How to work more efficiently with Robotic Process Automation

Robotic Process Automation
Robotic Process Automation

This case study shows which criteria you should definitely know for the successful use of Robotic Process Automation

A manufacturer of machine systems and special machines ensures more efficient work processes in the company with Robotic Process Automation (RPA). Together with the management consultancy ROI-Efeso, it determines areas of application for RPA in eight business areas. After only three months, the software bots complete 14 processes independently and reliably. You can find out how the experts from ROI-Efeso proceeded here.
The case study: initial situation and measures in brief
Starting position:

⦁ Data maintenance and information exchange in different formats cost time and generate errors
⦁ The process landscapes in operations, quality management, controlling and other business areas are to be redesigned.


⦁ Determination of fields of application and tasks for software bots (Robot Process Automation) including detailed recording and development in all departments involved
⦁ Immediate use after testing in practice

Solution approach:

⦁ Test phase with the real and the automated process running in tandem
⦁ Calculation of the ROI of the software bot for the sum of all use cases

Robotic Process Automation: What software bots can do

For work tasks that are very repetitive, time-consuming and error-prone, there are now smart technology assistants: In the context of digitization and Industry 4.0, software bots take on a wide range of tasks in the operations area.
Under the term Robotic Process Automation, they provide relief for simple but time-consuming work processes. In this case study, one objective was to centralize bookkeeping from five locations at the company’s headquarters without hiring additional staff. Instead, bots should take over tasks such as invoicing, dunning or the preparation of data from different sources for reporting from the first day of the changeover. The focus in the selection was on regular activities which are on the one hand so specialized that their automation in systems like SAP is not worthwhile – but which on the other hand tie up considerable time resources. 

Industry 4.0: Award from ROI-Efeso and the magazine Production

Digital assistance systems, data analytics, artificial intelligence or machine learning are changing the value creation processes in the manufacturing industry at breakneck speed. Companies that manage to successfully integrate these digitization technologies, tools and systems into their value creation processes are among the pacesetters of Industry 4.0. Together with the trade newspaper PRODUKTION, it has been honouring ROI-Efeso with the Industry 4.0 Award since 2013 – one of the most important benchmarks for digitization projects and Industry 4.0 best cases.
The second objective and special challenge of the project was to expand the change from manual to RPA-controlled work processes in just a few months to include operations, R&D, production and logistics. Together with ROI-Efeso, the company quickly implemented the appropriate bots. Just two weeks after the start of the project, the first bot was already saving half an hour a day with a previously manual inventory correction of shortfalls in SAP.

Testing and deploying

The project kicked off with an information event at which all employees involved got to know the deployment options of RPA. ROI-Efeso explained the limits of the technology very clearly – after all, those involved should be able to develop a realistic picture of the effort involved in introducing RPA to its benefit.
After this kick-off, the project team gathered ideas and suggestions on work processes that the employees believe could adopt RPA tools. In the days that followed, it selected 14 suitable processes from over 40 ideas in one-on-one discussions, and a detailed analysis was then carried out on each of them.

Industry 4.0 – An RFID module guides you through the Future Factory – Source: ROI-Efeso

On the basis of this knowledge, the project team created an automation concept for all areas involved, in which it prioritized the most attractive processes. The selection of the RPA software suitable for the company’s requirements and the assignment of authorizations were completed within two weeks in coordination with the IT department, so that implementation of the identified work processes could start immediately afterwards.
The project team now brought the RPA into everyday life – department by department, process by process – always via the detailed recording, development, and test stations. In the test phase, the previous and the automated process ran simultaneously according to the “tandem” principle. In this way, errors could be corrected or hurdles overcome without delays in day-to-day business. Once handed over, every work process ultimately ran through the RPA, which immediately relieved the workload.

If you hate it – automate it!

In the project, three experiences proved to be particularly valuable for the implementation of RPA projects:

  1. Identify “processes in the middle”! The maxim “If you hate it – automate it!” Is a good starting point for RPA projects. It is important to classify the intended use correctly: Which use cases are so simple that the programming effort for a system adaptation is not worthwhile – but automation? Software bots are intended for precisely these processes in the middle. In sum, this is where the greatest leverage for savings lies. 
  2. Exploit software bots 24/7! The license model of most RPA providers is designed in such a way that a software bot is available around the clock on every work and public holiday. As with employees, payment is made per bot. If the bot is not busy with tasks, the invoice is still due. Therefore, you should constantly look for other, suitable work tasks for the bot.
  3. Make your expectations realistic! RPA is not a ready-made software that takes care of all extensions independently after purchase. If the process that the software bot has taken over changes, this change must be adapted. This should be clearly communicated in RPA projects in order to achieve realistic expectations of the possible uses of the bots.

4 consultant tips for your successful RPA use!

