3 a.m., Monday morning. The phone wakes you from a deep sleep. On the other end is the panicked voice of your plant manager: "Production has stopped. We've run out of sensors." Sensors? You grab your laptop and open the ABC analysis. There they are: classified as unimportant C items. 15 euros per unit, just 0.02% of the total purchasing volume. How can 15-euro parts bring a 50-million-euro production to a standstill?
The answer is as simple as it is frightening: your ABC analysis works with yesterday's priorities, while today's world plays by completely different rules. What is classified as "unimportant" in your systems can determine the survival of your company.
Many traditional ABC analyses fail to predict critical supply chain disruptions. The problem? These systems were designed for a world that no longer exists, a world of stable markets, predictable risks, and linear developments.
In the next eight minutes, we'll show you why traditional ABC analyses are becoming a career threat and what you can do right now to future-proof your purchasing.
ABC analysis is based on the Pareto principle, developed in the 1960s for a world of stable markets and predictable developments. The 70/20/10 scheme worked perfectly back then: A items (70% of value, 10% of parts), B items (20% of value, 20% of parts), C items (10% of value, 70% of parts).
But here lies the fundamental problem: this logic assumes a constant world. A world in which priorities change slowly and predictably. A world that no longer exists.
Let's take a concrete example from the automotive industry: in 2019, microchips accounted for just 0.3% of the total purchasing volume of an average OEM, classic C items. In 2021, these very same "unimportant" components brought the entire industry to a standstill. Volkswagen had to halt production at several plants for weeks, Mercedes-Benz reduced vehicle production by 15%, and Ford postponed deliveries of over 100,000 vehicles.
The damage? In Germany alone, many billions of euros in added value were lost.
Value-based classification completely ignores systemic dependencies. A $5 chip can be more important than a $500 engine if nothing works without it. But traditional ABC analyses only look at the price, not the consequences of failure.
This reveals the second critical weakness: the speed of adaptation. Some German companies only update their ABC classification quarterly. Some even only annually.
While you are still working with Q2 data, raw material prices fluctuate by 10-15% daily.
A vivid example: in March 2022, the price of nickel exploded by 250% within 24 hours, from $29,000 to $101,365 per ton. Companies whose ABC analysis classified nickel-containing components as C items experienced a cost shock overnight. Tesla had to temporarily raise the price of the Model 3 by 8%. Other manufacturers halted entire product lines.
The problem: by the time the next quarterly ABC update was due, millions in damages had already been incurred.
But it's not just about price fluctuations. Geopolitical events, natural disasters, or pandemics can change the criticality of components overnight. Traditional ABC analysis reacts like a cumbersome tanker, too slow for today's world.
The third blind spot: traditional ABC analyses only consider monetary value. What they don't see:
Supplier concentration: 90% of your "unimportant" C-items come from one region? Geopolitical risk.
Environmental regulations: CO₂ tax makes "cheap" parts expensive overnight.
Technology dependencies: Old chip generations become critical when newer ones are not available.
Substitution risk: Are there alternatives, or are you dependent on this one supplier?
The world is becoming more multipolar, more fragile, more unpredictable. According to Deloitte's 2023 Global CPO Survey, 43% of chief procurement officers confirmed that procurement-related risks have "significantly increased," compared to only 20% in 2021. In addition, 70% of CPOs report increased supply chain disruptions in the last 12 months.
A BCG analysis shows that geopolitical risks are increasingly becoming one of the main causes of supply chain disruptions. However, your ABC analysis does not take geographical risks into account at all.
Climate change is not just an environmental issue, it is a business risk. And an immediate one at that.
Specific threats that are happening right now:
Panama Canal: The ongoing drought has caused water levels to drop significantly, reducing the number of ships that can pass through. Transport times are increasing considerably and costs are rising.
Low water levels on the Rhine: In 2022, the water level was so low that freighters could only be partially loaded. Transport costs for bulk goods rose dramatically.
Extreme weather: Winter storm Uri paralyzed Texas in 2021. Petrochemical plants stopped production. Plastic raw materials became scarce and prices tripled.
Your problem: Your ABC analysis does not take climate risks into account. It classifies a plastic raw material as a C item because it is cheap, but ignores the fact that it is only produced in a climate risk area.
