Note: This is the 3rd article in the 5-part “Supply Chain Data Strategy” series, a strategy to empower supply chain leaders on the value of data-driven decision making.
Recap: In our previous article (2nd part) we talked about why you need a supply chain data strategy and how a good strategy will enable you to effectively interpret the data at hand and set yourself apart from the industry’s competition.
Precap: In this article, we’ll take things a step further and discuss when it’s appropriate to put your supply chain data strategy into practice and how you can use predictive data analytics to improve the supply chain.
Check out other parts of the series:
In a constantly evolving and progressive world, everything is connected. The Fourth Industrial Revolution is witnessing a fusion of the physical, digital, and biological worlds, and we can be in control of the dizzying changes only if we collaborate. This is even truer for supply chain management as the ecosystem is undergoing digital transformation at scale.
Some enterprise scenarios call for immediate action to reset your data-enabled supply chain strategy at different stages of your supply chain data evolution.
Table of Contents
It will help if you accelerate your efforts to adopt a data-enabled supply chain strategy at this stage. To break free from a linear supply chain, one must move towards a unified data goal and make data sharing an enterprise-wide policy. Tear down data silos, centralize and integrate your data, and use a cloud-based data warehouse solution. There are many software platforms and techniques to do this.
This time is ripe for revamping the supply chain data analytics approach with an updated vision attuned to recent business, industry, and technology developments. A long-term transformation roadmap is drawn up based on an assessment of current performance. Hiring talent to set up a supply chain Centre of Excellence with unified data to lead change and promote innovation is essential. At this point, bridging technology gaps with the latest analytics solutions will aid transformation, which should include speculative changes and no-regrets improvements.
Predictive data analytics of the supply chain investigates past and present trends and combines the findings with business intelligence, market forecasts, and weather forecasts to recommend solutions. AI/ML-based solutions for supply chain data efficiency and maintenance are used to automate and optimize the supply chain in many ways.
Let us look at the following examples.
It is evident that when the flow of physical goods is faster than the flow of information about them, it is time to break down data silos, embark on supply chain digitalization, and initiate predictive analytics of your supply chain.
But there is another evolving situation in which the flow of data and information and resultant insights is faster than the flow of physical goods due to embedded technologies that have revolutionized supply chain transparency, production process visibility, and even business decision-making.
This is a juncture when the supply chain across industries will have data information flowing in rapidly from the Internet of Things (IoT) sensors. You must be ready with a Supply Chain 4.0 version of your supply chain data strategy.
There are estimates that by 2025, IoT-connected devices will amount to 30.9 billion globally, and the IIoT market may grow to USD 1.1 trillion by 2028.
The data aggregated from smart sensors have the power to transform your supply chain by tracking, authenticating, monitoring, identifying, and managing your goods – both stored and in movement. This means your supply chain data strategy can:
From disparate data to actionable supply chain insights – the path to achieving a robust supply chain involves several steps. Investing organizational efforts into building supply chain data strategy helps work through these steps. A good strategy will give an organization the ability to draw meaning from the data within the supply chain and convert it into actions.
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