approving a pilot, require:
A replication plan: how this moves to the next line, then the next plant
A support model: who owns it after go-live (not just during the pilot)
A training approach: onboarding, refreshers, and accountability
A success definition: what “good” looks like in daily operations, not just a demo
This is how you protect innovation transfer and reduce rollout friction.
Retiring legacy without disruption
Incremental tech investments often become permanent because no one plans the exit.
Here’s a simple sunset plan you can reuse:
Name the legacy tool and the replacement path. Put it in writing: what replaces it, and why.
Freeze new features. Only allow fixes required for safety, compliance, or uptime.
Define the “system of record” date. Pick a date when the new system becomes the official source of truth.
Run in parallel with clear rules. Short parallel periods work best. Decide which system wins when numbers differ.
Migrate or archive what you truly need. Don’t migrate everything by default. Migrate what’s required; archive the rest for audit needs.
Cut over with a rollback plan. The rollback plan reduces fear and improves adoption.
Decommission and remove access. Retiring a system includes eliminating logins, integrations, and vendor access paths.
Measure the outcome. Confirm adoption, data quality, and time saved, then reinvest that capacity.
This turns “legacy retirement” from a scary event into a managed transition.
Incremental technology investments feel cheaper because the bill is smaller. But in food manufacturing, the real cost is usually paid in:
Integration complexity
Inconsistent data
Cybersecurity exposure
Talent strain
Change fatigue
Slow scale from lab to plant to network
A Frankenstack isn’t solved by buying the next tool. It’s solved by making the foundations — architecture, integration, governance, and scale planning — part of the investment every time.
FAQ for food manufacturing leaders
Q: How do I know if we’ve built a Frankenstack?
A: If you recognize two or more of these, it’s likely:
The same metric shows up differently in different meetings
Teams export data to spreadsheets to “make it make sense”
Each plant has its own tool for the same job
Integrations break often and fixes depend on a few key people
Scaling a successful pilot feels like starting over
Q: Should we pause all incremental technology investments?
A: No. Some incremental investments are smart bridge investments, especially when they reduce risk or improve performance quickly. The key is to require:
Clear value
Minimal lock-in
A defined lifespan
Alignment to the target architecture
Q: What should we standardize first across plants?
A: Start where inconsistency creates the biggest business pain:
Production and downtime event definitions
Quality events tied to batch context
Master data (products, lines, SKUs, materials)
Then build integration patterns that make adding the next plant easier.
Q: How do we justify platform investments to finance?
A: Frame platform work as reducing ongoing “taxes” that quietly erode returns:
Less rework and manual reporting effort
Faster rollout of improvements across plants
Lower security and vendor risk exposure
Fewer one-off implementations
Tie the story to margin, risk reduction, and speed-to-value, not just “modernization.”
Q: How should cybersecurity influence technology prioritization?
A: Cybersecurity should be a gating requirement, not an afterthought. If a tool adds new access paths or connections, you need an owner for securing it before you scale it.
Q: What if our plants have different maturity levels?
A: That’s normal. The answer isn’t “one-size-fits-all,” it’s:
Common standards for data and security
A shared architecture for how systems connect
Phased rollout plans that respect site readiness
You can allow local flexibility inside a standardized framework.
