在过去的十年中，数字化转型在所有行业之间都加速了。这项新技术的革命改变了全球传统制造业和商业景观，并有望加快加速。
就在2018年，制造公司以数字方式进行了改造accounted for $13.5 trillion of global GDP。但是据估计，到2023年，这个数字将增加，包括53.3万亿美元的全球GDP。
In this article, we will discuss digital transformation in manufacturing and look at some of the trends driving this revolution.
Even before the pandemic began, the disruption and digital transformation of the supply chain were already underway. Smaller enterprises saw the new suite of technologies as a competitive advantage, or a means to address a lack of valuable skill sets through automation.
On the other hand, larger enterprises struggled with adoption. A2018 study by McKinseyfound that businesses with 100 employees or less were 2.7 times more likely to adopt digital transformation successfully. And the same survey found that overall, under 30% of these transformations succeeded.
While the pace of digitization in manufacturing was steady but slow with many fits and starts, it was widely recognized by C-suite executives and CEOs as the future of business. But as the COVID-19 pandemic hit in 2020, many businesses were forced to speed up adoption and make it work as a matter of survival.
Others, equally threatened in their business models by a once-in-a-lifetime disruptive event, quickly began looking for ways to continue operations during the pandemic while ensuring the safety of their workers. Today, as the effects of COVID-19 slowly recede, a new reality has taken shape in manufacturing, withas high as 91% of all manufacturing operationsstating that they have increased investment in digital transformation and will continue.
Despite the new urgency within the manufacturing sector, there are still challenges for adopters to overcome. These include:
尽管是早期采用者的中小型公司具有竞争优势，但这些较小的公司中的许多公司仍然缺乏资本资源和较大竞争对手的深厚口袋。
各种规模的公司也倾向于focus on ROI, and the formula for successful ROI is different for different-sized enterprises. There is also the problem of linking investment dollars to future profitability. Many companies struggle with the upfront cost, but the cost of not adopting may be higher or even fatal.
The solution may come through the modularity of many of these platforms. By using cloud-based software and cost-effective hardware, companies may develop incremental plans that focus on a critical area such as downtime with plans to scale the system as results pay for themselves through increased production.
许多制造行业发现，发现他们必须采购和培训人们成功部署数字技术是一项挑战。平台通常使用AI和机器学习算法运行。
But other technologies such as digital twins and 3D modeling also may be required depending on the industry. Retraining existing workers may not be possible. And these new skillsets are in high demand.
这也尤其受到技能问题的挑战。现在，昂贵的光纤，服务器和长电缆运行必须让位于具有不同要求的基于云的技术。
IT is also tasked with transferring data, ensuring security and access, and a host of other tasks that are either unfamiliar or run contrary to what they have done traditionally. And many of the legacy systems in place may have extended software licenses of their own that are now obsolete or not needed for a new digital world.
The manufacturing industry has always been seen as made up of predominantly manual workers. And while there will still be places within many industries for strictly manual labor, many new skills and training are required to bring operators to the technician's skill level.
These new technologies often utilize interactive screens, tablets, and other human-machine interfaces to input and receive data the operator needs. Training will need to be done to develop the skills for workers to understand and use these types of systems and leave behind paper-based ones.
Many company executives who understand the importance of digital transformation and see its current and future benefits also worry about security. Most of these systems are cloud-based and operate over the internet. They may also have Wi-Fi or cellular connectivity and Ethernet to connect legacy analog equipment to the floor. These物联网安全挑战do pose a risk, but can be mitigated.
With news of hacks appearing almost weekly, many are worried that such an event could stop production. Others fear that in the case of critical industries such as pharmaceuticals, these incidents could be life-threatening. While these challenges are real, security protocols are growing and will continue to focus on heightened and tightened security as adoption accelerates.
数字化转型是一个小组术语，它涉及一系列技术和方法，共同重塑了制造和创建新的业务模型。这是一些趋势。
Industry 4.0 is a global term for the第四工业革命。It encompasses all the technologies that refer to data capture and exchange in manufacturing. Industry 4.0 also includes technologies such as the Industrial Internet of Things, cloud-based computing and systems, connected manufacturing environments, 3D printing (additive manufacturing), and all components that make up the combined cyber-physical system. It refers to all things combined or networked in a modern factory that make such businesses "smart" in utilizing technology to realize efficiency gains and optimized processes.
IIOT代表工业互联网，属于行业4.0的框架。IIOT是包含A的传感器，数据收集设备，测量系统和执行器的集合smart factory。These systems are linked to powerful analytics that allows real-time visualization of the factory floor or shop floor and enable autonomous or semi-autonomous actions to be taken. It also will enable decision-makers and operators to act quickly on actionable insights provided by the analytics engines to gain greater efficiency and reduce downtime while improving the quality of products across the company.
Machine learning and AIwi是两个不同的东西,可以合作吗thin an IIoT platform. Machine learning is a term that describes advanced algorithms that can change the way equipment is run or suggest prescriptive steps to improve. The greater the volume and quality of data the system receives, the better the quality of the prescriptions. AI, or artificial intelligence, is the advanced intelligence engine that allows autonomous or semi-autonomous actions by the equipment itself. This is safer and more efficient as the artificial intelligence applications can work at speed, replacing actions previously done only and more slowly by humans.
As machine learning and AI drive advanced analytics within a production monitoring system, the system obtains trends, insights, information, and other data. This allows for the development of预防性维护的预测性维护within the manufacturing industry and its operations.Predictive maintenance在制造业实际使用食蟹猴e state and part condition monitoring to determine failure points, send alerts, and make parts inventory decisions and staging occur at the optimum time to prevent downtime.
机器人技术并非在每个制造业中使用。但是，随着数字技术的进步，它变得越来越普遍。Robotics从平台的AI和机器学习元素中获取说明，可提供更安全，更快，更高效和更准确的生产。机器人技术使制造能够以人类无法手动实现的速度进行。
Companies that invest in digital transformation will realize better profits and higher efficiency than their competitors. Benefits to adopting digital transformation within manufacturing include:
Companies that embrace digital transformation实现更大的效率as downtime decreases. Platforms such as MachineMetrics production monitoring can immediately reduce downtime and enhance efficiency and equipment utilization.
Higher equipment utilization and efficiency lead to reduced costs. Predictive maintenance can add to those lower costs with as high as 20% reductions in maintenance cost. And greater control over inventory and movement of materials within manufacturing can help control costs in the supply chain.
使用IIOT平台进行生产监控，例如机械学can help optimize processes。This may take the form of new methods as well as ongoing and more accurate process improvement strategies based on real-time data.
In addition to AI and machine learning capabilities, platforms can be scaled and customized to reflect the needs of different equipment and industries. This allows for flexible and agile responses to changes and challenges.
If you are looking for digital transformation, now is the time to jump in. With unprecedented disruption, manufacturers need the most advanced tools and best-in-class software to stay competitive. They should look tobuild an ecosystem of solutionsthat support rapid and continuous value achievement.
The foundation of a manufacturer's tech stack should be an Industrial Data Platform that can enable the autonomous collection, contextualization, and standardization of production data, as well as drive actionability on data to identify and resolve operational inefficiencies. Learn howMachineMetrics enables digital transformation for manufacturers orbook a demo to discover valuable use cases today。
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