But this question is often difficult to answer. Looking only at historical purchasing prices is not enough. Procurement needs external reference points: How has the market developed? Which cost components are driving the price change? And is the supplier’s price increase actually justified?

This is where WebCIS AI comes in. The solution helps connect internal procurement data with external market and price information. This creates a reliable basis for evaluating price developments more accurately and preparing negotiations on a stronger, data-based foundation.

Why Internal Data Alone Is Not Enough

Many companies have large amounts of procurement data: purchase orders, invoices, material numbers, supplier information and commodity groups. This data shows what has happened within the company.

What it often does not show is whether these developments are appropriate compared to the market.

For example, if a supplier requests a price increase of eight percent, procurement immediately needs to assess whether this request is plausible. Have raw material, energy, transport or labour costs actually increased accordingly? Or is the supplier’s request above market development?

Without external comparison data, this assessment remains difficult. With suitable benchmarks, procurement can evaluate much more effectively whether a price development is understandable and market-oriented.

The Role of NACE and Destatis

To compare procurement data meaningfully with external market information, materials, suppliers and commodity groups need to be classified correctly. Classifications such as NACE and product-related data from the German Federal Statistical Office, Destatis, can support this process.

The NACE code classifies companies and industries according to their economic activities. For procurement, this can be useful for assigning suppliers to industries, structuring market information more clearly and enabling international comparisons.

Destatis data is more focused on products and commodity groups. It can help link materials or product groups with suitable price indices and cost developments.

In short: NACE supports the classification of industries and suppliers. Destatis is particularly useful when evaluating product groups, materials and price developments.

How WebCIS AI Turns This Into Concrete Procurement Analyses

WebCIS AI connects these external reference points with existing procurement data from ERP systems. The assignment can take place on different levels, for example by supplier, commodity group or material number.

The advantage is clear: procurement no longer has to look only at internal price histories, but can compare them with suitable market and price indices. This makes key questions much easier to answer:

Is a price increase understandable?
Are our purchasing prices developing faster than the market?
Which materials or suppliers show anomalies?
Where is there a concrete need for negotiation?

This turns a simple data overview into a solid decision-making basis for procurement.

More Confidence in Price Negotiations

One major benefit arises in the preparation of price negotiations. When suppliers request price adjustments, procurement can better assess whether these requests are in line with market developments. By linking purchasing data with market indices and cost components, price developments become more transparent and easier to evaluate. Energy, transport, labour or material costs can play an important role here.

For procurement, this means that negotiations are based less on gut feeling and more on reliable data. This strengthens the internal argumentation basis and creates greater transparency towards suppliers and internal decision-makers.

Transparency for Indirect Procurement and Maverick Buying

Intelligent classification is particularly helpful where procurement data is not perfectly structured. This applies, for example, to indirect materials or maverick buying processes. In these areas, clear material numbers or well-maintained commodity group structures are often missing.

WebCIS AI can help generate suitable classification suggestions and validate them together with procurement. This also makes areas analysable that were previously difficult to capture.

In configurable dashboards, price developments, benchmarks, suppliers, commodity groups and materials can then be analysed in a targeted way. Procurement can identify more quickly where anomalies exist, which price developments are explainable and where action is required.

Conclusion: Bringing More Market Context Into Procurement Controlling

WebCIS AI helps procurement departments connect existing procurement data with external market information. This makes it possible to see whether price developments are understandable, how internal purchasing prices compare to the market and where concrete action is needed.

Especially in volatile markets, this is becoming increasingly important. Companies that do not only look at purchasing prices internally, but also connect them with market and price developments, can make more informed decisions and buy more in line with the market.

Der Beitrag Price Benchmarking in Procurement: How WebCIS AI Creates Market Transparency erschien zuerst auf SOFTCON CIS.

On May 19 and 20, 2026, SoftconCIS presented WebCIS 4.0 at the BME eSolution Days at Areal Böhler in Düsseldorf. The event once again brought together numerous solution providers, procurement professionals and experts to discuss current developments in digital procurement.

