AI is a term that often scares anyone who is not familiar with the technology and the application.
In this episode, we're going to cover some of the different use cases for AI in the procurement space, and then dive into master data as a specific case study.
Adriano Garibotto, Co-Founder and Chief Sales & Marketing Officer of Italian procurement data management company Creactives is my guest on this week's show to break down this not-as-scary-as-it-sounds technology!
The constant challenge of poor data, inaccurate or missing taxonomies and battling with free text PO descriptions is what ultimately led them down the path of creating a software business to solve this problem at scale.
Origins of what Creactives is today comes from some of the early stage AI utilised by their consultancy around 15 years ago. Together with a collaboration with the University of Verona, they then doubled down on developing a software solution which can help to classify, clean and structure complex master data from multiple ERP systems and sources.
AI can play a strategic role in the harmonisation of the data and helping to create a unique visibility. This is the fundamental building block which leads to other opportunities to use AI in the procurement space.
Data preparation historically required a large amount of work from procurement professionals. There is the classic Pareto of 80% of the time being taken doing the preparation, and only 20% conducting the actual added value activity for which the clean data is necessary.
If AI is able to do the lion's share of the 80%, this then allows strategic resources to be freed up to focus on more value-added activities which can actually implement the changes and the projects to drive the costs down, or reduce the supply chain vulnerability, or whatever the higher goal of the activity may be.
Data itself on its own doesn't intrinsically have value - it is the enabling factor that facilitates the journey to be able to deliver the value.
The way procurement teams operate in future will be beyond the category model, as a result of data being the driver of how organisations drive value in their business. Product launches and lean activities cut across numerous different categories, and the design of procurement departments must consequently adapt to this.
The same applies to procurement data and the AI solution you are using to clean it. If you have very complex data, then the process to clean and categorise that data will inevitably also need to be advanced.
Data has to be seen as the foundation. The more robust your data (foundation) is, inevitably the greater the percentage of data that can be cleaned using AI and the better the process will be.
However, where AI does come into its own is the application further downstream of being able to automate or flag issues in the procure-to-pay (P2P) process. The strain that tail suppliers cause when it comes to delivery issues, accounts problems, master data inaccuracy, quality problems etc is the invisible cost that the business doesn't see.
If automation can be applied to this part of the process, it ultimately avoids strategic procurement teams being dragged into more operational or transactional issues.
The long tail also consists of a lot of spot buying, where there is no clear pattern of historical spend. Automating or using AI for this application is harder, but also has immense value potential due to the resource-heavy nature of having to source and procure these goods and services.
The example Creactives have developed is a vertical solution - TAM - Technical Attribute Management - to manage material and service master data that comes from multiple ERPs. This enables them to remove duplicates, and also avoid the creation of duplicates any time in the future.
Adriano explains that it is definitely possible to show ROI using this method and cites a Forrester study (link below) which analysed the model they constructed of how ROI can be calculated built around the objective of reducing inventory of raw materials and spare parts.
