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Enterprise automation for automated business processes

Maximilian Schneider

This blog post covers several reasons why people are interested in the topic of automation in the enterprise and how enterprise automation can help achieve better results.

Main reasons of enterprise automation discussed in the article

  1. Understanding the Enterprise Automation and their benefits to achieve value in various functions and key performance indicators.
  2. Learn more about the process-oriented strategy in enterprise automation, which focuses on automating processes rather than isolated tasks to drive better value and efficiency.
  3. Explore components and technologies used in enterprise automation, including. Process mining, object-centric process mining, Robotic Process Automation (RPA) and task mining., as well as the role of artificial intelligence and machine learning.
  4. Identify challenges and limitations of certain automation approaches such as RPA and the importance of a holistic view of process improvement.
  5. Discover the Building blocks of enterprise automation strategiesidentified by Gartner, and the goals of driving efficiency, agility and effectiveness through these building blocks.
  6. Understand different Use cases and possibilities of automation in various business processes and areas such as order-to-cash, human resources, supply chain and customer experience.
  7. Learn more about the future of enterprise automation and how it will affect the Networking of companies and their ecosystems can extend to enable collaborative execution and create network externalities.

This blog post will cover these topics in detail and show how enterprise automation can be used in a variety of industries and functions to improve business operations and gain competitive advantage.

What is Enterprise Automation?

Enterprise automation refers to the use of technologies and systems to automate manual, time-consuming, and error-prone processes within a company.

This involves the use of various techniques and tools to mimic human capabilities, optimize data processing, and support decision making. Key enterprise automation technologies include artificial intelligence (AI), robotic process automation (RPA), machine learning (ML), optical character recognition (OCR), and workflow automation.

Building blocks of enterprise automation according to Gartner

Gartner has identified several building blocks of enterprise automation strategies that are critical to an organization's success in implementing automation solutions. These building blocks include:

  1. Hyperautomation: An approach that combines various technologies such as Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA) and other automation tools to achieve comprehensive and effective business process automation.
  2. DigitalOps: The combination of technologies, tools and methods to improve operations through automation, data sharing and collaboration between different departments within an organization.
  3. Robotic Process Automation (RPA): A technology that aims to automate rule-based and repetitive tasks by using software robots that can perform human-like actions.
  4. Process Mining: A technique for analyzing business processes by capturing event logs and identifying patterns to uncover potential improvements and automation opportunities.
  5. Task mining: A technology that aims to increase the efficiency of business processes by analyzing the actual activities of users and identifying potential automation opportunities.
  6. Artificial Intelligence (AI) and Machine Learning (ML): Technologies that can simulate human-like thought processes and recognize patterns in big data to make decisions, improve processes, and identify automation opportunities.
  7. Integration of low-code/no-code platforms: Using platforms that enable applications and automation solutions to be developed with little or no programming to accelerate the adoption of automation technologies across the organization.
  8. Automation orchestration: Coordinate and manage multiple automation technologies and solutions to ensure seamless integration and optimal efficiency in business processes.

Process-oriented strategy for enterprise automation

Which topics are the focus of strategic enterprise automation?

