In an era where data driven decision making is reshaping procurement, Pascal Evertz, CEO and Founder of GoBeyondProcurement along with AI tools like Juriaan.ai, brings a bold and unconventional perspective to the conversation. Drawing from the Best Value Approach AI (BVA AI), Pascal emphasises simplicity, transparency, and performance metrics as the foundation for procurement and project management success. In this Executive Insight, he shares how advanced analytics reduce risk, predictive models outperform traditional processes, and why true expertise lies not in managing complexity, but in eliminating it. His approach challenges long standing norms and offers a new blueprint for building efficient, resilient, and value focused vendor centric procurement systems.
How are you leveraging advanced analytics to improve procurement decisions?
I apply the Best Value Approach AI (BVA AI)—an artificial intelligence performance system that simplifies procurement processes, increases transparency, and predicts project management success. Transparency is achieved through the use of metrics: observable and countable performance information.
These metrics help us identify patterns and predict the likely outcomes of a project or service. By analysing historical vendor data from past projects, we can forecast future performance. For example, we can examine a vendor’s track record on similar projects and assess how their services contributed to successful outcomes delivered on time, within budget, and with high client satisfaction.
Metrics enable a more objective evaluation process, reducing the need for subjective decision making. Once it’s evident that a vendor has strong performance metrics that demonstrate expertise, the choice becomes clearer. Risk is minimised through transparency, and cost becomes a secondary concern, because true expertise naturally leads to cost efficiency.
What data do you prioritise when evaluating supplier performance?
Decision making often arises from a lack of information and becomes more difficult as complexity increases. The best data, therefore, reduces the need for decision making by providing clarity and predictability, enabling clients (buyers) to know less, think less, and decide less.
I prioritise data, specifically metrics, that clearly differentiate vendors, predict future performance (e.g., on time delivery, staying within budget, high client satisfaction), and demonstrate expertise. The best data is simple to understand, requires no technical knowledge, is objective and unbiased, relevant, specific, comparative, and supported by documented performance. Most importantly, it should be predictive of future outcomes.
We should only measure what is truly relevant. Metrics and minimum standards are not the same. Traditional KPIs or minimum standards are often generic, non-specific, and imposed without input from the experts doing the work. By focusing on meaningful, vendor generated performance metrics, we enable more informed, effective procurement decisions.
How has data analytics helped you mitigate risks in your supply chain?
Risk arises when expectations do not match reality. It occurs when someone is non-observant and unable to foresee future conditions. In procurement, risk is often, about 90% of the time, caused by the client (buyer) and the procurement system itself. Expert vendors don’t cause risk. Instead, the expert vendor’s project manager should use simplicity and metrics, observable and countable performance information, to help the client’s stakeholders see into the future and avoid creating risk.
When expert vendors are hired, empowered to pre-plan, and allowed to track their own time and cost deviations, risk is reduced to almost nothing. Risk mitigation is a direct result of transparency, and transparency comes from metrics.
To compare vendor performance effectively, we use a simple reporting tool that documents and tracks time and cost deviations, along with identifying who created the deviation (the problem or risk), based on a few core performance metrics. This approach not only reveals root causes but also makes the entire procurement process and project delivery more predictable and controlled.
How do you ensure data accuracy and reliability in your procurement process?
Transparency and confusion cannot coexist. It’s like shining a flashlight into a dark, unused room and watching all the cockroaches scatter, clarity drives out ambiguity. In a truly transparent environment, it’s not necessary for everyone to understand the technical details. What matters is that non-experts can clearly see that the expert vendor has done the service before, and can do it again for the specific project at hand.
Transparency means that the expert is able to communicate their expertise simply and clearly, in a way that non-experts can understand. Performance metrics are key to this. They should be observable, countable, and verifiable, supported by documented performance from previous projects.
In short, data becomes accurate and reliable when it is tied to clear, expert provided evidence that is easy to understand and difficult to dispute.
Can you share examples of how predictive analytics has improved procurement outcomes?
Industry testing has shown that project costs can be reduced by up to 50%, with client satisfaction reaching as high as 9.8 out of 10 through the application of predictive analytics within the Best Value Approach AI (BVA AI) framework.
The BVA AI structure is designed to:
- Minimise the bureaucratic burden of traditional procurement practices (zero waste).
- Emphasise the use of performance metrics to reduce complexity and the need for management, direction, and control.
- Enable expert vendors to fully pre-plan the project from beginning to end.
A strong example comes from a 2023 IT procurement project for an airline company. Using BVA AI—including predictive analytics—the procurement process was completed 67% faster, reducing the timeline by 121 days. Additionally, project costs were reduced by 38%, resulting in a highly competitive price aligned with the client’s scope.
Further evaluation highlights BVA AI’s performance against traditional procurement across several critical criteria, including simplicity, risk mitigation, efficiency, and stakeholder satisfaction. In every category, BVA AI outperformed traditional methods, with some criteria showing performance improvements of over 80%.
While stakeholders rated the BVA AI process highly, its real value becomes clear during project execution: delivering results on time, within budget, and with consistently high client satisfaction. As with all successful projects, this is not coincidental, initial conditions and final outcomes are intrinsically connected.
