Autonomous Supply Chain Planning with SAP IBP & AI | YASH Technologies

Executive Conversations on the Future of Mid‑Market Enterprise Transformation

LIVE WEBINAR · SEPTEMBER 29, 2026 · VIA ZOOM

The next era of supply chain planning is autonomous.

Here's how to get there.

Join SAP and YASH live to see how AI-driven IBP planning closes the gap between plan and reality, from master data and S&OP alignment to real-time, scenario-based decision-making.

Reserve your seat

$12.9M

The average annual cost of poor data quality to an organization is the exact failure mode that turns IBP recommendations into guesswork.

Gartner, gartner.com/en/data-analytics/topics/data-quality

70%

Many large organizations will adopt AI-based supply chain forecasting by 2030, most without a plan for the adoption gap.

Gartner, Sept 2025 press release

20–50%

forecast error reduction achievable with AI-driven forecasting, when the data and processes are ready.

McKinsey & Company

Every IBP transformation runs
into the same industry-wide hurdles

Data quality sets the ceiling

01 · MASTER DATAAny planning engine is only as good as the data feeding it, lead times, capacities, and BOMs. When legacy ERP data lags behind the business, the plan inherits that gap, no matter how advanced the underlying algorithm.

Old habits are sticky

02 · USER ADOPTIONEven with a centralized planning platform in place, planners often default to the spreadsheets they trust from experience. Closing that gap takes more than good software. It takes deliberate change management.

Three teams, three numbers

03 · S&OP ALIGNMENTSales push growth targets. Finance guards the budget. Ops protects capacity. Reconciling three numbers eats up the time that should go to deciding what to do about them.

Monthly cycles, real-time disruptions

04 · CADENCEBy the time a monthly S&OP cycle reaches executive sign-off, the disruption that shaped the plan is already old news, and a new one has started.

What it takes to get IBP running at its full potential

Fix the data at the source

Correct master data in ECC or S/4HANA before it reaches IBP, using a purpose-built data-sensing and correction utility rather than another manual cleanup project.

Deploy on accelerators, not from a blank canvas.

Industry-specific templates get baseline S&OP and demand models up and running in weeks, instead of the first three months of discovery.

Model what-ifs before you commit capacity

Digital twins and shadow pricing let planners pressure-test scenarios within IBP, turning the planning meeting into a decision meeting.

Move from monthly review to weekly response.

An agile S&OE cadence, layered with SAP Joule AI, keeps the plan current with what's actually happening.

Meet Our Speakers

Firdosh Kotwal

Head, SCM Practice

Catia Bongiardina

Domain Advisor Expert in
Supply Chain Management (SCM), SAP

Belal Ahmad

Principal Consultant

Two transformations
you'll see up close

Process Manufacturing - Scenario planning with digital twins & shadow pricing

How an IBP digital twin, paired with shadow pricing, lets planners simulate “what-if” scenarios and commit capacity based on evidence rather than instinct.

Life Sciences - From a monolithic cycle to an agile S&OP framework

How a life sciences business broke a rigid, monthly planning cycle into an agile rhythm built to keep pace with real disruptions.