  1. Defining the automation goals and priorities To implement a successful automation strategy, it is important to clearly define the goals and priorities of automation. This should involve analyzing which business processes can derive the greatest benefit from automation. It is advisable to first prioritize processes with high volume, low complexity and a high repetition rate.
  2. Selection of the right technologies and tools Choosing the right technologies and tools is critical to the success of an automation strategy. In doing so, companies should focus on solutions that integrate AI and ML to ensure continuous improvement of automation solutions. Technologies that can be used for enterprise automation include Robotic Process Automation (RPA), Business Process Management (BPM) and AI-powered decision making systems.
  3. Integration of automation solutions into existing systems To fully exploit the benefits of enterprise automation, it is important to integrate the automation solutions seamlessly into existing systems and applications. This ensures that all departments and employees involved have access to the automated processes and can use the information gained efficiently.
  4. Creation of a culture of automation The introduction of an automation culture in the company is a decisive factor for the success of enterprise automation. Employees should be trained and encouraged to use automation solutions and participate in the continuous improvement of processes. Open communication about the progress and benefits of automation helps ensure that all stakeholders recognize and support the value of automation.
  5. Measurement and optimization of automation success To ensure the effectiveness of automation in the company, it is important to continuously measure and optimize the success of automation solutions. Here, key performance indicators (KPIs) can help to monitor progress and evaluate the performance of the automated processes. By regularly analyzing these KPIs, companies can identify weaknesses and take targeted improvement measures. In addition, companies should develop and share automation best practices to further increase the effectiveness of automation solutions.
  6. Scaling of automation solutions Once automation has been successfully implemented in specific business areas, it is important to extend the solutions to other processes and departments. This enables companies to realize the full benefits of automation and increase efficiency across the enterprise. When scaling automation solutions, companies should take care to consider the individual requirements and specifics of different processes and departments.
  7. Ensuring data security and compliance Data security and compliance are key aspects when implementing enterprise automation. Companies must ensure that automation solutions comply with applicable data protection regulations and guarantee the confidentiality, integrity and availability of corporate data. This includes developing security policies and measures to ensure the protection of automated processes and associated data.
  8. Promoting innovation and continuous improvement Enterprise automation should be seen as a continuous process, constantly looking for new ways to optimize business processes and drive innovation. In doing so, companies should create an environment that promotes collaboration between different departments and enables the exchange of ideas and best practices. Regular reviews and adjustments to the automation strategy ensure that companies stay on the cutting edge of technology and maintain their competitive advantage.

In summary, enterprise automation offers companies the opportunity to make their business processes more efficient, faster and more cost-effective. By taking the above eight points into account, companies can develop and implement a successful automation strategy that leads to an increase in competitiveness and business success in the long term.

Enterprise Automation - Technologies

  1. Process Mining: Process mining is a technique based on the analysis of event logs that aims to visualize, monitor and optimize business processes. In the context of enterprise automation, process mining helps identify inefficient processes, uncover bottlenecks, and highlight areas for improvement. By analyzing event logs, companies can gain a better understanding of their process flows and develop targeted automation initiatives.
  2. Object-centered process mining: Object-centric process mining is an extension of traditional process mining and focuses on the analysis of object states and transitions in business processes. This approach enables a more granular analysis and provides additional information about the interactions between different process elements. In the context of enterprise automation, object-centric process mining can help develop even more precise automation solutions and increase the effectiveness of automation.
  3. Robotic Process Automation (RPA): RPA is a technology that uses software robots to automate manual, repetitive and rule-based tasks. These robots can perform human-like actions, such as filling out forms, moving files, or extracting information from various sources. In the context of enterprise automation, RPA enables companies to increase efficiency, reduce costs and improve process accuracy by reducing human error.
  4. Task Mining: Task mining is a technique that aims to analyze and understand how individual users perform tasks. Unlike process mining, which focuses on entire processes, task mining examines work at a microscopic level. In the context of enterprise automation, task mining can help identify inefficient work steps and identify automation opportunities for individual tasks.
  5. Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are technologies based on the development of intelligent algorithms that can learn from data and recognize patterns. In the context of enterprise automation, AI and ML can be used to automate complex and less structured tasks that go beyond the capabilities of RPA. Examples include decision making, speech recognition, or text understanding. AI and ML help extend automation to higher value tasks and help enterprises become more agile and intelligent.

Limitations of RPA compared to holistic process improvement

In the following table, we identify challenges and limitations in automation approaches of simple RPA and present the importance of a holistic view of process improvement.

