Applications of Artificial Intelligence in Manufacturing and Tips on How to Implement it

人工智能在制造业中的应用及实施技巧

The manufacturing sector faces numerous challenges, including difficulties in forecasting demands, skilled worker shortage, and keeping the equipment up and running.

制造业面临众多挑战，包括预测需求的困难，技术工人的短缺，以及保持设备正常运转。

Simultaneously, this sector generates plenty of data, which makes employing artificial intelligence in manufacturing a given. Many organizations have already realized this and started implementing AI-powered business solutions to enhance operations. Markets and Markets expect the global AI in the manufacturing market to reach $16.7 billion by 2026, growing at a CAGR of 57.2%. Interested in finding out how AI can help your company and which steps to take for successful deployment? Then keep reading this article.

同时，这个行业会产生大量的数据，这使得在制造业中采用人工智能成为必然。许多组织已经意识到这一点，并开始实施以人工智能为动力的业务解决方案，以加强运营。Markets and Markets预计，到2026年，全球制造业的人工智能市场将达到167亿美元，年复合增长率为57.2%。有兴趣了解人工智能如何帮助你的公司，以及为成功部署应采取哪些步骤？那么请继续阅读这篇文章。

Why Investing in AI Makes Sense for the Manufacturing Industry?

为什么投资AI对制造业有意义？

According to a recent study by Deloitte, the manufacturing sector is ahead of other industries when it comes to data generation. Artificial intelligence technologies possess an unmatched ability to analyze large amounts of data, so it is only natural for manufacturers to adopt this technology.

根据德勤公司最近的一项研究，在数据生成方面，制造业领先于其他行业。人工智能技术拥有无与伦比的分析大量数据的能力，所以制造商采用这种技术是很自然的。

Confirming the above, a recent MIT Technology Review published a graph showing the percentage of AI-enhanced business processes among different industries. Manufacturing is proudly located towards the top of the list, second only to the financial services sector.

为了证实上述情况，《麻省理工科技评论》最近发表了一张图表，显示了不同行业中人工智能增强的业务流程的百分比。制造业自豪地位于名单的顶端，仅次于金融服务部门。

So, How Can AI Improve Manufacturing Efficiency?

那么，AI如何提升制造效率呢？

Minimizing or even preventing equipment outage. AI-powered software can spot malfunctioning in factory devices before it causes actual damage and delays production.

最大限度地减少甚至防止设备停机。人工智能驱动的软件可以在工厂设备出现故障并导致实际损坏和延迟生产之前发现故障。

Enforcing quality standards. AI in manufacturing can monitor items on the production line, identify defects, and prevent low-quality products from reaching the market.

执行质量标准。制造业的人工智能可以监测生产线上的项目，识别缺陷，并防止低质量的产品进入市场。

Supporting human employees. AI assistants can change the manufacturing sector by freeing up a considerable amount of employees’ time. AI can take over routine inspections and automate repetitive duties. This technology can even perform creative tasks, such as generating product designs.

支持人类员工。人工智能助手可以通过释放大量员工的时间来改变制造业。人工智能可以接管常规检查，并将重复性的工作自动化。这项技术甚至可以执行创造性的任务，如生成产品设计。

Top 5 AI Examples in Manufacturing

制造业的五大人工智能范例

Artificial intelligence enables predictive maintenance.

人工智能实现了预测性维护。

AI forecasts demand and raw material prices

人工智能预测需求和原材料价格

AI supports generative design

人工智能支持生成式设计

Artificial intelligence helps build digital twins

人工智能助力打造数字双胞胎

AI inspects product quality

AI检验产品质量

1. Artificial Intelligence Enables Predictive Maintenance

1.人工智能实现预测性维护

Predictive maintenance is one of the most funded applications of AI in the manufacturing industry.

预测性维护是AI在制造业中获得资金最多的应用之一。

Equipment fault can cause significant disruptions, delays on production lines, and increase production costs. One minute of downtime at large factories can cost as much as $20,000. Additionally, regular diagnostics by human experts are relatively expensive.

设备故障会造成重大干扰，延误生产线，并增加生产成本。在大型工厂中，一分钟的停机时间可能会造成高达20,000美元的损失。此外，由人类专家进行的定期诊断也相对昂贵。

AI-powered solutions analyze equipment’s historical performance data to spot anomalies and predict when it will need maintenance before it malfunctions or comes to a halt. This allows employees to choose a suitable time for fixing the device instead of stopping everything in the middle of the production process when this machine is out of service.

