企业在做供应链数字化的时候面临的挑战有哪些？企业在做供应链数字化的时候面临的挑战之一：数字化准备和数据基础。数字化转型网

数字化升级进程中，需要将原本被零散丢弃、没有标准的数据，通过有规划的数据采集、清洗、挖掘，生成各种数据应用。可通过数据发现过去没有被发现的异常、问题，并找到解决办法。企业数字化升级的实际操作中，数据基础是供应链数字化项目所面临的第三个主要挑战。在《2021年制造企业供应链发展调研报告》中，关于供应链数据管理，仅有不到1/3的企业有供应链数据管理岗位（如图5）。数字化转型网

企业在做供应链数字化的时候面临的挑战有哪些？数字化转型网

解决方案是通过数据治理，对数据的资源整理、数据采集、数据存储、数据管理、数据使用、数据安全等，进行体系化的治理。企业需要预见到，合格质量的数据准备在项目初期是非常耗时，却是必须进行的工作。数字化转型网

在数据治理中，公司还应更加关注数据质量和相关性，而不是数量。与大部分人的普遍的认知相反，很多算法和人工智能在起初阶段不是收集的数据量越大越好，而是更注重数据的完整性，需要全维度、全过程、全场景的数据标签。数字化转型网

因为算法可以通过在开始时使用合理数量的高质量数据来增加价值，然后在新数据可用时逐渐丰富数据池。许多人工智能嵌入式的供应链模块已经优化了算法，使用较小的数据集来解决数据匮乏的问题。同时，使用其他外部数据源也是不错的补充，包括天气、宏观经济和人口统计数据。例如，为了预测冰激凌的销售，将天气数据添加到模型，可以提高预测的质量。数字化转型网

数据治理涉及业务流程梳理，要保证数据的准确性，并达到不同口径的数据对拢，是一项持续性工作；也是一个关注在数据的系统执行层面的体系。数据治理涵盖前端业务处理系统、后端业务数据库以及深入的数据分析，从源头到终端再回到源头，并形成一个闭环反馈系统。

同时需要注意的是，即便企业通过数据治理建立好数字化基础，是否就能够做到数字化赋能员工，辅助决策呢？“用数据说话”是企业数字化能够获得的主要价值之一，但另一个事实是，数字也是特别容易被操纵的，只要换一个说法，就能让数字只传递想传递的信息。

以百分比为例，我们在超市购买了一盒速溶热可可，包装上标明“99.9%不含咖啡因”，便可能觉得晚上喝一杯并不会影响睡眠。可我们也应知道，一杯500克的星巴克含有415毫克咖啡因，其咖啡因含量仅为0.075%，同样不到0.1%，即便星巴克的超大杯咖啡，也是99.9%不含咖啡因的。可以说，那盒速溶热可可包装上的咖啡因含量标注，虽然数字是真实的，却具有相当大的迷惑性。数字化转型网

因此，即便企业通过数据治理解决了数据基础问题，合理地挖掘数据价值也是数字化项目成败的关键因素。实际抛开技术本身，数字化在实施过程能否成功，归根到底是与企业的组织能力有关。这也就关系到企业供应链数字化面临的第四个挑战。

本文为科普类文章，不作为选择建议或投资建议。数字化转型网

如果你想了解更多供应链数字化全相关的资讯、科普、知识、方案、报告、资料、案例等可百度搜索中国数字化转型网。如果您对数字化感兴趣要记得去看看哦~

翻译：

What are the challenges companies face when digitizing their supply chains? One of the challenges enterprises face when doing supply chain digitization: digital preparation and data foundation.数字化转型网

In the process of digital upgrade, it is necessary to generate various data applications through planned data collection, cleaning and mining that were originally scattered and discarded without standards. Through data, we can find anomalies and problems that have not been found in the past, and find solutions. In the actual operation of enterprise digital upgrade, the data foundation is the third major challenge faced by supply chain digital projects. In the 2021 Manufacturing Enterprise Supply Chain Development Research Report, less than one-third of enterprises have supply chain data management positions (Figure 5).数字化转型网

What are the challenges companies face when digitizing their supply chains? Digital transformation network

EDITOR

The solution is to systematize the management of data resources, data collection, data storage, data management, data use and data security through data governance. Organizations need to anticipate that quality data preparation will be time-consuming but necessary early in the project.数字化转型网

In data governance, companies should also focus more on data quality and relevance rather than quantity. Contrary to the general cognition of most people, many algorithms and artificial intelligence do not collect the larger the amount of data, the better, but pay more attention to the integrity of the data, requiring full-dimensional, whole-process, whole-scene data labels.

Because algorithms can add value by using a reasonable amount of high-quality data at the beginning, and then gradually enriching the data pool as new data becomes available. Many AI-embedded supply chain modules have optimized algorithms to use smaller data sets to address data scarcity. It is also a good complement to use other external data sources, including weather, macroeconomic, and demographic data. For example, to predict ice cream sales, adding weather data to the model can improve the quality of the forecast.

Data governance involves business process sorting. It is a continuous task to ensure the accuracy of data and achieve data alignment of different caliber. It is also a system that focuses on the system execution level of data. Data governance covers front-end business processing systems, back-end business databases, and in-depth data analysis, from source to end and back to source, and forms a closed-loop feedback system.

At the same time, it is important to note that even if enterprises establish a digital foundation through data governance, will they be able to digitally empower employees and assist decision-making? "Speaking in data" is one of the main values that can be gained by enterprise digitization, but it is also true that numbers are also particularly easy to manipulate, by simply saying it differently, the numbers can only convey the information they are intended to convey.数字化转型网

For example, if we buy a box of instant hot cocoa at the supermarket and the packaging is labeled "99.9% caffeine free," we may think that drinking a cup at night will not affect sleep. But we should also know that a cup of 500 grams of Starbucks contains 415 mg of caffeine, its caffeine content is only 0.075%, the same less than 0.1%, even Starbucks large cup coffee, is 99.9% caffeine free. It can be said that the caffeine content label on the packaging of the box of instant hot cocoa, although the number is real, it has considerable confusion.

Therefore, even if the enterprise solves the data foundation problem through data governance, the reasonable mining of data value is also a key factor in the success or failure of digital projects. In fact, regardless of the technology itself, the success of digitalization in the implementation process is ultimately related to the organizational ability of the enterprise. This brings us to the fourth challenge facing the digitalization of enterprise supply chains.数字化转型网

This article is a popular science article and is not intended as selection advice or investment advice. Digital transformation network

If you want to know more information, science, knowledge, solutions, reports, data, cases, etc. related to supply chain digitalization, you can search China Digital Transformation Network on Baidu. If you are interested in digital, remember to check it out返回搜狐，查看更多

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