I begin with the fact that gives our current chapter its shape: our industrial robot shipments ranked first among all brands in China’s industrial robot market in the first half of 2026, and we became the first autonomous brand to ship more than ten thousand units in a single quarter. Since first reaching the top in 2025, our industrial robot business has held the leading position for six consecutive quarters, and we have remained the number one domestic brand for eight consecutive years. I do not see that record as a single event. I see it as the visible result of compounding work in core components, control systems, artificial intelligence, flexible manufacturing, delivery discipline, supply chain coordination, and global service. In this article, I want to explain how I interpret the industrial robot landscape, how our industrial robot platform creates value, and how formulas and tables can make the operating logic transparent and repeatable.
When I look at industrial robot leadership, I do not look only at a ranking. I look at whether the industrial robot portfolio can be produced, delivered, commissioned, serviced, and improved at scale. A leading industrial robot position is a systems problem, not merely a sales outcome. It requires a unified product platform, a modular architecture, a capable controller, a cloud platform, a partner ecosystem, and a field service organization that can respond across regions. It also requires a culture that treats every installed industrial robot as a long-term source of data, learning, and customer value.
$$ Rank_{t} = \begin{cases} 1 & \text{if } Shipments_t = \max(Shipments_{all,t}) \\ >1 & \text{otherwise} \end{cases} $$
The first dimension I want to make explicit is shipment leadership. In the first half of 2026, our industrial robot shipments again ranked first among all brands in the market. In the second quarter of 2026, we delivered more than ten thousand industrial robot units in a single quarter for the first time, becoming the first autonomous brand to cross that threshold. That number matters because it proves that an autonomous industrial robot brand can operate at the scale previously associated with established global leaders. It also proves that our manufacturing, supply chain, and commissioning systems can absorb a step change in demand without losing quality or service discipline.
| Leadership indicator | Record or value | Time window | Interpretation for industrial robot strategy |
|---|---|---|---|
| All-brand shipment rank | 1 | First half of 2026 | Our industrial robot shipments led all brands in the market. |
| Single-quarter shipment volume | More than 10,000 units | Second quarter of 2026 | First autonomous brand to exceed ten thousand industrial robot units in one quarter. |
| Consecutive leading quarters | 6 | Since first reaching the top in 2025 | Sustained industrial robot leadership rather than a short spike. |
| Domestic brand rank | 1 | 8 consecutive years | Long-term domestic industrial robot brand leadership. |
| Global service network | 75 service points | Current footprint | Service coverage supports industrial robot adoption across regions. |
| Core component autonomy in heavy-load class | 100% | 1200 kg payload class | Vertical integration strengthens industrial robot reliability and cost control. |
| Automation rate in a customer auto-parts factory | 90% | Core process automation | Industrial robot systems can transform complete production flows. |
| Robots commissioned in 60 days | More than 200 units | Auto-parts factory project | Rapid industrial robot deployment capability. |
| Clean robots deployed | More than 1,000 units | Electronics manufacturing environment | Precision industrial robot solutions for clean environments. |
I use the following formula to think about market share. If \(Q_{i,t}\) is the shipment quantity of industrial robot brand \(i\) in period \(t\), then market share is the ratio of that brand’s shipments to total industrial robot shipments. The change in market share from one period to the next shows whether leadership is strengthening or merely holding. For our industrial robot business, the meaningful signal is not only the absolute rank but the combination of rank, volume, and persistence.
$$ MS_{i,t} = \frac{Q_{i,t}}{\sum_{j=1}^{N} Q_{j,t}} \times 100\% $$
$$ \Delta MS_{i,t} = MS_{i,t} – MS_{i,t-1} $$
$$ L_{i,t} = \sum_{k=1}^{t} \mathbf{1}\{Rank_{i,k}=1\} $$
I also track cumulative leadership quarters. The formula above counts how many periods a brand has held the top industrial robot position. Six consecutive leading quarters since 2025 and eight consecutive years as the leading domestic brand are not just historical facts. They indicate that our industrial robot organization has learned how to repeat success across product generations, demand cycles, and customer segments. That repetition is what makes an industrial robot platform credible to large manufacturers.
