8. CONSIDERATIONS ON THE DIGITAL TR

8. CONSIDERATIONS ON THE DIGITAL TRANSFORMATION RELATED TO iMOM In previous chapters, we provided numerous evolving topics that revolve around the theme of digital technology and transformation. Many of the technologies are proven, yet many new technologies are continuously emerging. A few of these new technologies are an expansion of older technologies into new business areas. For example, future forecast based on current and historical data has been a well-established concept in advanced process control since the ’70s, yet the application of this technology into asset failure analysis has recently emerged. Perhaps the most evident example of an emerging powerful technology is that of data analytics. The need to analyze big data arose with the explosion in the amount of process-related data gathered and the increasing data storage and processing capabilities. These data need not be numeric. The recent developments in artificial intelligence also made it possible to consider descriptive texts, such as reports, as computer-analyzable data. There are a few main differences between how we used to handle information and applications in the past and how we foresee their future role in digitalization. In the past, industries used to bring applications that only served particular purposes. These applications looked at a specific spectrum of data to infer analysis and make conclusions. Moreover, each application survived in its own control space. It did not connect to spaces of other applications to get more data if and when required. This scheme mandated the use of segregated databases built for each and every created application. The unavailability of inter-application communication to exchange data limited the usefulness of much of the available data. At the plant operational level, the lack of proper communication and coordination between planning operations and maintenance departments resulted in tremendous losses to plant operability, product quality and assets. At the decision-making level, the lack of availability of the right information at the right time resulted in making improper decisions. At best, the right decisions are not made at the right time. Why Now? Currently the cost of the platforms to run the technology has been reduced dramatically. As of this writing, the price of a public unlimited cloud storage ranges from a fixed monthly fee of 80 cents to 10 cents per GB, depending on the service provider. Some of these clouds not only provide storage, but also act as computing platforms that offer capabilities for faster computing power through distributive computing. With time, more clouds are expected to provide computing power together with their storage capabilities. However, storage and computing power are not the only contributing factors. The availability of mature technologies that allow storage, management, analysis and retrieval of large amounts of such data have largely contributed to the rise of iMOM. Another main unnoticed drive is the expanded availability of open source software that facilitated the use of open-source big storage platforms such as Hadoop. What people sometimes overlook is that the availability of open-source software has contributed vastly to the development and enhancement of proprietary software, including proprietary platforms used for data storage and distributive computing. Open source is steadily gaining acceptance as an implementation platform for IIoT standards. The Gartner Hype Cycle for IoT Standards and Protocols lists 30 standards with, at least, half of that number considered as potential to deliver “high business benefit”. Of the potential standards, the following six are anticipated to evolve as mainstream in the next five years [13]. 