Predictive Analytics: The Future of Procurement

With advancements in AI and machine learning, a supply chain that can generate in-depth analysis is highly desirable and vital. The goal for many executives is to have an analytics system so advanced it can notify an organization of potential problems before they arise and effectively minimize risk exposure. But before we can predict what will happen in the future, we need to first figure out what happened in the past and why.

The Road to Predictive Analytics

It can be challenging to incorporate predictive analytics into existing supply chain functions, and it can seem like a daunting task to get started. While some organizations are currently utilizing AI to enhance their analytics, many aren’t sure where to start.

The first step of utilizing data analysis is to obtain historical data sets. Standardized, useable data sets can help indicate what has happened, but it can be difficult to harness the required data. Too often the essential data is spread out in a variety of formats in different locations. That data might be dispersed across a variety of different ERP systems or maybe even housed in individual Excel spreadsheets. With these scattered data sets, it can be impossible to evaluate anything meaningful.

Build Your Supply Chain Analytics Toolbox

Having standardized purchasing data collection throughout an organization means that the available supply-chain-related data will be more reliable and consistent. As a result, it’s important to have a tool or tools that will not only collect and standardize data, but also make that data accessible across an organization.

Once the data is available, most organizations will need AI services or tools to make use of that data. Most large businesses and organizations produce far more data than humans can effectively evaluate on their own. A tool that can integrate AI into business processes will help to make all that available data much more useful.

Additionally, it’s important to have a team or team member who can analyze the data and understand structures. As important as it is to have a team or consultant who can make the best use of the data, it’s equally important to have team members who understand the organization and the issues that are most important to stakeholders. After all, the most amazingly accurate insights and predictions aren’t of value if they concern issues of little importance.

With the right tools and the right team, it might be possible to get a glimpse of what lies in the future.