How Machine Learning and AI are transforming Supply Chain Management System


ByAditi Yadwadkar 0

Machine Learning and AI are transforming Supply Chain Management System

Amid the Fourth Industrial Revolution, the convergence of technology with various production processes, including supply chain and logistics, has become an indispensable part of doing business today. Businesses are voicing the need for tools to further enhance supply chain visibility and traceability, defining a new way to do amplify profits in the Information Age. Consequently, the digital transformation of the supply chain management system is emerging as one of the latest trends in the biz world.

In the past few years, investments in the latest technologies to bolster digital transformation of supply chain management have reached to new heights. With the integration of next-generation technologies such as cognitive analysis, Artificial Intelligence (AI) and Machine Learning (ML) with the supply chain management systems, manufacturers have been able to achieve high levels of efficiency at closing the gap between supply and demand.


Adoption of AI and ML to Grow Vastly in Supply Chain Optimization

A survey was recently published by JDA Software, Inc. – an American software company – and KPMG LLP – a multinational consulting company – found that more than three-fourth of the respondents considered visibility and traceability of supply chain as the highest investment areas for supply chain executives.


The survey also found that nearly 80% of the respondents viewed AI and ML as the most impactful technologies in this landscape owing to their applicability in dealing with the complex issues in supply chain and value chain systems. With predictive end-to-end visibility becoming one of the most important aspects in the modern ways to optimize supply chains, the ubiquity of AI and ML tool will increase dramatically in the supply chain management systems in diverse industrial areas.


As AI and ML are emerging as some of the most impactful technologies in the supply chain operations of any business, investment in these technologies will remain on the upward swing. However, it is of the immense importance to understand the exact impact of AI and ML, together, on supply chain management to ensure to capitalize on these technologies to their fullest potential. Artificial intelligence in supply chain management not only automate the process but also take decisions on procurement, inventory management, supply logistics etc without any human intervention.


Implementing AI/ML in the Management of VUCA as a Supply Chain Strategy

While the trend of Industry 4.0 is incurring both quantitative as well as qualitative changes in industries to boost organizational improvements, digitization of various industrial operations has also triggered lots of risk factors such as volatility, uncertainty, complexity, and ambiguity (VUCA). VUCA are the major roadblocks for standardizing supply chain management processes, and businesses how found a way to tackle these issues with advent of the advanced technologies such as AI and ML.


It is gaining popularity as an effective way to manage VUCA by integrating Artificial Intelligence and Machine Learning in supply chain management systems and logistics, which can not only identify but also define the contingencies throughout various processes. With the adoption of AI and ML-based tools in supply chain management, manufacturers have been able to manage ambiguities, complexities, and other VUCA challenges associated with high-tech products, while the trend of Industry 4.0 continues to remain on the rise.


Role of Artificial Intelligence in Supply Chain Management

As robotic process automation is becoming an inevitable part of most industrial operations as well as equipment, supply chain management systems are also undergoing a digital transformation. Thereby, technologies such as AI and ML are the part of not just manufacturing equipment, but also supply, value chains and warehouse management which mainly thrive on quick yet accurate decision-making.


The relentless pressure of making appropriate decisions faster than ever is triggering manufacturers to use AI and ML techniques to reduce⸺not replace⸺ human interference in supply chain management. Most AI and ML-aided tools implement human reasoning techniques as a model when they are integrated with decision-making processes in supply chain management, and this improves the speed and accuracy of insights about product as well as trends that are finally attained by such protocols.


As delayed decisions can have a significant impact on profits, revenue, cash flow, and even customer satisfaction in some cases. Thereby, AI and ML are enabling manufacturers to increase the speed of decision-making protocols in high-tech supply chain management systems. With the positive impact of AI and ML-powered tools on decision-making processes in supply chain, its adoption is likely to influence positive growth of businesses undergoing digital transformation.


AI and ML Techniques Influence a Synchronized Approach to Supply Chain Planning and Optimization

Supply chain management is always considered an interconnection of various data-driven and analytical processes, and synchronization of such huge amounts of data becomes imperative to ensure accurate supply chain planning. Furthermore, the increasing complexity of tech-driven supply chain has been bringing in a fundamental shift in the way the process of synchronized planning is carried out to ensure the optimization of supply chain.


AI and ML-powered tools are entering the supply chain planning landscape, facilitating the transition from a static to a dynamic sequence of multiple supply chain operations. Such tech-driven tools are being incorporated in today’s supply chain management systems, and this is highlighting their benefits in synchronizing end-to-end supply chain planning. These tools can also be used to automating procedures to match demand and supply as well as decision-making processes in real-time, which ultimately synchronize the planning ecosystem in the supply chain landscape.


Challenges in the Adoption of Artificial Intelligence and Machine Learning in Supply Chain Management

Though the global industrial landscape is making a move towards the adoption of next-generation technologies to bolster digital transformation, the adoption of these technologies in niche areas such as supply chain management remains significantly low. The gap between the hype of technologies such as AI and ML and the actual technological value is mainly attributed to the limitations in adoption of tech-driven tools in supply chain management.


Most managers and business executives fail to understand and visualize the exact benefits and impacts of AI and ML in supply chain management in the growth of business. Furthermore, AI and ML tools require periodic maintenance to ensure flawless working within the expected parameters of supply chain management systems, which translated into an additional cost. Such challenges have been heavily hampering the penetration of these technologies across all geographical regions in the world. However, as the awareness about the dramatically positive influence of AI and ML in supply chain management is growing rapidly, its adoption will become inevitable in the coming years, despite these challenges.

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