1. An IETF standard that delivers IPv6 connectivity over non-IP networking technologies like NFC and LoRa, possessing extremely low power. The application of this standard should allow devices to operate for years before their batteries dry out 2. Contiki is an open source operating system designed for low-cost, low-power IIoT microcontrollers 3. LiteOS is a Unix-like operating system that is specifically designed for wireless sensor networks 4. OneM2M is a machine-to-machine (M2M) service layer embedded within hardware and software to enable device-to-device connectivity 5. Random Phase Multiple Access (RPMA) is a proprietary standard established to facilitate connectivity between IoT objects 6. Sigfox is a proprietary low-power, low-throughput technology designed for use in IoT and M2M communications

8. CONSIDERATIONS ON THE DIGITAL TRANSFORMATION RELATED TO iMOM In previous chapters, we provided numerous evolving topics that revolve around the theme of digital technology and transformation. Many of the technologies are proven, yet many new technologies are continuously emerging. A few of these new technologies are an expansion of older technologies into new business areas. For example, future forecast based on current and historical data has been a well-established concept in advanced process control since the ’70s, yet the application of this technology into asset failure analysis has recently emerged. Perhaps the most evident example of an emerging powerful technology is that of data analytics. The need to analyze big data arose with the explosion in the amount of process-related data gathered and the increasing data storage and processing capabilities. These data need not be numeric. The recent developments in artificial intelligence also made it possible to consider descriptive texts, such as reports, as computer-analyzable data. There are a few main differences between how we used to handle information and applications in the past and how we foresee their future role in digitalization. In the past, industries used to bring applications that only served particular purposes. These applications looked at a specific spectrum of data to infer analysis and make conclusions. Moreover, each application survived in its own control space. It did not connect to spaces of other applications to get more data if and when required. This scheme mandated the use of segregated databases built for each and every created application. The unavailability of inter-application communication to exchange data limited the usefulness of much of the available data. At the plant operational level, the lack of proper communication and coordination between planning operations and maintenance departments resulted in tremendous losses to plant operability, product quality and assets. At the decision-making level, the lack of availability of the right information at the right time resulted in making improper decisions. At best, the right decisions are not made at the right time.  