In today’s increasingly complex procurement landscape, data has evolved from a support function into a true strategic enabler. Drawing on his experience leading procurement analytics and operational excellence across multiple markets, Angelos Gregoriades, former Head of Procurement Excellence & Compliance at Coca-Cola Hellenic Bottling Company, shares how structured data, fit for purpose tools, and a commercially minded approach can drive meaningful value. From spend transparency and supplier evaluation to risk mitigation, sustainability insights, and early stage predictive analytics, Angelos reflects on how embedding analytics into core procurement activities enhances decision making, strengthens operational performance, and supports long term strategic alignment.
How are you leveraging advanced analytics to improve procurement decisions?
Advanced analytics played a critical role in optimising procurement decisions by enhancing spend visibility, monitoring supplier performance, and identifying cost saving opportunities.
With the implementation of SAP Ariba, we leveraged its capabilities to structure and enrich procurement spend data, making it more actionable. By applying predefined rules for data cleansing and consolidation, we extracted key insights that improved visibility and revealed opportunities for cost optimisation and supply base consolidation. Additionally, we developed Power BI solutions to visualise spending and procurement KPIs, enabling senior management to better understand trends, monitor progress, and take informed action.
Beyond Ariba, custom built analytics tools supported a wide range of strategic procurement decisions. Excel based solutions provided flexible and user-friendly insights for managers handling complex tenders, while additional dashboards supported areas such as savings tracking, RM&P cost planning, sustainability analytics, and performance monitoring.
During the COVID period, procurement teams were tasked with renegotiating high value contracts. My team developed tools to analyse, track, and report the resulting savings, delivering tangible value and contributing to company profitability.
By integrating multiple data sources and maintaining a strong focus on data accuracy, we elevated procurement’s role in decision making, cost efficiency, and overall business effectiveness.
What data do you prioritise when evaluating supplier performance?
Supplier performance evaluation was a structured, data driven process focused on strategic and critical suppliers. Key performance areas included sustainability, quality of service and products, supplier agility, joint business planning, delivery performance, price competitiveness, reliability, and technical capabilities. Each area was weighted based on category specific priorities and procurement’s strategic goals.
We conducted annual internal evaluations involving both procurement and key business stakeholders, complemented by external sources such as sustainability assessments, financial risk data, and third party evaluations. A dedicated evaluation platform enabled structured assessments through digital questionnaires, automated scoring, and progress tracking dashboards.
This process helped identify underperforming suppliers and trigger corrective actions, such as renegotiations, supplier capability building, or transitioning to alternative suppliers. Evaluation results also informed future sourcing strategies, reinforcing continuous improvement.
Beyond the annual exercise, insights from supplier performance evaluation supported broader procurement initiatives such as supplier segmentation, compliance monitoring, contingency planning, and supply risk mitigation. By combining internal and external data, we ensured that supplier partnerships aligned with business needs, ESG priorities, and long term performance expectations.
How has data analytics helped you mitigate risks in your supply chain?
Data analytics played a vital role in identifying and mitigating supply chain risks by providing transparency across supplier performance, financial stability, and operational reliability. Although we did not have a dedicated risk management system in place, my team developed custom built tools to support risk identification and monitoring, while collaborating with IT to explore longer term digital solutions.
We relied on a combination of internal risk assessments and external intelligence, such as financial ratings and sustainability data, to flag suppliers with potential vulnerabilities. These efforts were supplemented by periodic reviews, including category level risk mapping conducted by strategic procurement managers.
A critical focus area was ensuring supply continuity and preventing disruptions. Procurement maintained clear KPIs related to business continuity, and we implemented contingency plans for high risk categories. This included identifying secondary suppliers, developing backup sourcing options, and collaborating with stakeholders to assess feasibility and potential exposure.
By combining structured risk monitoring with practical mitigation planning, we strengthened our ability to anticipate and manage disruptions, enhancing procurement’s contribution to business resilience and agility.
How do you ensure data accuracy and reliability in your procurement process?
Ensuring data accuracy and reliability in procurement was a top priority, given its direct impact on reporting quality, spend visibility, and strategic decision making. Our core data sources were SAP (ERP) and SAP Ariba, from which we retrieved and validated procurement data, including spend details, purchase orders, payment terms, contract status, and supplier hierarchies.
To enhance consistency, my team developed internal validation processes and leveraged external databases to structure supplier groupings, ensuring accurate classification. Month end data controls refined the information before it was published in dashboards and reports. We also implemented automated enrichment rules for spend categorisation, reducing manual corrections and improving reporting quality.
Aligned with internal control protocols, we used SAP BW reports to validate procurement data shared in external communications and corporate disclosures, maintaining complete audit records to support compliance. My team played a key role in ensuring this data was accurate and aligned with governance standards.
Beyond validation, my focus was on transforming data into insight. I encouraged my team not only to deliver reliable reports but to highlight key messages, prepare executive summaries, and consistently challenge the data to ensure it drives action. This approach positioned procurement analytics as a strategic enabler of business impact.
Can you share examples of how predictive analytics has improved procurement outcomes?
While predictive analytics was not yet fully implemented, we actively explored its potential to enhance procurement decision making. One promising area was the use of AI and RPA to automate PO classification, improving the accuracy of spend categorisation and reducing manual effort. Although full scale deployment was still in progress, the business case for such tools was clear.
In the meantime, we relied on historical spend patterns, supplier performance trends, and forward looking scenarios to anticipate risks and guide sourcing decisions. These insights supported more effective supplier negotiations, savings planning, and proactive alignment with business needs.
Looking ahead, predictive analytics holds significant promise. By combining machine learning with real time data, procurement functions can shift from reactive problem solving to proactive opportunity creation. My vision was to embed these capabilities into procurement strategy, enabling a more agile, resilient, and insight driven approach to supplier management, risk mitigation, and cost optimisation.
Our early efforts in this space, paired with strong analytics foundations, ensured that procurement was well positioned to embrace future digital transformation and continue delivering value to the business.
