Mondelēz’s $1.2B Digital Transformation to Cloud-Based ERP with AWS

Article Highlights
Off On

Mondelēz International is embarking on an ambitious $1.2 billion multiyear digital transformation aimed at enhancing its technology capabilities to increase market share and bolster revenue. This extensive overhaul includes data center exits, workload migrations, exploration of generative AI, and a comprehensive upgrade of the Enterprise Resource Planning (ERP) system.

Leading this transformation, Kostas Georgakopoulos, CTO and CISO at Mondelēz, emphasized the importance of aligning stakeholders, which include partners, technology teams, the board of directors, regional CEOs, and members of the C-suite. The groundwork for this project involved 18 months of meticulous planning, focusing on vendor partnerships, shared timelines, and enhanced governance.

The existing on-premise SAP ERP system at Mondelēz, which is nearing its end of life, necessitated a move to a cloud-based ERP. Although transitioning to a cloud ERP has numerous benefits, it also poses challenges due to the length and complexity of the process. This collaboration with SAP provided Mondelēz with critical support and resources, streamlining the migration process and minimizing potential disruptions.

The transition to a cloud-based ERP system represents a strategic pivot for Mondelēz, compelling it to rethink its operational methodologies.

With a looming deadline of 2027 for maintenance support, over half of SAP’s customers using on-prem solutions face potential risks. Strategically, Mondelēz decided to be proactive and undertake the upgrade early to secure better visibility, support, and resources from SAP.

Mondelēz has allocated around $9 million for the preliminary planning for the ERP implementation, expected to culminate by June 30, 2024. In December, AWS was chosen as the company’s strategic cloud provider after an extensive selection process where all three major hyperscalers were considered.

Mondelēz’s decision to invest substantially in early planning phases reflects the company’s commitment to meticulous preparation and risk mitigation. Allocating $9 million for planning underscores the importance of thorough groundwork in ensuring the success of the ERP transformation.

Factors such as cost efficiency, the capacity to transform operations, and the potential for innovation were pivotal in selecting AWS. Mondelēz calculated the total cost of ownership and found AWS offered the best overall package, considering both current needs and future scalability.

AWS’s capabilities on SAP S/4 would be accessible at least a year earlier compared to other providers, reinforcing their decision. By leveraging AWS’s advanced infrastructure and early access capabilities, the company aims to mitigate these market pressures through enhanced technological capabilities.

Their strategy includes leveraging SAP S/4’s out-of-the-box capabilities to minimize customizations and standardize processes across the business to enhance efficiency and reduce costs. The company’s thorough approach involves sifting through the existing ERP customizations, evaluating millions of lines of code to eliminate unnecessary complexities.

Furthermore, the company adopted Amazon Q to aid in software development, which includes writing shell scripts and enhancing productivity, yielding efficiency gains of around 20%.

Georgakopoulos expressed pride in the evolution of Mondelēz’s technological approach, highlighting how the company regained control over its strategic vision and operational management. Since his joining four years ago, the company’s paradigm has shifted towards leading the transformation, positioning itself ahead of its peers.

Mondelēz International is undertaking a bold $1.2 billion project over several years to digitally transform the company. The overall goal of these efforts is to create a more agile, efficient, and competitive business environment. By doing so, Mondelēz aims to leverage cutting-edge technology to stay ahead in the market, optimize resources, and improve overall performance.

Explore more

How Does Martech Orchestration Align Customer Journeys?

A consumer who completes a high-value transaction only to be bombarded by discount advertisements for that exact same item moments later experiences the digital equivalent of a salesperson following them out of a store and shouting through a megaphone. This friction point is not merely a minor annoyance for the user; it is a glaring indicator of a systemic failure

AMD Launches Ryzen PRO 9000 Series for AI Workstations

Modern high-performance computing has reached a definitive turning point where raw clock speeds alone no longer satisfy the insatiable hunger of local machine learning models. This roundup explores how the Zen 5 architecture addresses the shift from general productivity to AI-centric workstation requirements. By repositioning the Ryzen PRO brand, the industry is witnessing a focused effort to eliminate the data

Will the Radeon RX 9050 Redefine Mid-Range Efficiency?

The pursuit of graphical fidelity has often come at the expense of power consumption, yet the upcoming release of the Radeon RX 9050 suggests a calculated shift toward energy efficiency in the mainstream market. Leaked specifications from an anonymous board partner indicate that this new entry-level or mid-range card utilizes the Navi 44 GPU architecture, a cornerstone of the RDNA

Can the AMD Instinct MI350P Unlock Enterprise AI Scaling?

The relentless surge of agentic artificial intelligence has forced modern corporations to confront a harsh reality: the traditional cloud-centric computing model is rapidly becoming an unsustainable drain on capital and operational flexibility. Many enterprises today find themselves trapped in a costly paradox where scaling their internal AI capabilities threatens to erase the very profit margins those technologies were intended to

How Does OpenAI Symphony Scale AI Engineering Teams?

Scaling a software team once meant navigating a sea of resumes and conducting endless technical interviews, but the emergence of automated orchestration has redefined the very nature of human-led productivity. The traditional model of human-AI collaboration hit a hard limit where a single engineer could typically only supervise three to five concurrent AI sessions before the cognitive load of context