From Data to Decisions: How to Link Key Systems into a Single, Managed Model (S&OP, IBP, and AI)
THEMATIC SESSION

From Data to Decisions: How to Link Key Systems into a Single, Managed Model (S&OP, IBP, and AI)

15.09.2026
14:30-16:00
WIN Arena, Hall 1

Description

How can manufacturers improve operational efficiency? The problem isn't the lack of IT systems, but their fragmentation. A systematic approach to digital solutions, practical cases of IT system integration, and lossless data management.

What will we discuss:
  • S&OP and IBP in real life: what are the benefits, how to implement and use them effectively? How feasible is it to integrate them into the logistics system, including production?
  • Who is responsible for what, and what are the cross-functional KPIs? Who has implemented them and how, what are the shortcomings and challenges? Why isn't this primarily an IT task?
  • AI agents in production logistics: no longer a thing of the future
  • Production data architecture is a key aspect for S&OP/IBP and AI tools. What data is considered reliable, and who is responsible for it? IT integration as a management solution

Representatives of the following companies are invited to participate: InBev Efes, Grand A.V., Systeme Electric, Noosoft , Solvo , and others.

Attention

All event programme sessions are open to visitors holding an e-ticket to the exhibition. Get a ticket

PARTNERS
SPEAKERS
Valery Reshetnikov
Moderator
Valery Reshetnikov
Director of the Supply Chain Management Practice
Andrey Repin
Andrey Repin
Product Owner for Optimization, Demand, and Multi-Echelon Supply Chain Optimization, In.Plan
Anna Golovacheva
Anna Golovacheva
Director of Digitalization, Grand A.V.
Leonid Yakovlev
Leonid Yakovlev
Sales Director, AB InBev Efes
Pavel Murzakaev
Pavel Murzakaev
Leader in software development, Systeme Electric
Petr Pervoy
Petr Pervoy
Sales Director, Solvo
Sergey Kondratenko
Sergey Kondratenko
CEO of Noosoft
TOPIC
WIN RUSSIA. Conference
EVENT TYPE
Expert session
VENUE
WIN Arena, Hall 1
INTERESTS
IT management systems AI