The Future of Supply Chain Management

Regardless of industry, modern businesses are moving from an outdated linear supply chain model to a new paradigm that leverages technological tools to improve visibility, forecasting, and stock management. Let us introduce you to the key technologies and practices driving this shift and what they can offer for you and your business. The Evolution Traditionally, supply chains have operated in a linear network made up of parts that operate largely independently. This structure suffers from several issues that prevent optimization and reduce efficiency. Linear supply chains often employ an outdated silo approach that involves each link in the chain operating either independently or with limited collaboration with its immediate neighbours. Instead of freely sharing information to build a clear understanding of the company’s needs from one end of the supply chain to the other, each link in the chain works with incomplete information that often changes, interfering with efficiency and increasing operating costs. This approach misses the fundamental understanding that different parts of the supply chain are dependent on each other in complex ways. Each link contains knowledge, expertise, and information that could greatly improve the ability of other parts of the chain to operate effectively. To combat this problem, modern supply chain management has recently evolved toward a much more collaborative model that prioritizes information sharing, driven in part by the development of smart technology that allows rapid recording and processing large datasets. A New Supply Chain Paradigm In the last few years, industries have shifted towards using advanced digital tools and technology to develop a deeply connected and highly resilient supply chain. This approach leverages data-gathering across the entire network to create predictive models that drive strategy development and use automation and optimization tools to streamline performance. Analytics Recently, many businesses have benefited from data-driven software tools used to gather large amounts of performance data in different areas of the business and harvest them for insights into risk and opportunity. This creates a rich source of information that can be used to drive strategic planning. To get this data, industrial automation tools are being deployed that use IoT (Internet of Things) technology to continuously record data on equipment health and process performance, storing it in cloud-based servers that can be securely accessed from any geographical location. Machine Learning This decentralized data can then be operated on by advanced machine learning algorithms that look for ways to optimize the business's performance and spot opportunities for improvement. When both data and machine learning algorithms are deployed on the cloud, parallel computing power can be leveraged to massively speed up data processing and generate useful insights extremely fast. Having this type of aid enables managers to react to important signals in data gathered from all over their businesses, compare these signals to historical data and predicatively model future outcomes based on different actions they take right now. Real-timeVisibility With the advent of data-driven analytics and modelling tools, managers and operators are being provided with real-time visibility into key performance and health indicators, no matter where on the planet they are located. With the recent availability of 5G networks, massive amounts of low-latency data can be recorded and processed in record time, powering live dashboards that update on a moment-to-moment basis. Warnings about equipment health and performance can be broadcast to off-site personnel or even generate automatic orders for parts replacement, and information about potential opportunities sent directly to travelling business managers on their personal laptop or phone. Robotics and Automation Recent advancements in robotics and industrial automation to