⦁ Operate targeted stakeholder communication! An RPA introduction is only successful if it is (pro) actively driven forward by the specialist department – in cooperation with IT.
⦁ Clearly define roles and responsibilities! Determine at an early stage who will monitor the bots, make adjustments or pay attention to compliance requirements.
⦁ Look beyond the existing rules and limits! RPA is a technology that helps with process optimization – but only for those who are able or willing to think process-oriented.
⦁ Build up internal RPA competencies! Nobody knows the processes as well as your employees. You should therefore create a Center of Excellence with internal consultants for the (further) development of RPA as well as for support with its application.

Careful testing saves expensive mistakes

In this as well as in other RPA projects by ROI-Efeso, controlling the previous manual process alongside the new automated process proved to be an important success factor. This is the only way to compare whether the desired results can be achieved. This also makes special cases and sources of error visible that were not yet present in the process recording. With this start-up support for each process, the RPA introduction takes a little longer – the transferred bot then works more reliably than with an implementation that is too fast. And that in turn saves time and money for troubleshooting later.

Industry 4.0

Industry 4.0
Industry 4.0

Industry 4.0 is the digital transformation and automation of traditional production companies. Industry 4.0 is often referred to as the “fourth industrial revolution” or “smart manufacturing”. By digitizing machines and systems, including the Internet of Things (IoT), traditional processes and procedures should be made more intelligent and autonomous. Industry 4.0 affects a whole range of sectors such as construction, food and beverage, aerospace and the automotive industry.
Industry 4.0 increases productivity, lowers costs and improves international competitiveness. Read on to find out more about Industry 4.0. Learn more about the new technologies that are transforming traditional manufacturing operations into smart factories and get an idea of ​​the advantages and challenges facing production areas.

What is Industry 4.0?

Under Industry 4.0 means the optimization of traditional manufacturing industries through the Internet of Things (IoT) and other technologies. In addition to automating production, these new technological possibilities enable complete digital transformation. The aim is an optimized and data-driven production environment.
The driving factor of Industry 4.0 is data obtained through networking. Technologies from the field of Industry 4.0 record and collect data from production independently of company or machine systems. IoT, cloud computing, data lakes and other computer-based technologies are used. These technologies can improve traditional manufacturing industries through data-driven process optimization. Manufacturing facilities are becoming more efficient and flexible and can produce high-quality products at lower costs.

Industry 4.0 is the next after the three well-known and successful industrial revolutions. The first industrial revolution (around 1760) came about as a result of the invention of the steam engine and enabled the transition from manual to machine production. The second revolution (around 1870) is also known as the technological revolution and included numerous technical improvements such as electricity and railways. The third revolution (the 1960s) brought computerization and was characterized by the use of computer systems for mass production and automation. We are now experiencing the fourth revolution, which is driven by data and the IoT.

The term Industry 4.0 has existed since 2011 and came up as an idea and strategic initiative of the German federal government and German industry. The German economy is heavily dependent on traditional sectors such as mechanical engineering or the electrical, automotive and pharmaceutical industries. In order to remain internationally competitive, Industry 4.0 technologies are increasingly being used in German factories. 
The digitization of the manufacturing industry is also being promoted. In Germany Industry 4.0 is also associated with the term “Work 4.0”, a conceptual framework for the discussion about the future of work.
Let’s take a look at the technologies Industry 4.0 offers for smart production lines:

12 Industry 4.0 technologies

Industry 4.0 knows twelve essential industrial technologies and trends.

Additive manufacturing

3D printing and digital manufacturing to create lighter, better performing parts and systems. Additive manufacturing enables quick and individualized implementation. Manufacturing companies can manufacture the parts they need themselves, precisely when and in the required design.

Advanced Robotics

Modern autonomous robots will work side by side with humans. Robots enable new forms of collaboration, higher speed and higher efficiency. They also reduce costs and improve workplace safety.

Augmented Reality

Augmented Reality technologies can be used to control robots, for maintenance, assembly and repair work, for training purposes and quality controls or for monitoring production processes and for auditing systems. With augmented reality, companies can reduce errors, improve security, save time and cut costs.

Big data and data analysis

This means the acquisition and analysis of data from various sources, e.g. data from conventional machines and sensors or from customer relationship management software (CRM) or enterprise resource planning systems (ERP). In most cases, Industry 4.0 solutions enable manufacturing companies to systematically collect data from parts of the supply chain for the first time. With big data analyzes, managers can make data-based decisions, which leads to optimization and increased productivity.


The implementation of cloud computing technologies enables the storage, management and sharing of data. For the growing amount of data generated by smart factories, the cloud is essential. It enables scalability and improves collaboration.

Cyber security

Cyber security ensures secure communication between machines, devices and industrial systems, especially in systems that are being networked for the first time. A high level of cyber security is essential to maintain confidentiality and data protection. Cyber security prevents data breaches that could endanger the entire plant.

Horizontal and vertical system integration

This is about the complete integration of all data from all departments, teams, functions, production and components and that across the entire value chain. Complete integration is essential for end-to-end monitoring and for building a reliable database (SSOT-Single Source of Truth). This database serves as the basis for decision-making, the allocation of resources and the organization of cooperation in order to enable lean manufacturing. This is also one of the greatest challenges of Industry 4.0.