Environmental, social, and governance criteria are no longer just "nice to have"; they are becoming a legal obligation. And that fundamentally changes the evaluation of your components.
CBAM (Carbon Border Adjustment Mechanism) will come into full effect in 2026. Products from countries with low CO₂ standards will be subject to a tax. This can make traditionally "cheap" components from certain regions significantly more expensive.
CSRD (Corporate Sustainability Reporting Directive) will force you to check the entire supply chain for sustainability. As a result, components from non-compliant suppliers will become a reputational risk, regardless of price.
Artificial intelligence is not only changing how we work, it is changing what is important. Components that were unimportant yesterday suddenly become systemically critical thanks to AI.
Examples of AI-driven change:
Sensors: For autonomous driving, a car needs a variety of different sensors. In the past, these were €5 C-items. Today? Without them, the car cannot drive autonomously – and is therefore unsellable.
High-performance chips: AI training requires specialized processors. NVIDIA chips, which used to be a niche product, are now extremely expensive and difficult to obtain.
Battery raw materials: Lithium, cobalt, and nickel used to be C-items. The shift to e-mobility has caused demand to explode.
The problem: technology trends are not reflected in your historical ABC logic. You are buying for the past, while the future needs different components.
The solution does not lie in more frequent updates to the old logic, but in a fundamental paradigm shift. ABC Analysis 4.0 means continuous, automated revaluation based on live data from dozens of sources.
The new paradigm works like this:
Continuous market monitoring: Price data, availability information, and risk reports flow into the system in real time. If the price of nickel rises by 50%, all nickel-containing components are automatically classified higher.
Geo-intelligence: Satellite data, weather reports, and geopolitical analyses continuously assess the risks of your supply regions. Is the Panama Canal drying up? All components coming via this route are classified as higher risk.
Supplier Health Monitoring: AI monitors the financial stability, cybersecurity, and compliance status of your suppliers. If a supplier's credit rating deteriorates, all of its components increase in criticality.
Example: The system recognizes that your C-item supplier is located in Taiwan, geopolitical tensions are rising, and at the same time, demand for semiconductors is increasing. Result: Automatic warning three months before bottlenecks occur. You have time to react instead of being caught off guard.
The second revolution: moving away from one-dimensional value assessment to a 360-degree risk profile. Modern ABC analysis takes five dimensions into account simultaneously:
1. Traditional value (as before): Purchase price × quantity = share of total volume
2. Risk score:
3. ESG impact:
4. Technological relevance:
5. Substitution risk:
The result: Instead of A/B/C, there is now a multidimensional classification such as "A value, C risk, B ESG, A tech, C substitution." This gives a much more realistic picture of actual criticality.
The third game changer: AI recognizes patterns that humans overlook. Machine learning algorithms analyze millions of data points and identify correlations that are invisible to human analysts.
Specific examples:
Correlation analysis: The system recognizes that lithium prices always rise 6-8 weeks after political tensions in South America. When CNN reports on protests in Chile, the system warns you of rising battery costs.
Seasonal pattern recognition: AI learns that certain electronic components become scarce before Chinese holidays—not because of the holidays themselves, but because other companies increase their purchases during this time.
Supplier behavior prediction: Machine learning analyzes the behavior of your suppliers: delivery times, quality fluctuations, price adjustments. The system recognizes when a supplier is in trouble – often months before it becomes official.
Cross-Industry Intelligence: AI connects events in other industries with your purchasing. Boom in electric cars → bottleneck in battery raw materials → your industrial batteries become more expensive. The system tells you this in advance, not after the fact.
The concrete benefit: a 6-12 month head start on critical developments. Time to qualify alternative suppliers, build up inventories, or secure prices.
The fourth building block: Automatic adjustment in crises. Traditional ABC analyses are static; they do not respond to events. Intelligent systems automatically adapt to changing circumstances.
Specific trigger events:
Geopolitical crises: War in Ukraine → all components from the region are automatically classified higher. Taiwan conflict → real-time semiconductor reclassification.
Natural disasters: Earthquake in Japan → all components from the affected region are marked as high risk. Hurricane in Texas → petrochemical raw materials rise in priority.
Regulatory changes: New EU regulation comes into force → all affected components are automatically checked for compliance risks.