| Period | Industrial robot leadership state | Strategic meaning |
|---|---|---|
| 2025 onward | First reached the top of the all-brand industrial robot shipment ranking | An autonomous industrial robot brand can compete at the highest level. |
| Six consecutive quarters | Maintained the leading industrial robot shipment position | Leadership is operational, not episodic. |
| Eight consecutive years | Remained the number one domestic industrial robot brand | Domestic industrial robot scale and trust are durable. |
| Second quarter of 2026 | Delivered more than 10,000 industrial robot units in one quarter | First autonomous brand to achieve single-quarter five-digit delivery. |
| First half of 2026 | Again ranked first among all brands | Industrial robot growth continued across a full half-year window. |
The second dimension I want to explain is upstream research and development coordination. An industrial robot is not only an arm. It is a motion system, a control system, a sensing system, a software system, and a service system. In the heavy-load industrial robot class, especially the 1200 kg payload segment, we have achieved 100% self-development of core components. I see this as a foundation for reliability, cost discipline, and supply security. We have also addressed dual-motor synchronization control and dual-reducer hard synchronization. Those technologies matter because heavy-load industrial robot motion must remain stable when torque demand, inertia, and external forces are large.
For a heavy-load industrial robot, synchronization error can be expressed as the maximum absolute difference between two motor or joint positions over a motion cycle. If the two axes are not synchronized, the structure experiences internal stress, vibration, reducer wear, and reduced accuracy. Hard synchronization through dual reducers adds mechanical coupling intelligence to the control problem. The result is an industrial robot that can handle high payloads with better dynamic behavior and longer service life.
$$ SyncError = \max |\theta_1(t) – \theta_2(t)| $$
$$ Torque_{peak} = K_t I_{peak} $$
$$ \eta_{system} = \prod_{k=1}^{m} \eta_k $$
$$ Payload margin = \frac{Payload_{rated} – Payload_{application}}{Payload_{rated}} \times 100\% $$
| Heavy-load industrial robot capability | Technical focus | Value created |
|---|---|---|
| 1200 kg payload class | High structural stiffness, strong joint torque, thermal stability | Enables heavy industrial robot tasks in demanding processes. |
| Core component self-development | 100% autonomy in the heavy-load industrial robot class | Improves supply control, cost structure, and customization speed. |
| Dual-motor synchronization control | Coordinated torque and position control | Reduces internal stress and improves industrial robot motion quality. |
| Dual-reducer hard synchronization | Mechanical and control coupling | Increases stiffness and repeatability under heavy load. |
| Motion stability | Vibration suppression and trajectory accuracy | Supports precise industrial robot operation at high duty cycles. |
| Service life | Reducer wear reduction and thermal management | Lowers total cost of ownership for industrial robot fleets. |
The third dimension is industrial embodied intelligence. I use the term industrial embodied intelligence because the industrial robot must not only perceive and compute. It must decide and act in a physical environment. Our iER.OS control system is the core foundation. On top of it, we have built the RoboBase industrial embodied intelligence open platform. This platform connects perception, computation, decision, and execution. The purpose is to give the industrial robot the ability to adapt to complex non-structured scenarios. In a traditional automation cell, every position and every motion may be fixed. In an embodied intelligence cell, the industrial robot can interpret variation, adjust motion, and complete tasks that would otherwise require manual intervention.
We have also introduced the iER.Cloud AI industrial cloud platform. Together with the developer ecosystem and application software ecosystem, it forms a three-in-one full-stack technology chain for AI plus industrial robot solutions. I see this as a chain because value is created only when the control system, the cloud platform, the application software, and the developer ecosystem reinforce one another. A strong controller without cloud data limits learning. A strong cloud without field-grade control limits action. A strong application without an open ecosystem limits scale. The three-in-one structure is how we convert industrial robot data into deployable intelligence.
