Lincoln-based. Regionally focused. Enterprise-minded. info@withamtech.co.uk
Witham Technologies
01 · Industry

Data, cloud and AI for manufacturing and engineering businesses

From precision engineering shops to multi-site production, we help manufacturers turn machine, ERP and shop-floor data into fewer stoppages, faster reporting and better margins.

Book a discovery call → See example projects
CNC machining in a manufacturing plant
Common challenges

What we often see in manufacturing businesses

If any of these sound familiar, you're not alone. They're the problems that hold back performance in most operations in the sector.

Downtime you can't see clearly

Stoppages are logged on paper or in spreadsheets, so the real causes and costs only show up weeks later, if at all.

Systems that don't talk

ERP, MES, machine controllers and quality systems each hold part of the picture, and someone reconciles them by hand.

Reporting that takes days

Weekly production and OEE packs are rebuilt manually from exports, and are out of date by the time they're read.

Reactive maintenance

Critical assets fail without warning, and maintenance history isn't joined up with how the machines are actually being run.

Uncertain job costs

Quotes are based on estimates, and nobody can easily compare quoted against actual hours, materials and scrap.

Example projects

What working with us could look like

Three examples of the kind of work we do in manufacturing & engineering, from first conversation to measurable change.

Illustrative examples. These scenarios are based on common challenges in the sector and show how we'd approach them. They are not descriptions of specific client projects.

Example 1Analytics & Reporting

From paper downtime sheets to a live shop-floor view

The situation

A precision engineering business running around thirty CNC machines across two shifts. Downtime is written on paper, typed into Excel each Friday, and nobody trusts the OEE figure.

What we'd do

  1. Capture downtime reasons at the machine with a simple tablet app instead of paper.
  2. Bring machine status, job data from the ERP and the new downtime records into one data platform.
  3. Build Power BI dashboards for the shop floor, supervisors and management, all from the same numbers.

What changes

  • OEE by machine, shift and job, available daily rather than weekly
  • The biggest causes of lost time ranked and costed
  • Hours of Friday spreadsheet work removed

Typical shape: Data & Analytics Assessment → pilot on one cell → roll-out across the site

Example 2AI & ML Enablement

A predictive maintenance pilot that proves value first

The situation

A manufacturer has suffered repeated unplanned failures on a critical asset group. It's keen on predictive maintenance but unsure whether its data is good enough.

What we'd do

  1. Run an AI Readiness Workshop to review sensor, PLC and maintenance history data honestly.
  2. Build a focused anomaly-detection model on one asset group, not the whole factory.
  3. Feed alerts into the existing maintenance process so engineers act on them, not just look at them.

What changes

  • A clear, evidence-based go or no-go before larger investment
  • Early warning on the assets that hurt most when they fail
  • Maintenance planned around production rather than interrupting it

Typical shape: AI Readiness Workshop → single-asset pilot → scale to further assets

Example 3Data Platforms

Quoted versus actual: knowing which jobs really make money

The situation

An engineering firm quotes from experience. Margins feel thinner than they should, but the data to prove which job types lose money is spread across the ERP, timesheets and purchasing.

What we'd do

  1. Join quotes, labour bookings, material costs and scrap into a single job-level model.
  2. Report quoted against actual by customer, product family and machine.
  3. Feed the findings back into the quoting process with better default rates.

What changes

  • Visibility of which customers and job types are profitable
  • More confident, evidence-based quoting
  • Earlier warning when a live job is drifting over budget

Typical shape: Data & Analytics Assessment → job costing model → embedded in quoting

Your existing systems

We work with what you already have

Most projects start by connecting the systems you already run, not replacing them. Systems we often see in this sector include:

  • Microsoft Dynamics 365
  • Sage
  • SAP Business One
  • Epicor
  • MES & SCADA systems
  • PLC & machine data
  • CMMS / maintenance systems
  • Excel & SharePoint
Not sure where to start?

Begin with a Data & Analytics Assessment

A focused review of where you are today, with clear priorities and a practical roadmap, before any commitment to a larger programme.

Discuss an assessment → See all assessments

Let's talk about your operation

Tell us what you're trying to improve. We'll arrange an initial conversation, and visit site if that's useful.

Book a discovery call →
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