Framework for Unearthing Use Cases in SCM using Generative AI
Generative AI can enhance SCM efficiency and effectiveness but assessing its applicability for specific use cases is vital before implementation. This post examines how the framework for evaluating generative AI can be used in SCM.
Introduction
Supply chain management (SCM) is a highly complex and dynamic field that involves managing multiple parties and processes to deliver products and services to customers. In recent years, generative AI has emerged as a powerful tool for improving SCM efficiency and effectiveness. However, before implementing generative AI solutions, it's crucial to carefully evaluate its applicability to specific SCM use cases. In this post, we'll explore how the framework for evaluating generative AI use cases can be applied to SCM.
Framework for Ascertaining Applicability of Generative AI to Use Cases:
The framework for unearthing use cases where generative AI can be applied involves applying the lenses of
- Applicability to the Problem Domain
- Availability of Data
- Complexity of the Model
- Expertise in the Domain
- Legal and Ethical Concerns
- Costs
1. Problem Domain: The first step in evaluating the applicability of generative AI to SCM is to understand the problem domain. This involves identifying the data inputs required, the complexity of the data, and the nature of the problem. For instance, generative AI may be helpful in optimizing inventory management by analyzing data on customer demand, supplier lead times, and production capacity.
2. Data Availability: The availability and quality of data is a crucial factor to consider when evaluating the applicability of generative AI to SCM. Generative AI requires large amounts of high-quality data to be trained effectively. This may include data on order history, production rates, shipping times, and customer feedback. If the data is insufficient or of poor quality, generative AI may not be the best solution.
3. Model Complexity: Generative AI models can be highly complex, making them difficult to train and use. Evaluating the complexity of the model required to solve the problem and determining whether available computing resources can support it is essential. For example, a generative AI model that optimizes logistics routes may require a considerable amount of computing power to run.
4. Domain Expertise: Generative AI models often require deep knowledge of the problem domain to be trained effectively. It's essential to consider whether there are experts available who can provide the necessary expertise to train and use the model. In SCM, this may include experts in logistics, production planning, and inventory management.
5. Ethical and Legal Considerations: The use of generative AI in SCM may raise ethical and legal considerations, such as privacy concerns and bias in the data. It's essential to evaluate these factors and ensure that the use of generative AI is appropriate and ethical. For instance, the use of generative AI to optimize shipping routes may raise privacy and data security concerns.
6. Costs: Finally, it's essential to consider the costs associated with the development and deployment of a generative AI model in SCM. This includes the costs of data acquisition and processing, model development and training, and ongoing maintenance and support. However, the potential benefits of using generative AI to optimize SCM processes may outweigh the costs in the long run.
In conclusion, the framework for evaluating generative AI use cases can be applied to SCM to determine whether generative AI is a suitable solution for a given problem. By carefully considering the problem domain, data availability, model complexity, domain expertise, ethical and legal considerations, and costs, SCM professionals can make informed decisions about whether to pursue generative AI solutions to improve their operations.
