A look into how decision management enables organizations to automate their business decisions and processes.
As more organizations move further into their digital transformation to gain a competitive advantage, customers and employees increasingly expect streamlined and personalized digital interactions, including self-service options. By using automation as the cornerstone of digital processes, businesses can create and redesign processes to increase productivity, accuracy and satisfaction.
Decision management is the combination of machine learning with business rules to help organizations understand the appropriate actions to take in a process. Typically, companies use decision management as part of a larger business automation approach for business operations.
After defining which processes to automate, the organization creates workflows that outline the process. When a decision about what action to take next in the workflow arises, you can create a decision model to help determine what happens next.
The hallmark of decision management is that software makes the decision instead of a human. So, decision management effectively mimics the human decision-making process through decision modeling for digital tasks, especially those with clear guidelines for the job. By using decision management, you can combine multiple decisions together with automated tasks to create an end-to-end automated business process.
For example, if you’re automating the onboarding process for new hires, one step is setting up access to IT resources. However, the process is different depending on whether the position is on-site, remote or hybrid. You can use decision management to access employee records that can help determine an individual’s IT needs without human intervention. The software then launches the appropriate workflow based on the individual’s IT needs, such as provisioning the required network access, sending emails to employees and their manager, shipping physical equipment to employees’ home addresses or setting up a work ticket for the equipment to be delivered to their on-site workstations.
Using decision management for digital decisioning enables you to collect data from a wide range of sources in near real-time, which is typically not possible when humans perform the tasks. For example, the system can use multi-party content services, intelligent information extraction, first-party data and third-party consumer data.
How do business rules affect decision management?
Business rules are the cornerstone of decision management. When the automation system comes to a decision point in the workflow, the software uses a business rule to decide what happens next. Business rules consist of a conditional statement and then an action to take depending on which condition is met. You can modify business rules as your processes or situations change.
For example, a retailer organization uses automation for return fraud detection. When an employee enters a return into the POS, the automation system uses artificial intelligence (AI) to quickly process the customer’s return and purchase history. The system then uses the customer data with the business rule that’s been set up for the system so it can flag customers with more than four returns in the last 60 days. During the holidays, when returns are more common, organizations may change the rule to allow six returns in the last 60 days.
What role does decision management play in business process automation?
Business process automation (BPM) is often simply called business automation. It refers to the process of automating as many tasks as possible. Organizations are turning to automation software that is powered by AI and incorporates best practices across all workflows — all in the name of creating faster, digital customer experiences and optimizing internal processes. By using technology, organizations can replace manual processes with digitally automated processes, also referred to as business process management.
To complete the process of business automation, organizations use four steps for each process:
Discover: Identify opportunities for improvement.
Decide: Determine the course of action.
Act: Create business applications to quickly address changing requirements.
Optimize: Augment the workforce with AI-powered automation optimization.
Benefits of decision management
Organizations that use decision management as part of an overarching business automation approach often see the following benefits:
Empowered employees: With no- or low-code software, almost all employees can use decision management without additional support to automate tasks by defining business rules and creating models. In addition to creating more efficiencies, employees can test out-of-the box ideas on their own, which encourages innovation.
Reduced errors: Even the best employee is going to make a mistake, such as submitting the wrong work request for a new employee or missing a potential return fraud. However, machines do not get tired from long hours or stressed by a long line of frustrated customers in front of them. For many previously manual tasks, decision management significantly increases accuracy.
Smarter decisions: With decision management, the decisions are based on data and business rules instead of allowing human emotion or bias into the process. Additionally, decision management can be programmed to include data from other similar decisions; machine learning can also be applied to gain insight from other employees’ projects.
More engaged employees: Because decision management reduces manual tasks, employees are often more satisfied with their jobs. Organizations can then use the additional resources for tasks requiring the human touch, such as customer service or brainstorming.
Common use cases for decision management
Decision management can be used for any decision point in a digital process that can be defined with business rules and quantified with a conditional statement. Businesses use decision management for a wide range of processes, including the following:
Creating personalized experiences for customers
Customers are increasingly expecting digital experiences that meet their needs and preferences. By using customer data — including first-party, behavior data and third-party consumer data — businesses of all sizes and budgets customize all aspects of the interaction in near real-time. As organizations gather more data about the customer, they can improve the customer experience to more closely align with their needs, even after the interaction has already begun.
Let’s say a returning customer visits a company’s website. Based on the customer’s past purchase history, the company knows the individual is a female who likes to camp. So, the company customizes the website’s banner photo and promotions to focus on camping equipment. However, during this visit, the customer looks at ski equipment. Instead of sending a follow-up email that focuses on camping equipment similar to a previous visit, the automation software customizes the messaging with a link to a blog post on selecting the right ski equipment and sales on ski gloves.
Optimizing the supply chain
For a supply chain to run efficiently and effectively, multiple decisions must be made throughout the process — often based on changing data. Additionally, transparency into the process and frequent status updates are often required by (and for) key stakeholders. Decision management enables organizations to use advanced analytics to optimize the supply chain process from end-to-end.
For example, organizations can set up decision management to manage the ordering process when an item needs to be restocked. Predictive analytics gather the insights from multiple data sources to determine which approved vendor has the quantity needed in stock and has the highest satisfaction rating. The organization can then use advanced analytics to optimize the shipping options based on business rules that prioritize speed and cost as the factors for selecting the appropriate vendor and shipping option. Then, the business can automate the process to ensure a seamless experience from order placement to delivery.
Organizations that function in highly regulated industries use decision management to monitor operations that require a high degree of compliance. The healthcare industry is a good example. Because automation software uses AI and data analytics to analyze patient records, it can determine who hasn’t signed necessary paperwork related to HIPAA policy and flag the patient record. Business automation then includes a new HIPAA form for patients to sign during the check-in process at their next appointment.
Reviewing employment applications
Manually screening applications is time-consuming and invites human biases into the process. By setting up business rules based on the position requirements, such as number of years of experience and required skills, a human resources (HR) department uses decision management to automate the initial screening of job applications. Using automation, HR software can search for keywords on a resume based on rules to evaluate whether that candidate would be a good fit for a specific position. The automation software analyzes the applications and sends the ones that meet those requirements to the appropriate hiring manager for review.
What is business rules management software?
Many organizations use a business rules management system (BRMS) to create and manage business logic without manual intervention. A BRMS leverages AI and machine learning (ML) throughout the lifecycle to make precise and targeted decisions, and most have an easy-to-use interface that enables employees to create rules and models without code.
A BRMS contains the following components:
A development environment for defining and creating business rules
A repository where business rules are stored
A business rules engine
Decision management solutions and IBM
With IBM Cloud Pak® for Business Automation, you have everything you need to automate every business process. The solution integrates with IT systems to scale applications and automate decisions across multiple channels. Decision management is a key capability of the system that enables organizations in a range of industries to empower business users, make better decisions and optimize customer experiences. IBM Cloud Pak for Automation is built on top of automation services that enable you to gather insights on your business process and then use no- and low-code options to automate the workflow.