Kraljic Matrix Analysis Automation Overview
- Sergii Dovgalenko
- May 10
- 4 min read
Updated: Jul 26
Kraljic Matrix Overview
The Kraljic Matrix is probably one of the most widely known category management tools in procurement since its introduction in 1983. Developed by Prof. Peter Kraljic, this strategic tool helps organizations optimize their purchasing strategies by categorizing products and services along two key dimensions: supply risk and profit impact.
The matrix divides products into four distinct categories: strategic, leverage, bottleneck, and non-critical items, each requiring a different approach to procurement management, as follows:
The strategic items, which are high in both supply risk and profit impact, necessitate close collaboration with suppliers to ensure reliability and innovation.
Conversely, leverage items, characterized by a high profit impact but low supply risk, allow organizations to negotiate favorable terms due to the abundance of suppliers.
Bottleneck items, on the other hand, pose high supply risk but low profit impact, requiring careful management to avoid supply disruptions.
Finally, non-critical items are low in both dimensions and can often be managed through streamlined processes to minimize costs and administrative efforts.
By utilizing the Kraljic Matrix, companies can not only improve their procurement efficiency but also build stronger supplier relationships and drive overall business success.
And yet, in many organizations, the analysis process remains surprisingly primitive.
Someone exports spend data into Excel.
Category managers manually assign scores.
Someone redraws the matrix in PowerPoint.
Eventually, the organization receives a static picture that quickly becomes outdated.
Meanwhile:
operational dependencies evolve,
sourcing risks fluctuate,
nobody wants to repeat the exercise every month because the process itself is painful.
This creates an interesting paradox.
Many procurement teams discuss AI, predictive analytics, and digital transformation, but strategic category segmentation is still statically managed through spreadsheets, PowerPoint screenshots, and subjective scoring.
So, I attempted to automate the process using standard Microsoft 365 tools because having it in place means it can run more than once a year, when category plans are due.
What the Workflow Does
The solution runs on:
Microsoft SharePoint Online
Microsoft Power Automate
Microsoft AI Builder
Microsoft Outlook
Excel Online
The workflow:
imports category data from an Excel template,
calculates spend-share-based weighting,
evaluates Profit Impact and Supply Risk, including combined scoring (like SpendScore, which represents the category share of the total spend) and complex AI scores,
classifies categories into Kraljic quadrants,
generates AI commentary with the analysis outcome verification, category management advice, and risk assessment,
creates a visual SVG-based matrix,
builds a structured HTML report, stores it in SharePoint for audit purposes, and automatically distributes it via Outlook.
The entire process is auditable and SharePoint-native.
Process ideation by the Business Automation Coach
Other than the initial concept, the entire ideation process has been facilitated by the custom GPT - the Business Automation Coach.
It helps to turn a perception into a well-articulated process description and workflow structure. Then you can vibe code on your own or ask someone to.
Why SVG Instead of Power BI or Chart.js
One unexpectedly annoying problem was report rendering.
Most charting approaches work poorly inside generated HTML reports, SharePoint previews, or email-distributed files.
Chart.js introduced compatibility issues during report sharing and previewing.
Therefore, the workflow dynamically generates the matrix directly in SVG.
This turned out to be much more reliable:
cleaner rendering,
better portability,
simpler SharePoint compatibility,
and no dependency on external libraries.
The workflow now creates:
dynamic bubble coordinates,
proportional bubble sizes based on spend share,
color-coded quadrants,
and linked category legends automatically.
AI Should Not Merely “Talk”
One of the biggest mistakes in procurement AI experimentation today is using AI as a decorative text generator.
In this workflow, AI is expected to contribute to judgment.
The AI scoring evaluates:
supplier concentration,
sourcing complexity,
operational dependency,
continuity exposure,
spend materiality,
and market dependency.
The workflow then generates:
category commentary,
procurement recommendations,
sourcing risk signals.
Importantly, the AI assessment does not fully replace procurement judgment.
The final scoring combines:
procurement manager scoring,
operational stakeholder scoring,
automated spend-share scoring,
supplier dependency logic,
AI assessment layers.
This creates a much more balanced category positioning model.
The More Interesting Part
The more interesting observation is not technical. It is organizational.
The barrier is usually not technology. The barrier is process imagination.
Many procurement processes still assume:
manual preparation,
manual consolidation,
manual reporting,
and manual interpretation.
But category management is actually a very strong candidate for workflow automation because:
the logic is structured,
scoring models are repeatable,
governance rules are definable,
outputs are highly standardized.
Current Roadmap
The next development steps may include:
historical matrix movement tracking,
quarterly category trend analysis,
supplier market intelligence inputs contributing to the scores,
and Power BI dashboard integration.
Video Overview
Final Thought on Kraljic Matrix Automation
The Kraljic Matrix was never intended to become a yearly PowerPoint ritual.
It was supposed to support procurement decision-making.
The more repeatable, transparent, and automated the process becomes, the more useful the model actually appears.
If you want to test this process at no cost, please visit this page.
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