Improving Supply Chains with AI

The modern supply chain is growing increasingly complex. Companies must coordinate their global networks of suppliers, manufacturers, and logistics providers in a context of economic volatility from tariffs and changing regulatory environments.

Organisations are increasingly turning to Artificial Intelligence (AI) to revolutionise their supply chains, moving them from rules-based platforms towards adaptive and ultimately autonomous systems. SAP envisions this transformation as one characterised by enhanced data-driven intelligence, system-generated recommendations, predictive insights, and contextualised decisions.

The integration of AI offers almost endless possibilities for efficiencies and cost savings. It helps address many of the significant obstacles that organisations currently face in optimising their supply chains. To help companies better understand how they can leverage AI to optimise their supply chain, DXC Technology laid out some of the top use cases in this area.

Supply Chains of Data

AI enhances supply chains by overcoming challenges associated with complexity and a lack of visibility across data silos. Modern supply chains are intricate networks spanning global suppliers, manufacturers, and logistics providers. AI can break down these data silos and provide end-to-end visibility by unifying data from disparate systems.

Solutions such as the SAP Business Data Cloud facilitate visibility by harmonising master data and providing a unified semantic layer. This enables organisations to react more swiftly to disruptions and shifts in demand. Furthermore, AI’s ability to identify patterns and anomalies in vast datasets allows it to detect potential issues that human analysts might overlook, leading to proactive problem-solving.

DXC highlighted some of the most significant considerations companies should make ahead of any AI adoption which can be resolved with SAP Business Data Cloud.

Key Considerations

1. Data Quality and Consistency: AI systems depend on high-quality, well-governed data sets that reflect the business context.
2. Unified Data Models: Integrating SAP and non-SAP sources requires harmonising master data, metadata, and business semantics to ensure consistency, interoperability, and data quality across platforms. SAP Business Data Cloud and SAP Databricks support this by enabling a unified semantic layer. It bridges structured and unstructured data while retaining business meaning. This alignment empowers AI models to operate with a unified, real-time view of the enterprise. This enhances the accuracy of forecasting, planning, and decision execution across the supply chain.
3. Governance and Access Controls: AI systems must operate within a robust governance framework to ensure secure and compliant use of data assets. This includes defining access policies, role-based permissions, and auditability across all layers of the data platform. SAP’s partnership with Databricks enhances governance through the Unity Catalog. It provides centralised access control, data lineage tracking, and policy enforcement across multi-cloud environments. This supports a unified and secure foundation for AI deployment at scale.
4. Contextualisation: Business outcomes improve when AI is trained on data that retains process-specific context. This is an advantage of SAP Business Data Cloud. SAP Business Data Cloud ensures that data retains the original business meaning and context as it flows across systems.

SAP’s AI in Supply Chain use cases

“SAP has already delivered an incredible array of SAP Business AI features for supply chain that can deliver efficiencies in supply chains by automating routine tasks and sifting through high volumes of data to empower businesses to make informed decisions more quickly and effectively,” said Sam Atkinson, Director of Growth at DXC Technology’s SAP practice.

SAP still continues to build and deliver advancements such with the latest SAP S/4HANA Cloud Private Edition (2023 FPS03) release. Itincluded innovative AI capabilities to boost employee productivity and enhance decision-making with predictive insights.  The innovations included streamlining global trade compliance with intelligent proposals for customs tariff numbers and commodity codes. This reduces time and costs by up to 50%. It also introduces AI-assisted labour demand planning for warehouse supervisors to forecast workloads more accurately.

“We are seeing some of the strongest Supply Chain AI use cases involve forecasting and demand sensing using not just real-time quantitative data from market sources but also qualitative data from news networks and social media. There are also exciting developments with employing AI to detect anomalies in asset performance. For example, by feeding a CCTV stream through to AI visual inspection engines and using the output to trigger equipment servicing followed by dynamically adjusting production and transportation schedules. The end game will be to connect all of the steps and data points in a supply chain with an agentic AI workflow. This unlocks transformative opportunities for end-to-end process optimisation and orchestration at scale,” Atkinson said.

What This Means for SAPinsiders

Start with a Roadmap. Though AI is promising, companies cannot use it to overhaul their supply chains overnight. They must first ensure that they are well prepared to leverage this new technology. They should spend time evaluating your current state architecture and planning the introduction of available SAP Business AI solutions while drawing out a long-term roadmap to operationalisation at scale. Customers can be often surprised to learn that AI with SAP on-premise solutions is very much a viable option. DXC Technology works with their customers to develop comprehensive roadmaps tailored to client needs.

Build off quality data. While companies are understandably excited about AI, they must first ensure that their AI solutions are working off a base of high-quality data. This data should be harmonised and contextualised so it can be leveraged effectively.

Use AI where it is best placed. Supply chain bottlenecks can still be solved through business process redesign and fine tuning of current architectures with the addition of embedded AI solutions when it can deliver further cost-justified efficiencies.  However, the biggest gains will likely be to deploy the creativity of AI across multiple data sources. This delivers novel solutions that integrate seamlessly with the business processes supported by SAP solutions and SAP Business AI.