The world of supply chain planning has undergone significant changes in the last few years and companies have changed their approaches to deal with such changes. One important change has been the shift from traditional planning to insight-based planning. In traditional planning, stakeholders come together to discuss and agree on numbers and actions to take based on those numbers. In the case of insight-based planning, however, organizations come together to discuss drivers of demand and discuss how to influence that demand. This change is significant and when combined with the integration of large language models (LLMs) like ChatGPT, it can revolutionize how organizations plan for demand and supply.
Traditional planning to insight-based planning
Traditional planning has been the backbone of supply chain planning for many years. The process typically involves the following steps:
- gathering data,
- analyzing it,
- and creating a plan.
- Once the plan is created, all stakeholders come together to review the plan, discuss any concerns or questions, and make any necessary changes.
This traditional planning approach has been an excellent approach but has major limitations. The primary limitation is that the process does not account for the drivers of demand. In other words, traditional planning is backward-looking, whereas insight-based planning is forward-looking.
Insight-based planning is an entirely different way of working. With this approach, organizations come to planning meetings with the drivers of demand and can discuss how to influence demand. Instead of focusing on historical data only, insight-based planning takes into account market trends, customer preferences, and other factors that can impact demand. By taking a forward-looking approach, organizations can create more accurate plans that reflect the market realities.
The Integration of Large Language Models
The integration of LLMs like ChatGPT into insight-based planning will take this approach to a whole new level. LLMs are AI-based models that can process and analyze vast amounts of data, making them ideal for supply chain planning. By integrating LLMs into the planning process, planners and other stakeholders can access insights in a very easy manner.
This drastically improves collaboration and communication among stakeholders. With LLMs, planners can easily share insights and recommendations with other stakeholders, allowing everyone to work from the same set of data. This approach can help break down silos within the organization and ensure that everyone is working towards the same goals.
Another benefit of integrating LLMs into the planning software is that it can improve agility and flexibility. Traditional planning is often a rigid process, with plans created months or even years in advance. With insight-based planning and LLMs, organizations can create plans that are more responsive to changing market conditions. For example, if a new competitor enters the market, an organization can quickly analyze the impact of this new entrant and adjust its plans accordingly.
In conclusion, the transition from traditional planning to insight-based planning is a very exciting development in the world of supply chain planning. By considering the drivers of demand, organizations can create more accurate plans that reflect market realities. When combined with the integration of LLMs, the planning process can become even more powerful.
Jacobs Douwe Egberts has successfully made this transition from traditional planning to insight-based planning. With Logility, we’re making this insight-based planning accessible through a natural language interface.