Industry · Discrete and process manufacturing

Manufacturing & industrial

Scheduling, quality, equipment and procurement — watched by AI employees.

Book an industry diagnostic →See the sample case

Pain points

What keeps owners up at night

Rush orders break the schedule, and delivery dates depend on a few veterans

Paper QC records make root-cause tracing slow and inconsistent

Equipment issues surface late, causing unplanned downtime

Procurement and stock data are scattered — shortages and overstock at once

Roles

Which roles AI transforms, and which it augments

“Transform”: AI executes, people spot-check. “Augment”: AI drafts and prompts, people decide.

Role	Mode	What AI does / what people keep
Production planner	Augment	AI drafts schedules and rush-order impact; the planner signs off
QC recorder	Transform	Photo/voice to QC records; AI classifies and flags issues
Maintenance inspector	Augment	AI rolls up sensor and inspection data and opens work orders
Buyer	Augment	AI drafts replenishment and supplier reconciliation; people approve
Reporting clerk	Transform	Daily, weekly and output reports generated by AI

Department agents

AI employees we typically deploy

Scheduling & Materials AgentQC Records AgentMonitoring AgentReplenishment AgentSupplier Reconciliation AgentCockpit Agent

Super AI Workbench

Factory cockpit: the workbench for this industry

Demo UI with placeholder numbers.

BOOK.AI · Factory cockpitDemo UI · placeholder numbers

Cockpit
AI employees
Workflows
Knowledge
Audit log

Revenue¥ ——

Labor cost¥ ——

Output / person——

Open exceptions——

AI task completion by department (illustrative)

Sales

Ops

Finance

Supply

AI assistant · Monday briefing

Delivery: WIP, delay risk, rush-order impact
Quality: defect trend and traceability
Equipment: OEE, alerts and work orders
Procurement: shortage alerts, slow stock, supplier variances

Sample case

A sample case from this industry

A furniture manufacturer in east China (sample)

AI-generated

Sample caseManufacturing

A furniture manufacturer in east China (sample)

−24%Support headcount 132 → 100 (attrition + redeployment)

2 hrsCustom quote time (was 1–2 days)

4 daysMonth-end close (was 10)

“Quotes went from two days to two hours. Our sales team finally has time to visit customers.”— Chairman (sample quote)

What we did in this sample case

01 Diagnose, 3 weeks: two bases, 7 departments, 42 role groups scored
02 90-day pilot: sales quoting and finance reconciliation
03 Months 4–12: extended to scheduling, procurement and quality

Read the full case →

FDE in practice

Public FDE practice in this industry

Third-party cases from public reports — unrelated to the BOOK.AI sample above — to show how this industry is approaching AI.

Case information is compiled from public reports; figures are as reported by the original sources, and all rights belong to their owners. BOOK.AI was not involved in any of these projects.

Image from the source page: Panasonic Energy of North America — © Palantir / Panasonic Energy (Hannover Messe release); rights reserved by the owner · Image source

Public case · not a BOOK.AI clientManufacturing & industrial · US (Japanese subsidiary)

Panasonic Energy of North America × Palantir

FDE intensity: StrongFDE / co-build driven by user interviews

“10-15% Reduction in machine ‘wrench time’ … per ticket”; “<6 mos.” from user interviews to shop-floor value

As reported by Palantir · 2025 · provider-published

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Background

Tens of thousands of maintenance tickets a month, with a wide skill gap between new and veteran technicians.

How the FDE work was done

Started from interviews with front-line technicians, designed an interface both new and experienced staff could use, and built and iterated a maintenance assistant on AIP.

Results (as reported by Palantir)

“<6 mos.” from user interviews to shop-floor value; 10–15% less “wrench time” per ticket.

Takeaways

Interview the front line first, then design the interface — adoption depends on it.

Provider: Palantir
Delivery mode: FDE / co-build driven by user interviews
Region: US (Japanese subsidiary)

Read the original ↗Panasonic Energy of North America × Palantir AIP Impact Study

English summary by BOOK.AI; figures as reported by Palantir. Quotation marks indicate wording from the source.

Image from the source page: Anhui Conch Group — © Huawei; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientManufacturing & industrial · China

Anhui Conch Group × Huawei

FDE intensity: MediumJoint R&D

Strength-prediction accuracy over 85%; carbon emissions cut by 4,500+ tonnes a year

As reported by Huawei · 2025-04 · provider-published

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Background

Cement production relied on veteran know-how; quality checks lagged and energy and carbon pressure was high.

How the FDE work was done

Joint R&D since April 2024 mapped 200+ scenarios in 15 categories and embedded a large model into quality prediction, process tuning and safety recognition.

Results (as reported by Huawei)

Strength-prediction accuracy over 85%; standard coal consumption down a further 1% beyond tier-1 efficiency; 4,500+ t CO₂ cut per year; 95% recognition accuracy across 20+ scenario types.

Takeaways

Start by inventorying scenarios across the line, then embed a joint team to tackle them one by one.

Provider: Huawei
Delivery mode: Joint R&D
Region: China

Screenshot of the source page (captured 2026-10-19): Anhui Conch Group — © Huawei (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 海螺集团携手中国建筑材料联合会及华为发布水泥建材行业首个大模型 - 华为

English summary by BOOK.AI; figures as reported by Huawei. Quotation marks indicate wording from the source.

