AUTOMATION CHANGES THE FACE OF SUPPLY CHAIN MANAGEMENT
New technology solutions can be transformative, but only if executives properly prepare their organizations. In the supply chain of the future, digital and artificial intelligence will enable end-to-end transparency and faster decision-making.
Planning • Full transparency on execution through end-to-end digital control tower • Risk-adjusted end-to-end margin optimization
Marketing and sales • Unified and more accurate price and demand forecasts enabled by AI • Increased transparency and granularity on integrated margin by sale Procurement • Full data integration with suppliers • Optimization of raw materials recipes based on forecasted prices Boost Decision-Making Like control towers, digital twins can aid in decision-making. These are virtual representations of objects or systems that span their lifecycle, are updated with real-time data, and use simulation, machine learning, and reasoning to help decision-making, IBM explains. Companies can use digital twins to simulate the impact of an event, such as a supplier that halts plant operations due to a weather event. To provide the most value, a digital twin should concentrate on modeling hot spots and critical components. “Be smart about what you want to model,” Dekhne recommends. Improve Data Insights In the supply chain world, artificial intelligence (AI) is capturing attention. Successfully implementing AI-enabled supply chain management solutions enabled early adopters to improve logistics costs by 15% and inventory levels by 35%, among other benefits, compared to their slower-moving competitors, McKinsey research found. Generative AI is upping the ante. Generative AI empowers users to build their own interactive models, Deknhe says. For instance, a supply chain planner trying to determine the impact of a 20% jump in
Logistics and distribution • Dynamic optimization of routing, freight contracting, and vessel sharing, reducing costs and environmental impact
Production • Agile production planning and scheduling
Source: McKinsey & Company
Automate from End to End More companies are evaluating and automating supply chains in their entirety, rather than focusing on optimizing individual pieces. A more comprehensive approach can help supply chains better handle disruptions, such as geopolitical events. “End-to-end process automation can bring together siloed systems, connect people involved in different processes,
demand for a product has typically needed to manually assess supply levels and contact suppliers to get information on raw material supplies, among other data—a process that can take days. Generative AI can ask the same questions, work with the data, and adjust models to more quickly provide this insight. Advance with Software While traditional software solutions, like warehouse management systems, may not generate the same buzz as AI, they remain critical to many supply chains. They also continue to advance. Thirty years ago, companies could complete materials requirement planning (MRP) about once a week. “We would dim the lights whenever we hit the enter key,” recalls Steven Benz, senior consulting manager with Panorama Consulting. Today, companies can generate MRP reports every 15 minutes. Current algorithms can automate forecasting, inventory control, and supplier selection, among other functions. While algorithms—sets of finite rules or instructions to be followed in calculations or other problem-solving operations—aren’t true AI, they make software solutions more robust and useful.
36 Inbound Logistics • February 2024
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