Videos
Ericsson’s AI-enabled IBP transformation: Building the intelligent supply chain of the future
Insights
- For Ericsson, IBP unifies demand, supply, inventory, and S&OP into one platform, replacing siloed processes.
- AI and machine learning are embedded across forecasting, inventory optimization, and user enablement.
- Combining in-house models with SAP IBP algorithms boosts forecast accuracy and decision quality.
- A collaborative, multi-partner approach accelerates transformation and establishes a resilient, future-ready supply chain.
In this video of the Infosys Knowledge Institute, leaders from Ericsson, and Infosys explore how Ericsson is reinventing global supply chain planning through an AI-enabled Integrated Business Planning (IBP) program. With operations spanning multiple markets and complex product lifecycles, Ericsson is shifting from fragmented, spreadsheet-driven processes to a unified digital planning platform.The conversation highlights how SAP IBP, combined with Infosys expertise and Ericsson’s own AI capabilities, is transforming forecasting, inventory optimization, and decision making. Leaders discuss the introduction of machine-learning-based forecasting, early successes from Go-Live 1, and the integration of AI chatbots and self-help tools to modernize user adoption. This dialogue captures the lessons, vision, and technology foundation behind Ericsson’s journey toward a fully integrated, AI-first supply chain.
Kamish Mirza:
My name is Kamish Mirza. I am the IBP transformation director on behalf of Group Supply. The vision which we have for IBP transformation is essentially to integrate demand, supply, inventory, SNOP into one when we drive this transformation.
Anna Runellen:
My name is Anna Runellen. I have been the project lead for the first phase of IBP at Ericsson. AI is a machine learning and all these new techniques are so essential that we are incorporating and also establish as a natural way of working in our company.
Hans Hallgren:
I'm Hans Hallgren. I'm heading up supply chain management within Ericsson and supply at Ericsson. We have a program ongoing. We had the first release a couple of days back. In this release, we are using AI and also machine learning. So it's a part of our transformation making us more efficient. So it's already there.
Diego Moreno:
My name is Diego Moreno. I am the head of supply chain planning in Ericsson. Ericsson, before going to Infosys and SAP, IBP, we develop in-house, a solution, that is using machine learning to help us to improve our demand forecasting process. It is mainly aiming to our market areas. So it is not that you centrally like in my team, but is used to build the bottom up forecast. My personal expectation is that we will take the lessons learned while we and how we did in our in-house development, but we are not there yet to use machine learning as to replace this one. Go-live 1 we will have in our forecasting solution we have the input coming from our internal system and the input coming from SAP IBP. So then the people the forecaster is able to choose or to see which one is the ones that we should use. my expectation is that SAP IBP, we will have a higher forecast accuracy than what we have done in-house.
Nikhil Balkundi:
AI is the new way of life. So AI is leveraged in this program almost at every stage. So the new state of algorithms which are used for estimating your demand, new state of algorithm for doing the inventory optimisation.or even as basic as when I'm doing the training for the user then they can have an AI chat box to just refer to the most appropriate training documents or the videos. So AI is leveraged almost at every stage in this particular program.
Anna Runellen:
We have done that as a part of this first phase. We have combined it with an in-house AI model. We are looking forward to explore the SAP AI portfolio coming. We have built machine learning in-house that we're incorporating into IBP.We have combined it with an in-house AI model. We are looking forward to explore the SAP AI portfolio coming. We have built machine learning in-house that we're incorporating into IBP. So we will also have a time where we can evaluate how well does our own model work together with the IVP out of the box models.
Yehia Seraj:
We're definitely using machine learning across the platform, right? We're using it in forecasting. We're also using it for the segregation of the structure keys. But we also started to look into and using assistance AI as a chatbot, which we have developed together with Infosys as well, or with the support of Infosys.
Tarang Puranik:
In supply chain programs, we use AI to drive accurate demand and forecasting using advanced machine-learning algorithms to perform statistical forecast, inventory optimization, and logistics planning, all powered by SAP technologies like IBP, BTP, and S/4HANA. We also apply AI in org change management, using chatbots, and self help tools to guide users through training and adoption.
Kamish Mirza:
At the moment, we are already seeing some early wins as far as AI is concerned, where we are looking at statistical forecasting already happening in Go-live1. We able to use the AI for us to take decisions which we couldn't do it before we had our legacy system. And the future deployments which we are trying to do for IBP will further enhance the impact of AI in our decision making. We will introduce business rules, which basically means there will be touch of human interventions, the system will guide us with options and then the scenarios will have to be then chosen based on what parameters Ericsson would like to use on.
