Partnership Pantry
Mondelēz brings joy to billions through its brands.
IBM builds the global delivery infrastructure that powers that mission — across every market, every system, and every transformation ahead.
The IBM–Mondelēz relationship didn’t begin with a contract. It began with a shared conviction: that borderless operations are the foundation of a truly global consumer business. Since 2012, IBM has been the strategic partner Mondelēz has turned to when the transformation work is hardest.
From harmonizing ERP across 22 Central and Eastern European markets under the Kraft Catalyst program, to designing and operating the global Application Delivery Factory, to collaborating on blockchain-enabled food safety — IBM has been present at every inflection point in Mondelēz’s technology journey.
That track record isn’t incidental. It reflects IBM’s unique position at the intersection of CPG industry depth, SAP expertise, and the cultural discipline needed to operate at Mondelēz’s scale — a combination no late entrant can replicate.
IBM’s approach to Mondelēz isn’t a service catalog — it’s an integrated delivery framework where each dimension reinforces the others. Select any pillar to see what IBM has built and delivered.
Every engagement has built institutional knowledge that makes the next one faster, deeper, and more impactful. That compounding effect is IBM’s most durable advantage in this relationship.
Mondelēz’s $1.2 billion transformation isn’t a disruption to the partnership — it’s the clearest signal yet of what IBM’s work has made possible. A company that spent years stabilizing its ERP foundation is now ready to lead a widescale modernization.
As Mondelēz executes its region-by-region S/4HANA migration through 2028, the work IBM pioneered — harmonized processes, factory discipline, cultural change management — provides the baseline from which that migration can run cleanly and confidently.
IBM’s position in this next chapter is defined by the institutional knowledge no late entrant can replicate: the Mondelēz architecture landscape, the template governance, the IS leadership relationships, and the proven ability to lead multi-partner programs at this scale.
From global CPG programs to AI-embedded ERP transformations — a curated selection of IBM case studies that mirror the scope, complexity, and ambition of what Mondelēz International requires.
Migrated SAP Business Warehouse to SAP HANA, dramatically cutting nightly batch times as data volumes scaled across a global food and beverage portfolio.
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Colombian multi-brand food producer used IBM Rapid Move to migrate to SAP S/4HANA — enabling better retail analytics and improved product-mix visibility across its full brand portfolio.
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PepsiCo’s $18B snack division digitally empowered frontline employees — improving customer service execution and brand portfolio management at national scale.
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World’s leading glass packaging manufacturer for food and beverage brands migrated its SAP data to IBM Db2 — cutting costs and improving operational performance across global operations.
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Deployed SAP S/4HANA across global manufacturing operations using a standardized template — supporting AUDI’s transition to electric mobility without disrupting a complex, multi-market ERP estate.
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Migrated to SAP S/4HANA across a distributed retail network — standardizing processes and integrating systems to protect margins at scale across a multi-entity enterprise.
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Portuguese energy company executed a full SAP S/4HANA transformation on IBM Cloud — modernizing its integrated platform across multiple business units with zero operational disruption.
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On-premises and cloud SAP S/4HANA deployment with a full HANA migration — opening new market opportunities and establishing a scalable platform for future growth across Latin America.
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Built a centralized food traceability platform on IBM Food Trust blockchain — enabling farm-to-shelf provenance tracking with contamination isolation and recall capability in near real time.
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IBM built its own first cognitive supply chain using blockchain, track-and-trace, IoT, and AI — achieving 100% order fulfillment even during COVID and offering the model as a client blueprint.
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Demonstrates how AI, blockchain, IoT, and automation weave into end-to-end supply chain, finance, and procurement workflows — a reference architecture for the intelligent enterprise.
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Rome’s wholesale food market deployed IBM Blockchain Platform to trace provenance and ensure food safety across its distribution network — making unsafe products identifiable and removable in minutes.
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Full data center exit to AWS with hypercare support and structured handover — reducing costs while maintaining service agility and establishing a cloud-first operating baseline across Europe.
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Enterprise infrastructure transition managed with IBM Turbonomic and Instana — ensuring service continuity, ITIL alignment, and uptime during a complex operational handover.
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Used IBM Instana to maintain uptime and observability across a cloud migration to AWS — enabling digital transformation at scale without service disruption to a 24/7 mobility platform.
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IBM managed the transition to SAP S/4HANA in the cloud with zero disruption — governance controls and managed services kept global chemical manufacturing operations running throughout.
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Deployed IBM watsonx.ai LLMs to generate board-ready business insights from enterprise data — a reusable pattern for AI-powered decision support inside any ERP landscape.