$$ M_{fusion} = \sum_{m=1}^{K} w_m f_m(x) $$
$$ P(y|x) = \frac{P(x|y)P(y)}{P(x)} $$
$$ Autonomy = \frac{N_{autonomous\ tasks}}{N_{total\ tasks}} $$
$$ Closed\ loop = Observe \rightarrow Orient \rightarrow Decide \rightarrow Act \rightarrow Observe $$
| Industrial robot intelligence layer | Core element | Function | Outcome |
|---|---|---|---|
| Control layer | iER.OS control system | Motion control, logic execution, real-time coordination | Stable industrial robot behavior in production. |
| Embodied platform layer | RoboBase industrial embodied intelligence open platform | Perception, computation, decision, and execution linkage | Adaptation to complex non-structured scenarios. |
| Cloud layer | iER.Cloud AI industrial cloud platform | Data aggregation, AI model management, digital operations | Scalable industrial robot learning and management. |
| Developer ecosystem | Open tools and interfaces | Third-party and internal application creation | Faster industrial robot application expansion. |
| Application software ecosystem | Process-specific software | Welding, grinding, inspection, cutting, handling | Higher industrial robot process fit. |
| AI digital management platform | Analytics and orchestration | Fleet visibility and optimization | Better industrial robot utilization and uptime. |
The fourth dimension is AI plus embodied intelligence in industrial scenarios. I believe the industrial robot will become more valuable as vision, force, torque, position, and process data are fused. A purely position-controlled industrial robot can repeat a path. A multimodal industrial robot can understand a seam, a surface, a defect, a force profile, or a material variation. We are advancing AI plus industrial robot applications in visual and force modalities. In flexible sheet metal processing, intelligent grinding, AI teaching-free welding, intelligent high-speed flying capture inspection, and 3D laser cutting, we have formed leading intelligent solutions. These are not laboratory demonstrations. They are industrial robot solutions designed for real production environments.
In AI teaching-free welding, the industrial robot uses vision and process models to reduce manual teaching effort. In intelligent grinding, force control and vision help the industrial robot maintain consistent contact and material removal. In high-speed flying capture inspection, the industrial robot coordinates motion with imaging so that inspection can happen within the production cycle. In 3D laser cutting, the industrial robot follows complex geometries with high accuracy. Each of these applications increases the share of tasks that an industrial robot can perform without fixed fixturing or extensive reprogramming.
$$ Welding_{accuracy} = \sqrt{(x_a – x_t)^2 + (y_a – y_t)^2 + (z_a – z_t)^2} $$
$$ Grinding\ force = K_p e + K_i \int e dt + K_d \frac{de}{dt} $$
$$ Inspection_{speed} = \frac{N_{parts}}{T_{seconds}} $$
$$ Trajectory\ error = \int_{0}^{T} ||q_{actual}(t) – q_{desired}(t)|| dt $$
| AI plus industrial robot application | Key modality | Industrial robot value |
|---|---|---|
| Flexible sheet metal processing | Vision, position, process data | Adapts industrial robot motion to varying part geometry. |
| Intelligent grinding | Force, vision, torque | Maintains consistent industrial robot contact and finish quality. |
| AI teaching-free welding | Vision, seam tracking, process models | Reduces programming time for industrial robot welding cells. |
| High-speed flying capture inspection | Vision, synchronization, motion control | Integrates inspection into the industrial robot cycle. |
| 3D laser cutting | Vision, trajectory planning, laser process control | Extends industrial robot accuracy to complex cutting paths. |
| Automotive parts | Vision, force, safety, line integration | Supports high-mix industrial robot production. |
| Lithium battery | Precision, speed, cleanliness, traceability | Enables reliable industrial robot tasks in battery manufacturing. |
| Electronics | Cleanliness, precision, small-part handling | Deploys industrial robot systems in sensitive environments. |
| AIDC-related manufacturing | Data, automation, precision assembly | Connects industrial robot operations to digital infrastructure. |
The fifth dimension is delivery and flexible manufacturing. I have learned that an industrial robot order is only the beginning. The real test is whether the industrial robot can be manufactured, configured, tested, shipped, installed, commissioned, and supported on schedule. We rely on a unified modular product platform. That platform allows multi-model flexible mixed-line production. New product introduction cycles are shorter, and production efficiency and iteration speed improve. The modular approach also means that a customer solution can reuse proven industrial robot modules while still being customized for the process.