由人工智能驱动的解决方案分析设备的历史性能数据，以发现异常情况，并在设备出现故障或停工前预测它何时需要维修。这使得员工可以选择一个合适的时间来修复设备，而不是在这台机器停止工作时，在生产过程中停止一切。

General Motors gives one example of AI implementation in manufacturing. The company mounted cameras on its assembly robots and trained AI algorithms to analyze the data streaming from these cameras to identify signs of component malfunctioning. In a pilot test of this solution, it worked on 7,000 robots and identified 72 instances of component damage before they resulted in an unplanned outage.

通用汽车公司给出了一个在制造业实施人工智能的例子。该公司在其装配机器人上安装了摄像头，并训练人工智能算法来分析来自这些摄像头的数据流，以识别部件故障的迹象。在这个解决方案的试点测试中，它在7,000个机器人上工作，并在导致意外停工之前确定了72个组件损坏的情况。

2. AI Forecasts Demand and Raw Material Prices

2.AI预测需求和原材料价格

Forecasting Prices of Raw Material

原材料价格预测

Raw materials costs are volatile in nature. When manufacturers have this information in advance, they can adapt their operations to minimize expenses.

原材料成本本质上是波动的。当制造商提前掌握这些信息时，他们就可以调整他们的操作，使费用最小化。

A UK-based startup ChAI uses machine learning to forecast price fluctuation of raw materials, such as aluminum, oil, and copper, among others. The company was founded in 2017, and it secured €1.5 million in seed financing in 2020. ChAI targets Fortune 100 companies, including manufacturers, who rely on these materials as a part of their supply chain.

一家位于英国的初创公司ChAI利用机器学习来预测原材料的价格波动，如铝、石油和铜等等。该公司成立于2017年，它在2020年获得了150万欧元的种子融资。ChAI的目标是财富100强企业，包括制造商，他们依赖这些材料作为其供应链的一部分。

Predicting Demand

预测需求

AI analyzes behavioral patterns, socioeconomic data, location, and weather forecast to determine which products will be in demand, allowing manufacturers to focus on what matters and cease producing items that no one would purchase. AI can even predict which product will be a hit before they go to the market.

人工智能分析行为模式、社会经济数据、位置和天气预报，以确定哪些产品会有需求，让制造商专注于重要的事情，停止生产没有人会购买的物品。人工智能甚至可以在产品上市前预测哪种产品会成为热门。

Danone deploys machine learning in manufacturing to foresee variability in demand and adjust its production plan accordingly. Thanks to this approach, the company decreased its lost sales by 30%.

达能在制造业中部署了机器学习，以预见需求的可变性并相应地调整其生产计划。由于这种方法，该公司的销售损失减少了30%。

3. AI Supports Generative Design

3.AI支持生成式设计

Generative design is a program that relies on AI technologies to mimic a human engineer’s approach to designing products. Engineers feed different design parameters, such as size, materials, and cost constraints, into generative design algorithms, which generate different design options for one product. This method allows manufacturers to create hundreds of alternative designs for one item and experiment with how adjusting parameters reflect on the outcome. A human designer would not be able to come up with so many ways of building one item.

生成设计是一个依靠人工智能技术来模仿人类工程师设计产品的程序。工程师将不同的设计参数，如尺寸、材料和成本限制，输入生成式设计算法，为一个产品生成不同的设计方案。这种方法允许制造商为一个产品创建数百个备选设计，并试验如何调整参数以反映结果。人类设计师不可能想出这么多方法来制造一件物品。

The resulting designs can be further tested using machine learning to determine which options work best. Considering AI’s recommendations, a specialized workforce will select the design they want to pass to the development stage. For example, Nissan experimented with letting AI propose car designs hoping it would come up with something different. According to the company, their algorithms put forward a design that no one has ever seen before. It was not perfect, but it’s a good start. AI and ML in manufacturing can also assist designers with user experience. Typically, designers try to imagine possible ways the user might use a particular product. With its learning potential, AI can analyze data on how people utilize such products historically to come up with optimal designs.

