Use resources more efficiently - develop products faster
Goal
Use resources more efficiently, reduce waste and energy consumption while accelerating product development. The goal is sustainable, data-driven production that enables individualized products in a scalable manner and reconciles economic and ecological requirements.
Solution
With our modular digital platform, we are implementing an AI-supported solution for production planning and product development. Production, machine, material and quality data are brought together centrally and evaluated using intelligent optimization models.
AI supports both operational planning - from shift and capacity planning to material scheduling - as well as product development through generative design, simulations and feasibility tests for individualized variants. Key sustainability figures such as energy consumption and CO₂ footprint are taken into account transparently and actively optimized. The result is a holistic solution that increases efficiency, makes sustainability measurable and significantly accelerates innovation processes.
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Initial situation
High use of resources meets increasing requirements
A medium-sized manufacturing company with multi-variant production is faced with high material, time and personnel costs. Rejects, inefficient planning and increasing demands for sustainability and individualization increase the economic and organizational pressure.
The aim is to achieve resource-efficient production with optimized capacity planning, less waste and significantly faster, data-driven product development - without compromising on quality or flexibility.
Database: the basis for intelligent optimization
All relevant data is brought together in a central platform:
- Production and machine telemetry
- Material consumption, set-up times and quality data
- Customer requirements
- CAD and PLM data
This holistic database forms the foundation for AI-supported planning and development.
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Solution concept
Real-time optimization, sustainability and generative design
Production planning rethought
An AI scheduler continuously optimizes production plans:
- Minimization of set-up times, energy peaks and material waste
- Adaptive shift planning taking availability and capacity utilization into account
- Better synchronization of material, machines and personnel
Several AI models interlock: demand and batch size predictions to avoid overproduction, optimization models for material cutting and production sequence as well as workforce allocation with skills matching and load balancing.
Sustainability as an integrated decision-making factor
CO₂ and energy footprint per order are recorded transparently and actively managed. Costs, throughput times and environmental impact are optimized together. Sustainability aspects are already incorporated into product development - in the selection of materials, recyclability and product life cycle.
AI in product development: from the idea to the variant
With the help of generative design, components can be specifically optimized in terms of weight and material usage. AI-based surrogate models accelerate complex simulations and enable faster iterations. Intelligent variant configurators automatically check individual customer requirements for technical feasibility.
Integration & feedback loop
- Connection to ERP/MES for automatic order release and repeat orders
- Feedback of quality and customer data into planning and design models
- Continuous improvement through learning systems
Pilot phase & results
Focused entry with measurable benefits
POC (60-90 days)
- Scope: 2 product lines, 1 production line, approx. 1,000 orders p.a.
- Deliverables: AI scheduler MVP, material optimizer for one product, generative design PoC, KPI dashboard (material, scrap, energy)
Success criteria:
- ≥10 % reduction in material waste
- ≥15 % improvement in throughput time
Expected results after 6-12 months
- -15-30 % material consumption
- -20 % scrap rate
- -25 % throughput time
- +12 % machine utilization
- -10-20 % energy consumption / CO₂ footprint per unit
- -30-50 % time-to-market for new product variants
Risks & Digital Platform
Launch safely - prodot Digital Platform
Risks & safeguarding
- Data quality: data governance and sensor baseline ensure reliable models right from the start
- Production interruptions: Gradual rollout and simulation before live operation minimize operational risks
- Acceptance: training, transparent decisions and people-in-the-loop increase trust and usage in the team
With our modular construction kit for digital platforms, we offer standardized and proven modules for specific requirements of industries and specialist departments, which can be combined and adapted to individual needs with little effort. You benefit from the development expertise and experience that we incorporate into our standard modules.
We would be happy to discuss your requirements with you in a non-binding online appointment.
Find out more now!
