Event Details

We've been in the process of transitioning to Industry 4.0 ever since 2013. It's proven to be a long and complex journey. The machines in your production facilities are increasingly equipped with IoT-sensors, creating data that can help reducing production downtimes by doing Predictive Analytics/Maintenance. The huge amount of datathat it produces, pose new challenges to the efficient analysis:

自2013年以来,我们一直处于向工业4.0过渡的过程中。事实证明,这是一段漫长而复杂的旅程。如今,越来越多的生产设备配有IoT传感器。通过利用所产生的数据,进行预测性分析和维护以减少生产设备的停机时间。但因此产生的大量数据,却为有效分析带来了新的挑战:


When we are talking about these fast applications with smart devices, what's the challenge for us?

当我们谈论这些智能设备的快速应用时,我们面临的挑战是什么?


How to resolve big data volume problem in platform side?

如何解决平台端数据量过大的问题?


How to implement a Real-Time decision based on sensor data?

如何实现基于传感器数据的实时决策?


How to provide edge computing service instead of the service from data center side?

如何提供边缘计算服务,而不是数据中心端的服务?


In manufacture industry, how to do the predictive maintenance more efficiently?

在制造业中如何提高预测性维护的效率?


In the first Big Data wave some years ago, all information was sent into data lakes in backend systems. This led to bottlenecks in the compute and volume of data to be processed in the backend, overloaded the network to the backend and created unwanted latency. Building intelligent computing for the loT edge is not easy. Providing security and integrating applications is difficult, getting wireless and embedded systems working together is tough, and integrating with your ERP (SAP etc.) to influence your business decisions can be a complete nightmare.

几年前,大数据技术尚处于初期阶段,所有数据都会被传送到后台数据湖。这种方式带来了后台对数据计算和处理方面的瓶颈;并且网络负荷过重,导致了不必要的延迟。为物联网的边缘设备打造智能计算并非易事;保证安全性并将所有应用程序集成到一起也是困难重重;让所有的无线设备和嵌入式系统协作无间更是难上加难;将物联网与ERP(SAP等)核心系统结合起来,去影响业务决策简直如噩梦一般。


The new trend of Edge Computing addresses this, with analytics servers next to the machine park: Computing takes place at the edge where the data is produced, only refined data is transferred into backend systems. The information won from the Big Data brings more business value when interconnected into your production systems, such as ERP and MES. With increasing amount of intelligent machines connected into your LAN, ports and machines need to besecured against hacking.

边缘计算的新趋势解决了这一问题,数据分析服务器位于机器区域旁边:计算发生在产生数据的边缘,只有精确的数据被传输到后端系统。而当这些从大数据中获取的信息与您的生产系统比如ERP和MES系统集成时将能呈现更多的业务价值。随着越来越多的智能机器连接到你的局域网,端口和机器需要保护以防黑客攻击。


In this webinar, T-Systems introduces holistic approach to Edge Computing, Big Data and interface into SAP, and securitization of OT (Operational Technology), to show you how IoT can help your company run more intelligent and bring a business growth.

在此网络研讨会上,T-Systems会介绍边缘计算,大数据与SAP的结合,以及OT (Operational Technology)安全的整体方案, 向您展示物联网如何帮助您的公司更智能地运行并带来业务增长。



Agenda 活动流程:


Introduction of T-Systems and presentation topic (5mins, Martin Muens)

T-Systems公司介绍 (5分钟, Martin Muens)



Edge Computing, Big Data Analytics for Predictive Maintenance

(20mins, Weng Xiaojie)

边缘计算,预测性维护的大数据分析(20分钟,Weng Xiaojie)



Interfacing into SAP; pre-conditions of SAP (15mins, Ivan Sun)

与SAP接口,先决条件 (15分钟, Ivan Sun)



Securitization of OT (10mins, Naosad Hossen)

OT 安全 (10分钟, Naosad Hossen)



Q&A (10mins)

问答 (10分钟)


Language | 活动语言:English | 英文


Please note:

  • Please register before September 16, 2pm!
  • Please only register if you´re certain to attend the webinar, otherwise you might block the seat for another person.
  • Registration and payment upfront are mandatory.
  • Registered participants will receive a confirmation mail including login link (zoom) and access information after September 16, 46PM.
  • Electronic Fapiao will be sent to your email within 10 business days after the event.

Speakers

  • Naosad Hossen (Portfolio and Solution Design Manager at T-Systems China)

    Naosad Hossen

    Portfolio and Solution Design Manager at T-Systems China

  • Martin Muens (Global Account Manager at T-Systems China)

    Martin Muens

    Global Account Manager at T-Systems China

  • Ivan Sun (SAP Portfolio Manager at T-Systems China)

    Ivan Sun

    SAP Portfolio Manager at T-Systems China

  • Xiaojie Weng (Dedicated SI Portfolio Manager at T-Systems China)

    Xiaojie Weng

    Dedicated SI Portfolio Manager at T-Systems China

Tickets

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