Syntegon: Developing scalable machines with Cquenz
No product without modularity: Matthias Börner develops process machines at Syntegon, where
Product portfolio management: Strategically managing and successfully positioning products
Objectives
Designing data-driven product portfolio management
Effective product portfolio management does not begin with assumptions, but with reliable data. The goal is to
Odego supports companies in analyzing their product portfolio based on data, optimizing it in a targeted manner, and thus combining market success with efficiency.
- tailored the product portfolio to market needs, strategically aligned the focus of product
- development projects avoided redundancy, thereby
- reducing complexity, costs, and inefficiencies.
Odego supports companies in analyzing their product portfolio based on data, optimizing it in a targeted manner, and thus combining market success with efficiency.
Definition
What is a product portfolio?
DEFINITION OF COMPACT PRODUCT PORTFOLIO:
The product portfolio comprises all of a company's products, variants, and offerings.
A product portfolio refers to all of a company’s products, variants, and offerings, including both existing and planned products. It forms the basis for decisions on product range structure, variant diversity, and strategic market orientation. Efficient product portfolio management uses data to effectively shape diversity in the market and within the company. The importance of product portfolio management
lies in the active shaping of market presence: a good portfolio reflects a company’s interaction with the market—it connects customer needs with strategic goals.
lies in the active shaping of market presence: a good portfolio reflects a company’s interaction with the market—it connects customer needs with strategic goals.
Our USP
Analysis based on real order data
A unique feature of the Odego approach is its use of historical order data. Every analysis is based on real sales information – down to individual orders. This creates a very detailed picture of the product portfolios and their use. The big advantage is that the method works independently of the data quality of the systems or their networking. Even in fragmented IT landscapes with incomplete master data, reliable results can be achieved.
Procedure
Data-driven product portfolio management – here's how it works
The first step toward sound product portfolio management is exploring the data landscape. Almost every company has relevant data—spread across CRM, CPQ, ERP, and PLM systems, usually in very different formats. The challenge is to consolidate this data, understand it, and make it systematically usable. Odego helps companies identify data sources and link information from different systems. Data from Excel lists, system extracts, or free text information can also be included. Depending on the complexity, generative AI tools are used to make unstructured data usable. The goal is to create a consolidated data basis for analyzing the product portfolio without placing high demands on the source data.
A key advantage of the Odego approach is that data quality is not an exclusion criterion. Instead, it works with what is available. Relevant information from ERP exports, technical data sheets, or CRM systems is extracted and converted into an analyzable format. Intelligent plausibility checks are used to make even incomplete or incorrect entries editable. GenAI can help to automatically classify technical descriptions, equipment variants, or customer-specific requirements. The structured preparation of data is a prerequisite for analyzing the product portfolio in its entirety and identifying real scope for action.
The actual analysis phase begins with the prepared data set. The aim is to reveal patterns, redundancies, and gaps in the product portfolio. Odego uses a variety of methods to do this, including:
- Shopping cart analyses: Which product combinations are sold together?
- Lost order analysis: Which product variants lead to lost sales?
- Market segment analyses: What are the requirements in specific segments and how well does the portfolio cover them?
- Feature links: Which features occur together, which combinations are contradictory or rare?
This allows dependencies, duplications, or untapped market potential to be identified that remain hidden in day-to-day business. Seasonal trends or regional differences in demand can also be taken into account.
The analysis not only provides insights, but also forms the basis for concrete decisions in product portfolio management.
Key questions here are:
- Which market segments are strategically relevant and how well are they being served?
- Which variants are really necessary for market success?
- Where do unnecessary complexities arise from similar but poorly differentiated product options?
On this basis, companies can, for example, realign their modular focus, consolidate their product range, or specifically tap into new market opportunities. The goal is a balanced product portfolio that both addresses customer needs and can be managed efficiently internally.
Trend
IoT data as an additional source of analysis
A growing area in product portfolio management is the evaluation of IoT data. Connected products provide valuable information about actual usage, operating conditions, and customer preferences. This data can be used to tailor the product portfolio even more precisely to actual requirements—for example, by identifying rarely used features or developing new variants based on live data.
Industry Cases
Case studies: Targeting product portfoliostfolios gezielt ausrichten
In an analysis in the food processing sector, systematic evaluation of order data identified overlaps and gaps between machine sizes. A small adjustment to the size range in the product range meant that existing customer segments could be better served. Additional sales of €2 million were achieved in the first half of the year after implementation. This product portfolio example shows how data-based product portfolio management can achieve concrete economic effects..
In a project with a manufacturer in the field of intralogistics, IoT data from networked vehicles was evaluated. The analysis showed which functions were actually used under real conditions – and which options were of little relevance in practice. On this basis, the product could be specifically tailored to customer needs and new accents set in the competitive environment. This is clear evidence of the potential of IoT-supported portfolio optimization.
Read more industry cases
Baader: Modular product system in mechanical engineering – focus instead of variety
The more volatile the market, the more important the focus. Product modules
Eppendorf: Developing products faster
In order to serve different application areas in medical technology more efficiently,
WAS ambulances: From manufacturing to configuration
From manufacturing to configuration – is that really possible? How Wietmarscher Ambulanz-
Conclusion
Data diversity for strategic management
Well-thought-out product portfolio management combines market orientation with internal efficiency. It allows products to be tailored specifically to customer needs, reducing complexity while identifying potential for innovation. The basis for this is structured data analysis—ideally based on real order data. Odego supports companies in pursuing this path even without perfect data quality or uniform system landscapes. This turns diversity into a strategic advantage.
FAQ
A product portfolio encompasses the entirety of all products, variants and offerings of a company, which together form the market presence and the range of offerings.
Product portfolio management refers to the systematic planning, control, and optimization of the product portfolio in order to efficiently meet market requirements and support corporate goals.
A good product portfolio ensures that the offering is optimally aligned with customer needs, market potentials are utilized and at the same time complexity and costs are reduced.
The analysis is carried out through evaluation of data from various systems, pattern recognition in product combinations, market segments and customer needs as well as identification of redundancies and gaps.
Examples range from mechanical engineering to consumer goods to IT solutions, where companies strategically structure their products according to customer segments, variants or functional areas.