由此产生的设计可以使用机器学习进一步测试，以确定哪些方案效果最好。考虑到人工智能的建议，专门的工作团队将选择他们想要传递到开发阶段的设计。例如，日产公司试验了让人工智能提出汽车设计，希望它能提出不同的东西。据该公司称，他们的算法提出了一个从未有人见过的设计。这并不完美，但这是一个好的开始。制造业中的人工智能和ML也可以协助设计师进行用户体验。通常情况下，设计师试图想象用户使用特定产品的可能方式。凭借其学习潜力，人工智能可以分析人们在历史上如何利用这种产品的数据，从而提出最佳设计。

4. AI Helps Build Digital Twins

4.AI帮助构建数字双胞胎

Products

产品

IBM defines a digital twin as a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to help decision-making. To set up a digital twin, you need to collect data from sensors attached to the physical item and project this data onto the digital twin. This way, when you look at the virtual item, you can see what is happening to its real-world counterpart.

IBM将数字孪生体定义为一个物体或系统的虚拟代表，它跨越其生命周期，根据实时数据进行更新，并使用模拟、机器学习和推理来帮助决策。为了建立一个数字孪生体，你需要从连接到物理项目的传感器收集数据，并将这些数据投射到数字孪生体上。这样，当你看到虚拟物品时，你可以看到其现实世界中的对应物正在发生什么。

For example, if you create a plane engine’s digital twin, it will receive data from the real engine upon landing and takeoff. You will be able to evaluate the condition of the actual engine by examining the digital twin. Researchers can use this technology to conduct simulations and anticipate malfunctioning.

例如，如果你创建了一个飞机发动机的数字孪生体，它将接收来自真实发动机着陆和起飞时的数据。你将能够通过检查数字双胞胎来评估实际发动机的状况。研究人员可以利用这项技术进行模拟，并预测故障的发生。

Manufacturers can also use digital twins to make design modifications tailoring to customer preferences.

制造商还可以使用数字双胞胎来根据客户的喜好进行设计修改。

Manufacturing Processes

制造工艺

Digital twin technology is not limited to products. You can create a digital twin of the whole production line to optimize the manufacturing process. You will need to position sensors along the production line and use the generated data to analyze performance indicators.

数字孪生技术并不局限于产品。你可以创建整个生产线的数字孪生，以优化制造过程。你需要沿着生产线定位传感器，并使用生成的数据来分析性能指标。

Unilever partnered with the Marsden Group and used Microsoft Azure to set up eight digital twins of its factories. Algorithms embedded into the digital twins can suggest improvements to production based on the data they receive. For instance, one of the digital twins analyzed the shampoo production process data and could predict the correct order of processes to get the best batch time. Also, using this technology, Unilever reduced the number of production-related alerts by 90%, freeing up operators’ time.

联合利华与马斯登集团合作，利用微软Azure建立了八个工厂的数字双胞胎。嵌入数字双胞胎的算法可以根据它们收到的数据对生产提出改进建议。例如，其中一个数字双胞胎分析了洗发水的生产过程数据，可以预测正确的流程顺序，以获得最佳的批处理时间。同时，利用这项技术，联合利华将与生产有关的警报数量减少了90%，解放了操作员的时间。

5. AI Inspects Product Quality

5.AI检验产品质量

To make sure that products are up to par with quality standards, manufacturers use in-line visual inspection. However, it is time-consuming for human employees to examine all products manually. Cameras, computer vision, and other AI technologies for manufacturing can perform a fast inspection in real time, detecting flaws at the earlier manufacturing stages allowing engineers to make adjustments before the product can cause further delays.

为了确保产品符合质量标准，制造商使用在线视觉检查。然而，人类员工手动检查所有产品是非常耗时的。照相机、计算机视觉和其他用于制造的人工智能技术可以实时进行快速检查，在早期制造阶段检测出缺陷，让工程师在产品造成进一步延误之前做出调整。

Audi installed an image recognition system at its Ingolstadt press shop to capture and evaluate the quality of pressed sheets. This AI-powered system was trained on millions of test images and can identify even the finest cracks that could easily escape the human eye.

奥迪在其英戈尔施塔特冲压车间安装了一个图像识别系统，以捕捉和评估冲压片的质量。这个由人工智能驱动的系统在数以百万计的测试图像上进行了训练，甚至可以识别出很容易逃脱人眼的最细微的裂纹。

Another example of AI in production comes from a large food processing organization, which produces over 200,000 eggs per hour. Human operators used to inspect these eggs employing the sampling method, but it was prone to errors as inspectors couldn’t spot every damaged egg. Realizing this problem, the company switched to an AI-enabled quality control system. It was trained to identify several defects, including holes, leakage, and cracking in eggshells. This innovative solution can scan one egg in less than 40 milliseconds and spot any of the classified defects.

