What are the emerging trends and innovations in PDM that you should watch out for?

From our experience: - We use AI models to automatically scan and clean product data, removing duplicates and inconsistencies. For example, an AI model is able to automatically categorize products into the correct categories and subcategories based on their characteristics and descriptions. For data enrichment and validation we use AI to enhance existing product data by adding missing information or verifying the accuracy of details. An example is product description generation based on product attributes, in demanded style and language, Another example would be data types recognition, correction or normalization done automatically by AI.

The PDM landscape is rapidly evolving. Here are key trends to watch: AI and ML Integration: Intelligent systems are enhancing data analysis, prediction, and automation, optimizing product development cycles. Cloud-Based: Flexible, scalable, and cost-effective cloud solutions are gaining traction, improving accessibility and collaboration Digital Twin Technology: Creating virtual replicas of products for simulation and analysis, enabling better decision-making and innovation. IoT Integration: Connecting physical products with digital systems for real-time data collection and analysis, optimizing product performance. AR/VR Applications: Immersive experiences for product design, review, and maintenance, enhancing collaboration and efficiency.

AI-powered Product Data Management (PDM) revolutionizes the way we handle product-related data. By leveraging machine learning algorithms, AI-PDM enhances data accuracy, automates repetitive tasks, and provides predictive insights for better decision-making. It optimizes product lifecycle management by detecting patterns and anomalies, enabling proactive adjustments. Moreover, AI-PDM integrates seamlessly with existing systems, ensuring a unified data ecosystem. My experience shows that implementing AI-PDM leads to significant efficiency gains, improved product quality, and accelerated time-to-market, offering a strategic advantage in competitive markets.

KI im Produktdatenmanagement ist zweifellos ein spannender Trend. Die Technologie kann viele Prozesse effizienter machen, etwa bei der Datenbereinigung und Analyse. Aber man sollte nicht blind auf die Automatisierung setzen. KI-Systeme sind nur so gut wie die Daten, mit denen sie gefüttert werden. Schlechte Daten bedeuten schlechte Ergebnisse – das darf man nicht vergessen.

We often advise clients to focus on the bottleneck where automation (with AI) can have the most leverage: - During onboarding: mapping/classifying, cleaning and transforming product data to your own product data model - During enrichment: to automatically generate product descriptions or even fill attributes on any information you have availale (product number, existing descriptions or even just an image) - During publicaiton: to map your product data to the output channels (internally and externally)

I personally have used a range of Backend as a service (Baas) for a range of products that I have worked with. I don't regret it at all. In my opinion, the flexibility and scalability that cloud-based PDM can offer is noteworthy. Also, the security for most part would be much superior unless you have the financial bandwidth to afford the costs of having your own infrastructure. If PDM is important for your organization that leveraging these solutions can be great for you.

As an authorized implementation partner of OpenBOM, our cloud-based PDM solutions offer significant benefits to SMEs. They provide cost-effective, scalable, and accessible tools for managing product data, streamlining collaboration, and improving efficiency. SMEs benefit from total cost of ownership. This enhances agility and competitiveness, allowing SMEs to focus on innovation and growth while ensuring data security and compliance through cloud infrastructure.

I haven't come across an industry where consistency of data wasn't sought after. Most organization that were unable to adapt to the newer PDM standards have suffered much friction in their data-related pursuits like analytics and maintaining efficient infrastructure.

When you have data coming in from a variety of sources, you obviously want to be able to integrate them in one unified place at some point. This is why PDM practices, with a focus on data integration capabilities, become so crucial.

Die Tatsache, dass viele Geschäftspartner abseits von Standards wie beispielsweise BMEcat ihre Produktdaten austauschen, hat schon immer zu Herausforderungen geführt. Es ist zwar immer möglich strukturierte Daten aus beliebigen Datenformaten zu extrahieren, schnell und flexibel ist man hierbei aber dennoch nicht. Auch APIs bringen ihre Herausforderungen mit sich, da viele Systeme andere Ausprägungen und Arbeitsweisen (bspw. in der Variantenbildung) haben. Eine effektive Datenintegration - Syndikation - ist hier entscheidend um alles unter einen Hut zu bekommen (optimiert!).