Global supply chain control and digital production scheduling work together. Real-time capacity monitoring and intelligent scheduling help ensure that orders are fulfilled on time. Batch delivery capability has been recognized by leading customers in new energy, automotive, and other demanding sectors. I see this as a feedback loop. The more industrial robot units we deliver, the more field data we collect. The more field data we collect, the better we can design the next industrial robot generation. The better the next generation, the stronger the delivery capability becomes.
$$ NPI_{reduction} = \frac{T_{old} – T_{new}}{T_{old}} \times 100\% $$
$$ FlexIndex = \frac{1}{M}\sum_{m=1}^{M} n_m $$
$$ Capacity = \frac{Available\ time}{Cycle\ time} \times Efficiency $$
$$ LeadTime = T_{proc} + T_{prod} + T_{log} + T_{inst} + T_{ramp} $$
$$ Throughput = \frac{N_{units}}{T_{operating}} $$
| Delivery and manufacturing factor | Mechanism | Industrial robot benefit |
|---|---|---|
| Unified modular product platform | Shared modules and interfaces | Faster industrial robot configuration and customization. |
| Multi-model flexible mixed-line production | Mixed model scheduling | Higher industrial robot production flexibility. |
| Shortened new product introduction | Reusable design and validation | Quicker industrial robot generation turnover. |
| Digital production scheduling | Real-time capacity monitoring | More reliable industrial robot order fulfillment. |
| Global supply chain control | Supplier coordination and risk management | Stable industrial robot component availability. |
| Intelligent dispatch | Dynamic allocation of production resources | Higher industrial robot line utilization. |
| Batch delivery | Standardized commissioning and logistics | Scalable industrial robot deployment for large customers. |
The sixth dimension is customer outcomes. I prefer to measure industrial robot value through what changes inside the customer’s factory. In one auto-parts factory built with a well-known smart vehicle brand, core process automation reached 90%, and more than 200 industrial robot units were commissioned and put into production within 60 days. That combination of automation rate and speed is a strong proof point. It shows that industrial robot systems can be deployed at scale without extending the project timeline beyond commercial expectations.
In lithium battery manufacturing, we provide solutions across the full industrial chain, including cell manufacturing and module assembly. The goal is flexible and efficient production. In electronics manufacturing, we have deployed more than 1,000 lightweight clean industrial robot units and deep customization solutions to ensure high-precision stable operation in clean environments. Each of these cases reinforces the same idea: an industrial robot is not a commodity arm. It is a configured production asset that must fit the process, the environment, the quality system, and the customer’s growth plan.
$$ Automation\ ratio = \frac{Automated\ steps}{Total\ steps} \times 100\% $$
$$ OEE = Availability \times Performance \times Quality $$
$$ FPY = \frac{Units\ passed\ first\ time}{Units\ started} \times 100\% $$
$$ PPM = \frac{Defects}{Total\ units} \times 10^6 $$
$$ ROI = \frac{\sum_{t=1}^{T} (B_t – C_t)}{(1+r)^t} – I_0 $$
| Customer scenario | Industrial robot deployment | Measured or targeted outcome |
|---|---|---|
| Auto-parts factory for a smart vehicle brand | More than 200 industrial robot units | 90% core process automation; 60-day commissioning and production start. |
| Lithium battery cell manufacturing | Industrial robot solutions for cell production | Higher precision, speed, and traceability. |
| Lithium battery module assembly | Flexible industrial robot assembly lines | Improved line flexibility and throughput. |
| Electronics clean manufacturing | More than 1,000 lightweight clean industrial robot units | High-precision stable operation in clean environments. |
| Flexible sheet metal | AI-enabled industrial robot cells | Adaptation to variable parts and reduced manual programming. |
| Intelligent grinding | Force-controlled industrial robot systems | Consistent surface quality and less rework. |
| AI teaching-free welding | Vision and process-model industrial robots | Shorter setup time and higher arc-on time. |
| High-speed inspection | Flying capture industrial robot inspection | Inline quality control at production speed. |
| 3D laser cutting | High-accuracy industrial robot cutting | Complex geometry processing with repeatable quality. |
The seventh dimension is reliability and quality. I cannot separate industrial robot leadership from reliability. A factory may accept a new industrial robot brand for a pilot, but it will only scale that industrial robot brand if the mean time between failures, mean time to repair, first pass yield, and process capability are strong. We therefore treat reliability as a design output, not a service afterthought. Core component autonomy helps because we can control the quality of critical parts. The control system helps because we can monitor motion, torque, temperature, and vibration. The cloud platform helps because we can aggregate fleet data and detect patterns before they become failures.
