Despite spending millions on SaaS platforms, nearly every company is ill-equipped to manage supply chain infrastructures. What’s more, chronic component and raw material shortages are expected to continue for the foreseeable future. Read on to find out how AI helps in organizing supply chains.
Despite spending millions on SaaS platforms, nearly every company discovered that they were ill-equipped to effectively manage their just-in-time, multigeography supply chains in the face of supply-side uncertainty, labor shortages, climate disruptions, price volatility, tariffs, and cyberattacks. What’s more, chronic component and raw material shortages, inflation, supply chain snarls, geopolitics and conflict are expected to continue for the foreseeable future. Today, business leaders are writing blank checks to accelerate the transformation of their just-in-time supply chains, mitigate risk, build resilience, minimize the impact of inflation and price volatility and improve profitability.
We’re entering an arms race, and the company with the most tangible data set will win the race to global supply chain dominance! The top performers who focus on improving AI effectiveness, treat data as strategic assets and simplify AI|ML will gain significant competitive advantage. Moreover, the next three to five years are pivotal in how AI|ML will transform supply chains that were built to be cost-optimized. Without strategic leadership, the implementation of AI will dehumanize decisions, reward the lowest-cost suppliers by default and completely ignore waste, CO2 emissions and inequality. Gartner predicts that by 2024, 75% of organizations would have operationalized AI — ranging from simple data science use cases to simulating and operating complex supply chain systems, shifting from “informing humans” to “taking decisions for humans.”
So ,as we rapidly adopt AI | ML, we need to build compassion into the algorithms companies’ use in procurement to source suppliers and run the world’s supply chains. Here’s how:
1. Combine Quantitative Methods with Qualitative Analysis: Companies must deploy AI|ML on higher-quality data to improve demand and price forecasting accuracy, as well as enable better collaboration with suppliers.
Cloud-based and API-driven platforms allow seamless integrations between buyers and suppliers outside the firewalls of the manufacturer to aggregate and generate insights with greater speed, accuracy and transparency.Leading companies are upending traditional methods like ‘should-cost modeling,’ which focused on weighting material input costs, in favor of advanced price forecasting models that use up-to-date market indices such as labor statistics, duties and tariffs restrictions, commodities futures, regional suppliers, scorecards, etc. These practices, although in the early stages of adoption by the broader supply chain community, are quite commonly used in retail banks to optimize the cost of their cash supply chains.
2. Track Impact and Drive Sustainability: AI|ML can not only mine and provide insights about the business but also determine risk factors by bringing in the key ESG related metrics, like carbon dioxide emissions, product recyclability rate, water consumption per ton, product produced, packaging materials recycling rate, and waste recycling rate. Use ML-based recommender algorithms to incorporate company-specific goals like carbon net-zero, waste, etc., to recommend actions for driving compassionate and sustainable value. For instance, the Port of Montreal developed a logistics system called CargO2ai that uses AI to deliver medications, equipment and food products as quickly as possible by navigating supply delays and stock shortages.
3. Enable Agility Through Digital Twins and Scenario Planning: Supply chains simply must become more agile and real time. To quickly respond to unanticipated disruptions with greater intelligence, companies are establishing AI-powered control towers as single sources of truth with end-to-end real-time data connections across multiple systems, raw material flow, warehouses, logistics, people and processes. Control towers have been successfully applied to health supply chains even before the pandemic. New accelerated digital business models are increasingly forcing supply chain ERPs to evolve from a system of records to a system of results. ERPs through API-based integrations are now collecting more real-time sensor and equipment data, as well as other business system data, so that ML models can create a live execution environment and simulate scenario planning through digital twins. Control towers are aggregating KPIs, producing alerts for specific personnel and automating low-hanging rectification decision-making. Because AI learns over time, the supply chain history, decisions and preferences can be preserved to develop playbooks for handling future situations.
The success of AI in providing the solutions in this context is not just about model accuracy but also about incorporating them into the organization’s environmental, social and governance (ESG) priorities. It is still early days, but formal efforts have begun towards establishing frameworks for responsible AI. However, the full potential of AI cannot be realized if we do not humanize the algorithms with trust, transparency, and collaboration. It is a mutual partnership between humans and AI to transition from a linear objective of profit maximization to solving problems for the greater good – a sustainable, ethical and responsible world that puts equity for all at the center.
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