Growth Initiatives

Al Aziz Dress Co. Digitising Initiatives

Al Aziz Dress Co. operates a high-volume woven and knitwear facility supplying both domestic and export buyers. Until 2016, most of the planning and load-balancing work was done on paper. Fabric markers were drawn manually, loom assignments handled by shift supervisors, and any imbalance or shade mismatch triggered rework that quietly ate into margins.

Average fabric waste stood above 15 percent; shade re-dye cycles averaged two per lot; and production managers lacked objective data to explain delivery slippages.

Digital Transformation Initiative

Textile Factory Machinery

The management authorised a pilot of MaxobizTex technologies MaxTex for AI-driven cutting and sequencing, and LoomIQ for utilisation analytics to see whether automation could stabilise output without increasing headcount.

Implementation was led on site by Ahsan Sharif, working with the in-house IT engineer and the production superintendent. His initial task was to pull machine-runtime and QC data into usable form; this required building API links between SAP B1, shop-floor PLC feeds (OPC-UA), and MaxTex's data interface. The goal was not immediate savings but a repeatable digital baseline of how the floor actually behaved.

Within six weeks, one cutting hall and a block of ten looms were connected. Nesting algorithms were tuned against local fabric characteristics, and loom operators began classifying downtime causes through handheld tablets. After calibration, the deployment extended across all lines over the following quarter.

Measurable Impact & Results

By the end of the first six-month cycle, internal reports showed:

Fabric Yield

Improved from 84% to 94%, roughly a 12–18% saving depending on style

Throughput

Per operator up ≈ 20% without staff increase

Shade Re-dye

Cycles down ≈ 22%, confirmed by QC logs

Loom Utilization

Runtime up ≈ 10–11% once stoppages were categorised

Operational Efficiency

Weekly summaries that once took two days to compile are now generated automatically at shift close.

Implementation Leadership

Ahsan Sharif's contribution centred on calibration and change management rather than coding. He verified the model outputs against manual logs, ensured that variance stayed within tolerance, and built the first KPI dashboard still used by the supervisors. After hand-over, Al Aziz's team retained the configuration and continued internal reporting without external support.

Supporting Documentation

Supporting material kept by the factory includes the SAP integration schema, yield comparison sheets, QC re-dye register excerpts, and LoomIQ OEE dashboard screenshots all time-stamped and internally audited.

Project Outcome

The implementation demonstrates how a traditional textile line can reach higher efficiency using AI-assisted planning without large capital expenditure.