We weren't data people before, so we didn't learn professionally. KNIME really helped us get there, but already bring the domain knowledge with us from our past, which is really great. It's the best combination that you can have in the field of supply chain.
Antonina Polkovnikova, Manager of Analytics & Performance, Kärcher
Why KNIME for supply chain analytics
Sophisticated analytics, intuitive interface
Stay ahead of the changing, often volatile and unpredictable global supply chain.
Enable experts across warehousing, fleet operations, logistics, and more, to improve productivity by gaining insights self-sufficiently.
Allow data experts to perform complex analyses such as demand forecasting, predictive maintenance, and capacity planning. All with KNIME’s low-code/no-code interface.
A single environment for the entire data science lifecycle
Offer end-to-end support for the entire data science life cycle, allowing data experts to access, prepare, explore and analyze data, and productionize models across the enterprise. From fleet monitoring, to demand forecasting, to environmental sustainability.
Optimize the supply chain with faster insights. Create, deploy, monitor, and manage projects without coding, and with optional use of Python, R, and other scripting languages.
Future-proof with open source
Keep supply chain teams at the forefront of data science innovation with KNIME’s open source approach.
Access new technologies, methods, and strategies all regularly introduced by the active community of data science experts.
Access an inexhaustible source of innovative ideas to enhance supply chain planning and operations, even in a constantly changing global landscape.
Enterprise-scale data analytics platform
Scale as your data sources and systems grow. KNIME offers connectivity to over 300 data sources and easily integrates with existing platforms, regardless of the data type.
Ensure privacy and security, with centralized administration and auditability trails digitized and built into workflows
Allow supply chain teams to streamline data capture through automation.
Save time and effort - schedule tasks, automate processes, share and reuse models, and deploy them wherever you want, either on-premises or in the cloud.