AI in Bangladesh RMG: Lessons from 2025’s Pressure-Test

ADP implementation hit 8.33% in the first four months of FY 2025–26, Dhaka saw 1,604 road blockades in a year, and non-performing loans reached 24% of total loans. If you’re running a Bangladesh garment factory, those numbers aren’t “news”—they’re the background noise that shaped every production meeting in 2025.

Here’s the uncomfortable truth: 2025 didn’t just expose macro risks for Bangladesh’s RMG industry. It exposed how much of the sector still runs on manual judgment, scattered spreadsheets, and last-minute firefighting. And when incentives are cut, credit tightens, and lead time gets punched by disruptions, firefighting becomes expensive.

This post is part of our series “বাংলাদেশের টেক্সটাইল ও গার্মেন্টস শিল্পে কৃত্রিম বুদ্ধিমত্তা কীভাবে পরিবর্তন আনছে”—and I want to take a clear stance: AI adoption in Bangladesh RMG isn’t a “nice to have.” It’s a defensive capability. In 2025, the factories that invested in better data, automation, and AI-ready processes had one big advantage: they could respond faster with fewer costly guesses.

2025 proved Bangladesh RMG’s biggest problem is predictability

Answer first: Bangladesh’s RMG sector didn’t struggle in 2025 only because of costs—it struggled because planning became unreliable, and unreliable planning destroys efficiency.

The annual review highlighted a perfect storm: slowed investment, a fragile banking system, reduced cash incentives (around 50% across 43 sectors), packing credit jumping from 6% to 14–15%, EDF shrinking from USD 7B to USD 2B, port cost increases, labour agitation, and tariff pressure in the US market.

When all of that hits at once, the classic RMG operating model—bulk planning early, then chasing execution—starts breaking down. 2025 made three planning assumptions unsafe:

• Raw material availability isn’t guaranteed (L/C constraints, supplier refusal from low-rated banks).
• Transit time isn’t stable (blockades, transshipment policy shifts, port congestion).
• Capacity isn’t fully controllable (unrest, compliance interruptions, sudden closures).

This is exactly where AI in garment manufacturing becomes practical, not theoretical. AI doesn’t “remove” disruption. It helps you forecast, sense, and replan faster—before losses compound.

The myth most factories still believe

Most companies get this wrong: they think AI starts with buying software.

In reality, AI starts with decisions that repeat daily—line balancing, shade sorting, defect classification, delivery date promises, replenishment, overtime planning. If those decisions are still driven by gut feel and fragmented Excel files, your AI investment won’t land.

Banking stress + incentive cuts: AI’s role in cash discipline

Answer first: When credit becomes expensive and incentives shrink, AI helps protect cash by reducing waste, rework, and working-capital lock-up.

2025’s banking constraints weren’t abstract. Exporters struggled to open back-to-back L/Cs, and foreign suppliers refused to honor L/Cs from about 18 low-rated banks. That kind of friction shows up as delayed inputs, emergency buying, and higher buffer inventory.

AI can’t fix bank liquidity, but it can help factories operate with tighter financial margins:

1) Demand and materials forecasting that’s actually usable

Instead of planning fabric and trims purely from a static booking sheet, AI-assisted forecasting combines:

• Order history by buyer/style
• Lead time variability by port route
• Supplier on-time performance
• Fabric consumption variance by size mix

The payoff is simple: fewer panic imports and less deadstock. In a year where EDF support dropped sharply, reducing “inventory that looks safe but behaves like debt” matters.

2) WIP visibility to cut working capital

A lot of factories still don’t know, in real time, how much value is trapped in:

• cut panels waiting for sewing
• sewing output waiting for finishing
• finishing output waiting for QA

AI-enabled production analytics (even basic anomaly detection on hourly output) flags bottlenecks early. That reduces WIP pileups, which reduces cash tied up in half-finished goods.

3) Price pressure needs cost truth, not cost estimates

The review noted 30% order decline, 30% price reduction, and 40% increase in production costs. Under that squeeze, “average SMV” costing is a trap.

AI-driven costing uses real line performance, changeover loss, defect rates, and operator learning curves to tell you:

• which styles you should refuse
• where you can negotiate MOQ/lead time based on factual constraints
• what efficiency target is realistic without turning overtime into a habit

Blockades, port changes, and policy shocks: AI for resilient planning

Answer first: 2025’s logistics shocks made dynamic planning mandatory; AI helps factories replan shipments and production with fewer penalties.

Dhaka’s 1,604 road blockades (Aug 2024–Aug 2025) created shipment volatility. India’s transshipment revocation and Bangladesh’s yarn import restrictions via land ports altered lead time assumptions. Port/ICD charges rose sharply (reported 41% and up to 60% increases), raising the cost of every delay.

The common “solution” in 2025 was air freight. It works, but it’s brutal for margin and buyer negotiations.

What AI changes in this situation

AI-based scheduling tools don’t magically create capacity. They do something more valuable: they quantify trade-offs quickly.

