Growth System Design - Case Studies #MS Access

Master data cleanup

Sun, 12 Jul 2026 00:54:49 +0000

Master data cleanup to stabilise ERP (products, locations, vendors, BoMs)

Client

Manufacturing and warehousing businesses using ERP for purchasing and planning.

The challenge

Poor master data breaks purchasing and planning. When product records, locations, vendors, and BoMs are inconsistent, the ERP cannot produce reliable signals. Teams then revert to manual workarounds.

What we did

We stabilised ERP performance by cleaning up master data and introducing standards.

Standards introduced:

• Naming conventions

• Mandatory fields

How we prevented relapse

• Clear owners of information were assigned

• Ongoing responsibility to maintain data quality was made explicit, so master data did not become “everyone’s problem and no one’s job”

Results

• Purchasing and planning became more reliable because the system inputs were consistent

• Teams spent less time correcting errors downstream

Why it matters

Master data is not an admin task. It is operational infrastructure. When it is owned and maintained, the ERP becomes a tool for decision-making rather than a source of confusion.

Non-conformance

Sun, 12 Jul 2026 00:42:12 +0000

Non-conformance and corrective action workflow (CAPA-lite) that actually gets used

Client

Small and mid-sized manufacturers who needed practical quality accountability without heavy bureaucracy.

The challenge

Many SMEs either have no non-conformance process, or they have a process that is too complex to maintain. The result is repeat issues and a familiar pattern: “We had this problem before. What did we do last time?”

What we did

We implemented a lightweight non-conformance and corrective action workflow that teams could actually sustain. Depending on the environment:

• In some systems, the native quality app was used for logging

• In other cases, a controlled Excel-based log was implemented where it was the most practical option

What ‘good enough’ looked like

• Basic logging of issues

• Tracking of resolutions

• Clear accountability through assigned owners and follow-up

Results

• Clearer accountability

• Faster containment and resolution

• Reduced repeat issues because previous actions were visible and searchable

Why it matters

A simple quality workflow that gets used beats a perfect workflow that gets ignored. The goal is not paperwork. The goal is learning and repeatability.

Routing and work centres

Sun, 12 Jul 2026 00:32:42 +0000

Routing and work centre setup for realistic capacity planning

Client

Manufacturing businesses that needed realistic capacity planning and better labour utilisation.

The challenge

When run times, setup times, and work centre capacity are not defined properly, schedules become optimistic and delivery promises become unreliable. Teams compensate with overtime and constant rescheduling.

What we did

We implemented routing and work centre data that reflected real work.

This included:

• Defining run times and setup times

• Using motion studies to validate time assumptions What became visible Once routing data was realistic, it became clear that some tasks could be split into two linked activities:

• A higher-skilled portion of the work

• A lower-skilled portion of the work This maintained one-piece flow while allowing higher-skilled workers to focus their energy where it created the most value.

Results

• Higher output with less overtime

• Reduced bottlenecks because labour allocation matched the true constraint

Why it matters

Capacity planning is only as good as the data behind it. When routing and work centre assumptions match reality, scheduling becomes a practical tool instead of a constant negotiation.