Before LineLab: How Production Planning Gets Done with Excel and Simulation
In many of the world’s most advanced manufacturing organizations — across aerospace, defense, automotive, and biopharma — production system modeling is an essential craft. Teams rely on a deep combination of institutional knowledge, spreadsheets, simulation tools, and years of experience to plan high-stakes operations: greenfield factories, rate increases, and new product introductions.
These models are not improvised — they are the result of hard-earned expertise, often built and refined over years by highly capable industrial engineers and operations teams. Excel remains the most widely used tool, often enhanced with VBA, Monte Carlo plug-ins, and bespoke logic to evaluate cycle time, staffing, or equipment trade-offs. Simulation tools like Simio, AnyLogic, and ProModel are deployed with care by trained experts when dynamic analysis is needed. And in many cases, individual engineers have built remarkably sophisticated workflows that stitch these tools together in support of critical decisions.
But these tools, as powerful as they are, are often hard to share, slow to adapt, and difficult to extend beyond the team that built them. That’s where LineLab is starting to play a role — not by replacing this knowledge, but by capturing and extending it into a more flexible, collaborative, and scenario-driven environment.
Some teams have already made the transition. For many others, understanding what modeling looks like before LineLab helps clarify where the effort currently goes — and how quickly those same Excel-based process flows can be carried forward into a full, more reliable, and dynamic system model.
Excel: The Ubiquitous Backbone
Whether designing a new line for an aerospace component, scaling up biopharmaceutical production, or rebalancing automotive final assembly, most techno-economic decision-making still starts — and often ends — in Excel.
There are good reasons for this:
- Flexibility: Excel accommodates everything from equipment lists to capacity models to cost forecasts.
- Ubiquity: Everyone uses it, so models are easy to share and discuss.
- Institutional momentum: Many Excel models are multi-year work efforts, tweaked and updated with every new detail that is learned.
But there are crucial trade-offs. Spreadsheets can't handle the nonlinear dynamics of flow. Things like blocking, starvation, batch behavior, or rework loops introduce feedback effects that far exceed what spreadsheets can capture. This leads to significant accuracy limitations.
And as models grow in complexity, they become harder to audit, validate, or adapt to new scenarios. Relationships between variables are encoded in nested formulas and linked sheets that resist transparency. And even in cases where the stakes are high — $100M+ capex or multi-year ramp timelines — the underlying assumptions are often buried in a file only a few people truly understand.
Advanced Toolchains
Occasionally, we encounter a standout example of individual initiative: a manufacturing engineer or process analyst who has built a semi-automated workflow using tools like:
- Minitab for statistical process control or DoE analysis,
- Excel/VBA for cost rollups and equipment modeling,
- Python scripts or Power Query to clean and connect data,
- And simulation like Arena or AnyLogic for discrete-event simulation.
These toolchains can be very complex. Chris Tonn of SpiritAerosystems demonstrated a toolchain that combined Excel, Simio and Minitab to enable an advanced sensitivity analysis similar to the one that's built-in to LineLab.[1]
This is a common theme: these solutions are often sophisticated and require advanced skill to use and understand all the elements required to make sure they accurately represent reality.
Simulation: Specialized Modeling for Determining Throughput
Simulation tools — like Siemens Plant Simulation, Tecnomatix, AnyLogic, ProModel, or Simio — are used in many corporations, but they tend to arrive late in the project timeline. Sometimes, simulation is seen as a validation tool once a layout or process design is nearly finalized, rather than a tool for exploring alternatives.
Why?
- Required Expertise: Simulation requires specialized training.
- Data needs: Simulations require detailed input — installed capacity, number of kanbans and inventory, cycle times — that aren’t always available early on.
- Lead time: Building a validated simulation can take weeks. For fast-moving teams, that’s too slow to guide early trade-off decisions.
As a result, simulation is often decoupled from the upstream economic modeling and process planning work. A rough “optimization” of system capacity is often done in Excel, the throughput for various assumptions is then determined via simulation, and finally the cost calculation is carried out in another Excel spreadsheet.
Startups and Small Teams: The Challenge of Data Gathering
Startups often rely on simpler tools — typically Excel or expert-driven estimates in the early stages — to plan production systems. Some try simulation, but run into the challenge of sourcing all the required inputs: capacity, resource counts, routing assumptions. Preparing this data can take weeks or months, while startups burn through runway.
The result is that valuable time gets spent trying to build models that rely on key questions — like flow bottlenecks or cost drivers — already being answered.
Where Do We Go From Here?
This dynamic is changing — with integrated modeling that bridges engineering, finance, and operations starting in early development.
In our work with advanced manufacturing teams — across aerospace composites, microfactories, biopharma scale-up, and new defense platforms — we’ve seen a clear pattern: the knowledge already exists. Engineers have built thoughtful Excel models, structured cost breakdowns, and layout sketches that reflect real manufacturing understanding.
What’s often missing isn’t insight — it’s integration. These models are hard to adapt, slow to share, and disconnected from flow behavior. That’s why, in practice, many teams now start by importing their existing Excel flows directly into LineLab. Within minutes, they get a working production system model that reflects station-level logic, considers buffers and resource constraints, and supports immediate exploration of scale, rate, and cost trade-offs.
The transition is fast because it doesn't require rethinking how teams work. It builds directly on what they already do — and makes it easier to test scenarios, share findings, and collaborate across departments.
Excel isn’t going anywhere. But for a growing number of teams, LineLab is where production modeling now begins.
[¹] Chris Tonn: „Advanced Sensitivity Analysis with Excel, Simio and Minitab”, Simio Sync 2019.