智能工厂：从小处着手，快速迭代，以设计思维扩展
When the idea of Industry 4.0 was first introduced, it centered around the concept of cyber-physical systems that enable machine-to-machine communication. Over the last several years, however, the term has come to represent much more: today, it includes smart factories not only run by direct machine-to-machine interactions but also advanced data processing made possible by connected sensors.
Successful Industry 4.0 and smart factory strategies require an interconnected and scalable architecture, a strong focus on value-driven project selection and the buy-in of all stakeholders involved, from IT developers and production managers to maintenance teams and machine operators. And industry leaders are well aware of the challenges involved: in a joint study conducted by Intel and Microsoft, industrial companies reported that “incomplete IT/OT convergence” was one of the key barriers standing in the way of scaling their Industry 4.0 initiatives.
Success comes from finding ways to move quickly toward a smart factory while keeping an eye on scalability and security. This often means relying on modular, adaptable solutions that can evolve as your application or environment grows.
Taking an iterative approach to your digital transformation projects can help you cultivate a smart factory without unnecessary risk or disruption.
Taking an iterative approach to smart factory and IoT projects: what it means
As the leading figure in design thinking, former IDEO CEO and Partner Tim Brown introduces the concept as: “a discipline that uses the designer’s sensibility and methods to match customer needs with what’s technologically feasible and what a viable business strategy can convert into customer value and market opportunity.”
The term is defined as an iterative process that must pass through three stages:
- Inspiration
- Ideation
- Implementation
Along the complex journey toward a smart factory, strategies built on incorrect assumptions can lead to high costs and dissatisfaction. Challenging these assumptions as early as possible is critical. Ideally, the first implementation of your Industry 4.0 application should be achieved quickly so you can uncover any unexpected challenges and gather feedback from users to continuously improve the system.
The 3 phases of the design thinking process
Technology from CloudRail, a Belden connected brand, can play a crucial role in facilitating the three phases of design thinking to drive your Industry 4.0 projects.
By leveraging the platform’s capabilities, IoT project workflows can be streamlined, cross-divisional collaboration can be fostered and important ideas can be realized quickly and efficiently.
Phase 1: inspiration
During this phase of design thinking, we work to define the underlying problem that needs to be resolved. To do this, we identify your requirements and challenges through interviews, observations and techniques that help us a deeper understanding of your environment.
For instance, CloudRail projects usually start with tackling this list of questions:
- How can value be created from machine data connected to the cloud?
- Will implementation result in financial and/or non-financial benefits that justify the costs and efforts involved?
- What are the project’s scope and key requirements?
As we gather this information, we begin to make initial determinations about possible solutions. By drawing on our experience with IT/OT integration, we can quickly connect your first machines with minimal investment, making it easier to validate ideas and adapt as new needs arise.
As Rimsha Tariq, continuous improvement and digital transformation technician at NGF Europe Limited, states, “[I] 非常感谢与 AWS 服务的无缝连接。 它减少了设置时间，并允许我们快速运行 PoC 来识别有前景的项目。”
第 2 阶段：构思
在此阶段，将收集并评估大量有关潜在智能工厂解决方案的想法。 在收集和评估潜在的解决方案之后，下一步就是使这些想法更加具体和详细。 通过真实的故事，您可以了解其他人在现实世界中如何使用或体验该解决方案。
我们帮助您思考以下概念：
- 组织中的不同关键用户需要从解决方案中获得什么才能取得成功？
- 哪些数据需要存储在云中，应该多久更新一次？
- 对于所需的解决方案，建议使用哪种体系结构？
在整合方面， 云轨 支持与 AWS 和 Microsoft Azure IoT 服务无缝集成，实现适应性架构。 通过灵活使用数据源（包括超过 12,000 个传感器、一个 OPC UA 服务器和 Modbus 设备）提供了额外的灵活性。
自动数据转换 IO-Link 传感器 使得将运营技术 (OT) 与信息技术 (IT) 连接起来变得非常容易，而无需专门的自动化专家参与项目。
在此阶段，价值主张设计和KANO模型等优先级方法通过向您展示哪些最有可能在您的情况下创造价值，帮助您从产品功能和价值主张列表中选择最佳创意。
第三阶段：实施
这一阶段的设计思维侧重于在项目早期构建最小可行产品（MVP），以促进直接互动。 这意味着构建简单的早期产品版本，以便用户可以试用并立即提供反馈。 收集反馈对于了解现实生活中的需求至关重要。 然后可以使用这些见解来验证或驳斥假设，并在迭代循环中改进原型。
在由CloudRail驱动的物联网项目中，实施阶段通常包括：
- 物理安装和布线
- 集成在OT和IT网络中
- 收集日常系统用户（如生产计划人员、作员和维护团队）的反馈
- 重申在第二阶段（构思）阶段起草的技术产品
- 验证在第一阶段（灵感）阶段所做的假设
CloudRail 的远程配置意味着 IT 团队和自动化专家不必亲临现场；更多用户可以参与实施过程。
此外，基于云的设备管理可以快速实施变更。 由于 CloudRail 设备已准备就绪并包含开箱即用的所需安全标准，工业制造商可以快速创建真实的 MVP。
正如 nexineer digital GmbH 总经理 Tobias Haungs 所说，“AWS 和 CloudRail 的结合使我们的开发团队能够在数小时内（而不是数天或数周）设置和验证新的用例。 随着数据提取的繁重工作被消除，他们可以专注于构建推动业务发展的应用程序和流程，而不是与基础设施作斗争。”
迭代和灵活性在智能工厂成功中的重要性
物联网项目的成功主要取决于它为您创造的价值。 因此，持续监控有关商业利益、用户需求和技术设计的关键假设并尽早验证它们至关重要。
非托管网关和 DIY 解决方案使迭代任务变得手动，因此很麻烦。 由于在此过程中收集到的发现无法轻易地融入系统设计中，该项目可能会逐渐变成一系列不连贯的修复。
CloudRail 提供了灵活的边缘到云层，可以轻松适应即将到来的需求，即使在项目开始时还不知道这些需求。 例如，如果您想使用 AWS IoT Core 过渡到 AWS IoT Sitewise，则只需在 CloudRail.DMC（设备管理云）控制台。
特别是在构建预防性或预测性维护解决方案时，灵活更改数据采集点或连接其他传感器可以显著提高模型的长期准确性。
通过采用迭代方法并利用 CloudRail 等适应性解决方案，您可以快速响应新的见解和不断变化的需求，从而随着时间的推移提供持续的价值。 这种方法不仅可以最大限度地降低风险和复杂性，还可以让您的物联网计划随着需求的变化而实现可扩展、安全的增长。