One topic was particularly present throughout the event: artificial intelligence in procurement. At many booths, in presentations and in conversations, it became clear that AI is becoming increasingly important for procurement organizations. At the same time, it also became apparent that while many companies are actively exploring the topic, practical implementation is still at an early stage.

A particular highlight was our expert presentation by Bastian Wagner on the topic “AI in Procurement: Doing Instead of Talking!”. The high number of participants confirmed that procurement departments are currently looking above all for practical answers:

• Where can AI provide concrete support in procurement?
• What data foundation is required?
• How can price developments, benchmarks and KPIs be evaluated more effectively?
• What role does procurement controlling play in data-based decision-making?
• How can real value be created from existing ERP data?

In the discussions at our booth, it became clear that many procurement departments already have access to large amounts of relevant data – for example from SAP and non-SAP systems, purchase orders, invoices, supplier master data or category structures. However, the challenge often lies in making this data quickly, structurally and decision-oriented usable.

This is exactly where WebCIS 4.0 comes in. As a specialized platform for strategic procurement controlling, WebCIS supports companies in transparently analyzing procurement data, preparing key performance indicators and making potential savings visible.

The event also showed how dynamic the market around AI in procurement has become. Many providers are now positioning themselves with AI functionalities, AI agents or automated workflows. For procurement decision-makers, this creates a new challenge: it is no longer just a question of whether AI is relevant in procurement, but which applications actually deliver measurable value.

Because AI alone does not solve procurement problems. What matters is whether it works on a clean data foundation, can be embedded into existing system landscapes and answers concrete procurement-related questions. For example: Where do price deviations occur? Which suppliers are developing critically? Which categories are affected by market changes? Where are potential savings hidden? And which developments will become relevant to future business results?

From our perspective, this connection will be decisive: AI must not be viewed separately from procurement, but as an intelligent extension of reliable procurement controlling. WebCIS 4.0 provides the foundation for this by bringing together procurement data from SAP and non-SAP systems, analyzing it and translating it into decision-relevant information. AI can build on this foundation, identify patterns faster, detect anomalies and support procurement teams in making more fact-based decisions.

The BME eSolution Days therefore confirmed an important impression: the future of procurement will not be determined by new technologies alone, but by the ability to integrate technology meaningfully into existing processes, data structures and decision-making paths. Companies that want to use AI successfully in procurement should therefore first strengthen their data foundation, KPI logic and transparency in procurement controlling.

We would like to thank all visitors for the insightful conversations, the open exchange and the great interest in WebCIS 4.0.

Der Beitrag AI in Procurement in Focus – SoftconCIS at the BME eSolution Days 2026 in Düsseldorf erschien zuerst auf SOFTCON CIS.

Der Beitrag ERP Integration in Purchasing Controlling: Optimally Connecting SAP and Other Systems erschien zuerst auf SOFTCON CIS.

Between Euphoria and Skepticism

Hardly any other topic polarizes strategic procurement as much as artificial intelligence. On one side are the enthusiasts who see AI as the solution to every challenge, from spend analysis to supplier development. On the other side are the skeptics who distrust AI results and rely on the tried-and-true combination of experience, market knowledge, and their own judgment.

Both camps are right, and both are wrong when they ignore the other side.

Because AI in strategic procurement is neither a panacea nor just hype. It is a powerful tool, but only in the right hands.

What AI can really do in procurement

AI’s strengths lie where humans reach their natural limits: in processing large amounts of data, recognizing patterns, and the speed of analysis.

Specifically, this means the following in strategic procurement:

Price index comparisons at the click of a button. Instead of manual research and time-consuming Excel models, AI can automatically compare market indices such as those from Eurostat or the Federal Statistical Office with your own product groups and immediately highlight discrepancies. What used to take days can now be done in minutes.

