Results in Practice

Starting Situation

A mid-sized mechanical engineering supplier from the Reutlingen region with around 85 employees faced a typical but critical problem in sales. Quotations were created entirely manually.

• Excel-based calculations
• Copy-pasting from previous projects
• Individual adjustments without a clear structure
• Quotation time: 2 to 5 days
• High error rate in bills of materials and pricing

Objective

The focus was not on introducing a new system, but on improving an existing process: reducing quotation time, minimizing errors, and creating a scalable structure.

Methodology

1. Structuring the Quotation Logic

Existing ERP data was used systematically: standardization of variants and pricing, definition of recurring quotation structures, building a consistent data foundation.

2. Automating Quotation Creation

Automatic generation of quotations including calculation, defined text modules, consistent pricing logic. Optionally supplemented by AI-assisted email preparation for sending quotations.

3. Centralizing Data

Uniform pricing structure, clear variant logic, elimination of media breaks.

"We didn't introduce anything revolutionary — we just finally brought order to what we do every day anyway."

This case study is fictional, but based on real project experience and typical challenges faced by mid-sized industrial companies.

Starting Situation

An automotive supplier (Tier 2) from the Stuttgart area with around 120 employees was confronted with a growing problem in day-to-day customer communication.

• Cluttered email threads with OEMs
• Manual forwarding of requests within the company
• Delays in handling technical queries

"The request is somewhere in the inbox."

Objective

Transparency over all incoming requests, clear assignment to responsible parties, faster and more consistent responses.

Methodology

1. Structuring the Email Inbox

Automatic analysis and classification of incoming emails: detection of request types (technical, commercial, operational), prioritization by relevance and urgency.

2. Automatic Assignment

Sales → commercial topics · Engineering → technical queries · Purchasing → supplier-related topics. Manual forwarding as a bottleneck was eliminated.

3. Support for Responding

Suggested replies based on previous emails, consistent wording in communications with OEMs.

4. Transparency on Status

All open requests made centrally visible, prevention of duplicate handling, clear responsibilities.

"We don't have more emails — we finally have control over them."

This case study is fictional, but based on real project experience of automotive suppliers.

Starting Situation

A metal processing company in the Ulm area with around 60 employees faced a structural efficiency problem in order processing.

• Order data was manually transferred from emails into the ERP system
• Delivery notes and documentation were maintained in multiple places
• High time expenditure in production planning

The real problem was not individual major errors, but the cumulative effect of many small, manual steps.

Objective

Creating a stable, end-to-end process: reduction of manual data entries, minimization of transfer errors, increased process reliability.

Methodology

1. Extraction of Order Data

Automatic reading of information from emails and PDF documents, structured preparation of relevant data.

2. Integration into the ERP System

Automatic creation of orders in the ERP, elimination of manual intermediate steps, consistent data foundation for all downstream processes.

3. Automated Document Creation

Delivery notes, order confirmations, and other documents — all from a single, unified data source.

4. Standardization of Data Structure

Clear data logic, no duplicate maintenance, clean handoffs between departments.

"We didn't speed anything up — we just removed what was slowing us down."

This case study is fictional, but based on real project experience in the metal processing industry.

Starting Situation

A technical wholesaler from the Karlsruhe area with around 40 employees faced increasing pressure on its inside sales team due to a high volume of recurring customer inquiries (availability, pricing, data sheets).

• Long response times for standard requests
• Little capacity left for more complex customer needs
• The majority of daily work consisted of repetitive tasks

Objective

Clear relief for the inside sales team without compromising communication quality: automation of standard requests, faster response times, focus on value-adding activities.

Methodology

1. Introduction of a Digital Assistant

Automatic recognition of typical inquiry types, access to relevant product and inventory data, generation of appropriate responses in real time.

2. Integration of Existing Data Sources

Direct connection to product databases, inventory levels, and pricing structures — for reliable, consistent responses.

3. Clear Separation of Responsibilities

Standard requests → automated. Complex or individual requests → inside sales team. Control stays within the company.

"The simple questions are now answered by the system — the important ones by us."

This case study is fictional, but based on real project experience in technical wholesale.