Policy documents often generate complex issues that require detailed explanations and personal or technical interpretation. They are written by teams of people, which can lead to multiple perspectives and conflicting ideas being incorporated into the final document. Moreover, there is an abundance of personal interpretations in policy documents. Due to these complications, it can be challenging to implement these policies in real-world scenarios. Decision automation-driven policy implementation can be reduced these challenges.

By using this approach, organizations can make smarter, data-driven decisions based on specific policies and guidelines, rather than relying on human intuition or gut feelings. This means that organizations can define specific rules and guidelines that the decision automation system must follow, ensuring that the system is aligned with the organization's goals and values.

A few benefits of this approach are:

  • Easy to understand the policy
  • Automated decisions based on the policy
  • The policy model is the live specification of the policy so they don’t stale
  • Policy can be deployed as a REST API to on-prem or cloud
  • Decision of policy is explainable

In this blog post, we will explore how university libraries can implement these automated decisions based on their policy specification, to make the most of this approach.

Implementing Policy Automation for a Library

In this example, we are going to model a borrowing ability decision that uses decision graphs to represent business decisions based on pre-existing library policy specifications.

  • Loan type, Grace period, fine rate, and maximum fine are defined in the specification of the library policy.
  • According to the policy specification, the students’ system sanctions are classified as preventing students from getting access to their results, transcripts, graduating, or re-enrolling.

Create a Decision Model

The Decision Graph frames the borrowing ability decision into its decision units such as the decisions, inputs, and sub-decision graphs as required. In this model:

  • The Inputs are the student details that relate to the library.
  • The Decisions will calculate overdue days, fines, and overdue status and then determine the borrowing ability along with other suspensions.

Decision Graph

This model consists of five decision units:

  1. Determine Loan Details
  2. Calculate Overdue Days
  3. Determine Overdue Status
  4. Calculate Fine
  5. Determine Borrowing Ability

Determine Loan Details

We use Decision Table to determine the loan detail decision. For this decision, loan period will be the only input, and based on that, the grace period, fine rate per day, and possible maximum fine will be assigned to the respective parameter of the student.

1st DT

Calculate Overdue Days

Based on the grace period, retuned date, and borrowed date, the overdue days will be calculated if the lending duration exceeds the grace period.

Calculate overdue days

Determine Overdue Status

This is a subgraph that is used to determine the overdue status. Based on the loan period and overdue days, the overdue status will be determined if it is overdue by more than the specified number of days with respect to the loan period. And also we have used the Skip action in order to skip the logic execution if the loan period does not match with the logic inside.


Calculate Fine

decision calculates the fine based on the overdue days and fine rate then sum it up with the student fines previously had.

Calculate fine expression

Determine Borrowing Ability

Based on the previous decisions such as fine, overdue status and maximum fine exceed, this decision table determines the borrowing ability of the student along with a sanction in the student system which may prevent access to the results and graduating.

Final DT

To explore more about this project, visit FlexRule Resource Hub.

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One of the key benefits of policy automation in libraries is the ability to streamline processes. By automating certain tasks, such as determining users’ borrowing ability or processing the limit of access to the system, libraries can free up staff time and resources to focus on other important tasks. Additionally, automated policy implementation can help to improve efficiency by reducing errors and minimizing the need for manual intervention.

Another benefit of policy automation is the ability to make more informed decisions. By using this approach, libraries can gain a deeper understanding of user needs and preferences and make more accurate and fair decisions about borrowing ability or resource allocation. This can help to improve the overall user experience and increase satisfaction with library services.

In addition, because these policy implementations are automated, it eliminates the need for human decision-makers to constantly review data and make decisions.

Last updated May 20th, 2024 at 09:49 am Published January 27th, 2023 at 02:36 pm