Unilever +Veeva: A Strategic Approach to Quality Transformation

This video provides an in-depth exploration of Unilever’s five-year digital quality transformation journey, led by Ahmed Maklad, Head of Quality for Digital Transformation and Quality Strategy. The central purpose of the initiative was to move away from slow, disconnected, and siloed legacy systems that hampered decision-making and operational efficiency. The strategic approach focused not just on solving current problems but on building a future-proof, agile digital platform capable of adapting to emerging technologies, particularly Artificial Intelligence (AI). This transformation was rooted in creating a unified digital ecosystem that provides full visibility into quality processes and democratizes data across the organization.

The progression of the strategy involved several critical steps. Initially, the focus was on gaining a complete understanding and full visibility of existing processes and their differences. This foundational understanding allowed the team to identify high-value use cases—a strategy described as "bigger, fewer, and better"—rather than attempting to "boil the ocean" with countless small projects. By prioritizing key areas, the team was able to deliver results with excellence, leading to significant measurable improvements. The core technical achievement was the creation of a completely integrated platform, connected seamlessly with enterprise resource planning systems like SAP, ensuring that essential production and purchase order data flowed automatically into the quality system, thereby simplifying data input for end-users and enhancing traceability.

The tangible results of this data-driven approach underscore the success of the transformation. Unilever achieved a 30% reduction in quality non-conformances across various operational areas. Furthermore, they realized a 40% improvement (reduction in time spent) related to the collection, aggregation, and dashboarding of quality data, demonstrating substantial gains in operational efficiency and speed of decision-making. Beyond technology and metrics, the speaker emphasized the critical role of cultural change. A key methodology was fostering a "culture of ownership," ensuring that users felt the system was built "for them and by them," rather than being imposed externally, which is essential for long-term adoption and continuous improvement.

Ahmed Maklad stressed the importance of selecting strategic partners who can "stretch and inspire" the organization, helping to define problem statements with clear, measurable value. The overall strategy was built on agility and continuous improvement, acknowledging that a "perfect system" is not achievable on day one. Crucially, the transformation was designed with an eye toward the future, recognizing that while AI capabilities were a distant dream five years ago, they are now a reality. The unified, data-rich platform serves as the necessary foundation to leverage advanced technologies like AI, ensuring the organization is prepared for the next wave of digital innovation in quality management.

Key Takeaways: • Future-Proofing Strategy is Essential: When embarking on digital transformation, the strategic goal must be to solve the problems of tomorrow, not just the problems of today, ensuring systems are agile and flexible enough to integrate future technologies like AI and LLMs. • Unified Data Ecosystem is Foundational for AI: The prerequisite for leveraging advanced AI capabilities is dismantling siloed systems and establishing a single, integrated digital platform where data is democratized and easily accessible, enabling comprehensive storytelling and faster decision-making. • Integration Drives Efficiency and Traceability: Connecting the quality platform (Veeva) directly to core enterprise systems like SAP allows for the automatic flow of critical data (e.g., production orders, purchase numbers), significantly improving process visibility, traceability, and reducing manual data input burdens for users. • Focus on High-Value Use Cases: Adopt a "bigger, fewer, and better" approach by focusing resources on a limited number of key use cases that deliver maximum measurable value, rather than attempting to address every minor inefficiency simultaneously ("boiling the ocean"). • Measurable Quality Improvement: The transformation resulted in a significant 30% reduction in quality non-conformances, demonstrating the direct business impact of moving from disconnected systems to a unified, data-driven quality management platform. • Operational Efficiency Gains: The project achieved a 40% reduction in the time spent collecting, aggregating, and dashboarding quality data, highlighting the substantial efficiency gains realized through data democratization and automated reporting. • Prioritize Process Visibility: The initial step in the transformation journey must be achieving full visibility and understanding of existing processes and their variations, which is the necessary prerequisite for driving targeted improvements. • Foster a Culture of Ownership: Successful system adoption requires ensuring that end-users feel the system is built "for them and by them," promoting ownership and long-term engagement rather than viewing the new platform as an external imposition. • Continuous Improvement Mindset: Recognize that digital transformation is a journey, not a destination; systems should be built with agility to support continuous improvement rather than aiming for a "perfect system" on day one. • Strategic Partner Selection: Choose partners who can "stretch and inspire" the organization, helping to clearly define problem statements and quantify the value proposition of proposed solutions.

Tools/Resources Mentioned:

• Veeva: The core platform vendor used for the quality transformation (implied to be Veeva QualityOne, based on the channel).
• SAP: The enterprise resource planning (ERP) system that was integrated with the quality platform to ensure data connectivity and automation of production and purchase order data flow.

Key Concepts:

• Data Democratization: The process of making data accessible to the average non-technical user, enabling faster, data-driven decision-making across the organization.
• Quality Non-Conformance: Incidents or deviations where a product, process, or system fails to meet specified quality standards or regulatory requirements.
• Culture of Ownership: A workplace culture where employees feel personally responsible for the success and maintenance of the systems and processes they use, leading to higher adoption and better data integrity.