Harnessing the Power of AI for Digital Supply Chains
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Key Takeaways
⇨ AI-enabled supply chain management significantly improves logistics costs by 15%, inventory levels by 35%, and service levels by 65% for early adopters, highlighting the necessity for organizations to leverage advanced technologies.
⇨ Building resilient supply chains is essential in managing pressures from diverse product portfolios, supply and demand fluctuations, regulatory shifts, and geopolitical risks, emphasizing the adaptive use of AI and analytics.
⇨ SAP and Microsoft showcase effective integration of AI in the latter's digital supply chain processes, utilizing advanced analytics for demand forecasting and operational efficiency, while emphasizing the importance of accurate data for training AI models.
Managing diverse product portfolios, predicting fluctuations in supply and demand, complying with regulatory shifts and mitigating geopolitical risks are just some of the pressures supply chain professionals face today.
Thus, building resilient supply chains is of paramount importance. A recent McKinsey study found that AI-enabled supply chain management allows early adopters to improve logistics costs by 15%, inventory levels by 35%, and service levels by 65%, compared with slower-moving competitors. 47% of companies surveyed by SAPinsider also recognized the significance of artificial intelligence and machine learning (AI/ML), with 43% of respondents highlighting its increasing prominence on the corporate agenda.
During a recent webinar powered by SAP, expert partner CloudPaths and their SupplyChainPaths practice was showcased by Mindy Davis, Global VP of Product Marketing and Digital Supply Chain at SAP. “With the influx of new technologies, not only AI, but advanced analytics, and machine learning too, we are going into this adaptive mode of how customers can actually harness the power of AI to ensure that they have the resiliency and agility in their supply chain,” Davis said.
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Microsoft’s Digital Supply Chain
Davis highlighted Microsoft as one of the organizations that has successfully harnessed AI to strengthen its digital supply chain. Dhaval Desai, Microsoft’s Group Engineering Manager, emphasized the need for AI to run the cloud ERP efficiently and forecast the demand for its cloud. Microsoft uses data science models, generative AI, and machine learning to create demand forecasts and provide planners with the right business insights to meet each data center’s consumer demand, according to Desai.
SAP and Microsoft: Building a Resilient Supply Chain
Microsoft Cloud Supply Chain’s order-to-cash and procure-to-pay processes are powered by the SAP ERP platform. It also leverages SAP’s SaaS solutions like SAP Integrated Business Planning (IBP) for supply chain planning and data.
SAP IBP offers an AI-enabled capability called master data anomaly detection, which is deployed in a human-assisted mode as opposed to autonomous AI. It combs through master data and detects patterns and inconsistencies, presenting cases back to a human for correction or intervention. As part of SAP’s Digital Manufacturing, AI-enabled virtual inspection also observes quality and improves productivity.
AI used in solutions like SAP IBP can provide the right data insights into user queries and offer alternative suggestions to design engineers for specific components ensuring an effective outcome. Through SupplyChainPaths, and their expertise in SAP IBP, CloudPaths facilitates real-time data integration and predictive analytics, enhancing decision-making and operational efficiency. It also emphasizes the importance of data cleansing and harmonization to ensure AI models are trained on accurate, real-time information, thereby improving forecast accuracy and responsiveness to market changes.
What this means for SAPinsiders
Sharpen Your Supply Chain AI Focus: Supply chain operations encompass a vast array of functions, systems, and business processes, a target-rich environment for AI. When it comes to proving AI’s value, it is important to pick your battles. Focus on the pockets of waste and inefficiency that stand to benefit the most from AI’s advanced decision-improving capabilities.
Mind your Metrics: Like with any investment, when testing the potential impact of AI in your supply chain it’s important to tackle problems worth solving, per the point above, but also to have an idea of what success looks like. What is a meaningful progression across which metrics, like forecast accuracy, inventory reduction, fill rate, or cost, even on a small scale under the scope of a proof of concept? Document and communicate these metrics with internal sponsors and external partners like CloudPaths so you have an objective basis for either continuing to invest or changing course.
Participate in SAPinsider research: AI is transforming the supply chain management landscape by introducing new capabilities that enhance efficiency, prediction accuracy, and operational resilience. Related SAPinsider research indicates that 79% of companies are currently using or implementing AI in the supply chain, plan to implement AI within 24 months, or are evaluating AI for the supply chain. Leaders in the SAP ecosystem are quickly moving past the hype to leverage AI to drive tangible benefits in areas like demand forecasting, inventory optimization, route optimization, logistics planning, and predictive maintenance. SAPinsider will be publishing a research report in February on AI in the Supply Chain that will explore the key drivers causing companies to adopt AI in the supply chain, the primary strategies companies are deploying to operationalize AI in the supply chain, the critical underlying capabilities that must be in place in order for AI to deliver durable outcomes, and the leading technology tools and providers companies are using to embed AI into supply chain processes and systems. Take the survey today and let your voice be heard!
