In today’s supply chain landscape, digitalization plays a central role in monitoring the entire lifecycle of products. This digital transformation leverages various tools such as algorithms, artificial intelligence, machine learning, and large datasets to analyze historical and real-time data, enabling predictive modeling.
The primary objective of this digital shift is to attain complete transparency across all supply chain stages. It offers real-time insights into progress, identifies risks and inefficiencies, facilitates reporting on all supply chain stakeholders, streamlines business processes, enhances financial clarity, and enables a shared visualization of high-level supply chain operations.
Back in 2016, McKinsey introduced the concept of “Supply Chain 4.0,” foreseeing the transformative potential of the internet of things, robotics, and advanced big data analytics in the industry. Today, many major companies are harnessing the power of enhanced networking, continuous analysis, and workflow automation to boost their performance.
At its core, a digital supply chain can be as simple as creating a visual representation of the physical supply chain, often referred to as a digital twin. This concept is not limited to the supply chain domain; for example, MIT researchers have employed digital twins at scale to create predictive virtual models in aeronautics and astronautics.
Digital twins offer valuable diagnostic, analytical, and feedback capabilities. An illustrative use case involves assessing the condition of a delivery drone mid-flight, deciding whether to proceed or reroute. A “virtual” drone following the same flight path can provide the data necessary to make an informed decision.
When applying this technology to a broader supply chain context, complexity grows, but the fundamental principles remain unchanged. In a virtual environment, businesses can transmit data signals that allow for pivoting, rerouting, or reallocating resources. Multiple scenarios can be tested in the virtual realm, reducing risks associated with real-world experimentation.
The linchpin for this digital transformation is data—clean, accurate, and dependable data. The importance of data quality cannot be overstated. Furthermore, data needs to be cross-referenced, compared, and aggregated from reliable sources to present a comprehensive picture.
Data originating from one segment of the supply chain can have far-reaching effects on others. A change in weather conditions or political unrest in a distant location can trigger issues affecting the entire supply chain. The answer to the question “What does that have to do with the price of tea in China?” is, quite a bit. Today, making sense of vast volumes of both structural and dynamic data requires advanced analytics, not just human analysis.
Advanced analytics can decipher insights from diverse data sets, including market fluctuations, weather patterns, travel trends, government policy shifts, international regulations, and more. These insights can lead to proactive course corrections or issue prevention.
The tangible benefits of these modern technological tools can be summed up in one word: resilience. In today’s business environment, it is essential for companies to sustain operations and even thrive in the face of challenges and shifting market conditions. These technological solutions are no longer a luxury but a necessity for survival and growth. Technology offers a means to accelerate progress, making its adoption a prudent choice.
Your go-to for supply chain report news updates: The Supply Chain Report. For international trade tools, see ADAMftd.com.
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