Digital Supply Chains
Digitization of supply chains has happening for close to two decades. As supply chains become more complex, geographically distributed, and more susceptible to shocks, digitization is no longer sufficient – the priorities have evolved into designing resilient and sustainable supply chains!
As we set out to achieve these priorities, connecting entire supply chain processes from design-to-operate becomes very important. Once connected, gaining visibility, preparing for suitable actions, and executing them becomes much easier. Data plays a big role in making this happen, be it collaboration between various parties, understanding past trends, or predicting the future state.
The Right Time and Place
In the ever-evolving landscape of supply chain management, the integration of Artificial Intelligence (AI) has become a game-changer. In just the last 12-18 months, our knowledge of generative AI has multiplied manifold with products like OpenAI’s ChatGPT, Anthropic’s Claude AI, Google’s Bard, and SAP’s very own Joule AI co-pilot. This quick deployment of AI products marks the start of a new revolution, similar to the dot-com boom of the early 2000s.
Many of these AI models focus on different purposes, from general-purpose large language models (LLMs) to text-to-speech models for specific industries. But what about the digital supply chain? How does generative AI or for that matter, general AI fit?
To explore this topic, we must first delve into what the differences between these two even are.
Traditional AI
Traditional AI focuses on simpler models with smaller datasets. It is generally what we think of when we hear about AI. The Google search we use is an example of traditional AI. Additionally, content-specific search/recommendation engines on Amazon Prime and Netflix are powered by traditional AI, based on our own search history. Apple’s Siri and Amazon’s Alexa are examples of traditional AI as well.
Traditional AI is smart and valuable, but it doesn’t have the responsiveness that generative AI can provide. It’s better suited for the analysis of data and making predictions for use cases such as predicting sales forecasts, asset failures, etc.
Generative AI
Generative AI utilizes models with billions and trillions of parameters on massive datasets. It enables the generation of complex outputs like speech, text, and videos which traditional AI can’t achieve on its own. It opens up doors to completely new service possibilities and even brand-new business models. Some examples include generating art/designs, synthesizing new ideas, assisting learning, etc.
Role of AI in Supply Chains
SAP has been utilising traditional AI for several years to create valuable insights for decision-making, whether it is to enhance efficiency, achieve cost-effectiveness, improve customer satisfaction etc. Specifically for digital supply chains, traditional AI has been employed in supply chain planning, manufacturing quality inspection, logistics document matching and predicting asset performance.
Generative AI could extract insights from unstructured supply chain data, speeding up tasks and improving overall performance for example, via Joule AI copilot. Joule, integrated into SAP’s cloud portfolio, revolutionizes the user experience by swiftly deriving actionable insights from diverse sources. This innovation reflects SAP’s commitment to advancing enterprise management, streamlining decision-making, and optimizing operational efficiency. Built to understand and adapt to customer processes, Joule is set to become a key component across SAP’s solutions, potentially positioning SAP as a major player in the global AI enterprise landscape.
Joule is poised to redefine the landscape of supply chain management by offering contextualized insights and intelligent recommendations. The transformative potential of AI in supply chain operations is underscored, with the expectation that it will facilitate better decision-making, improve efficiency, reduce costs, and contribute to meaningful sustainability initiatives. The introduction of Joule is seen as a milestone in integrating AI capabilities into SAP’s portfolio, potentially providing supply chain professionals with unprecedented data insights and influencing strategic decisions at the boardroom level.
Consuming AI with Caution
Like any technology, generative AI has limitations. The most important ones are as follows:
- Generative AI models, like ChatGPT, can produce plausible yet false information, leading to issues in areas such as customer service interactions, report citations, and mathematics problems, impacting the reliability of business applications.
- Generative AI models are frozen at the time of training, making them prone to conflicting and outdated information in rapidly evolving business landscapes, emphasizing the need for up-to-date and relevant data for accurate outputs.
- The accuracy and reliability of business-oriented generative AI heavily depend on the quality of training data. Biases within the data can result in skewed outputs, posing ethical concerns and necessitating careful consideration and management.
- The complex nature of generative AI models poses challenges in understanding their decision-making processes, particularly in high-stakes business domains. Ethical considerations related to privacy, bias, and accountability require a robust framework to ensure compliance with data protection laws and ethical standards in business applications.
Emphasizing transparency in AI systems enhances trust among users and stakeholders. In the logistics and supply chain industry, generative AI serves as a gateway technology for broader AI adoption. Its potential to solve challenges, improve efficiency, and optimise operations has a positive impact, encouraging the exploration of other AI applications and leading to wider integration.
Despite the transformative potential, acknowledging the ethical, legal, and practical limitations of generative AI is crucial for its responsible and effective utilization in various domains. Understanding these limitations ensures that businesses harness the power of generative AI while navigating the complexities and potential pitfalls associated with this cutting-edge technology.
What is SAP doing?
Enabling Business AI in its Digital Supply Chain offerings
SAP’s overall strategy is to embed business AI into its applications and make them available via Joule AI copilot. Digital Supply Chain Cloud applications will increasingly be enhanced using AI use cases.
Supply Chain AI initiatives with Strategic Global Partners
SAP works with several strategic partners on Digital Supply Chain. Generative AI especially is seen as the number one innovation priority by these strategic partners as well. Some examples are as follows:
- Deloitte has strategically expanded its practices to integrate Generative AI solutions to further its “Clean Core + Edge Innovation” approach to digital transformation. This initiative aims to leverage cloud application development and AI capabilities to provide clients with optimized use cases for cloud solutions and enhanced value to digital supply chain platforms
- Accenture is partnering with SAP to create a comprehensive supply chain nerve center that leverages cloud, data, AI, and analytics to enhance supply chain resilience, reduce risk, and support sustainability goals. With over 30 industry use cases already developed, the focus is on exploring generative AI collaboration with SAP.
- Infosys has developed a Demand Sensing solution for the consumer products industry, that integrates generative AI and digital supply chain. It is also available on the SAP Store for potential customers who would like to enhance their core ERP capabilities.
SAP along with its strategic partners are well-positioned to unlock the value of AI, especially in Supply Chain. However, the ability to address high-value use cases in key industries would be critical for success.
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