Training course · Make · Manufacturing

Make for Manufacturing Teams

Automation with Make (formerly Integromat) and AI modules: build scenarios, routers and AI steps while keeping data protection in check.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Make for Manufacturing Teams” is designed for roles such as Production and plant managers, Shift leaders and supervisors and Manufacturing and process engineers and shows step by step how to use Make productively, safely and transparently in Manufacturing – from the fundamentals to advanced workflows.

Make, formerly known as Integromat, is a visual automation platform that lets you connect applications and automate recurring processes without writing code. Over two days you will learn to build scenarios systematically, from triggers and modules through routers and filters to iterators, aggregators and data stores. You will understand how data flows through a scenario, how to reshape it with functions and how to keep an eye on operations consumption.

The second focus is AI. You connect language models through Make’s AI modules and provider integrations to classify text, extract information from emails and documents, draft replies or enrich content. You learn to catch errors with error handlers, test scenarios and document them clearly. Finally, you address data protection topics such as hosting region, data processing agreements and data minimisation, so that your automations can be run safely within your organisation.

Manufacturers face cost pressure, skills shortages, complex product variants and high expectations for on-time delivery. Generative AI adds a new layer to existing Industry 4.0 solutions: AI assistants make knowledge from manuals, fault logs and work instructions accessible, support shift handovers, summarise KPIs from MES and ERP exports and help with root cause analysis using methods such as 5 Whys or Ishikawa. AI agents and automation create shift reports, analyse machine breakdowns and prepare maintenance orders. Experienced staff in particular benefit when their knowledge is documented and made available to new colleagues. Adoption succeeds when it starts with concrete shop-floor problems, involves works councils and health and safety early and keeps production data secure.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Production and plant managers
  • Shift leaders and supervisors
  • Manufacturing and process engineers
  • Maintenance teams
  • Lean and operational excellence leads

Typical challenges we address

  • Capturing and passing on the know-how of experienced staff
  • Analysing breakdowns quickly and reducing downtime
  • Presenting KPIs such as OEE from various systems in an understandable way
  • Keeping work instructions current and clear across many variants

What you will be able to do

  • You can build Make scenarios independently using triggers, routers, filters and data transformations.
  • You can process lists and records with iterators, aggregators and data stores.
  • You can integrate AI modules for classification, extraction and text generation into your processes.
  • You can safeguard scenarios with error handlers, validation and human approval.
  • Use AI for knowledge management, root cause analysis and shift reporting in production.
  • Analyse production KPIs with AI and derive improvement actions.
  • Take data security, co-determination and occupational safety into account in manufacturing AI projects.

Course outline

Day 1

Module 1 Fundamentals: scenarios, modules and data flow
  • Interface layout, scenario editor and execution history
  • Triggers, actions and searches: module types and their role in a scenario
  • Understanding bundles, mapping and data types and using them deliberately
  • Choosing between schedules, webhooks and instant triggers
Module 2 Logic: routers, filters, iterators and aggregators
  • Using routers and filters for branching and conditional flows
  • Splitting lists with iterators and combining them with aggregators
  • Applying text, date and maths functions to transform data
  • Using data stores and variables for interim values and duplicate checks
Module 3 AI building blocks in Make
  • Connecting language models via AI modules and provider integrations
  • Automating classification, structured data extraction and text generation
  • Building prompts from scenario variables and processing output as JSON
  • Combining AI agents in Make with tools and scenarios as actions
Module 4 Integrations, error handling and testing
  • Connecting common business applications and any API via the HTTP module
  • Applying error handlers such as Resume, Ignore, Break and Rollback
  • Validating AI output and routing unusable results to a person
  • Testing scenarios, optimising operations and documenting them for the team

Day 2

Module 5 Data protection, governance and operations
  • Assessing hosting region, data processing agreements and data flows to AI providers
  • Applying data minimisation and pseudonymisation within scenarios
  • Managing connections, teams and permissions across the organisation
  • Defining approval steps, monitoring and ownership for live operation
Module 6 Practice lab: Manufacturing
  • Shop-floor knowledge assistant
  • Root cause analysis
  • Shift reports and handovers
  • Work instructions
  • KPI analysis
  • Maintenance preparation
Module 7 Data protection, quality & transfer to daily work
  • Production data and engineering know-how are trade secrets and belong only in approved AI environments; in Germany, AI-based evaluation of employee performance or behaviour requires works council co-determination. AI components in safety-related machine functions fall under the EU Machinery Regulation and may be high-risk under the EU AI Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Shop-floor knowledge assistant

An AI assistant answers questions about machines, changeovers and fault patterns based on manuals, breakdown history and work instructions, always citing the source for verification.

Root cause analysis

AI structures breakdowns and quality issues using 5 Whys, Ishikawa or 8D, suggests possible causes from similar cases and helps document actions and responsibilities.

Shift reports and handovers

Notes, machine data and fault reports are turned into structured shift reports with output, breakdowns and open points, making handover to the next shift easier.

Work instructions

AI turns process descriptions, photos and expert interviews into illustrated, multilingual work instructions and checklists in plain language and keeps variants consistent.

KPI analysis

MES and ERP exports are analysed, OEE losses broken down by availability, performance and quality and anomalies explained clearly for shop-floor meetings.

Maintenance preparation

Automations collect fault reports; AI groups recurring failures, drafts maintenance orders and prioritises actions based on frequency and impact, before maintenance planning takes them over.

Prerequisites

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. You work in production, production planning or maintenance and know the processes at your plant.

Technical requirements

You will need a laptop with an up-to-date browser and a Make account; the free plan covers most exercises. API access for a language model is set up together during the course.

Data protection & compliance

Production data and engineering know-how are trade secrets and belong only in approved AI environments; in Germany, AI-based evaluation of employee performance or behaviour requires works council co-determination. AI components in safety-related machine functions fall under the EU Machinery Regulation and may be high-risk under the EU AI Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Salzburg Request
  • Wed, 16 December 2026 – Thu, 17 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Bremen Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Augsburg Request

Frequently asked questions

Is Make the same as Integromat?

Yes. Integromat was renamed Make and substantially developed along the way. The core principle of visual scenarios made of connected modules remains, while a redesigned interface, many new integrations and AI building blocks have been added. If you used Integromat in the past, you will quickly feel at home and discover the new capabilities on the course.

How do I keep operation costs under control?

Every module that runs consumes operations or credits, and AI calls also use quota with the respective model provider. On the course you learn to design scenarios so that filters apply early, unnecessary runs are avoided and data is processed in batches. This keeps your automations economical and predictable as volumes grow.

Can Make be used in a GDPR-compliant way?

In principle yes, provided you set up the framework deliberately. This includes choosing the hosting region, signing a data processing agreement, reviewing connected AI services and applying data minimisation. On the course you work through these points with a checklist and learn to filter or pseudonymise sensitive data within scenarios.

Do we need to connect AI to our machines?

Not necessarily. Many use cases, such as knowledge assistants, work instructions or root cause analysis, work with documents and MES or ERP exports. Connecting directly to machine data is a later step that requires IT and OT security. In the course we start with use cases you can implement quickly.

How do we gain acceptance from the workforce?

By making AI visibly ease work, for example through faster troubleshooting or simpler documentation, rather than being perceived as a monitoring tool. Involve employees and their representatives early, start with pilot areas and clearly define which data will not be used for performance evaluation.

How much does “Make for Manufacturing Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

More courses for Manufacturing Teams

More Make courses

All 45 Make courses

Make · MunichMake · SalzburgMake · BerlinMake · AugsburgMake · HanoverMake · BremenMake · StuttgartMake · KaiserslauternMake · Online