From Paper to Pipeline: Digitizing Multi-Factory Precast Slab Production, Warehouse, and Delivery

Precast concrete manufacturers — hollow core slab producers especially — tend to run a genuinely sound physical process on top of a genuinely fragile paper trail. The casting itself is proven: prestressed strands, extrusion or long-line casting, a curing schedule that doesn’t change much year to year. What breaks down is everything around the casting — the same project dimensions typed into a spreadsheet three times, a panel layout sketched by hand before anyone checks whether an offcut in the yard could have covered it, a factory that has no idea what another factory in the same group is producing this week.

This post lays out a digitization framework we’ve used for a multi-factory precast producer — four casting plants running the same hollow core process — generalized because the underlying pattern (sound process, fragile tooling, a real ERPNext-vs-custom decision) shows up in almost every precast or make-to-order manufacturing digitization project, not just this one.

1. Where the friction actually lives

Reviewing a precast producer’s actual production sheets, warehouse forms, and delivery records — rather than starting from a generic manufacturing-software template — tends to surface the same handful of gaps:

• Production numbers and project data get tracked manually, with the same multi-thickness project data re-entered at each stage.
• Panel-layout planning happens by hand before anything reaches a production system, with no automated optimization to reduce material scrap.
• Warehouse intake and finished-goods stock live in disconnected spreadsheets, re-entering data that already exists somewhere else in the process.
• Delivery scheduling and truck-load planning stay manual and spreadsheet-based.
• Quality records — non-conformance reports — are handwritten, with no link back to the production record that actually caused the defect.
• Each factory operates in isolation. Moving a job from one plant to another means re-entering everything from scratch, because there’s no shared visibility across sites.

None of this calls for automating the casting process itself — labor- and crane-assisted precast production doesn’t need to become PLC-driven to fix these gaps. What it needs is the workflow around it digitized and connected, from order intake through delivery.

flowchart LR
    A[Sales and order intake] --> B[Production planning and optimization]
    B --> C[Warehouse and stock management]
    C --> D[Delivery and logistics]
    B --> E[Quality management]
    D --> F[Installation tracking]
    B --> G[Shop drawing approval]
    C --> H[Cross plant reporting dashboard]
    D --> H
    E --> H

Capture project details once at intake, and everything downstream — the production schedule, the stock ledger, the delivery plan, the quality record — should draw from that single source rather than re-typing it at each handoff. A cross-plant dashboard sits on top, giving management the one thing paper and disconnected spreadsheets structurally can’t: real-time visibility across every site at once.

One capability is worth calling out on its own, because it’s usually the single highest-value line item in a project like this: automated panel-layout optimization. Arranging cutting and casting sequences to minimize offcut waste, and automatically checking existing stock for reusable offcuts before cutting new material, is exactly the kind of task that’s tedious and error-prone by hand and mechanical for software — a straightforward bin-packing-style optimization problem, not a research project.

2. The real decision: platform foundation, not feature list

Once module scope is agreed, the decision that actually shapes cost, timeline, and long-term ownership isn’t which features to include — both realistic paths deliver the same scope. It’s which technology foundation to build on.

Option A — build on an open-source ERP platform (e.g. ERPNext). A mature platform with strong native sales, manufacturing, and warehouse support lets the core workflow get delivered faster, while the parts unique to precast — panel-layout optimization, dimension drawings, quality traceability, multi-plant reporting — still get fully customized on top. The standard back-office interface such a platform ships with is built for office data entry, not a factory floor in work boots, so the right version of this option pairs it with a dedicated field/floor web app — lane and casting-bed screens for production staff, warehouse check-in, delivery and load planning for drivers, installation progress reporting for site technicians — that talks to the platform via API while the platform quietly manages the underlying records and business logic.