$$ MTBF = \frac{\sum t_{operation}}{N_{failures}} $$
$$ MTTR = \frac{\sum t_{repair}}{N_{repairs}} $$
$$ Availability = \frac{MTBF}{MTBF + MTTR} $$
$$ Reliability = e^{-\lambda t} $$
$$ Cpk = \min\left(\frac{USL – \mu}{3\sigma}, \frac{\mu – LSL}{3\sigma}\right) $$
$$ RPN = Severity \times Occurrence \times Detection $$
| Reliability and quality metric | Formula or definition | Why it matters for industrial robot fleets |
|---|---|---|
| MTBF | Total operating time divided by number of failures | Shows how long an industrial robot can run before failure. |
| MTTR | Total repair time divided by number of repairs | Shows how quickly an industrial robot can return to service. |
| Availability | MTBF divided by MTBF plus MTTR | Directly affects industrial robot line uptime. |
| Reliability | Exponential function of failure rate and time | Predicts industrial robot survival probability. |
| Cpk | Process capability relative to specification limits | Confirms industrial robot process consistency. |
| RPN | Severity times occurrence times detection | Prioritizes industrial robot risk reduction. |
| FPY | First-time pass units divided by started units | Reflects industrial robot production quality. |
| PPM | Defects per million opportunities | Provides a high-resolution industrial robot quality measure. |
The eighth dimension is economics. I want every industrial robot investment to be defensible in financial terms. The customer does not buy an industrial robot merely because it is advanced. The customer buys productivity, quality, flexibility, safety, and cost reduction. Therefore, I look at payback, net present value, internal rate of return, total cost of ownership, and life-cycle cost. When an industrial robot reduces cycle time, improves yield, lowers scrap, reduces downtime, or increases flexibility, those benefits can be modeled. The model does not replace engineering judgment, but it makes the industrial robot business case more transparent.
$$ Payback = \frac{Initial\ investment}{Annual\ savings} $$
$$ NPV = \sum_{t=0}^{T} \frac{CF_t}{(1+r)^t} $$
$$ IRR: NPV(IRR) = 0 $$
$$ TCO = CAPEX + OPEX + Service + Energy + Training $$
$$ LCC = TCO + End-of-life – Residual\ value $$
$$ Productivity\ gain = \frac{Output_{new}/Input_{new}}{Output_{old}/Input_{old}} – 1 $$
$$ Cost\ reduction = \frac{Cost_{old} – Cost_{new}}{Cost_{old}} \times 100\% $$
$$ Downtime\ reduction = \frac{DT_{old} – DT_{new}}{DT_{old}} \times 100\% $$
| Economic lever | Industrial robot impact | Typical formula |
|---|---|---|
| Labor productivity | More output per labor hour | Output divided by labor hours |
| Cycle time | Shorter time per unit | Throughput equals one divided by cycle time |
| Quality | Lower defects and scrap | Quality equals good units divided by total units |
| Downtime | Higher availability | Availability equals MTBF divided by MTBF plus MTTR |
| Flexibility | Faster changeover | Flexibility gain equals old change time minus new change time divided by old change time |
| Energy | Lower energy per unit | Energy efficiency equals useful work divided by energy input |
| Payback | Faster capital recovery | Initial investment divided by annual savings |
| NPV | Long-term value creation | Sum of discounted cash flows |
| IRR | Return rate of the industrial robot project | Discount rate where NPV equals zero |
| TCO | Total cost over ownership | Capital plus operating plus service plus energy plus training |
The ninth dimension is globalization. As the first industrial robot company in China’s industrial robot sector to be listed on both A-share and H-share markets, we are accelerating global business layout. I see global expansion as a service problem as much as a sales problem. An industrial robot installed far from the factory needs local response, spare parts, technical support, training, and application engineering. We currently have 75 service points worldwide and are expanding in Europe, North America, Southeast Asia, and other mainstream markets with global辐射 impact. I avoid thinking of globalization as simply exporting an industrial robot. It is building a local operating system around the industrial robot.
In Europe, we are building a regional structure that brings research, sales, service, and manufacturing together. Local branches and partners help cover Western, Central, and Southern Europe. In Asia, we are building a local service provider and partner ecosystem, starting from Southeast Asia and coordinating closely with the domestic market. The goal is to serve both outbound brands and local platforms with automation and intelligent solutions. For an industrial robot leader, global coverage means that a customer can deploy the same industrial robot platform in multiple regions and receive consistent support.