For example, when a shipment is at risk:

• Which lines should be resequenced with minimal efficiency loss?
• Which orders can be partially shipped without creating shade/assortment issues?
• Which suppliers consistently miss dates so you should stop promising their lead time?

A practical approach I’ve seen work is building a “control tower” view (even if it starts simple):

• Production status (cut/sew/finish/pack)
• Material readiness
• QA pass rate trend
• Actual vs planned output by line
• Logistics risk score by route

Then you add AI gradually: first alerts, then recommendations, then semi-automated replanning.

Labour unrest, BLA amendments, and compliance: AI for worker-first productivity

Answer first: AI can support productivity without squeezing workers—by reducing chaos, improving safety, and making compliance evidence easier.

2025 saw intense labour unrest, factory closures, and later reforms: wage increment rate increased from 5% to 9% of basic wage; BLA amendments simplified union formation, enhanced maternity benefits, mandated provident fund contributions, and shortened wage review cycles from 5 to 3 years. Bangladesh also ratified ILO conventions C190, C155, and C187.

Some factory leaders treat compliance as paperwork. Buyers don’t. And GSP+ readiness will demand traceability and proof.

AI use cases that align with compliance expectations

• Computer vision for safety monitoring: PPE detection, restricted area alerts, congestion risk—implemented carefully with privacy policies.
• Digital SOP adherence tracking: not for punishment, but for identifying training needs.
• Smart audit trails: automatic logs for corrective actions, maintenance schedules, incident response, and worker training completion.

Here’s the stance I take: If your AI project can’t be explained to workers as “less chaos, fewer last-minute crises,” you’re doing it wrong.

Quality and fire risk: AI that pays back fast

Answer first: In a year of heightened scrutiny, AI-driven quality inspection reduces rework, claims, and shipment risk—often with clearer ROI than bigger automation bets.

2025 included a major fire at the airport cargo village that destroyed export samples during peak season, plus additional factory fires that increased global scrutiny. When scrutiny rises, tolerance for quality drift drops.

Where AI fits in quality management

• Fabric defect detection: camera-based inspection can standardize defect grading.
• Sewing defect detection: identifying broken stitches, puckering, skipped stitches.
• Shade sorting: AI-assisted shade grouping reduces rejection and rework.

A good starting KPI set for AI quality projects:

• DHU reduction target (e.g., 15–25% in 90–120 days)
• Rework hours per 1,000 pcs
• Claim rate / chargeback incidents
• Final inspection pass rate trend

Even modest improvements matter when prices are down and costs are up.

LDC graduation pressure: AI as a competitiveness strategy

Answer first: LDC graduation and GSP+ compliance raise the bar; AI helps Bangladesh compete on reliability, transparency, and speed—not only price.

Bangladesh’s EU dependency is well known, and losing a price advantage of up to ~12% (as the review warned) would push buyers to compare Bangladesh against countries that already compete through FTAs, automation, and diversified product mix.

Bangladesh can’t negotiate its way out of every disadvantage. It can, however, become harder to replace by delivering:

• shorter sampling cycles (AI-assisted tech packs, pattern optimization, virtual sampling)
• more accurate OTIF (on-time, in-full) through predictive planning
• better traceability (digital thread from yarn/fabric to finished goods)

If you’re thinking, “Sounds expensive,” I agree—if you start big. If you start with the right operational problem, AI becomes a cost-control tool.

A pragmatic 90-day AI roadmap for RMG factories

1. Pick one pain point with a cash impact: rework, WIP, late shipment penalties, fabric waste.
2. Fix data capture at the source: line output, defect codes, machine downtime, inspection results.
3. Build one dashboard that people actually use daily.
4. Add AI only after the dashboard is trusted: anomaly alerts, defect classification, predictive delay risk.
5. Measure weekly and publish results internally.

The goal isn’t “AI transformation.” The goal is operational control.

What buyers and investors will reward in 2026

Answer first: The factories that win in 2026 will be the ones that can prove reliability with data—and AI helps produce that proof.

After 2025, “trust me” doesn’t work as a management system. Buyers are tightening compliance and delivery expectations. Banks and investors are cautious. The factories that can show consistent performance metrics will get better conversations on price, volume, and relationship longevity.

A simple way to frame it:

AI won’t replace your merchandisers or IE team. It will replace the uncertainty that slows them down.

If your leadership team is planning next year’s competitiveness moves, I’d prioritize AI where it intersects with the year’s real pain: cash, disruption, compliance, and quality.

The series you’re reading focuses on exactly this—বাংলাদেশের টেক্সটাইল ও গার্মেন্টস শিল্পে কৃত্রিম বুদ্ধিমত্তা how it’s changing daily work inside factories. The next step is deciding where you’ll start: quality, planning, or compliance evidence.

What’s the one operational decision in your factory that still depends too heavily on “experience”—and costs you money when the environment shifts overnight?