Simulation / digital twin

A digital twin is a virtual copy of a real process with real data. Before a decision is made, simulation tools are used to test and optimize processes. In this way, error rates can be reduced, costs lowered and processes optimized.


Sensors connected to machines enable performance, system and environmental characteristics to be recorded. Manufacturing companies can use sensors to collect data from production. This data can be used for monitoring purposes, for predictive maintenance, to optimize processes and improve machine availability, or in general for data analysis.

AI (Artificial Intelligence)

AI enables the optimization of tools, technologies and processes through intelligent machine algorithms. In principle, AI technologies can always be used to improve Industry 4.0 solutions, for example, to improve monitoring, maintenance, robotics or operational management.

11. ML (machine learning)

With machine learning, a branch of AI, manufacturing processes can be optimized on the basis of recorded and collected data. This also includes calculation models and algorithms from the field of operational excellence.

12. Industrial Internet of Things (IIoT)

This means the digitization of all devices and machines used in industry. Older machines without their own control can also be digitized. The IIoT enables collaboration, data acquisition and optimization in real-time. The industrial Internet of Things forms the basis for generating data for most other Industry 4.0 technologies.

Advantages of Industry 4.0 for manufacturing companies

Industry 4.0 is useful in many ways for manufacturers in a wide variety of industries, from food production to automotive suppliers. The following benefits are mentioned as examples:

Productivity and efficiency

The main benefit of Industry 4.0 and smart factories is that the respective industries can increase their productivity and improve their efficiency. How does it work? With Industry 4.0 technologies, production companies can collect data and use the analysis of this data to improve their key business figures. The new processes ensure data-based and therefore more intelligent decision-making. Bottlenecks can be identified and resource allocation optimized. Barriers are removed and, as a result, machine availability is increased, so that performance and production quality increase.

Cost reductions

As a result of improved productivity and efficiency, costs decrease and profits increase. The costs also decrease thanks to improved resource allocation, shorter production times and fewer downtimes, thanks to improved quality and less waste or rejection. In addition, so-called predictive and preventive maintenance enable optimized machine maintenance. Wear and tear are reduced and depreciation is increased. Although Industry 4.0 naturally requires a certain initial investment in new technologies, the return on investment (ROI) is still significant.

Traceability and transparency

The networking of all machines and systems enables data to be collected. This information can be used to transparently track the performance and output of the machines. This enables managers to better monitor and control operational processes. This enables the optimization of production processes and the improvement of product quality. Key figures can be set and monitored. In addition, the data can also be used to comply with legal regulations more easily.

Agility & Innovation

Industry 4.0 enables deeper insights into production. You will then better understand where the bottlenecks are, introduce scalable technologies and work together better on processes. Production companies are thus given the flexibility and agility they need to reorganize the allocation of their resources, introduce new processes and implement ideas.


Many manufacturers are still competitive due to lower costs and increasing product quality, even without cuts in wages or even outsourcing to low-wage countries.

Industry 4.0 challenges

Even if Industry 4.0 brings many advantages, there are also some challenges

Initial investments

Industry 4.0 offers a very advantageous return on investment (ROI), but also requires a certain investment that not all companies may be able to shoulder. This includes, for example, investments in new technologies, training courses and machines. Adjusting business models can also result in costs.

Data protection and security risks

When communicating between devices (D2D – Device to Device) and machines (M2M – Machine to Machine), personal data can also be exchanged. Data breaches in the company’s own production facility or at third-party providers could make this data accessible to attackers and endanger data protection with regard to PII (Personal Identifiable Information). Furthermore, there are fundamentally new attack surfaces and weak points from networking via IoT. These two topics require appropriate government regulations and also new cybersecurity technologies.

Training of the workforce

Operating a conventional machine requires different skills than operating a conventional machine connected to a computer. Managing traditional industrial production is different from managing data-driven, automated production. Industry 4.0 brings new technologies and functions that require a change in the mindset of employees and other skills. For this reason, companies have to train their employees and also ensure a new corporate culture. This takes time, effort and resources.

Dependence on technology

Industry 4.0 technologies can “free” older processes, but companies are still restricted by existing technologies and their operating personnel. For example, networked devices and machines require IT teams in the factories. Companies have to analyze large amounts of data and are therefore dependent on AI algorithms and computer scientists. While Industry 4.0 is gradually gaining ground, many of these technologies are still in development. Therefore, not all challenges of today’s modern factory can be answered by Industry 4.0.


The connection of different systems and machines to a common business process requires standardization and technologies that are not always available.

An entry into Industry 4.0

Switching to Industry 4.0 is not always easy. In the beginning, an initial investment is always required and you have to find the right technological solutions for yourself. Employees have to be trained and given confidence in the process of collecting and evaluating data.
A simple way to get started with Industry 4.0 is to implement a universal, non-invasive technology.
3d Signals offers an IoT solution that can be set up easily and non-invasively on your machines (without interfering with the machines). You can use it to collect data and gain usable insights immediately. Increase the availability and productivity of your machines right from the start. View data from your machines in real-time, optimize your production processes and increase productivity.

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