$$ R_{service} = \frac{N_{covered}}{N_{installed}} $$
$$ Coverage = \frac{Covered\ regions}{Target\ regions} \times 100\% $$
$$ Global_{growth} = g_{domestic} + g_{international} + g_{synergy} $$
$$ Fleet\ utilization = \frac{\sum busy_i}{\sum available_i} $$
$$ Service\ uptime = \frac{Available\ hours}{Total\ hours} \times 100\% $$
| Globalization element | Current approach | Industrial robot value |
|---|---|---|
| Service points | 75 service points worldwide | Local response for industrial robot fleets. |
| Europe | Regional coordination with local R&D, sales, service, and manufacturing | Closer industrial robot support for European customers. |
| Asia | Local service provider and partner ecosystem | Stronger industrial robot adoption in Southeast Asia and beyond. |
| North America | Expansion in mainstream markets | Broader industrial robot market access. |
| Outbound brands | Automation and intelligent solutions across regions | Consistent industrial robot platform for global operations. |
| Local partners | Ecosystem development | Faster industrial robot integration and service. |
| Spare parts | Regional logistics and planning | Shorter industrial robot downtime. |
| Training | Local technical enablement | Higher industrial robot user competence. |
| Application engineering | Process-specific support | Better industrial robot fit for local industries. |
The tenth dimension is the future. I believe the next phase of industrial robot competition will be defined by embodied intelligence, cloud-edge-end collaboration, and AI-enabled process knowledge. We are exploring AI plus embodied intelligence and pushing AI plus industrial robot applications faster into industrial scenarios. We have signed a joint research agreement with an industrial artificial intelligence research institute to deepen work in industrial embodied intelligence, intelligent welding processes, and cloud-edge-end collaboration. I see this as a necessary step because the industrial robot must become better at learning from data, reasoning about tasks, and acting under uncertainty.
Cloud-edge-end collaboration is especially important. Some decisions must happen on the industrial robot controller within milliseconds. Some decisions can happen at the edge for line-level coordination. Some learning and fleet management can happen in the cloud. If we place every computation in the cloud, latency and network risk may harm the industrial robot cycle. If we place every computation at the edge, we lose fleet-level learning. The right architecture distributes intelligence according to latency, bandwidth, safety, and privacy requirements.
$$ Cloud_{latency} = T_{edge} + T_{network} + T_{cloud} $$
$$ Edge_{decision} = \arg\max_{a \in A} U(a|s) $$
$$ Digital\ twin\ fidelity = 1 – \frac{|y_{real} – y_{sim}|}{|y_{real}|} $$
$$ Predictive\ maintenance = P(failure | sensor\ data) $$
$$ Remaining\ useful\ life = E[T_{failure} – t | data] $$
$$ Anomaly\ score = ||x – \mu||^2_{\Sigma^{-1}} $$
| Future industrial robot direction | Technology focus | Expected outcome |
|---|---|---|
| Industrial embodied intelligence | Perception, reasoning, planning, control, action | More adaptive industrial robot behavior. |
| Intelligent welding | Seam tracking, process models, AI parameter control | Higher quality and less manual teaching for industrial robot welding. |
| Cloud-edge-end collaboration | Distributed computation and low-latency control | Scalable and responsive industrial robot fleets. |
| AI plus robotics | Vision, force, multimodal fusion | Broader industrial robot task coverage. |
| Digital twin | Simulation and real-time synchronization | Faster industrial robot line design and commissioning. |
| Predictive maintenance | Sensor analytics and remaining useful life | Less unplanned downtime for industrial robot systems. |
| Open ecosystem | Developer tools, APIs, application software | Faster industrial robot solution innovation. |
| Global service | Local support and spare parts | Higher industrial robot fleet uptime worldwide. |
The eleventh dimension is organizational learning. I think an industrial robot company must convert every deployment into reusable knowledge. A welding project should improve the next welding industrial robot. A grinding project should improve force control. A clean-room electronics project should improve contamination control. A heavy-load project should improve synchronization and thermal design. A global service event should improve reliability engineering. The formula for data value is not only about volume. It is about variety, velocity, veracity, and value. When an industrial robot fleet generates data, the organization must have the processes to label it, analyze it, and feed it back into design and service.
