Agile Is Pull-Based — Waterfall Is Batch-Based — And That Makes All The Difference

In manufacturing, there’s a world of difference between a batch-and-queue process and a pull-based flow like the (Toyota Production System)[https://global.toyota/en/company/vision-and-philosophy/production-system/]. The same is true in software development.

When you strip away the jargon, Agile (especially when practiced through Scrum or Kanban) is a pull-based, demand-responsive approach to delivering value, while Waterfall is a batch-based, queue-heavy approach that tries to deliver the entire solution all at once.

Waterfall: The “Solve It All at Once” Model

Waterfall projects follow a fixed sequence:

1. Requirements → 2. Design → 3. Build → 4. Test → 5. Deploy.

In this model, teams decide up front what the full solution should be — based on estimates, projections, and assumptions about the future — and then work to deliver it as one large release.

This means:

• You commit to solving the entire estimated problem before you even know if parts of it are needed.
• Value is delayed until the whole package is done.
• Risk piles up because any wrong assumption affects the entire build.
• Feedback arrives too late to change course without major rework.

It’s the equivalent of manufacturing an entire car model before finding out if anyone likes the design.

Agile: The “Solve the Next Bottleneck” Model

Agile Project Management Methods like Scrum and Kanban both operate as pull-based systems:

• Scrum pulls the most valuable work from a prioritized backlog into short, fixed-length sprints.
• Kanban continuously pulls the next highest-priority work item when capacity is available, with explicit limits on work-in-progress.

Instead of designing and delivering everything in one shot, Agile teams decide what to build based on the most pressing constraint or opportunity right now.

They solve that, see what it unlocks, and then decide the next move.

Over time, this creates a chain reaction:

1. Solve a small problem → it unlocks the next problem.
2. Address that → it illuminates the next opportunity.
3. Repeat.

Because decisions happen closer to the point of need, Agile teams adapt in real time to market shifts, customer feedback, and operational realities.

Vision Still Matters

Being pull-based doesn’t mean you’re wandering aimlessly from bottleneck to bottleneck. Vision is still required.

A clear overall vision is still essential:

• You need to know the big problem you’re trying to address.
• That problem must align closely with your business strategy.
• And that strategy? It’s also a hypothesis — an informed guess that needs validation through real-world results.

Agile simply means you validate that vision in smaller, safer steps instead of betting everything on one massive, untested plan.

Why This Matters for Responsiveness

The real difference isn’t just batch size — it’s how you decide what to build next:

Real-World Example: ERP Rollout vs. Agile Product Development

Waterfall ERP Rollout

A company wants to modernize its operations. In Waterfall style, they gather requirements from every department — finance, HR, operations, sales — and plan a full ERP implementation to replace everything at once.

Two years later, the system goes live… but:

• Some features are obsolete because the market changed.
• Teams have to adjust to entirely new workflows overnight.
• Early design decisions that seemed fine are now costly to change.

Agile Product Approach

Same company, different method. Instead of tackling the whole ERP at once, the team starts with the most painful current bottleneck — say, slow purchase order approvals. They deliver a small workflow app in Excel (what they’re currently using) that fixes just that.

Next, – after observing, reassessing, and learning – they tackle inventory tracking. Then payroll integration. Each release is based on current demand, informed by real-world usage of the previous step.

In 18 months, the company has a fully modernized system — but every part has been tested, adapted, and tuned along the way.

Pull Beats Push for Business Agility

Pull-based systems — like Scrum and Kanban — are inherently more customer-focused and market-aware:

• You only build what’s needed right now, not everything you guessed months ago.
• Lead time is shorter — small wins ship faster.
• Feedback arrives sooner, so course corrections cost less.
• Risk is spread out instead of concentrated in a single launch.

This keeps your business aligned with reality instead of a static plan.

The Bigger Picture

Manufacturing learned long ago that massive batches slow everything down and hide problems. Software development is no different.

If you want to be responsive to demand, flow value quickly, and adjust to the market, you can’t just shrink your batches — you have to change how you decide what to build and ensure every step stays aligned with your larger vision and strategy.

That’s the true difference between Waterfall and Agile.

Slicing

When you’re dealing with a big, cross-system, multi-team, multi-location rollout like a big Quick-service Restaurant switching labor management software across all stores, the key is to define “small” not in terms of the entire project, but in terms of a measurable, testable slice of the end-to-end outcome.

Most people get stuck because they think small batch means “cut the project into technical pieces” (e.g., database, middleware, front-end) — but that’s still just partial work-in-progress. The right way is to slice by value and learning:

What “Small Batch” Could Look Like in This Case

1. Roll out to a single store first
   • Pick a representative store (average size, average complexity, average turnover) and run the new labor management system end-to-end in just that one location.
   • Goals:
     • Verify technical integrations in a live setting.
     • See how managers and staff adapt.
     • Identify operational hiccups.
   • Output: Real data + first-hand feedback.
2. Expand by store type
   • After validating in one store, pull in the next set of stores based on meaningful variation:
   • A high-volume location.
   • A location with unusual staffing patterns (e.g., late-night hours).
   • A remote or hard-to-support location.
   • Goals:
     • Validate the system under different workloads and conditions.
     • Expose edge cases early.
3. Use “day-in-the-life” scenarios
   • Instead of enabling all features at once, turn on the minimal set needed to run scheduling, time tracking, and reporting for a real shift.
   • Then, progressively enable advanced features:
     • Mobile scheduling approvals.
     • Automated shift swaps.
     • Labor compliance alerts.
   • This lets you de-risk each feature area separately.
4. Parallel run for data confidence
   • For a period, run the old and new systems side-by-side for a small set of stores, and compare:
     • Clock-in/out accuracy.
     • Labor cost reporting.
     • Payroll integration.
   • This small batch is about data trustworthiness, not geography.
5. Treat “small” as a learning unit, not a delivery unit
   • A batch is small if:
     • It can be delivered and validated in 1–2 weeks.
     • It produces actionable feedback for the next decision.
     • It can be rolled back without derailing the rest of the program.

Why this works

• Risk containment — Problems are found before the whole network is impacted.
• Learning speed — Feedback from live use guides the rollout.
• Adaptability — You can change your plan mid-rollout without rewriting everything.

Our Slicing Dimension

Using a single store as the slice would usually be classified along the deployment dimension — sometimes called a geographic rollout or location-based slice.

Here are some common ways to frame that dimension:

1. Geographic Slice – Rolling out by physical location (city, region, or store).
2. Market Segment Slice – Choosing a store that represents a certain customer or operational profile (e.g., high-traffic urban location).
3. Pilot Site – Using one location as a controlled, real-world test environment.
4. Deployment Cohort – Treating the store as part of a small, intentional group for early adoption.
5. Operational Context Slice – Selecting the store based on operational patterns (late-night hours, high turnover, drive-thru only, etc.).

If we were building a taxonomy for small batches in large rollouts, “single store” would fall under:

Dimension: Deployment / Geography
Subtype: Pilot location (representative or strategically chosen)

The “slice” is not a partial feature or technical layer — it’s a complete vertical implementation limited by scope of deployment.

Small Batch Slicing Dimensions