Wertschöpfung aus Maschinendaten 4.0 - Die Praxisanleitung für den Mittelstand

VDMA 2021
Download: Guide (PDF) and analysis tool (XLSX)
70 pages / 11 worksheets

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Guide
Creating value from machine data 4.0
The practical guide for SMEs
Application scenarios - analysis tool - fields of action

Summary:
The aim is to strengthen the value chain Machine operators, machine manufacturers and automation suppliers: The successful exploitation of machine data can only be achieved together. For everyone involved, the benefits must outweigh the costs, and the risk must be known and acceptable. This sounds trivial, but is often difficult to achieve in practice. The guide provides instructions and tools for tackling such projects together with partners: Eleven catalogs as a source of ideas and reference works as well as an analysis tool as an evaluation aid support the selection of the appropriate application scenario, the assessment of cost-benefit risk and the processing of the fields of action.


Runtime data from machines and systems is valuable. Valuable for the operator, valuable for the machine manufacturer and valuable for their suppliers. These groups are involved in the development and operation of the machines and systems and together support the cost-effective design and efficient operation of the machine. These parties also work together when it comes to improvements, maintenance or troubleshooting. Machine data is the most important source of information here.

Extracting data from the depths of machines and refining it into useful information is an important process of modern value creation. The IT sector in particular has discovered a lucrative market for itself here. Large tech companies are trying to occupy this market with strong expertise in data refinement and information processing.

Mechanical engineering currently has a knowledge and expertise advantage in its core business. However, there is a growing danger that mechanical engineering, which is dominated by SMEs, will become an interchangeable hardware manufacturer with high cost pressure and that profits will be made by platform and IT companies. To counter this danger, it is necessary to develop new skills in the use of machine data and to convert the current advantage into a lasting, profitable advantage.

Awareness of the opportunities offered by the exploitation of machine data is increasing. But there are also increasing questions about the risks that arise from sharing machine data.

This guide is intended to create the basis for self-determined, transparent and partnership-based value creation from machine data. By making it possible to jointly define goals and evaluate use cases, the guide strengthens trust-based cooperation between manufacturers and operators and provides specific recommendations for action.

The guide shows a way for potential business partners to jointly overcome obstacles in the run-up to a project and achieve fair value creation from machine data for all parties involved.


Contents:
Machine data is a promising source of modern value creation
Management Summary
Introduction

1 Application scenario
1.1 Selecting an application scenario
1.2 Specifying the project
Catalogue [1]: Application scenarios

2 Assessment of benefit, effort, risk
2.0 Determinations
2.1 Assessment 1: Machine data (availability & sensitivity)
2.2 Assessment 2: Together with the partner (building trust)
2.3 Assessment 3: Detailed evaluation (effort / benefit / risk)
2.4 Decision-making
Catalog [2]: WaM-ANALYSETOOL
Catalog [3]: Machine data
Catalog [4]: Benefits
Catalog [5]: Efforts
Catalog [6]: Hazards
Catalog [7]: Departments & Processes
Catalog [8]: Risk mitigation measures

3 Fields of action
Catalog [9]: Fields of action
Catalog [10]: Guidelines and publications on implementation

Practice & outlook
Catalog [11]: Project checklist