$$ Data_{value} = f(Volume, Variety, Velocity, Veracity, Value) $$
$$ Knowledge\ transfer = \frac{Hours\ trained}{Employees} $$
$$ Innovation\ rate = \frac{New\ products}{Total\ products} \times 100\% $$
$$ Adoption\ rate = \frac{Deployed\ industrial\ robots}{Potential\ lines} \times 100\% $$
$$ Market\ penetration = \frac{Installed\ base}{Addressable\ base} \times 100\% $$
| Learning loop | Input | Output for industrial robot development |
|---|---|---|
| Design feedback | Field failure and performance data | More reliable industrial robot components. |
| Process feedback | Welding, grinding, cutting, inspection results | Better industrial robot process libraries. |
| Service feedback | Repair time, spare parts, diagnostics | Faster industrial robot support. |
| Customer feedback | Cycle time, yield, flexibility needs | More targeted industrial robot solutions. |
| Cloud feedback | Fleet utilization and anomaly patterns | Improved industrial robot fleet management. |
| Partner feedback | Integration and application experience | Stronger industrial robot ecosystem. |
The twelfth dimension is sustainability and productivity together. I do not believe an industrial robot must choose between environmental performance and economic performance. A well-designed industrial robot can reduce energy per unit, reduce scrap, reduce rework, improve safety, and extend equipment life. The sustainability equation includes energy, material, labor, and waste. When an industrial robot improves first pass yield, it reduces material consumption. When it improves energy efficiency, it lowers operating cost. When it improves safety, it reduces human risk. Those outcomes are aligned with long-term manufacturing competitiveness.
$$ Carbon = \sum E_i \times EF_i $$
$$ Energy\ reduction = \frac{E_{old} – E_{new}}{E_{old}} \times 100\% $$
$$ Scrap\ rate = \frac{Scrap\ units}{Total\ units} $$
$$ Rework\ rate = \frac{Rework\ units}{Total\ units} $$
$$ Safety\ index = \frac{Incidents_{baseline} – Incidents_{new}}{Incidents_{baseline}} \times 100\% $$
| Sustainability lever | Industrial robot contribution | Measurement |
|---|---|---|
| Energy efficiency | Optimized motion and standby management | Energy per unit produced. |
| Material efficiency | Higher first pass yield and less scrap | Scrap rate and rework rate. |
| Labor safety | Removal of dangerous manual tasks | Incident reduction index. |
| Equipment life | Predictive maintenance and thermal control | MTBF and remaining useful life. |
| Production resilience | Flexible automation and faster changeover | Flexibility gain and lead time reduction. |
| Carbon intensity | Lower energy and waste per unit | Carbon emissions per unit. |
The thirteenth dimension is the formula for industrial robot leadership itself. I do not think leadership can be reduced to one number, but I do think it can be modeled. It combines market position, technology autonomy, AI capability, delivery reliability, service coverage, customer outcomes, and organizational learning. If any one of those factors is weak, the industrial robot business becomes fragile. If all of them improve together, the industrial robot business becomes compounding. That is why I pay attention to both the shipment ranking and the underlying operating system.
$$ Industrial\ robot\ leadership = f(Market, Technology, AI, Delivery, Service, Quality, Cost, Ecosystem) $$
$$ Market = \sum (Share \times Growth \times Persistence) $$
$$ Technology = Autonomy \times Reliability \times Performance $$
$$ AI = Data \times Models \times Compute \times Action $$
$$ Delivery = Capacity \times Flexibility \times LeadTime^{-1} $$
$$ Service = Coverage \times Uptime \times ResponseSpeed $$
$$ Quality = FPY \times Cpk \times PPM^{-1} $$
$$ Cost = TCO^{-1} \times Productivity $$
$$ Ecosystem = Developers + Partners + Applications + Standards $$
I also use a compact table to summarize the main formulas that guide my industrial robot thinking. This is not a substitute for engineering detail. It is a way to keep the business logic visible when the industrial robot portfolio becomes large and the customer base becomes global.
