SANY: How digital twins create value for the enterprise

As early as 2008, SANY Group Co., Ltd. (referred to as: SANY Group) was facing fierce competition with its international counterparts. Since that time, SANY has devoted itself to improving the technical strength of its products to catch up with the world’s top leaders in the industry. What has SANY done?

All industrial equipment has one purpose, that is, to serve production. That’s why the industry evaluates equipment suppliers by measuring equipment service availability as a key metric. When a fault happens, costs occur not only in equipment maintenance, but also in the high indirect production losses as a result of downtime. For example, assume that an infrastructure project worth 1 billion RMB needs to be completed in 1 year. If production breaks down for just one day, 3 million RMB will be lost due to delay of progress. At that time, even though the international counterparts have powerful technical strengths, faults like this can still occur to them, and maintenance time is not guaranteed. Therefore, SANY set a goal to make itself more competitive than its international counterparts by offering a portfolio of “products + services”.

Researches and trials have been carried out on many types of construction machinery, but still, transformation can be quite difficult. First of all, construction machinery mostly works in harsh environments that are hard to access, and the machinery itself cannot move around too often. Repair personnel are required to go to the site to repair and maintain construction machinery. To improve service response efficiency and repair equipment as soon as possible, personnel need to know the specific location, operating status, and faults of the equipment to determine whether there are suitable engineers and parts within the corresponding service radius to reach the site as soon as possible.

Therefore, digital twins of the equipment are very important for engineers. Before they arrive at the site, they can check and diagnose the overall operation of the equipment through the data collected from more than 70 collection points on the equipment to ensure that problems can be understood and solved in the shortest time. In some cases, the engineers may not need to go to the site, but only communicate with the equipment remotely to make relevant program adjustments or remotely guide the field personnel to solve the faults. In the traditional way, engineers need to go to the site to investigate and then determine the maintenance plan. If there is a shortage of spare parts, they need to order them first. The long time spent waiting for the parts can bring huge production losses to customers

SANY integrated loT-based digital twin technologies in its after-sales service system using several key metrics during the service process to evaluate the quality of services, such as engineer response time (from the time of receiving a demand call to the time of dispatching an engineer), readiness of common spare parts, one-time repair rate, and equipment fault rate. Utilizing real-time operational equipment data, fault parameters and engineers’ maintenance knowledge, SANY Group has created digital models of all kinds of equipment, services and other related parties to continuously improve the corresponding service response and quality.

Data show that loT-based intelligent digital twin services have improved the response rate by 33%, and the loT data modeling based on fault modeling and equipment operations has directly improved the accuracy of spare parts prediction by 55%.

The ultimate benefits for SANY are very obvious. The engineer response time is shortened from the original 300 minutes to 15 minutes; now, it only takes 2 hours to arrive at the site within its main service areas, and equipment can be repaired in 24 hours; improvement in service response has also reduced spare parts inventory from 1 billion to 700 million, with one-time repair rate increased from 75% to 92%.

With the accumulation of data, loT digital twins based on services can provide the most realistic data for R&D, such as overall equipment operating status and health condition, characteristics of the parameters corresponding to faults, and driving behaviors of operators. The construction of loT digital twins based on equipment has a great role in improving R&D.

Every day, SANY’s R&D team has to deal with TBs of experimental and application data. In the past, its data storage was chaotic, with low analysis efficiency and insufficient value mining; its analysis relied on traditional ways, such as Excel, VB or even manual analyses which took a long time with low accuracy and low data utilization and wasted experts’ efforts. With the help of ROOTCLOUD Platform’s big data abilities, SANY realized the fast import and cleaning and accurate classification of R&D data. According to the experts’ analysis experience and mechanism models, AI abilities are converted into algorithms on the ROOTCLOUD AI Platform, which continuously accumulates experience and greatly improves the analysis efficiency and data accuracy by adopting AI technologies.