Pattern recognition in supplier and pricing structures. AI detects inconsistencies in price trends, illogical tiered pricing structures, or conspicuous volume fluctuations, across all material numbers and suppliers simultaneously. No human eye could sift through this volume of data in a reasonable amount of time.

Predictive analytics for better negotiations. Based on historical price and market data, AI can model price trends and provide the buyer with a fact-based framework for supplier negotiations.

Homogeneity indices for clean product groups. One of the biggest weaknesses of many procurement organizations is an inadequate product group classification system. AI-based homogeneity indices objectively show how consistent the classifications are and make suggestions for optimization.

Where AI Reaches Its Limits

As impressive as these possibilities are, AI cannot replace the strategic thinking of an experienced buyer. And for good reason.

AI works with patterns from the past. It cannot assess whether a supplier is a strategically indispensable partner despite poor metrics. It does not understand political market dynamics, personal negotiation relationships, or company-specific priorities.

AI provides suggestions, not decisions. It highlights potential that would otherwise remain invisible. But whether and how this potential is realized remains the task of humans.

This is not a weakness of AI. It is the natural division of labor between humans and machines, and that is precisely where the real added value lies.

The crucial prerequisite: the data foundation

AI is only as good as the data it works with. This is where many procurement organizations fall short not because of the technology, but because of data quality.

Missing master data, inconsistent product category systems, and data from various sources that isn’t harmonized, all of this makes AI analyses unreliable or simply impossible. Anyone who wants to use AI effectively first needs a clean, complete, and consistent data foundation.

A powerful procurement information system like WebCIS 4.0 creates exactly this prerequisite: consolidated data from various sources, uniform product group classifications, and interactive dashboards as a solid foundation for integrable AI models that deliver real added value.

Conclusion: AI as an Enhancer of Human Expertise

AI in strategic procurement is not just hype, but it’s also not a surefire success. Its true value unfolds where it does what it does best: analyzing data, recognizing patterns, and identifying potential faster and more comprehensively than any human could.

Strategic assessment, decision-making, and negotiation remain the responsibility of the experienced buyer. Not because AI is incapable of doing so, but because procurement remains, at its core, a human endeavor.

The question, then, is not whether AI creates value in strategic procurement. The question is whether your organization has what it takes to leverage that value.

Der Beitrag AI in Strategic Procurement – Hype or Real Value erschien zuerst auf SOFTCON CIS.

AI Creators vs. Skeptics

Two camps emerge when mathematical AI models are viewed as a supplement to reliable KPIs or as idea generators in strategic procurement: on one side, the advocates and innovators who immediately recognize AI models as progress in their work environment. On the other side, the skeptics who criticize the lack of perfection in AI-generated suggestions and reject AI results compared to their own numerical frameworks.

The fact is—without denying the imperfections of AI models—never before has it been possible to generate suggestions, ideas, and potential opportunities at such speed, enabling an expansion of established ways of thinking, based on facts rather than gut feeling.

An information system like WebCIS 4.0, which provides reporting, interactive dashboards, and decision support in procurement at the push of a button, is an excellent foundation for analyses using integrable AI models.

Market benchmarks from price indices, the detection of implausible price and quantity structures, cost driver analyses, and predictive analytics all stem from patterns that would be inconceivable without AI.

Index Comparison of Price Trends

If material cost changes are used as the key metric for measuring the impact of procurement actions on company performance, the natural question arises: how have material costs developed compared to market indices?

Through AI-supported mapping of indices—NACE, Eurostat, Federal Statistical Office—to product groups, not only can individual checks be performed, but all product groups can be compared at the push of a button, including breakdowns by suppliers and materials. This is essential for deeper analysis.

Homogeneity Index and Product Group Structure

Index comparisons often raise doubts about their validity due to potentially insufficient product group classifications. AI-based homogeneity indices help provide a more objective view of how well assigned materials match within groups.

As an extension of this idea, one could also imagine an alternative product group system designed solely to maximize homogeneity within groups. AI models embedded in WebCIS provide valuable suggestions and comparisons for this purpose.