| Domain | Formula | Industrial robot interpretation |
|---|---|---|
| Shipment leadership | \(Rank_t = 1\) when shipments are maximum | Top industrial robot brand position. |
| Market share | \(MS_{i,t} = Q_{i,t} / \sum Q_{j,t}\) | Share of total industrial robot shipments. |
| Consecutive leadership | \(L_{i,t} = \sum \mathbf{1}\{Rank_{i,k}=1\}\) | Durability of industrial robot leadership. |
| Synchronization | \(SyncError = \max |\theta_1 – \theta_2|\) | Heavy-load industrial robot motion quality. |
| System efficiency | \(\eta_{system} = \prod \eta_k\) | Compounded efficiency across industrial robot subsystems. |
| Autonomy | \(Autonomy = N_{autonomous} / N_{total}\) | Share of tasks an industrial robot can perform autonomously. |
| Multimodal fusion | \(M_{fusion} = \sum w_m f_m(x)\) | Vision, force, and process fusion in industrial robot control. |
| Delivery lead time | \(LeadTime = T_{proc} + T_{prod} + T_{log} + T_{inst} + T_{ramp}\) | Time from industrial robot order to production. |
| OEE | \(OEE = Availability \times Performance \times Quality\) | Overall industrial robot line effectiveness. |
| MTBF | \(MTBF = \sum t_{operation} / N_{failures}\) | Industrial robot reliability. |
| Availability | \(A = MTBF / (MTBF + MTTR)\) | Industrial robot uptime. |
| Payback | \(Payback = Investment / Annual savings\) | Industrial robot investment recovery. |
| NPV | \(NPV = \sum CF_t / (1+r)^t\) | Long-term industrial robot project value. |
| Service coverage | \(R_{service} = N_{covered} / N_{installed}\) | Global support for industrial robot fleets. |
| Data value | \(Data_{value} = f(V,V,V,V,V)\) | Volume, variety, velocity, veracity, and value of industrial robot data. |
| Leadership | \(Leadership = f(M,T,A,D,S,Q,C,E)\) | Integrated industrial robot competitiveness. |
As I bring these dimensions together, I return to the opening fact. Our industrial robot shipments ranked first among all brands in the first half of 2026. We became the first autonomous brand to ship more than ten thousand industrial robot units in a single quarter. We have held the leading industrial robot position for six consecutive quarters since 2025 and have remained the number one domestic brand for eight consecutive years. Those achievements are meaningful because they are supported by heavy-load industrial robot autonomy, iER.OS control, RoboBase embodied intelligence, iER.Cloud AI, multimodal AI applications, modular flexible manufacturing, digital scheduling, global service coverage, and customer deployments in demanding industries.
I do not treat industrial robot leadership as a finish line. I treat it as a responsibility. Every industrial robot shipment creates an expectation of uptime, accuracy, safety, and productivity. Every industrial robot application creates a new data point for learning. Every customer factory creates a new standard for what automation should deliver. My first-person view is therefore simple: we must continue to improve the industrial robot at the component level, the control level, the AI level, the delivery level, and the service level. We must keep the industrial robot open enough for partners, robust enough for factories, intelligent enough for non-structured work, and efficient enough for mass adoption.
I believe the next chapter of industrial robot competition will reward companies that can combine physical precision with embodied intelligence. The industrial robot will not only repeat. It will perceive, reason, decide, and act. It will not only sell as a machine. It will deliver as a platform. It will not only serve one process. It will connect across welding, grinding, inspection, cutting, assembly, and logistics. The industrial robot will become a core node in smart manufacturing, and the companies that lead will be those that can scale trust as quickly as they scale shipments.
That is why I continue to focus on the industrial robot as an integrated system. The market ranking confirms that customers trust our industrial robot platform. The single-quarter ten-thousand-unit milestone confirms that our industrial robot delivery system can operate at scale. The heavy-load autonomy confirms that our industrial robot technology foundation is deep. The embodied intelligence stack confirms that our industrial robot software architecture is forward-looking. The customer deployments confirm that our industrial robot solutions create measurable value. The global service network confirms that our industrial robot business can support international growth. Together, these elements form a durable industrial robot leadership model.
$$ Industrial\ robot\ future = Physical\ precision + Embodied\ intelligence + Cloud\ collaboration + Global\ service + Customer\ value $$
$$ Sustainable\ industrial\ robot\ advantage = \sum_{t=0}^{T} (Innovation_t + Quality_t + Delivery_t + Trust_t) $$
I end where I began, but with a broader view. The industrial robot is one of the most important tools for modern manufacturing because it converts software, data, and control into physical work. When that physical work becomes more intelligent, more flexible, and more reliable, the entire factory becomes more competitive. I see our industrial robot journey as part of that transformation. I see the ranking, the shipment milestone, the technology stack, the customer projects, and the global network as connected proof points. I see the future as a continuing effort to make the industrial robot more capable, more accessible, and more valuable in every industrial scenario.