Image from the source page: TCL Air Conditioner Wuhan smart-manufacturing park — © Hubei Dept. of Economy & IT; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientManufacturing & industrial · China

TCL Air Conditioner Wuhan smart-manufacturing park × Getech (TCL’s industrial-internet company)

FDE intensity: MediumIn-group provider embedded

Equipment-maintenance efficiency up about 85%; nearly 100 AI robots on the job

As reported by Hubei Dept. of Economy & IT (repost) · 2025-06-15 · media report

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Background

Lots of equipment and scattered maintenance knowledge.

How the FDE work was done

TCL’s industrial-internet company deployed LLM-based AI robots into equipment maintenance and other role workflows.

Results (as reported by Hubei Dept. of Economy & IT (repost))

Maintenance efficiency up about 85%; nearly 100 AI robots in the plant.

Takeaways

Rolling out AI “co-workers” role by role builds organizational capability, not just point tools.

Provider: Getech (TCL’s industrial-internet company)
Delivery mode: In-group provider embedded
Region: China

Screenshot of the source page (captured 2026-10-19): TCL Air Conditioner Wuhan smart-manufacturing park — © Hubei Dept. of Economy & IT (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 新质生产力在湖北丨AI工友上岗！工厂增智生产提质-湖北省经济和信息化厅

English summary by BOOK.AI; figures as reported by Hubei Dept. of Economy & IT (repost). Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): ZPMC (Shanghai Zhenhua Heavy Industries, part of CCCC) — © www.leadigital.cn (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientManufacturing & industrial · China

ZPMC (Shanghai Zhenhua Heavy Industries, part of CCCC) × Leadigital

FDE intensity: MediumCo-build

Material kitting rate up more than 15%; labour cost optimised by 90%+ (as reported by vendor)

As reported by Leadigital · 未标注（2025年前后） · provider-published

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Background

Engineer-to-order projects are complex, with long cycles, hard cross-department coordination and low material kitting rates.

How the FDE work was done

The two sides built a multi-agent system on the Baigong model, covering design review, material-status visualisation with automatic alerts, and replenishment.

Results (as reported by vendor: Leadigital)

Material kitting rate up more than 15%; labour cost of tracking materials and production progress optimised by 90%+, with agents replacing shift-based manual monitoring (as reported by vendor).

Takeaways

Engineer-to-order heavy equipment suits multi-agent setups, with agents linking design, materials and replenishment.

Provider: Leadigital
Delivery mode: Co-build
Region: China

Read the original ↗Original title (Chinese): 羚号工厂——客户成功案例

English summary by BOOK.AI; figures as reported by Leadigital. Quotation marks indicate wording from the source.

Public case · not a BOOK.AI clientManufacturing & industrial · China

Changan Automobile × Geega (with Chongqing University of Posts and Telecommunications and CloudWalk)

FDE intensity: MediumCo-build (industry–academia; platform + engine + templates)

Defect escape rate down 80%; weld troubleshooting cut from 3 hours to 5 minutes (as reported by vendor)

As reported by Geega · 2025-09-12 · provider-published

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Background

Welding and body-dimension defect troubleshooting was slow, and rush orders made production scheduling complex.

How the FDE work was done

The provider led a joint effort with the automaker and a university on multimodal data transfer and fusion, with 12 types of agents coordinating scheduling, warehousing, logistics, equipment and quality.

Results (as reported by vendor: Geega)

Defect escape rate down 80%, process-design efficiency up 50%, scheduling cycle cut 83%; weld troubleshooting from 3 hours to 5 minutes with a 99.5% first-pass weld rate; scheduling from 6 hours to 1 hour (as reported by vendor).

Takeaways

Quality inspection and scheduling are the manufacturing agent scenarios with the most solid data — good entry points.

Provider: Geega (with Chongqing University of Posts and Telecommunications and CloudWalk)
Delivery mode: Co-build (industry–academia; platform + engine + templates)
Region: China

Read the original ↗Original title (Chinese): AI大模型在汽车工业的应用：广域铭岛的创新实践

English summary by BOOK.AI; figures as reported by Geega. Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): Midea Group (Jingzhou washing-machine plant) — © Midea Group (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientManufacturing & industrial · China

Midea Group (Jingzhou washing-machine plant) × Midea in-house digital team (not an external FDE)

FDE intensity: LightIn-house embedded team

14 agents across 38 core scenarios; first-article inspection cut from 15 minutes to 30 seconds

As reported by Midea · 2025 · company statement

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Background

Flexible multi-category production; scheduling and first-article checks were manual and slow.

How the FDE work was done

Midea’s in-house digital team embedded in the plant, assigning agents to scheduling, QC and other role tasks, rolled out scenario by scenario.

Results (as reported by Midea)

14 agents covering 38 core scenarios; average efficiency gain of 80%+; scheduling response 90% faster; first-article inspection from 15 minutes to 30 seconds.

Takeaways

One agent per role task, launched scenario by scenario, keeps AI “employees” scoped and measurable.

Provider: Midea in-house digital team (not an external FDE)
Delivery mode: In-house embedded team
Region: China

Read the original ↗Original title (Chinese): 首个智能体工厂获世界纪录认证，美的引领智能制造范式跃迁

English summary by BOOK.AI; figures as reported by Midea. Quotation marks indicate wording from the source.

See public FDE practice across all 14 industries →

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