In the past, the analysis of an R&D batch took as long as 1 week, but now it has been shortened to less than 3 hours; before, personnel need to spend 2-3 days dividing the R&D stages manually, but now ROOTCLOUD’s AI capabilities can automatically divide them through algorithms, which can be completed within 3 minutes. Data utilization rate has increased from less than 20% to 80%; in the past, it took 2-3 hours to analyze 10G of test data, but now it can be completed within 3-5 seconds. More than 30 kinds of experience factors have been accumulated, and data analysis efficiency has been increased by 50 times to support SANY’s 96 kinds of newly launched construction machinery and develop 58 new technologies every year.

SANY’s technical strength has kept improving in recent years, and has become the construction machinery enterprise of the world’s second largest market value, the world’s No.1 pumping equipment provider, and China’s No.1 excavator provider, all thanks to the support from the irreplaceable digital twin R&D system.

In addition to good R&D support, the production of good products also requires a strict, advanced, and efficient management of production organizations. In more than 20 regions in China, SANY has more than 30 companies, over 90 factories, and more than 8,000 pieces and 200 kinds of equipment, of which nearly more than 6,000 pieces are dumb equipment (i.e. the equipment that has not been connected through IoT technologies), involving 15 major production processes. The group was bearing more and more costs and pressure to manage such a wide range of production organizations, which can be seen in the following 2 aspects.

• In the traditional way, equipment is managed by privately deployed software like SCADA system. Each of these pieces of software finally turns into an information silo, which is difficult to manage and requires a cross-region and cross-business cloud platform.
• The connection is very difficult for it needs to connect a large number of different machines, and also 65% of them are dumb machines.

With ROOTCLOUD LEEMS, an innovative solution to identify equipment operating status by detecting electric current with AI technologies, the costs and difficulty of connecting common equipment are greatly reduced with such a high efficiency that more than 8,000 machines are connected in just about six months.

Through the real-time metrics of equipment assets, you can view the online or historical statistics of all the equipment in the group, and then classify equipment statuses such as online, working, standby, downtime, bottleneck, and redundancy, and derive decision-guiding data including online rate, power-on rate, working rate, utilization rate, bottleneck rate, redundancy rate, total power consumption, etc.

From 2019 to 2020, SANY Group’s compound output growth exceeded 55%, and especially, in the first half of 2019, sales grew by 60% year on year without SANY’s additional investment in equipment. The intelligent equipment management system plays a very critical role in helping SANY to accurately identify problems in production management. Lean management requires accurate data imported for analysis. With IoT data analysis, SANY obtains more and more insights into business; at the same time, SANY also finds out more than 500 units of superfluous equipment, saving tens of millions of dollars.

Sufficient, real-time, accurate data are very critical for lean management and intelligent manufacturing. In the past, manufacturing enterprises mainly relied on the management of scattered process data with low structuring and efficiency. In the era of industrial Internet, one of the biggest benefits is to network equipment in the physical world and get real-time data directly from the equipment. No matter how big a factory or a business group is, we can gain real-time insights into all aspects of its problems, and connect all the management processes and physical spaces, and control the operations on the site directly online. In the future, real-time data, coupled with artificial intelligence and other technologies, will revolutionize management in terms of accuracy and efficiency.

Industry chain finance is an application for which ROOTCLOUD has applied for innovation patents. Evaluations and analyses based on excavator IoT data and equipment maintenance and parts replacement data are carried out for big data mining and modeling of equipment usage and fault maintenance, and for the establishment of quality assessment indexes. Data-oriented products are developed for actuarial pricing and risk selection based on model results, assisting actuarial and product departments of insurance companies in providing technical, data and operational support for user usage scenarios, risk management, and pricing of the products with extended warranty by referring to data such as excavator quality assessment indexes and other variables.

SANY Group sold more than 50% of the equipment by leasing equipment worth more than 30 billion yuan each year, so it is very important for it to control its financial risk. How can people establish the indexes to define customer’s repayment abilities based on equipment operating status and utilize machine locks to control the risk of leasing machines? Digital twin technology is an answer to that.