Why Now? Currently the cost of the platforms to run the technology has been reduced dramatically. As of this writing, the price of a public unlimited cloud storage ranges from a fixed monthly fee of 80 cents to 10 cents per GB, depending on the service provider. Some of these clouds not only provide storage, but also act as computing platforms that offer capabilities for faster computing power through distributive computing. With time, more clouds are expected to provide computing power together with their storage capabilities. However, storage and computing power are not the only contributing factors. The availability of mature technologies that allow storage, management, analysis and retrieval of large amounts of such data have largely contributed to the rise of iMOM. Another main unnoticed drive is the expanded availability of open source software that facilitated the use of open-source big storage platforms such as Hadoop. What people sometimes overlook is that the availability of open-source software has contributed vastly to the development and enhancement of proprietary software, including proprietary platforms used for data storage and distributive computing. Open source is steadily gaining acceptance as an implementation platform for IIoT standards. The Gartner Hype Cycle for IoT Standards and Protocols lists 30 standards with, at least, half of that number considered as potential to deliver “high business benefit”. Of the potential standards, the following six are anticipated to evolve as mainstream in the next five years [13]. 1. An IETF standard that delivers IPv6 connectivity over non-IP networking technologies like NFC and LoRa, possessing extremely low power. The application of this standard should allow devices to operate for years before their batteries dry out 2. Contiki is an open source operating system designed for low-cost, low-power IIoT microcontrollers 3. LiteOS is a Unix-like operating system that is specifically designed for wireless sensor networks 4. OneM2M is a machine-to-machine (M2M) service layer embedded within hardware and software to enable device-to-device connectivity 5. Random Phase Multiple Access (RPMA) is a proprietary standard established to facilitate connectivity between IoT objects 6. Sigfox is a proprietary low-power, low-throughput technology designed for use in IoT and M2M communications

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8.注意事项对数字化改造相关iMOM 在前面的章节中，我们提供了许多发展的主题，围绕数字技术和改造的主题。许多技术已被证明，但很多新技术不断涌现。一些这些新技术都是较旧的技术进入新的业务领域的扩展。例如，基于当前和历史数据预测未来一直处于先进过程控制行之有效的概念，因为上世纪70年代，但这项技术的应用进入资产故障分析最近出现。 也许一个新兴的强大技术最明显的例子是，数据分析。分析大数据需要出现在聚集过程相关的数据量爆炸和增长的数据存储和处理能力。这些数据不一定是数字。在人工智能最近的事态发展也使人们有可能考虑描述文本，如报告，计算机分析的数据。 有间我们如何用来处理信息，并在过去的应用程序，以及我们如何预见自己的未来在数字化作用的几个主要区别。在过去，用于将只担任特定目的的应用行业。这些应用看着数据的特定频谱推断分析和做出结论。此外，每个应用程序在其自己的控制空间存活。它没有连接到其他应用程序的空间，如果有需要时获得更多的数据。该方案授权使用的每一个创建的应用程序内置隔离数据库。应用程序间通信的交换数据的不可用限制的许多可用的数据的有效性。 在工厂操作层面，缺乏规划的运营和维护部门之间适当的沟通和协调，造成了巨大的损失植物的可操作性，产品质量和资产。 在决策层面上，缺乏在正确的时间将正确的信息可用性导致做出不正确的决定。在最好的情况，正确的决策是不正确的时间做。 为什么现在？ 目前该平台的运行技术的成本已经大大降低了。在撰写本文时，公共无限云存储的价格范围从80美分，至每GB 10美分的固定月租费，这取决于服务提供商。有些云不仅提供存储，而且还作为计算平台，提供了通过分布式计算更快的计算能力的能力。