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Built a watsonx.ai-powered solution letting enterprise users query structured operational and ERP-adjacent data in natural language — delivering reliable business insights at scale.
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Deployed IBM watsonx Assistant with LLM capabilities to eliminate customer wait times entirely — demonstrating generative AI automation at enterprise scale across a major utility operation.
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Modernized IT operations and finance functions using watsonx.ai and watsonx Orchestrate — embedding generative AI and intelligent automation directly into enterprise workflows at scale.
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The 2019 site visit to IBM's Bangalore delivery center ran on paper — a 23-page workshop booklet mapping three practitioners through the work the SAP application delivery pitch was built on. This app has 23 screens. One for one. The sessions, the personas, and every quote shown are from the live engagement. The app is the reimagination.
Thirteen chapters, three personas, full sessions. Arrow keys step through every screen in sequence.
Mondelēz wanted to industrialize SAP application delivery — fewer delays, more automation, predictable outcomes. IBM's 2019 site visit mapped what that work looked like for the people doing it: one change governance lead, one developer, and the only Mondelēz-side voice in the room. The app opens here — choose a practitioner to begin their session.
Two screens that anchor every session before a single observation is captured. The brand card maps Mondelēz's seven product categories against the seven IBM commitments — the scope of what IBM is proposing, all on one screen. The inspiration deck offers four provocations: Cognitive Cocktails, Food Trust traceability, JOY-targeted personalisation. The room reacts before choosing a persona. Ambition first, prioritization later.
Rajiv Mathur — 39, SAP Lead Consultant, IBM India. Every SAP change request on the Mondelēz account passes through his hands, from raise to deploy. "A plan is a promise" — his line, and the weight of his role. The same template carries Smita's ambition and Nirmal's forecasting load.
"Too many mails, cannot handle!" sits next to "When will I get my promotion?" — one person's working life, mapped in four quadrants. By the end, there's a shared picture of what Rajiv's day costs him. That picture is what the IBM proposal has to respond to.
Rajiv's change governance runs five stages — raise the request, estimate effort, document, build and test, deploy. The journey tab puts an IBM opportunity inside each one. "Dynamic automation" in step three. "Efficient deployment" in step five. An IBM answer at every stage, grounded in the specific friction that caused it.
All the empathy notes and journey observations land in one canvas — Interpret in IBM's Design Thinking framework, the step where collecting ends and deciding begins. "Communication improvement," "automation," "historical data" — each tagged as a Big Bet, No-Brainer, Utility, or set aside. The debate shifts from "which category is this?" to "what does IBM actually do about it?"
Every tagged observation gets placed on the impact-vs-effort grid. The IBM opportunities from the journey reappear here — this time the room decides which ones lead. When consensus stalls, "Suggest with AI" plots the rest — and the room argues with that. That's the point.
When the session ends, everything Rajiv produced — empathy quotes, journey opportunities, the placed matrix — becomes one scrollable artefact. One tap sends it to the team's channel. The proposal meeting starts with a document, not with reconstructing what the room said.
The same tools, an entirely different kind of day. Smita is 26 — IBM India's tech developer on the Mondelēz account. "Wow, there is so much to learn!" reads as ambition, not complaint. What her session reveals is quieter: the system has no structure for that energy. "Am I proficient enough?" in the Thinks quadrant and "confusion about career goals" in the pain points name the same gap — IBM's opportunity is giving her ambition somewhere to land.
The interpret tab pulls Smita's observations into a single canvas — "Am I proficient enough?" alongside "Review mechanism is too slow." The matrix slots them: easier peer review in No-Brainers; a personalised growth plan backed by Watson as the Big Bet. By the summary screen it's a complete story: a specific IBM offer for every stage of her day.
Nirmal is 37 — Mondelēz's Forecasting Manager and the only client-side participant in the room. Four stages of statistical forecasting work, most of it spent waiting: SAP runs to complete, reports to consolidate, exceptions to resolve. "So many reports, so little time" — the signal the IBM offer is built around.
Nirmal's empathy map holds "Cautious when he plans alternatives" alongside "Reassured when reports are accurate" — a person who wants certainty, operating in a system that makes certainty expensive. The matrix puts consolidated real-time analysis in Big Bets — IBM's Smart Materials Planning mapped directly to a forecasting cycle that currently runs one exception at a time.
The synthesis screen names what the three sessions produced together — where the personas converge, and where the IBM offer should split. The automation gap Rajiv described, the career structure Smita needs, the reporting time Nirmal can't recover — they all point to the same proposition: less manual work, more predictable outcomes. The combined matrix surfaces which opportunities cluster across all three roles. Those lead the proposal.
