Big Data, Big Impact. How Big Data Analytics Influences Supply Chains | 7wData

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Some 40 years ago, supply chains were domestic or local, and they presented a pretty simple process. The globalization paired with the technological boom added new moving parts to supply chains, making them complex. Ultimately, big data as a user-friendly and significant asset arrived to change supply chains once and for all. But what was the most valuable thing that big data brought to the industry? The answer is — an ability to create a wealth of knowledge to be shared.

The ‘Data as an asset’ report powered by KPMG revealed that17%of responding companies claim their effectiveness in maximizing value from available data. A tiny 18% of surveyed organizations state they successfully sustain an enterprise-wide strategy of data management.67%of CEOs think their businesses could enhance the understanding of their clients. As per the Harvey Nash infographic,91%of surveyed CIOs believe they could do much more to build customer trust.

According to the Mordor Intelligence report, the supply chain BDA market value was worth$3.55 billionin 2020 and is expected to reach $9.28 by 2026. Given these projections, businesses involved in the supply chain must be well aware of the advantages of big data analytics and areas of implementation.

I will outline five major challenges companies face in their path to sustainability and growth. While these problems existed before COVID-19 broke out, they are dramatically exacerbated by the pandemic.

The lack of agility. Difficulty in differentiating customer offerings on-demand due to lack of flexibility in manufacturing and supply chain.

Weak design of the ecosystem. Failure to identify the relevant partners to innovate, create and deliver value on demand.

Insufficient architecture.While the framework must ensure seamless collaboration between multiple stakeholders, some tech architectures fail to foster co-creation and innovation.

Lack of visibility. The inability to provide real-time visibility across the entire journey hinders the establishment of trusted relationships.

The current uncertainty has pushed businesses to find new agile approaches, reinvent themselves and get on their feet again. Whatever the circumstances are, a company either adapts or ceases its existence. Over time, artificial intelligence has become more accessible. In the context of supply chain and logistics, the industry is network-based. Therefore, its origin opens doors to various applications of AI as well as the opportunities of scaling it.

As far as the conventional SCM methods are concerned, every element in the chain faced barriers: it was limited to its own corporate tracking. The approach demanded manual work concerning the status updates and communication across departments, etc. Regardless of how swiftly the message travels from one office to another, a human factor generated a certain gap which was inevitable. Things could get much worse when big data consolidation takes place between the companies. One mistake and the whole dataset or several of them might go to waste.

The main capability of tech is that it consolidates and glues together the supply chain data, provides the framework for data maintenance, storage, and then turns it into specific optimization measures. When implementing cloud-based analytics, a company can unite and base metrics on a real-time updated source of truth.

Once a company gets a hold of in-depth analytical tools, the available insights optimize the supply chain in many ways:

Let’s take Amazon as an example. One of the biggest names in the e-commerce world, Amazon uses big data analytics to address client demand.