Future-proof innovation
Smart assistance in the intralogistics control center
With intelligent modules for advanced analytics, better predictions and optimized recommendations for control and scheduling, TUP.AI helps you master the complexity in your warehouse while relieving your employees. Intralogistics AI provides suggestions and recommendations and also empowers further automation steps.
Central aspects and advantages
New possibilities for your warehouse
Artificial intelligence in the industrial environment has enormous potential.
Two aspects stand out in particular, which can be named as central arguments:
- Draw conclusions from the previously impenetrable noise of information
- Plan for the future based on small quantitatively qualified views
As a data-rich, complex environment, intralogistics is predestined for use that quickly brings enormous economic benefits.
Smart Warehouse
Predictive applications - Gallery
Automatisierte Vorhersage von Arbeitslasten und benötigten Kapazitäten.
Intelligente Steuerung der Lagerprozesse von Wareneingang zu Warenausgang.
Optimierung der Artikelpositionierung, um Laufwege verkürzen.
Optimierung des Packprozesses, um Packmittel und Transportkosten zu verringern.
Concrete added value
Smart, data-driven intralogistics
TUP.AI identifies optimization potential along pragmatic guard rails.
- Which parameters were responsible for a separate day going particularly well?
- How much pickload is waiting in storage area y in the next x days?
- How much of it can be covered internally, how much can be served by variable external capacities?
TUP.AI answers questions like these and is a practical, efficient and transparent tool.
Optimize beyond human capabilities
Pattern recognition penetrates complexity
A vast amount of data is generated in the course of warehouse management. Artificial intelligence can analyze this data in unimagined depth. The diverse application opportunities in intralogistics are combined with the competencies of AI. Insights from previously non-transparent information patterns make it possible – and processes and operations more efficient.
Central to the use of AI is knowing the strengths of such solutions and using them accordingly. Contrary to frequent statements, the concept of AI should not be regarded as absolute. For example, solvers are very likely to be the simpler and more efficient solution for a batch formation. On the other hand, as soon as the system is unpredictable and complex, AI excels. Machine pattern recognition helps to gain insight.
The technology is a powerful, but above all complementary, component in the services of optimization: It allows untapped potential to be harnessed.
Artificial intelligence in the intralogistics world of TUP
TUP.AI is the umbrella term for numerous AI-driven warehouse management solutions. Beyond the applications below, the portfolio will be further expanded.
Knowing loads and requirements in advance
In order to control procurement, production and capacities, a continuous and reliable forecast of expected quantities and workloads is essential. It is often helpful to group similarly reacting groups together. This can be done at different levels down to the item level.
Optimizing picking waves and lead times
To improve delivery reliability, avoid over- and underloading and thus prevent stress among employees, optimized release and scheduling of deliveries to be picked contributes. Capacities, transport time windows, service level agreements and forecast lead times are taken into account as hard and soft factors.
Predictive capacity and deployment management
In addition to long-term capacity planning, TUP.AI also automates short-term operational management of staff deployment in warehouse zones. Combined with load forecasting and lead time optimization, the solution learns from human planners (imitation) or makes situation-based suggestions (optimization).
Perfect packaging and reduction of packaging material
The number of items, the required filling material and, in the best case, the transport costs can be reduced by cleverly distributing the picked items among the packing materials.
Optimization takes into account delivery units, dimensions and weights, and item lists, as well as sequence and orientation, can be specified.
Optimization of the use of space and routes
Optimizing the use of space and routes is a complex task, taking into account factors such as topology, picking strategy, and anticipation of frequent item combinations as assortments and demands change dynamically. This process is supported by pattern recognition and intelligent clustering.
Optimization of stock levels and recommendation of products
Recognizing and using typical patterns in the ordering behavior and structure of stores and customers can not only help to optimize inventories in advance. By recommending items that are frequently ordered in conjunction with other items, sales can be increased and repeat orders due to forgotten items can be avoided.
TUP.AI
Economic advantage through intelligent warehouse solutions
Rather than a mere necessity, intralogistics is rightly seen as an important competitive factor. From shipping time to overall profitability, efficiency makes a big difference. AI brings additional improvements. Well-known customers, such as BOSCH, already use TUP.AI and benefit from insights, optimizations and savings.
Application as optimization tool
The spectrum of AI in industry
Machine learning processes follow a methodical approach whose steps can also be viewed as individual use cases:
- Anomaly detection
- Localization
- Prognosis
- Diagnosis
- Optimization
Industrial artificial intelligence
Our Machine Learning expertise
TUP.AI is a joint development with strategic partners, the companies use the synergy effects from structured data acquisition and two-sided process focus to allow users to analyze or forecast more precisely.
TUP partners with various actors to facilitate the idea of creating ‘smart warehouses’ with its know-how on industrial AI. TUP.AI is based on field-proven AI components and gains further effectiveness by bundling the domain expertise of both intralogistics and machine learning.
Ihr Ansprechpartner
Sie haben eine Projektanfrage oder wollen individuelle Beratung zu TUP.AI?
Unser Vertriebsleiter Swen Weidenhammer ist Spezialist für die Verbindung von Automatisierungslösungen mit moderner Informationstechnologie.
