Managing High-Level Problems in Business Processes

In organizations, managing Business Processes can be challenging. These processes often involve many tasks that need to be completed quickly. Issues can arise due to overloaded resources, delays, and other factors. When these problems occur, they can affect the overall performance of the process.

This article discusses how various problems in business processes relate to the specific instances that create them. We will look into the relationship between high-level problems and the specific cases that influence these problems.

#Understanding High-Level Problems

High-level problems in business processes cannot be understood by looking at individual tasks alone. Instead, they emerge from the overall behavior of the process. For instance, high-level behavior includes patterns like workload, process delays, and the way tasks are handed over between resources.

To better understand this relationship, we will explore how we can track and analyze these problems. We aim to identify which specific cases contribute to these high-level issues and how we can improve the process as a whole.

#The Impact of Business Process Characteristics

Business processes can face challenging situations where tasks overlap, causing delays. This is similar to cars on a busy road, where too many vehicles can lead to congestion. When cases in a process become active at the same time, they may create a backlog that affects the overall workflow.

High-level behavior arises from these interactions and can lead to further complications for specific cases. A demanding case may occupy resources longer, while busy periods can prevent other cases from receiving the attention they need. Therefore, there is a clear interplay between high-level problems and the characteristics of the cases involved.

#Methods for Analyzing High-Level Behavior

We can monitor high-level behavior by detecting patterns using event data from a process. This involves identifying sequences of interactions that arise from specific case types. By doing this, we can gain insight into how certain cases may contribute to high-level problems.

For example, we can spot trends in which combinations of delays and resource use are linked to positive or negative outcomes in the process. By analyzing these patterns, we can determine which case characteristics lead to successful or unsuccessful outcomes.

#A Practical Illustration

Let's consider a loan application process where events occur in different segments. When many applications enter the system at the same time, it could strain the resources available. We can observe high workloads or significant handover of tasks among resources.

If we analyze a specific case that is part of a busy group, we might find that it has a lower chance of success than other cases that are not part of such congestion. Therefore, the performance of a case can be directly affected by the behavior of other cases in the process.

#Analyzing Outcomes

By applying our method to the data from a real loan application process, we can see how certain patterns impact the outcome of the applications. For instance, if many applications are processed simultaneously, the chances of a successful outcome can diminish.

We can also determine how long each application takes to complete. This throughput time is important because it helps us identify areas that need improvement. By evaluating these aspects, we can make informed decisions about how to streamline the process.

#Process Mining Techniques

Process mining techniques help analyze event data stored in information systems. These techniques aim to provide insights into business processes, allowing organizations to reduce costs and improve efficiency.

Key Performance Indicators (KPIs) are often used to measure performance. These may reference the average time taken to complete a case, the costs involved, or the success rates of applications. However, it’s important to remember that each case does not operate in isolation.

#Traffic Analogy in Processes

Think of processes like traffic on a road. When too many cars attempt to occupy the same space, delays occur. This is similar to how multiple cases in a process can congest resources and lead to delays. The way cases interact can create patterns of behavior that might not be visible at the level of individual cases.

When cases are active simultaneously, they can experience delays, excessive waiting times, or interruptions in service. This behavior may lead to lower success rates for the cases involved. It’s crucial to understand how these patterns form to manage processes more effectively.

#Exploring Case Characteristics

Our research focuses on analyzing how the behavior of cases, particularly their unique characteristics, can influence high-level problems. We explore whether specific types of cases are more likely to lead to unfavorable outcomes when they become part of high-level behavior patterns.

For instance, if a case type typically leads to delays, we must evaluate its behavior in the process to understand its effect on the outcome. This insight can help organizations make informed decisions to alter process elements that lead to inefficiencies.

#Identifying High-Level Events

To identify high-level events, we need to define what these events look like in the context of the business process. This includes recognizing various forms of high-level behavior related to congestion, workload, and delays.

By conceptualizing high-level behavior as events, we can analyze them more effectively. Each occurrence is linked to specific segments and timeframes in the process, allowing us to build a clearer picture of the interactions at play.

#Correlating High-Level Events

High-level events are interconnected and may not stand alone. We assess how these events relate to each other based on time and location. When high-level events happen in close proximity, we can assume a correlation and analyze their sequence.

By examining these connections, we can understand how underlying cases contribute to specific episodes of high-level behavior. This relationship becomes apparent when we observe patterns that emerge over time.

#Case Participation

To measure case participation in high-level behavior, we can determine which cases are involved in high-level events. We categorize cases into participating and non-participating groups to analyze their characteristics further.

This comparison allows us to gauge the impact of participation in high-level behavior on case outcomes. For instance, we can identify whether participating cases tend to have longer throughput times or lower success rates than non-participating cases.

#Experimentation with Loan Application Data

In our study, we applied our method to a loan application dataset to explore high-level behaviors. We analyzed various patterns of activity to see how they relate to case outcomes. For example, we observed that cases that experience delays in the validation process tend to have lower success rates.

We also categorized cases based on their outcomes, measuring how participation in specific high-level paths influenced their chances of success. By doing this, we aimed to uncover the underlying connections between process behavior and case characteristics.

#Outcome Analysis

Analyzing the success rate of loan applications, we found that crowding areas of the process negatively influenced the likelihood of success. Cases that joined the main flow in busy periods showed less favorable outcomes.

In addition, we noticed trends in throughput times. Applications that were subjected to heavy workloads or delays took longer to process. Understanding these trends helps organizations identify which parts of the process need improvement.

#Throughput Time Trends

Throughput time can be categorized based on how long cases take to complete various tasks. Our analysis revealed that cases finishing in under ten days had different participation patterns compared to those taking longer than thirty days.

These patterns indicate that high-level problems can significantly impact throughput time. If a case is caught in a cycle of delays or resource congestion, it is likely to take longer to complete, which can lead to dissatisfaction for customers.

#Conclusion

Through our exploration of high-level problems and their relationship with the cases that generate them, we gain valuable insights into business processes. By identifying how specific case characteristics influence the behavior of the process, organizations can make informed decisions to enhance efficiency.

Our findings emphasize the importance of monitoring interactions among cases and the patterns that emerge from these interactions. The insights gained from such analyses can help organizations address issues proactively and improve overall performance.

In future efforts, further studies could focus on understanding the cause-effect relationships associated with high-level behavior. By doing so, organizations can refine their processes and optimize outcomes based on data-driven decisions.