面向读者:供应链管理从业者 | 供应链管理系统的IT从业者 | 供应链管理高管 | 企业IT高管 | 供应商 阅读目标:这篇文章可以帮助您宏观的快速了解大数据技术对供应链管理的影响,帮助您的企业未来在供应链环节讨论是否引入大数据技术打下一个概念基础。你不一定真的看完或者理解全文,但至少可以为大家提供一些可信的数据参考,而不是盲目毫无头绪的去“猜”大数据到底对未来的供应链管理有什么影响。 关键标签:供应链管理 | SCM | 大数据 | Big Data 概 要 大数据可以为供应商网络(Supplier Networks) 提供更好的数据准确性(Accuracy)、清晰度(Clarity)和洞察力(Insights),从而在共享的供应网络中实现更多的情境智能(Contextual Intelligence)。 Bottom Line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains. 有前瞻目光的制造商们正在将80%或更大比例的供应网络经营活动构建在其企业外部,他们利用大数据和云计算技术来突破传统ERP系统和供应链系统的局限性。对于商业模式基于快速产品周期迭代和产品上市速度的制造商,传统的ERP/SCM系统仅仅是为了完成订单交付、发运和交易数据而设计的,这样的传统系统的扩展性极其有限,根本无法满足当下供应链管理所面临的种种挑战,已经成为企业供应链管理的瓶颈。 Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today. 如今的制造商都立足于在准确性(Accuracy)、速度(Speed)和质量(Quality)方面开展市场竞争,这一定位迫使企业的供应商网络必须具备一定程度的情景智能的能力,传统的ERP/SCM系统是无法帮助企业达成这一竞争目标的。然而当今大多数企业还没有将大数据技术引入其供应链运营当中,本文介绍的十大要素将成为企业未来供应链战略变革的重要催化剂。 Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey. 举个小实例来说明大数据分析(BDA – Big Data Analytics)如何在准确性、速度和质量方面对供应链管理提升的作用: 亚马逊Amazon利用大数据来监控、追踪、确保其15亿库存商品准确的存放于全球200个订单履行中心(fulfilment centers)当中。亚马逊利用预测分析(Predictive Analytics)技术可以实现“预期发货(anticipatory shipping)”的情景,即,当客户打算购买一件商品的时候(注意是打算购买尚未正式下单),亚马逊就将货物提前发运(pre-ship)到离客户最近的仓储中心。这种对供应链管理的优化极大的提升了其客户的体验。 大数据变革供应链 1、情境智能 Contextual Intelligence目前,由供应链产生的数据的规模(scale)、广度(scope)和深度(depth)都在加速增长,为情景智能(contextual intelligence)驱动的供应链提供了充足的数据基础。 The scale, scope and depth of data supply chains are generating today is accelerating, providing ample data sets to drive contextual intelligence. 下面“图1”很有意思,它收集了整个供应链中的52种不同的数据源(包括结构化/半结构化/非结构化数据),并从大数据的三个维度(3Vs)进行了统计分析,数据量(Volume)/数据速度(Velocity)和数据多样性(Variety)。其中很明显绝大部分数据都是从企业外部产生的。有前瞻性的制造商已经开始将大数据作为更广泛供应链协作的催化剂。 The following graphic provides an overview of 52 different sources of big data that are generated in supply chains Plotting the data sources by variety, volume and velocity by the relative level of structured/unstructured data, it’s clear that the majority of supply chain data is generated outside an enterprise. Forward-thinking manufacturers are looking at big data as a catalyst for greater collaboration. 图 1:点击查看高清大图 |