How IIoT and AI are Transforming the Supply Chain

This article appeared in今天的来源并已在此处出版。

What you’ll learn:

• How IIoT and AI can alleviate challenges that have impacted the supply chain over the past two years.
• 资产跟踪如何改善供应链。
• Aggregating and analyzing data more quickly with artificial intelligence.
• What’s next for the supply chain.

先进技术和人工智能(AI) and sensors have enabled the supply chain to continually evolve over the past several years, with the goal of keeping operations lean. Less inventory in the pipeline has meant using electronic components and raw materials in the most cost-effective and efficient ways—a huge benefit for many companies. However, when the COVID-19 pandemic took hold and caused global supply-chain disruptions, many industries were left reeling, including the electronics industry.

The industry has experienced an immense bullwhip effect, seeing an initial drop in demand for end-product at the outset of the pandemic, followed by skyrocketing demand for products, alongside a delivery and inventory storage shortage. Unfortunately, this zig-zagging demand in unpredictable places revealed many pain points within the supply chain—primarily, a lack of robustness.

尽管为保持供应链精益的努力是充分意图的，但该过程变得如此集中，以至于当一个事件仅影响一个制造商时，世界各地都感受到了这种影响。供应链根本不足或不足以将足够的原材料带入植物中，许多供应链完全被关闭。劳动问题还加剧了干扰，工厂关闭和人员短缺加深了整个供应链的延误。

好消息是，AI和IoT传感器在开发一个更健壮，更具弹性的系统方面有很大的作用，可以修复大流行揭示的供应链中的许多差距。实际上，工业互联网（IIOT）已经影响了供应链和制造业，从而带来了更具预测性，智能和积极主动的解决方案以及可提高效率的新功能。

Enhanced Asset Tracking

供应链中IIT传感器越来越多地用于资产跟踪的众多方式之一。监视过去不透明的资产的位置和状态。如今，由于传感器的广泛使用和降低的价格，客户可以在任何给定时间实时访问其资产的数据。

For instance, with IIoT trackers, a customer could see if their container is still on a ship at a port, or if it’s arrived and is en route to its final destination. That said, knowing the location and status of just one certain asset isn’t enough if it takes, for example, 20 or more components, to produce a product.

这强调了为什么对所有不同输入（例如组件，原材料和其他产品）的传感器收集的所有数据的统一观点如此重要。汇总所有这些数据至关重要，这样对可能在家中跟踪其订单的客户和消费者都更有意义和富有成效。

Many different sensors are used beyond location tracking as well, to provide more data throughout the supply chain. Environmental sensors that track pressure, temperature, and humidity, and positioning sensors utilizing gyroscopes and accelerometers to track a product’s orientation, position, etc., offer a much more robust view into a product’s journey to its end destination. They also can shed light, for example, into the exact time and place a product may have been damaged.

The widespread application of sensors and trackers is largely due to the reduced cost associated with their use. However, all of the available data from these IIoT powerhouses is only useful when it’s managed properly, analyzed, and acted upon.

Using AI for Data Management

运营经理需要大量信息来确定工厂是否能够在特定时间运行生产，尤其是在适当的时间将其用于工厂的所有必要输入方面。在IIOT解决方案变得如此普遍之前，由于缺乏对实时（和准确）数据的可见性，传统上这是一个“猜测游戏”。

现在，由于IIOT，AI可以轻松地汇总和分析传感器的大量数据，以更准确地预测何时将所有运动部件到位。例如，AI系统可以将传感器数据与第三方数据一起使用，例如端口供稿和天气信息，以主动预测必要的输入何时到达，以及是否会延迟。

边缘计算也启用了此过程的很大一部分。数据处理更接近其源或收集点，而不是首先将其传达回云，最终提高效率。

AI通常可以比人更快，更准确地分析来自不同来源的大量数据。结果，工人可以在平凡的手动任务上花费更少的宝贵时间，例如追踪输入的位置和状态。这些聪明的自动化系统使工人释放了更多的创造性，熟练的工作，需要批判性思维技能，例如管理系统，与客户建立关系以及在需要时进行艰难的判断。

Data-management systems aren’t designed to “replace” workers. Instead, these systems bring to light more meaningful insights to the individual overseeing it. Ultimately, data management through AI unearths new value through improved accuracy and increased efficiency across the board.

Data from Sensors Can Predict Disruptions

IIoT sensors are key to improving operational efficiency and enhancing worker safety, particularly when it comes to enhanced traffic and environmental monitoring. For example, the increasing frequency of wildfires in the U.S. have affected many areas of the supply chain, including creating unsafe driving conditions and closing roads. Companies that incorporate environmental data into their planning, alongside data from asset trackers, were able to shift as needed to account for the disruption.

天气状况和交通拥堵通常被视为外部因素。但是，通过将他们带入计划操作，供应链专业人员可以预见到地平线上断。

What’s Next

IIOT技术正在创造大量新功能，其中大量实施相对便宜，例如低成本处理器。从成本的角度来看，现在比以往任何时候都更容易将传感器集成到各种产品中以监视和收集数据，而过去可能在经济上访问了数据。其中一些技术包括视觉传感器。此类传感器正在迅速前进，尤其是与AI配对以分析来自相机中每个像素的所有数据。

The intersection of AI and real-time data, as is the case with many new sensors, has created numerous methods for gaining insight into the supply chain. IIoT will continue to fuel the future of how we work in manufacturing, shipping, and many other areas of the supply chain. These technologies will provide a more holistic view and control over the supply chain, and with any luck will help prevent future disruptions in the global supply.