人工智能在生产中的另一个例子来自一家大型食品加工机构，该机构每小时生产超过20万个鸡蛋。人类操作员曾经采用抽样方法检查这些鸡蛋，但这很容易出错，因为检查人员无法发现每一个受损的鸡蛋。意识到这个问题后，该公司转而使用人工智能质量控制系统。该系统经过训练，能够识别若干缺陷，包括蛋壳上的孔洞、漏水和裂缝。这个创新的解决方案可以在不到40毫秒的时间内扫描一个鸡蛋，并发现任何分类缺陷。

8 Steps to Successfully Implement AI in Manufacturing

在制造业中成功实施AI的8个步骤

Recently, Deloitte surveyed the manufacturing sector. The respondents confessed that 91% of their AI projects failed to meet timely expectations. There are things that you can do to minimize the chances of your AI project joining the deck.

最近，德勤调查了制造业。受访者坦言，他们91%的人工智能项目未能及时达到预期。有一些事情你可以做，以尽量减少你的人工智能项目加入甲板的机会。

1. Align AI With Strategic Objectives

1.使AI与战略目标保持一致

It is best if the AI applications you are planning to adopt are in line with your business goals, be it cutting down costs, finding new revenue streams, increasing operations efficiency, etc. This tactic will ensure that business units are involved. AI efforts also need to match your established business goals timeline. Before employing a more advanced AI in the manufacturing industry, check if your schedule can handle the likely delays.

如果你计划采用的人工智能应用与你的业务目标相一致，无论是削减成本、寻找新的收入来源、提高运营效率等，这是最好的。这种策略将确保业务部门的参与。人工智能的努力也需要与你既定的业务目标时间表相匹配。在制造业采用更先进的人工智能之前，请检查你的时间表是否能处理可能出现的延误。

Highlight the business goals you want to achieve with AI in manufacturing and specify how to measure improvements. For example, increasing operations efficiency by reducing equipment downtime by 20%. It can help compose a roadmap with the business applications where you want to use AI in the short, mid, and long terms.

突出你想通过人工智能在制造业中实现的业务目标，并说明如何衡量改进。例如，通过减少20%的设备停机时间来提高运营效率。它可以帮助构成一个路线图，包括你想在短期、中期和长期使用人工智能的业务应用。

2. Prioritize Use Cases

2.确定用例的优先级

Even if you have ambitious plans regarding AI, it is a good practice to start with a few carefully selected use cases. As the company’s capabilities and experience grow, it can expand its AI in manufacturing efforts to more applications. You can prioritize use cases based on their feasibility, total value, and time needed to achieve this value.

即使你有关于人工智能的雄心勃勃的计划，从几个精心挑选的用例开始也是一个好的做法。随着公司能力和经验的增长，它可以将人工智能在制造业的努力扩展到更多的应用。你可以根据用例的可行性、总价值和实现这一价值所需的时间来确定其优先次序。

In his interview with Capgemini, Luis Miguel del Saz Rodriguez, Head of Digital, Design, and Manufacturing Services at Airbus, explained how he approaches use case selection at his company: “First, we organize a team workshop where we discover the pain points and the opportunities. We also consider the scale and impact in the business. Next, we take these pain points, or opportunities, and work on the digital solutions, analyze budget and the associated business case.”

空中客车公司数字、设计和制造服务主管Luis Miguel del Saz Rodriguez在接受Capgemini采访时，解释了他如何在公司进行用例选择。"首先，我们组织一个团队研讨会，发现痛点和机会。我们还考虑业务中的规模和影响。接下来，我们利用这些痛点或机会，研究数字解决方案，分析预算和相关的商业案例"。

3. Organize Your Data

3.整理数据

Data is the main foundation of any AI-related endeavor. Your system needs to be able to capture data from different sources in various formats. It must be clean and accessible.

数据是任何人工智能相关工作的主要基础。你的系统需要能够以各种格式从不同的来源捕获数据。它必须是干净和可访问的。

Discrepancies are large between what companies want and what they can afford data-wise. In its recent study, Forrester Research discovered that 90% of the surveyed decision-makers view deriving insights from data as a business priority, while 91% described this task as rather challenging. Before starting with AI in manufacturing, it is advisable to examine your data and determine your level of maturity. This will show which opportunities you can explore with AI and prevent you from targeting solutions that your data foundation can’t adequately support.

