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.

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.