AI-Based Use of Tiered Pricing

Quantity-dependent pricing conditions remain a common way to handle planning fluctuations. But according to which rules are price tiers and quantity breaks defined? This seemingly simple question rarely has a clear answer.

Two key questions arise when defining tiered pricing:

• Which materials are suitable for tiered pricing at all?
• Are the tiers structured in a way that fixed and variable costs result in a logical pricing model at each level?

AI models reveal inconsistencies and propose solutions at the push of a button.

The next step is to compare existing pricing tiers with better pricing alternatives. By weighing price advantages, inventory costs, and capital commitment, savings of 5–10% are realistic.

What Is Increasing Procurement Volume Worth?

What is commonly referred to as quantity or volume discounts also offers measurable savings potential in industrial procurement. Economies of scale, improved planning reliability, and freight optimization often accompany increased volumes.

However, in the reality of hard data and facts, even significant volume increases often show little measurable cost reduction—frequently justified by the lack of reliable data correlations.

With just one click in WebCIS, potential material numbers and suppliers can be identified with whom bonus agreements or tiered incentives might be negotiable. Combined with AI-based mapping of price indices, volume increases can be evaluated in the context of market index developments.

This creates valuable opportunities for argumentation and action in both direct and indirect procurement.

These few examples demonstrate how mathematical and statistical AI models are already becoming a distinct resource factor in strategic procurement—and will continue to evolve in sophistication in the future.

Der Beitrag WebCIS AI: The Perfect Data Booster for AI in Strategic Procurement erschien zuerst auf SOFTCON CIS.

Currently, the priority for purchasing is to secure supplies for the company’s own production and maintain its performance. Materials and services must be procured – at almost any price. Skyrocketing raw material and energy prices are rendering many existing supply contracts worthless. In order to continue offering its own services and products on the market, purchasing is currently forced to accept price reductions from suppliers beyond the scope of existing contracts.

What is necessary in the current situation will become a balancing act in the coming fiscal year. And the questions posed to purchasing by management are becoming more urgent:

How will purchase prices develop in 2022 and beyond?

Without scenario-based purchasing planning that can take many influencing factors into account, the answer becomes a guessing game. And even if the influences of raw materials and price components are reasonably clear, the question arises as to how volatile the negotiated terms are.

How large is the potential range of price fluctuations in the coming years?

Whatever the purchasing department’s response to such questions, without sound planning it will definitely be fraught with uncertainty. Serious predictions about the future development of material costs are never exact. Such statements can only be presented within a more or less wide range. The size of the range and the reliability and quality of the forecasts depend on the scenarios underlying the purchasing planning.

This is where the great strength of WebCIS 4.0 purchasing planning comes into play. Scenarios and simulations can be created and varied in a highly automated manner. Quantities, prices, and quotas for all planned services and materials can be flexibly “played with.” Entire groups of prices can be provided with underlying price simulations. New parts, material changes, graduated prices, exchange rate influences, supplier changes, and quota changes are incorporated into these scenarios and provide precise indications of the impact purchasing will have on future P&L.

The added value of WebCIS purchasing planning lies in the precision of the simulation scenarios. Each simulation is broken down into materials and suppliers at the end, even if the simulation values are entered at aggregated supplier, product group, or other levels. Credible and measurable planning only makes sense at the lowest level and not at aggregated levels.

Dashboards integrated into WebCIS 4.0 accompany the planning process and immediately highlight anomalies and outliers. Are price and/or quantity changes plausible? What opportunities and risks are associated with them?

Since planned scenarios can be compared with each other at all aggregation levels, this inevitably results in reliable and comprehensible statements about the range of fluctuation of future purchase prices.

Der Beitrag WebCIS 4.0 Purchasing Planning Module: Making the future calculable erschien zuerst auf SOFTCON CIS.

A perfectly structured information system like WebCIS 4.0—which enables reporting, benchmarking, and strategic purchasing decisions at the push of a button—is an excellent foundation for analyses performed by integrated AI models.