Option B — a fully custom-built system. Built from the ground up around the manufacturer’s exact process, with no dependency on a third-party platform: full ownership of design and source code, maximum flexibility for future customization, and the fastest possible interfaces for factory-floor and mobile use, at the cost of a longer build and a higher price point.

flowchart TD
    A[Same module scope] --> B{Platform foundation}
    B -->|Faster delivery, platform dependency| C[Option A: open-source ERP plus field app]
    B -->|Full ownership, longer build, higher cost| D[Option B: fully custom system]

Neither option is a strictly better choice — it’s a genuine trade-off between delivery speed and long-term platform independence, and it’s the first thing worth deciding before scoping detail goes any further, because it changes both the timeline and the investment by a wide margin. As a rough order of magnitude across comparable four-factory precast projects we’ve scoped: an ERPNext-foundation build tends to land in the 3–4 month range, a fully custom build in the 5.5–7 month range, with cost following the same ratio — the custom path typically runs 60–80% more than the platform-foundation path for the same module scope.

3. Starting smaller: the MVP path

Not every manufacturer wants to commit to the full eight-module scope up front. A two-module MVP — Production Planning & Optimization and Warehouse & Stock Management — covers the highest-impact areas first: automatic production numbering, lane/schedule planning, panel-layout optimization, and real-time location-level stock tracking that updates automatically as panels complete production. The on-site field app for lane/production staff and warehouse check-in comes with the MVP, since those are exactly the users it’s built for. Sales & order intake, delivery & logistics, quality management, installation tracking, shop-drawing approval, and the cross-plant dashboard follow in a second phase, while the rest of the operation keeps running on the current paper/spreadsheet process in the meantime. In our experience the MVP scope runs at roughly half the cost and timeline of the corresponding full build, for either platform foundation.

4. Where AI genuinely helps — and where it’s premature

Automated panel-layout optimization belongs in the initial build; it’s the module the business case rests on. A second tier of AI capability is worth naming explicitly, and worth deliberately not building on day one:

• Automated visual quality inspection — camera-based detection of visible panel defects at the point of production, with automatic photo documentation attached to the quality record.
• Intelligent document search — natural-language search across historical production and quality records, including digitizing existing paper archives.
• Predictive insights — early detection of unusual patterns in scrap rates, quality trends, or delivery performance, once enough operating history exists to make a pattern meaningful.

That last qualifier matters. Predictive and pattern-detection features need real operating data to be worth anything — building them before the core system has produced that history just means building against synthetic assumptions. The honest sequencing is: ship the core system, let it run, then scope the second tier against what the actual data shows, not what looks impressive in a proposal.

5. Hosting: cloud by default, on-prem where policy requires it

A system spanning four factories, a head office, and field devices doesn’t benefit from any one site owning the server — cloud hosting gives every plant and every field user the same live system over a standard internet connection, with the provider handling hardware, power, and network reliability. Ongoing hosting is a separate, typically monthly cost from the one-time development investment — sized to server capacity, backups, and security monitoring, and scaled to actual user count and data volume once confirmed during discovery, commonly in the low five figures per month in local currency for a deployment of this size. For organizations with a specific data-residency or IT-policy requirement, on-premise or hybrid deployment is a reasonable alternative worth raising during discovery rather than defaulting away from.

6. Structuring payment around real delivery gates

Milestone-based invoicing tied to actual delivery — kickoff, a mid-project point once core modules are functionally complete and ready for UAT, and final payment on UAT sign-off and go-live — keeps both sides honest about progress instead of paying on a calendar. Hosting is billed separately and monthly from go-live onward, since it’s an ongoing operating cost, not a development milestone; and any second-phase AI capability gets scoped and invoiced under the same milestone structure once it’s actually agreed, rather than folded into the original fixed fee.

7. Who this fits

This framework fits any multi-site, make-to-order or engineer-to-order manufacturer running a proven physical process on a fragile administrative one — precast concrete producers most directly, but the same shape applies to precision fabrication, modular construction, and other plants where project data gets re-entered at every handoff and each site can’t see what the others are doing. The ERPNext-vs-custom decision, the MVP phasing option, and the "ship the core system before building predictive AI on top of it" sequencing all generalize past hollow core slabs specifically.

8. Getting started

If your production, warehouse, or delivery process still runs on paper and disconnected spreadsheets across more than one site, the useful first step is a discovery session against your actual forms and workflows — not a generic manufacturing-software demo. Simplico scopes and delivers both the ERPNext-foundation and fully custom paths, including the field/floor app, panel-layout optimization, and cross-plant reporting.

Reach out at hello@simplico.net to talk through your factory count, current tooling, and where the friction actually is.