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Digitalization > Business Analytics – Process Innovation

Business Analytics – Process Innovation

Learn From the Past and Leverage Existing Data to Strategically Optimize Current and Future Business Processes.

Huge amounts of data are generated during individual process steps. Our image processing systems are an example of this, where machines are able to see and form the basis of extensive inspection tasks. Process data also drive feedback in control loops and control systems in our automation technology portfolio.

Using modern big data processing methods, such as machine learning, data mining or statistics tools, enables us to enhance the value of data. What's more, it can all be expanded further by networking several systems, using metadata, internet sources or additional sensor technology.

Learning From the Past

With Business Analytics, the questions to ask must go beyond "What happened?"; instead it's about answering "What will happen?" or "What needs to be done?". Data is the raw material for such decision-making and forecasting.

One of the most important factors for deriving value from data is understanding business processes. At PSI Technics, we use our long-standing experience from a wide variety of industries to co-create processes with our customers and partners in order to achieve optimum results.

Our Approach

Our approach is based on the ASUM-DM (Analytics Solutions Unified Method for Data Mining) process, which divides projects into five phases and is supported by project management. Here too, the preliminary basis for using data is understanding business processes. The individual phases are:

1. Analyze

It is important for us to understand our customer's entire process. Tasks cannot be considered autonomously; they are integrated into business processes. Collaboration with our customers forms the foundation for understanding business processes and the requirements and objectives that flow from them.

2. Design

Tasks are separated into various modules and their definitions and relationships to one another are defined.

3. Configure and Build

The modules are implemented in this phase. Here too, our approach is iterative and agile, and coordinated with our customers.

4. Deploy

The solution is implemented at the customer's premises and the staff is trained in its use.

5. Operate and Optimize

While the system is being used, feedback is provided to implement continuous improvements and optimize the solution.

6. Project Management

Project management oversees the entire process in order to ensure milestones are achieved on schedule and to ensure goal-oriented communication.

 

This standardized process, which was first introduced by IBM, has the advantage that it can be implemented for any Business Analytics project and is independent of industry and application. This reduces implementation costs and achieves high quality results.

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Opportunities for Business Analytics

Big data, deep learning and business analytics are buzzwords that everyone is using today. But where are they actually used in practice? Here are a few examples of how real added value can be achieved using data and modern methods.

Process Mining
Process optimization stands at the forefront of process mining. The most varied existing process data can be combined with newly collected data, such as additional captured timestamps, to identify optimization potentials with various methods, for example, deep learning.

Predictive Maintenance
The goal of predictive maintenance is forecasting maintenance and repair. Using past data and current information, maintenance and repair tasks can be predicted and planned.

Automated Quality Inspections / Quality Assurance
Increasingly complex quality assurance tasks can be performed by processing large amounts of data or by using methods such as deep learning. In manufacturing, image processing plays a leading role in the application of deep learning and in implementing Industry 4.0.

Digital Twin
The digitalized replica of processes, products, machines, and much more, stands at the forefront of digital twins. For example, changes can be simulated using a digital twin in order to predict their effects prior to implementation.

 

Our application experts are standing by to answer your calls or written requests. Please call +49 (0) 2630 91590-0, use the contact form to submit your queries or send an e-mail to support@psi-technics.com

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Orchestrator of a great plan
PSI Technics plays leading role in business ecosystem

A "Silicon Valley of digitization of technical business processes" in Rhineland-Palatinate – that is the ambitious goal of the medium-sized technological leader, PSI Technics. 

PSI Technics of Urmitz on the Rhine river develops automation and digitization solutions and has recently focused increasingly on automated image processing to aid in fault detection, maintenance and inspection. This is a broad area that can be applied in numerous industries. From food inspection to the inspection of railway trains, PSI Technics has established deep competencies and, among other things, has developed scalable and expandable cutting-edge solutions using artificial intelligence, digital twin and software engineering.

Meeting the tremendous global demand, further developing these technologies and building interfaces for adjacent fields are significant tasks that can offer work and revenue for other business entities too. For this reason, CEO Karl-Heinz Förderer is proactively engaged in establishing a business ecosystem. "We aim to establish a competence center for the purposes of implementing groundbreaking applications in the field of the automation and digitization of technical processes in enterprises. We aim to be a market leader in this field within the next five years", Förderer said of his vision.

(Excerpt from a PSI Technics press release published 02/01/2021)

Prof. Dr. Gordon Müller-Seitz, professor of strategy, innovation and cooperation at the Technical College in Kaiserslautern and Karl-Heinz Förderer, CEO of PSI Technics, describe how the vision based on a business ecosystem can succeed in a White Paper. It is available for download at https://psi-technics.com/EN/Whitepapers.php.

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Image sources: PSI Technics, Inodo, AdobeStock

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