随着时间的推移，更多的云预计将提供与他们的存储能力一起计算能力。然而，存储和计算能力并不是唯一的影响因素。成熟的技术，让存储，管理，分析和大量的此类数据的检索的可用性在很大程度上促成了iMOM的崛起。 另一个主要驱动忽视的是开源软件的扩展可用性，便利使用的开源大的存储平台，如Hadoop的。人们有时会忽略的是，开源软件的可用性已大大促进了专有软件，包括用于数据存储和分布式计算专有平台的发展和提高。开源正在稳步获得接受作为实现平台IIoT标准。Gartner的炒作周期为物联网标准和协议列表30个标准，至少，被视为潜力人数的一半，提供“高的商业利益”。潜在的标准中，以下六个预期演变为主流在未来五年内[13]。 1. IETF标准，超过像NFC和LORA非IP网络技术提供IPv6连接，拥有极低的功耗。该标准的应用程序应该允许在他们的电池干出设备运行若干年 2半自助旅游旅行社是一个开源的操作系统，专为低成本，低功耗微控制器IIoT 3 LiteOS是一种类Unix操作系统，它是专门设计用于无线传感器网络 4. OneM2M是嵌入式硬件和软件中，以使设备到设备连接5.随机相位多址（RPMA）的机器对机器（M2M）服务层被建立，以方便连接的专有标准物联网对象之间 6. Sigfox是专有的低功率，低通量技术设计用于的IoT和M2M通信中使用

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8. 与 iMOM 相关的数字化转换的注意事项 在前几章中，我们围绕数字技术和转型的主题提供了许多不断发展的主题。许多技术已经得到证实，但许多新技术正在不断涌现。其中一些新技术是将旧技术扩展到新的业务领域。例如，基于当前和历史数据的未来预测自 70 年代以来一直是高级过程控制中一个公认的概念，但该技术在资产故障分析中的应用最近出现了。 也许，一个新兴的强大技术最明显的例子就是数据分析。随着与流程相关的数据量的激增以及数据存储和处理能力的增强，对大数据进行分析的需求也出现。这些数据不需要是数字。人工智能的最新发展也使得将描述性文本（如报告）视为计算机可分析的数据成为可能。 过去我们处理信息和应用程序的方式和我们如何预见它们在数字化方面的未来角色之间，有一些主要区别。过去，行业过去只提供特定用途的应用。这些应用程序研究了特定的数据范围，以推断分析和得出结论。此外，每个应用程序都在其自己的控制空间中存活下来。它不连接到其他应用程序的空格，以便在需要时获取更多数据。此方案要求使用为每个创建的应用程序构建的隔离数据库。应用程序间通信无法交换数据，这限制了许多可用数据的有用性。 在工厂运营层面，规划运营和维护部门之间缺乏适当的沟通和协调，给工厂可操作性、产品质量和资产造成了巨大损失。 在决策层，由于无法在正确的时间获得正确的信息，因此作出了不适当的决定。充其量，正确的决定不是在正确的时间做出的。 为什么是现在？ 目前，运行该技术的平台成本已大大降低。在撰写本文时，公共无限云存储的价格范围从每 GB 80 美分到 10 美分的固定月费不等，具体取决于服务提供商。其中一些云不仅提供存储，还充当计算平台，通过分布式计算提供更快的计算能力。随着时间的推移，预计会有更多的云提供计算能力以及存储功能。然而，存储和计算能力并不是唯一促成因素。能够存储、管理、分析和检索大量此类数据的成熟技术，在很大程度上促成了 iMOM 的兴起。 另一个被忽视的主要驱动器是开源软件的扩展可用性，它促进了使用开源大型存储平台（如 Hadoop）。人们有时忽视的是，开源软件的可用性极大地促进了专有软件的开发和增强，包括用于数据存储和分布式计算的专有平台。作为 IIoT 标准的实施平台，开放源码正稳步获得认可。Gartner IoT 标准和协议的宣传周期列出了 30 项标准，其中至少一半被视为具有"高业务收益"的潜力。在潜在标准中，以下六项标准预计将在未来五年内演变为主流[13]。 1. IETF 标准，通过非 IP 网络技术（如 NFC 和 LoRa）提供 IPv6 连接，功耗极低。本标准的应用应允许设备在电池干涸之前运行多年 2. Contiki 是一款开源操作系统，专为低成本、低功耗 IIoT 微控制器而设计 3. LiteOS 是一款类似 Unix 的操作系统，专为无线传感器网络而设计 4. OneM2M 是嵌入硬件和软件中的机器对机器（M2M）服务层，可实现设备到设备的连接 5。随机相位多重访问（RPMA）是专为便利 IoT 对象之间的连接而建立的专有标准 6. Sigfox 是一种专有的低功耗、低吞吐量技术，设计用于物联网和 M2M 通信

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8个。与iMOM相关的数字化转型思考 在前几章中，我们提供了许多围绕数字技术和转型主题的不断发展的主题。许多技术已经被证实，但许多新技术正在不断涌现。其中一些新技术是将旧技术扩展到新的业务领域。例如，基于当前和历史数据的未来预测，自70年代以来已经成为先进过程控制中一个很好的概念，然而这项技术在资产失效分析中的应用最近才出现。 也许最明显的例子就是数据分析技术。随着与过程相关的数据收集量的激增以及数据存储和处理能力的提高，人们对分析大数据的需求与日俱增。这些数据不必是数字。人工智能的最新发展也使人们有可能将诸如报告等描述性文本视为计算机可分析的数据。 我们过去处理信息和应用程序的方式与我们如何预见它们在数字化中的未来作用之间存在一些主要差异。在过去，行业通常只提供特定用途的应用程序。这些应用程序查看特定的数据光谱，以推断分析并得出结论。此外，每个应用程序都在自己的控制空间中生存下来。它没有连接到其他应用程序的空间以在需要时获取更多数据。此方案强制使用为每个创建的应用程序构建的隔离数据库。应用程序间通信无法交换数据，限制了许多可用数据的有用性。 在工厂运行层面，计划运行部门和维护部门之间缺乏适当的沟通和协调，导致工厂的可操作性、产品质量和资产损失巨大。 在决策层，由于缺乏及时、准确的信息，导致决策失误。充其量，正确的决定不是在正确的时间做出的。 为什么是现在？ 目前，运行该技术的平台的成本已经大大降低。在撰写本文时，根据服务提供商的不同，公共无限云存储的价格从每GB 80美分到10美分不等。其中一些云不仅提供存储，还充当计算平台，通过分布式计算提供更快计算能力。随着时间的推移，更多的云将提供计算能力和存储能力。然而，存储和计算能力并不是唯一的影响因素。允许存储、管理、分析和检索大量此类数据的成熟技术的可用性在很大程度上促成了iMOM的兴起。 另一个未被注意到的主要驱动因素是开源软件的扩展可用性，它促进了开源大型存储平台（如Hadoop）的使用。人们有时忽略的是，开源软件的可用性极大地促进了专有软件的开发和增强，包括用于数据存储和分布式计算的专有平台。开源作为IIoT标准的一个实现平台正逐渐被接受。Gartner的物联网标准和协议宣传周期列出了30个标准，其中至少有一半被认为有潜力提供“高业务效益”。在潜在的标准中，以下六项预计将在未来五年内成为主流[13]。 一。IETF标准，通过非IP网络技术（如NFC和LoRa）提供IPv6连接，具有极低的功耗。本标准的应用应允许设备在电池耗尽前运行数年 2。Contiki是一个为低成本、低功耗IIoT微控制器设计的开源操作系统 三。LiteOS是一个类Unix的操作系统，专门为无线传感器网络设计 四。OneM2M是嵌入在硬件和软件中的机器到机器（M2M）服务层，用于实现设备到设备的连接5。随机相位多址（RPMA）是一种专有标准，旨在促进物联网对象之间的连接 6。Sigfox是一种专为物联网和M2M通信设计的低功耗、低吞吐量技术

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