As SAP Showcases AI-Backed Innovations for Manufacturing, Enterprises Have to be Mindful of Implementation Caveats

Last month during Hannover Messe, software solutions giant SAP announced Artificial Intelligence (AI) advancements in its supply chain management solutions. The goal is to help enterprises derive their own data to make well-informed decisions across different supply chain stages. This shows SAP’s recognition of the importance of agility and intelligence that feeds into making data-driven decisions. Enterprises, especially those with complex supply chains and logistical processes can enhance efficiency and be more risk-averse during supply chain disruptions being more data-driven. AI advancements have helped SAP’s cloud business grow quite considerably due to the advanced use cases AI applications have presented. The company’s Cloud Contracted Bookings (CCB), which represent subscription revenue secured over the span of next year, grew by over 28%.

SAP has made several key upgrades for its customers with its AI-centric strategy:

• Optimizing Decision-Making across the Supply Chain: Enterprises can now leverage larger volumes of data from disparate sources and integrate AI-driven visual inspection into their production.
• Streamline Research and Development (R&D): R&D engineers and product developers can use SAP’s AI copilot Joule to gather and enhance new product ideas rapidly.
• Prognostic Capabilities: AI can also be leveraged to proactively address potential breakdown of assets and equipment such as forklifts and robotics using sensor data collected from smart devices and edge gateways.
• Improving Field Response: Use cases such as route optimization for drivers can be realized with integrated real-time traffic data.

These advancements can present a lot of benefits for manufacturers and their supply chain partners. The fact that decision makers can access a variety of data sources such as real-time sensor data, historical transaction data, etc., which can be analyzed by AI algorithms to detect patterns, trends, and correlations, can present a lot of improvement opportunities. This capability allows enterprises to make informed decisions based on comprehensive insights and predictions.

Although solutions like SAP’s AI-integrated supply chain suite present potential for operational improvements, enterprises need to consider aspects such as feasibility, data quality, and implementation, and make this part of their long-term strategy for Industry 4.0. When it comes to big data models, “garbage in, garbage out” is a very real issue. Successful AI implementation stems from quality data. Therefore, fostering a data-driven culture is crucial at an organization level. Data should not only be leveraged for business reporting and intelligence, but also for use cases like ad hoc analysis and augmented analytics to enable proactiveness among decision makers.

When it comes to implementation, enterprises should go for the low-hanging fruit, which can enable them to gain momentum in terms of scaling up. Focusing on fragmented areas such as warehousing or yard operations can enable organizations to gain quick wins and scale up accordingly.

For SAP or any other vendors such as IBM, Oracle, and Blue Yonder introducing an AI-enabled supply chain solutions, offering integration with complementary platforms will also be key. Partnering with the right system integrators, hardware solutions providers, and consultants will also play a key role. AI-integration will be a two-sided effort during the early stages, so vendors working alongside enterprises to create long-term implementation strategies will be necessary.