Supplier events: Supplier files for bankruptcy → all of its components are immediately classified as critical. Cyberattack on suppliers → risk assessment of all of its components increases.
Your advantage: No more manual updates. No more missing crises. The system reacts faster than any human being.
The fifth success factor: No more Excel chaos. Modern ABC analysis is seamlessly integrated into your existing systems.
Integration includes:
ERP systems: Direct connection to SAP, Oracle, Microsoft Dynamics. Classifications are automatically synchronized. Purchase orders automatically take the current risk assessment into account.
Supplier management: Connection to your SRM system. Supplier evaluations flow directly into the ABC classification.
Market intelligence: Connection to Bloomberg, Reuters, S&P for real-time market data. Price fluctuations are taken into account immediately.
Compliance systems: Integration with ESG rating platforms, sanctions lists, sustainability databases.
Business Intelligence: Dashboards and reports for management, controlling, compliance. Everyone works with the same data.
The result: a uniform, always up-to-date database for all decisions. No media breaks, no version conflicts, no manual maintenance.
Modern procurement platforms such as Mercanis are already demonstrating what automated ABC analysis can look like. Instead of manual Excel lists, suppliers and items are automatically classified on the platform based on spend volume.
Suppliers are automatically segmented, from strategic A partners to C suppliers. This classification flows directly into analytics dashboards, where purchasing teams can filter specifically by A, B, or C categories. The advantage: instead of manual quarterly reviews, reevaluation takes place continuously based on current transaction data.
Such integrated solutions point the way forward: away from isolated analyses and toward consistent, data-driven decision-making processes. Classification is transformed from a time-consuming reporting exercise into an automated intelligence layer that supports strategic decisions.
Step 1: Analyze data quality Before you can use intelligent algorithms, you need to know what you are working with. A KPMG study shows that 67% of German companies have incomplete or inconsistent purchasing data.
Specific tasks:
Step 2: Integrate external data sources The most important sources for modern ABC analysis:
Step 3: Identify quick wins While preparing your data, you can already start identifying the biggest blind spots in your current ABC list:
Result after 4 weeks: Cleaned-up database and list of the top 20 risk components.
Smart start with a critical product category Select a category with high learning potential for the pilot:
Parallel operation: old vs. new Run both systems in parallel for four weeks:
Measurement of forecast accuracy Define clear KPIs:
Results after 8 weeks: Proof that the new system works and initial measurable improvements.
Company-wide introduction Based on the pilot experience, you roll out the system gradually:
Training of purchasing teams The most important success factor: Your employees must understand and accept the new system.
Training content:
Change management through success People accept change when they see its benefits:
The three critical success factors:
1. Leadership commitment Management must be behind the transformation. Without C-level support, most digitization projects in purchasing fail.
2. Data quality Even the best AI system is useless with poor data. "Garbage in, garbage out" applies especially to machine learning algorithms.
3. User adoption Even the best technology is useless if your employees don't use it. Invest heavily in training, support, and change management.
The transformation to intelligent ABC analysis may seem complex, but it starts with simple steps: honestly analyzing your current blind spots and gradually integrating more modern evaluation logic.
Companies like Mercanis accompany such transformations on a daily basis—but the most important success factor is not technology, but the willingness to question familiar ways of thinking.
The question is not whether you have perfect systems, but whether you are willing to become better than yesterday.
ABC analysis is a method for classifying materials, suppliers, or products according to their value share of the total volume. Typically, A parts are classified as particularly valuable and C parts as rather insignificant.
Traditional ABC analyses are based on rigid categories (e.g., 70/20/10). They do not take into account market volatility or risks such as supply chain disruptions or sustainability factors. This results in an incomplete picture.
AI-supported systems dynamically reclassify and continuously adapt categories to market and risk data. In addition, they can use predictive analytics to forecast which C items could become critical for production tomorrow.
Fabian Heinrich is the CEO and co-founder of Mercanis. Previously he co-founded and grew the procurement company Scoutbee to become a global market leader in scouting with offices in Europe and the USA and serving clients like Siemens, Audi, Unilever. With a Bachelor's degree and a Master's in Accounting and Finance from the University of St. Gallen, his career spans roles at Deloitte and Rocket Internet SE.
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