公司的需求和他们在数据方面的承受能力之间存在着巨大的差异。在最近的研究中，Forrester Research发现，90%的受访决策者将从数据中获得洞察力视为业务优先事项，而91%的人将这项任务描述为相当具有挑战性。在开始使用制造业的人工智能之前，建议检查你的数据并确定你的成熟度。这将显示你可以利用人工智能探索哪些机会，并防止你针对你的数据基础不能充分支持的解决方案。

4. Think About Integrating AI in Manufacturing Solutions With Legacy Systems

4.考虑将制造业解决方案中的AI与遗留系统集成

You probably have some legacy manufacturing systems, such as enterprise resource planning (ERP) and product lifecycle management apps that can generate valuable data. Discuss with your vendor the possibility of integrating such software in your AI solutions.

你可能有一些遗留的制造系统，如企业资源规划（ERP）和产品生命周期管理应用程序，可以产生有价值的数据。与你的供应商讨论在你的人工智能解决方案中整合此类软件的可能性。

You can consider placing a standardized equipment purchase policy. Neeraj Tiwari, Director of Manufacturing JV Organization at Fiat Chrysler, explained how this is done at his company: “We have a centralized process for purchase of equipment, their subsystems, and associated software. This brings a level of standardization and makes integrating AI applications much easier and results in far fewer issues.”

你可以考虑放置一个标准化的设备采购政策。菲亚特克莱斯勒公司的制造合资组织主任Neeraj Tiwari解释了他的公司是如何做到这一点的。"我们有一个集中的程序来购买设备、它们的子系统和相关软件。这带来了一定程度的标准化，使整合人工智能应用变得更加容易，并导致问题大大减少"。

It is also a good practice to examine your manufacturing devices and attached sensors. Some of them might be generating data in formats that you cannot use. Forrester Research Analyst, Paul Miller, spoke about such equipment:

检查你的生产设备和附加的传感器也是一个好的做法。他们中的一些人可能正在生成你无法使用的数据格式。Forrester研究分析师保罗-米勒谈到了这类设备。

“Many [devices] may have been in use for a decade or more, and they either have no sensors at all or they have proprietary sensors that send commercially sensitive data in proprietary formats, which can be hard to decode.”

"许多[设备]可能已经使用了十年或更久，它们要么根本没有传感器，要么有专有的传感器，以专有格式发送商业敏感数据，这可能很难解码。"

Miller also added that such problems have a solution. Some companies sell specialized sensors that manufacturers can fit into their old devices if they know what they want to measure.

米勒还补充说，此类问题有一个解决方案。一些公司出售专门的传感器，如果制造商知道他们想要测量什么，就可以把这些传感器装进他们的旧设备。

Deloitte highlights five data maturity levels:

德勤重点介绍了五个数据成熟度级别：

Level 1: key business data is lacking

一级：关键业务数据缺失

Level 2: the basic data is available but located in isolated data silos

第2级：基础数据可用，但位于孤立的数据筒仓中

Level 3: the data is highly integrated but can’t support decision-making activities

第三级：数据高度集成，但不能支持决策活动

Level 4: it is possible to make data-driven decisions, but the system can’t reflect real-time changes

第4级：可以做出数据驱动的决策，但系统不能反映实时变化

Level 5: all the previous points, in addition to supporting real-time recommendations

第5级：除了支持实时推荐之外，所有前面的要点

If your data is not at the maturity level you need to support AI; it is worth investing in a reliable data foundation. It is paramount for the long-term success of AI and will allow you to roll out new AI-powered applications in the future. Furthermore, you might want to establish strong data governance practices. This includes determining:

如果你的数据没有达到支持人工智能所需的成熟度，那么就值得投资一个可靠的数据基础。这对人工智能的长期成功是最重要的，并将使你在未来推出新的人工智能驱动的应用程序。此外，你可能想建立强大的数据治理实践。这包括确定。

Which business functions and devices generate relevant data

哪些业务功能和设备生成相关数据

Who is the data owner

谁是数据所有者

How this data is stored

如何存储这些数据

Which data formats are acceptable

哪些数据格式是可接受的

5. Recruit Talent and Build Expertise

5.招聘人才，建立专业知识

When moving towards machine learning and AI in manufacturing systems, you will need to hire people with specific analytical skills. Limiting talent search to data scientists might not suffice. Your organization will need other specializations, such as data engineers and data stewards. Also, make sure your data experts collaborate with internal domain experts who have a deep understanding of the business problems AI in manufacturing is intended to solve. Some companies initiate upskilling programs for their in-house employees by teaming up with academia and startups.

