It has become nearly impossible to predict changes in supply and demand patterns. On the supply side, there are increasingly frequent supply disruptions, part shortages and cost pressures. On the demand side, customer preferences remain volatile, and this is further exacerbated by the growing threat of inflation plus the rise in omni-channel. This not only hurts revenue, profit and customer satisfaction, but makes it difficult for organizations to achieve sustainability objectives of reducing waste.
As external and internal challenges have become more acute, new technologies have emerged to address them. The rise in platforms like Azure has accelerated the deployment of SaaS solutions. New data management strategies are changing the way businesses organize, structure and manage data. And the promise of artificial intelligence (AI) and machine learning (ML) has compelled many organizations to invest in new technology – with the goals of optimizing and automating business processes, driving better performance, and increasing profitability.
Data silos and disjointed point solutions are making it impossible to achieve desired profitability or resilience
Advanced technologies, including AI and ML, hold the potential to revolutionize the world’s supply chains. By monitoring real-time conditions along the end-to-end supply chain and across the extended partner network, AI- and ML-enabled optimization engines can sense anomalies, project the results of various resolution strategies, and autonomously take corrective action.
Yet for many companies the promise of these advanced technologies is not being realized, despite their growing investments. In fact, it’s been reported that 85% of AI and ML projects fail to deliver their intended business outcomes. The primary reasons? A lack of strategic data management or a well-designed data infrastructure, and disjointed point solutions. Even with big investments in AI-powered point solutions, companies today are still relying on decades-old databases, offline algorithms, and systems that lack interoperability or easy data sharing.
Today, businesses have all the information they need to optimize their supply chains. But most companies are simply overwhelmed by the sheer volume of available data – from suppliers, customers, partners, and third-party sources. They also lack the infrastructure to collect it, harmonize it, analyze it and apply it to their everyday decisions. Instead, data is scattered across the extended supply chain in disparate point solutions. It’s not centralized, accessible or actionable. It’s no longer enough to simply throw more resources at the problem. As data volume and velocity accelerate, businesses cannot possibly process all the necessary data amidst increasing market complexities.
Advanced technologies such as AI and ML depend on data for their success. Unless companies are equipped to digitally capture and apply real-time data on demand changes, inventory levels, product availability, and other key factors, their advanced supply chain solutions will never achieve their full potential