Challenges / LimitationsRPAHolistic view of process improvement
ScalabilityRPA is mainly suitable for rule-based, repetitive tasks. Scaling problems can occur when the complexity of tasks increases or business processes change.The holistic view enables better integration of technologies and greater scalability by combining different automation technologies.
AdaptabilityRPA robots can have difficulty adapting when there are changes in the processes or systems they rely on.A holistic view takes into account the constant change of processes and ensures that automation solutions are adaptable and flexible.
ComplexityRPA is limited in complex tasks with little structure and high cognitive demand.Integrating AI and ML into process improvement can handle more complex tasks.
Long term improvementRPA can accelerate existing processes without fundamentally improving them. This can lead to short-term efficiency gains without long-term improvements.A holistic view of process improvement focuses on achieving long-term change and sustainable improvement.
Dependence on existing systemsRPA robots often rely on existing systems and user interfaces, which can reduce their effectiveness when those systems change.A holistic view recognizes the dependency on existing systems and enables solutions to be developed that are better integrated into the existing IT landscape.

Use cases and possibilities of automation

A large retailer with a wide range of products from food to household goods.

Challenges: This company was struggling to optimize inventory management and demand forecasting, resulting in overstocked warehouses and regular product shortages.

Technology: Artificial Intelligence and Machine Learning (AI/ML).

Solution: Using AI and ML, the company improved its demand forecasting and optimized inventory management by identifying patterns and trends in sales data.

Result: The company was able to better manage its inventory, make the supply chain more efficient and increase customer satisfaction through improved product availability.

A medium-sized bank offering a comprehensive range of financial products and services.

Challenges: The bank was struggling to speed up customer service while reducing the cost of manual processes, such as reviewing loan applications.

Technology: Robotic Process Automation (RPA)

Solution: The bank implemented RPA to automate the manual review of loan applications, using software robots that extracted relevant information from the applications and matched it against the bank's criteria.

Result: The bank significantly reduced the processing time of loan applications, lowered operating costs and improved customer satisfaction.

A large pharmaceutical company with a wide range of drugs and medical devices.

Challenges: The company was struggling to accelerate its research and development processes while meeting compliance requirements.

Technology: Low Code/No Code Platforms

Solution: The pharmaceutical company implemented low-code/no-code platforms to develop custom applications for data management and collaboration between different departments without resorting to complex programming.

Result: The company accelerated its research and development processes, improved collaboration and ensured compliance requirements were met.

A medium-sized manufacturing company that produces various machine components.

Challenges: The company was struggling to increase production efficiency while ensuring product quality.

Technology: Process Mining

Solution: By using process mining, the company analyzed its production processes, identified bottlenecks and inefficient operations, and implemented targeted improvements.

Result: The company was able to increase its production efficiency, improve product quality and reduce operating costs.

An international logistics company providing freight and delivery services.

Challenges: The company was struggling to ensure on-time delivery of packages, manage growing demand for faster delivery times, while reducing fuel consumption and environmental impact.

Technology: Internet of Things (IoT) and Artificial Intelligence (AI)

Solution: The logistics company implemented IoT devices in its vehicle fleet to collect real-time data on the location, condition and efficiency of vehicles. It then used AI algorithms to determine optimal route planning and vehicle maintenance based on the collected data.

Result: The company improved on-time delivery of packages, reduced fuel consumption and environmental impact, and was able to improve overall customer service.

Conclusion - Enterprise Automation

Despite the clear benefits, many companies are reluctant to adopt new technologies such as enterprise automation, wary of the changes they can bring. However, companies are also under intense market pressure to operate more efficiently and deliver a better customer experience. By using enterprise automation, companies can speed up and optimize their operational processes without compromising the quality of their services, which means that everyone wins.

Efficient data management has become the order of the day. If a company wants to reduce operational costs, increase customer retention and satisfaction, or improve overall performance, how effectively it automates its processes matters. For a company to remain competitive and achieve these goals, introducing enterprise automation technologies such as Robotic Process Automation (RPA), process mining, and artificial intelligence (AI) into its business processes to increase productivity is essential.

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