当在制造系统中走向机器学习和人工智能时，你将需要雇用具有特定分析技能的人。将人才搜索局限于数据科学家可能是不够的。你的组织将需要其他专业人员，如数据工程师和数据管理员。此外，确保你的数据专家与内部领域专家合作，他们对制造业中的人工智能要解决的业务问题有深刻的理解。一些公司通过与学术界和初创企业合作，为其内部员工启动了技能提升计划。

Manufacturers typically begin with fragmented uses of AI experts and slowly move to more coordinated centralized efforts. Some end up establishing AI labs or centers for excellence, which will define best practices of using AI in the company.

制造商通常从零散地使用人工智能专家开始，慢慢转向更协调的集中努力。一些人最终建立了人工智能实验室或卓越中心，这将确定在公司使用人工智能的最佳做法。

6. Conduct a PoC and Scale Up Your AI solution

6.进行PoC并扩大你的AI解决方案

When your data is at the desired maturity level, run a proof of concept with your vendor of choice. This will help you better understand what to expect and what you still can fix before a large-scale adoption. Do not forget to integrate AI solutions into the end users’ workflow. According to McKinsey’s research, overlooking this step is one of the major obstacles to AI adoption.

当你的数据达到理想的成熟度时，与你选择的供应商一起运行一个概念验证。这将帮助你更好地了解在大规模采用之前，应该期待什么以及你仍然可以解决什么问题。不要忘记将人工智能解决方案整合到终端用户的工作流程中。根据麦肯锡的研究，忽视这一步骤是采用人工智能的主要障碍之一。

7. Monitor AI Algorithm’s Performance

7.监控AI算法的性能

When your AI solutions are fully up and running, it is advisable to keep monitoring the results. Assign dedicated staff members to make sure that ML in manufacturing is delivering on expectations, and if not, find out why and what to do to improve the situation. Also, someone will need to adjust AI to any change in your operations. AI algorithms will need retraining with new data categories. Or, if you installed the AI system in a different location, it might need to be retrained with location-specific data.

当你的人工智能解决方案完全启动和运行时，建议继续监测结果。指派专门的工作人员确保制造业中的ML正在实现预期目标，如果没有，找出原因，以及如何改善这种情况。另外，有人需要调整人工智能以适应你的运营中的任何变化。人工智能算法将需要用新的数据类别重新训练。或者，如果你在不同的地方安装了人工智能系统，它可能需要用特定地点的数据进行重新训练。

8. Be Patient and Adjust Your Expectations

8.保持耐心，调整自己的期望

Both your employees and AI need to learn how to do their job together optimally. There is a good possibility that this technology will produce false results frustrating everyone involved. Especially people who were not that excited about adopting artificial intelligence in manufacturing.

你的员工和人工智能都需要学习如何以最佳方式共同完成他们的工作。这种技术很有可能会产生错误的结果，让每个人都感到沮丧。特别是那些对在制造业中采用人工智能不那么兴奋的人。

Siddharth Verma, Global Head and VP  —  IoT Services at Siemens, shared his AI adoption experience with Capgemini. Here is what he said: “In the early days, when the accuracy of the system was low, it predicted a few failures which turned out to be false alarms. At these points, it is important to remind everyone that it is a prediction which has a probability of being right or wrong. As accuracy improved, the system was able to predict many failures in advance and saved a lot of cost and downtime, proving its worth.”

西门子物联网服务全球负责人兼副总裁Siddharth Verma与凯捷公司分享了他的AI采用经验。以下是他所说的。"在早期，当系统的准确性很低时，它预测了一些故障，结果是错误的警报。在这些时候，有必要提醒大家，这是一个预测，有可能是对的，也有可能是错的。随着准确性的提高，该系统能够提前预测许多故障，并节省了大量的成本和停机时间，证明了其价值"。

Want to use AI to enhance your manufacturing operations? Contact ITRex AI experts! They will help you build the right solution and integrate it into your existing system.

想使用人工智能来提高您的生产运营？请联系ITRex AI专家! 他们将帮助您建立正确的解决方案，并将其整合到您的现有系统中。