Solutions for industry and manufacturers
Every maintenance visit becomes a sales opportunity.
The knowledge about your installed base and your customers sits in ERP, CRM, service tickets and machine data. Agents bring it together and turn it into forecasts, reorders and service that your team handles manually today.
The starting point in industry
Industrial products run at the customer’s site for years. Service knows what happens there, sales does the planning, the ERP does the booking. The lever usually lies at the handovers.
Order history in the ERP, pipeline in the CRM, capacities in the MES: each source knows one part. Sales planning takes shape in spreadsheets and alignment meetings.
Solution: One data foundation for planning
We bring ERP, CRM, service and, where available, sensor data together on one data platform. Forecasts per item and region run on it, with early warnings of bottlenecks.
The technician replaces a component and documents it in the service tool. That is where the information ends. Sales and the customer portal only learn about it when the customer calls.
Solution: Service data in commerce
We close the data flow from the service ticket to the customer portal. Every maintenance visit triggers a matching reorder recommendation, in the portal, in the CRM or for field sales.
Group, plant and technical buyer each follow their own logic in ERP, CRM and online shop. Every analysis starts with the question of who is actually ordering.
Solution: Master data first
We clarify the hierarchies before the first model is trained. The technology is then in place within weeks, and the results stand up to scrutiny by sales.
Manufacturers selling directly to end customers change product management, logistics and customer service at the same time. Growth in your own channel needs clear priorities and clear ownership.
Solution: A clear course for direct business
We prioritize the roadmap, assign responsibilities and build the platform on which your team runs direct business itself.
When service, sales and planning work on the same data, the spare parts business becomes predictable.
Value levers in industry
Every agent is assigned to a value lever. For manufacturers, the strongest lie in after-sales, planning and inside sales.
An after-sales score combines order history, service tickets and installed-base data into the probability that a customer needs a part. The recommendation reaches the customer in the portal or your field sales team as a targeted reason to get in touch.
Result: higher spare parts margin, targeted service contacts.
Forecasting agents calculate demand per item and region on a rolling twelve-month basis and flag bottlenecks week by week, before they delay deliveries.
Result: lower safety stock, fewer bottlenecks in peak season.
Agents capture orders, answer parts and status inquiries and prepare service assignments. Your team focuses on projects, exceptions and customers.
Result: lower cost per transaction and more time for projects and customers.
Autonomy at the machine.
In the foobar Labs project ViscoCircle, an AI model readjusts the injection molding process on its own when plastic batches vary in viscosity, with people supervising. Funded by the German federal ZIM program, with BKW Kunststoff and Aalen University. What emerges there flows into your projects.
References
Manufacturers start at different points: with the data platform, the customer portal or their own direct business. Our references show how they proceed step by step.
Frequently asked questions
With a clean SAP source, a first productive forecast is typically ready after eight to twelve weeks, provided the master data hierarchies are clarified early. In one industrial project, the first after-sales score was running after eight weeks; after four months, the recommendations reached the customer portal.
Both use the same data foundation and answer different questions. Predictive maintenance predicts the failure of a specific machine. After-sales prediction predicts which customers will need which parts and services and when, which places it in sales and commerce.
The methods are largely the same. The difference lies in the data: customer hierarchies from group to plant to buyer, contract terms, equipment life cycles, service histories and technical master data. We model these characteristics from the start.
SAP remains the leading system. We build on the interfaces and data paths your SAP team or SAP partner provides, such as OData services, existing extracts or SAP Datasphere, and take over the data platform, models and agents from there. We decide on the right path together with your SAP team, based on system load and real-time requirements.
Within your company. Agents work with clear permissions, logging and approvals, and you define where a person decides. Every step is traceable.
The Impact Check, at a fixed price: afterwards, you know your maturity level, the hurdles between service, sales and planning and the blueprint for your first agent.
We look forward to your enquiry.
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