Market comparisons using price indices, detecting illogical price and quantity structures in tiered pricing, AI-generated suggestions based on learned patterns, and forecasts derived from learning processes are methods that would be unimaginable without AI.

From our experience, those interested in mathematical AI models fall into two groups: opportunity seekers who immediately perceive the results of AI models as added progress in their work environment, and skeptics who criticize insufficient perfection in the assignment of market indices and reject comparisons of AI results with their own numerical world. The fact is—without denying the errors of AI models—never before has it been possible to obtain suggestions, ideas, and potential improvements at such speed, providing an expanded perspective on existing thought patterns. Fact-based rather than gut-feeling.

Market Index Comparisons as Benchmarking at the Push of a Button

When evaluating the performance of successful purchasing activities, one key question arises: Have material cost developments per product category been better or worse compared to market indices? Through AI-assisted assignment of product groups to the appropriate indices—NACE, Eurostat, Federal Statistical Office—not only selective comparisons but all product groups can be classified at the push of a button, including drill-downs to suppliers and materials. This creates an impressive basis for deeper analyses.

Homogeneity Index as a Measure of Product Group Structuring

The index comparisons above naturally provoke doubts about the meaningfulness of existing product group assignments. AI-based homogeneity indices often show only low levels of consistency among the underlying comparison features within product groups. A compelling way to broaden one’s perspective would be to introduce an additional alternative system with the sole aim of achieving the highest possible homogeneity index within product groups. AI models provide valuable suggestions here.

Multiple Regression Analyses to Identify Cost Drivers

How do the characteristics of a purchased part influence its price? One option would be a time-consuming cost breakdown. But how many parts can be analyzed in a reasonable amount of time? And even then, internal discussions can be extensive depending on the outcome. A multiple regression analysis retrieves characteristics of materials—weight, size, material type, manufacturing processes, and other features influencing price. The regression equation shows which influencing factor contributes what share to price formation and where there is room for negotiation.

Tiered Pricing: Logical Design or Coincidence?

Lot-size-dependent prices remain a common method for handling planning fluctuations. But according to what rules are tier quantities and tier prices actually defined? Rarely is there a plausible explanation to this simple question. Two essential questions arise when defining tier prices: Which materials are even suitable for tiered pricing, and are the tiers set up such that fixed and variable costs enable logical price calculations for each tier? AI models reveal possible inconsistencies and solutions at the push of a button.

In the next step, actual tiers should be compared with potentially more economical tier prices. Considering the price advantages, and despite higher capital commitment, savings of 5–10% are realistic.

Does Increasing Purchasing Volume Influence Purchasing Results?

What is commonly referred to as quantity or volume discount also exists in industrial procurement. Fixed-cost degression, improved planning reliability, and freight optimization often accompany rising volumes. It is surprising how often our system uncovers volume increases with barely noticeable cost reductions or missing bonus agreements.

In WebCIS, a single click is enough to identify potential suppliers with whom bonus tiers may be feasible.

All these examples illustrate how mathematical and statistical models of artificial intelligence already represent an essential resource in the procurement of the future—and will continue to mature.

Werner Güntner (CEO)

Der Beitrag WebCIS AI: P&L-Relevant Potential Thanks to Artificial Intelligence in Strategic Procurement erschien zuerst auf SOFTCON CIS.

Both bring extensive leadership experience and in-depth knowledge of the organisation, having been with the company for many years — Martin Mayr since 1998 and Oliver Schmidt since 2007.

Their appointment to the executive management board represents a logical and significant step, recognising their long-standing responsibilities and continued commitment to the company’s development.

By expanding the executive management board, we are strengthening our leadership structure and establishing a future-oriented framework that combines experience, innovation and strategic perspective. Together with the company’s founder, they form a strong leadership team that unites continuity with new momentum.

Der Beitrag Strategic Strengthening of the Executive Management Board erschien zuerst auf SOFTCON CIS.