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制造业正在令人兴奋的道路上解锁工业4.0的新可能性 - 一种范式转向机器的转变,朝着更加联系,智能,最终,使用物联网和数据的力量。即使在今天,商店楼层也会产生大量数据。但是,我们是否有效地使用它迈向行业4.0?在里面15th Manufacturing Leadership SummitMindtree在2019年举行了一个智库 - 在制造4.0中调配数据洪水。来自一系列制造公司的许多领导者讨论了该主题的四个关键方面:

1. What are the challenges presented by this data deluge?
2. What are some of the paradigms to deal with these challenges?
3.如何使用此数据进行可操作的见解?
4.需要什么类型的治理在瞧ng run?

It’s easy to see that the key reason for the data deluge is the ever expanding number of data sources – from the machines on the shop floor to the enterprise, partner systems, and the world. The granularity of the data collected further compounds the challenge. One participant mentioned that their organization collects 12 billion data points in a year. Participants also agreed that often data is collected “just in case it is needed at some point.”

Let’s deep dive into some of the aspects of the discussion:

1. What are the challenges presented by the data deluge?


布朗菲尔德是一个接受的生活,持续存在于一段时间内设置的工厂。它们具有略微不同的命名约定,不同的标签,分类,层次结构以及作为一个制造商提到的,同一事件的不同故障代码。这导致消毒数据的基本挑战,同时仍然能够在多个工厂中跨多条线凝聚力地凝聚力。

作为另一个参与者,他们的组织一直在收集20年的数据,而不是清楚地与该数据有关。除了大数据的三维(卷,速度,品种)之外,我们现在还有两个更多的VS(准确性和值),必须在寻找收集时考虑。

Another participant had questions about solving potential data conflict problems that exist in a siloed environment. Mindtree’s response was: instead of trying to connect the siloes physically, create unique identities to segment the data and create a data lake as a landing zone. As a next step, develop a catalog and to organize it. Finally, create microservices-based intelligent consumption models.

2. What are some of the paradigms to deal with data deluge?


如果我们将问题陈述分为三个部分:(i)数据的清洁度(ii)数据(iii)的处理使用数据,然后具体方向出现来处理问题:
o数据词典可以帮助解决不同的标记名称/源变化,但标记数量可能是挑战手动处理。数据目录来到这里救援。一些目录可以自动消除元数据,而其他目录可以使用像机器学习(ML)和NLP等先进技术以识别关系。对于新数据,最好建立一个正式的公约 - 一种声明性方法,并使用自动化方法等传统数据的衍生方法。
o Once that’s done, processing becomes much more straightforward, especially leveraging the scalability of cloud. Additionally, the data required to train an ML model is fine grained. But once this is accomplished, the model itself can be used to filter out pertinent data on the edge, reducing eventual processing volumes.
o数据使用问题引起了许多有趣的回应。一位参与者提到他们有20年的价值交易数据,并且能够在票务和工作的历史和职业历史和历史上产生200k的影响。
o Another participant emphasized the Pareto principle or the 80:20 rule. Using the principle, their organization was quickly able to determine that they needed to hire additional sales folks in a specific region, something that was not obvious earlier. Discussion also revolved around how it is crucial to operate with a goal in mind and identify problem domains and fix use cases that have business value.

3. How can one use voluminous data for actionable insights?


一个好的数据策略是这个的起点。但重要的是保持简单。分析有三个阶段 - 描述性,预测和规定性。描述性是这里和现在运行/操作风格;预测是识别可能发生的事情 - 例如即将发生的失败;虽然规定性是在具体行动方面要做的事情。虽然许多参与者使用主要是运行方面的数据,但是较小的数字表示他们已经开始预测模型的旅程,并且大多数人尚未使用规范模型。通常,运营效率形成用于利用数据的推力的关键区域,然后是行业特定的数据货币化。

4.必须在长期运行中建立哪些类型的治理以维持价值?


Unsurprisingly, this topic was highly debated. Earlier, both the generation and consumption of data rested with the same organization. Now, with multiple consumers of data and multiple correlations across data sets, it is important to identify not just governance but also stewardship. A well-established method to achieve this is to use a cascaded model:
a)在组织级别,建立语义,安全和运动相关的“规则”。
b) At a domain level (for example, manufacture, supply chain), establish structure, design, and consumption models.
c)在团队/用户级别,仅启用消费。

According to another participant, their organization started at the domain level and never went to the organization or other levels as technology took the front seat, causing everyone to lose sight of the structure. Similarly, another company got caught up in the security aspects of data and didn’t look into its structure or other areas. It’s important to note that technology cannot be the leading agent.

Overall, the think tank corroborated a number of observations and approaches discussed above. The bottom line: there’s always opportunity in the guise of a potential problem. The most important aspect to identifying that opportunity is to move from an intuition-based approach to one that is insight-based and data-driven.

在Mindtree,我们帮助全球制造商使用设计思维和想象的研讨会,以优先考虑其数据和分析挑战,并推动业务价值。再加上决策时刻,一个有助于快速探索数据并生成建立方向的初步见解的平台,我们可以帮助为您的行业设定4.0旅程。在我们的数字南瓜中安排设计思维研讨会。我们很乐意听取您的意见。

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About the Author

Srinivas Rao Bhagavatula.
Associate Vice President, IoT Practice

Srinivas heads the IoT Practice in Mindtree. His group focuses on building end-to-end solutions spanning devices, clouds and enterprises for Mindtree's customers. Srinivas has 20+ years of experience in the IT industry and has played the role of a solution architect in the recent past, and is now responsible for business development and capability building in the IoT space.

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