Hardly any other area of a company has such a significant impact on the bottom line as strategically aligned purchasing. To meet these expectations, meaningful key figures and action-oriented information are essential. In practice, however, there are enormous shortcomings. There are many data pools, little structured information, and certainly nothing that can be used at the touch of a button, and it is rarely geared toward purchasing activities and potential suggestions.
Statistical Federal Office and purchasing key figures: The same intention
The Statistical Federal Office reports and comments on the inflation rate on a monthly basis and publishes price indices for goods and services. Why? Because these figures influence business expectations and decisions. The analogy to business-oriented purchasing is easy to see: Purchasing is largely responsible for the costs of purchased services and intermediate products. Between 40% and 65% of all company costs are payments to third parties and are therefore relevant to purchasing. But which purchasing department can provide information about the price development of the invoiced purchasing portfolio in the income statement, both as year-to-date values and for the future? In our projects, our customers are always surprised at the boost in options for action that an integrated information platform such as our WebCIS 4.0 provides for purchasing. Information at the touch of a button for almost every conceivable question – transparent, traceable, and tailored to all levels of action. Too often, purchasers are given opaque reports that merely elicit a shrug of the shoulders. Professional information comes in the form of dashboards that are geared toward purchasing scenarios (strategy, negotiation, management) so that, for example, a lead buyer can handle any situation in both internal and external communications.
Three levels of information for modern purchasing
A suite such as WebCIS 4.0 provides purchasing with three levels of information: perfect data structuring as a basis, data analysis as mathematical and statistical support, and AI models for chatbot control and pattern recognition.
Data structuring at the touch of a button, the basis for every purchase
Linked to the ERP world, WebCIS 4.0 provides all important information: Negotiation results with and without P&L reference – Budget deviations in project business – Cost avoidance – Currency and cost driver analyses – Supplier transparency and risks – Contract and catalog usage – Process monitoring – Product group structures and benchmarks – Scenario planning and measure tracking – Maverick buying – Payment behavior – Inventory analyses including demand forecasts and much more. Best-of-breed dashboards and favorites collect, bundle, and structure detailed data into informative information boards with values, graphics, push news, and interactive buttons.
Data analysis using commercial and mathematical models
As much as excellent data structuring increases market transparency in purchasing, mathematical analysis provides further support for purchasing decisions and arguments. Cost breakdowns, simulation calculations, benchmarks, and regression analyses are mathematical and statistical methods that are also helpful in purchasing. Why are these tools used so little? Because the effort involved is enormous. Data downloads and a lack of integration into the big data world quickly diminish the appeal of these methods. The models must be part of a fully integrated information suite such as WebCIS 4.0.
Identifying and assessing market potential often requires mathematical principles. Are suppliers’ price demands and cost arguments realistic? How can changes in purchasing volume be linked to bonus agreements? How can tiered pricing/quantities be handled in a modern way?
Large language models (LLM), digital assistants, and AI-based patterns as sources of ideas
It is already clear that AI models tailored to purchasing will provide helpful services in the future. Both finding exactly the right information based on prompts from our LLM “Curator” and intelligent suggestions for additional questions, favorites, and dashboards are a great support for purchasers in WebCIS 4.0. The topic of information gathering and searching for potential improvements is made much easier and accompanied by the integrated digital assistant. Our experience has taught us to take a critical approach to AI suggestions. AI-based restructuring of more homogeneous product groups, similarity codes for identical parts analysis, and automatic suggestions for suitable price indices for parts and services are popular AI topics. Many things can be done faster, but good preparatory work on data quality, critical examination of the samples provided, and corrections by human beings remain essential.
