Leading Utility Company Transforms Workload Process With ignio™

For utility service providers and retailers, key business functions like compiling meter readings, invoice generation, payment processing, and customer request processing need to run smoothly. These functions rely on workload automation to execute a series of associated IT tasks (batch processes) in a timely manner and ensure business continuity. Any failure or delay in these batch processes causes operational issues, interfering with the customer experience and revenue streams.

The customer has millions of customers, accounting for over 12 million utility contracts. The contracts have varying billing cycles and meter connection types, which means The customer needs to conduct two million meter readings and generate over 150,000 invoices almost every day. To meet this massive scale of requirements, the customer must run a complex series of workloads, which fetches data from various applications, updates records, generates invoices, and tags incorrect invoices and bills. The customer uses a Linux based scheduler to orchestrate these workload automations across a complex IT infrastructure, involving a wide range of applications such as CRM, billing engines, financial applications, printer vendor systems, and various ETL tools and file systems.

Since the batch processes serve critical business needs, the customer needs to monitor and detect issues, diagnose them, and resolve them quickly.

The company’s existing setup had no provision for automated monitoring or forecasting. This meant the IT team needed to monitor every application, in the scheduler as well as in the SAP ERP system, to ensure that the entire process was completed. Also, the IT platform had no mechanism to generate alerts or notifications. This meant that if any component malfunctioned, the IT people would only realize it after key processes stopped.

Manual monitoring/remediation was both inefficient and risk-prone, increasing the customer’s operational cost and often leading to inaccuracies and delays in revenue generation. The customer needed a solution that could monitor the workload automation processes across its extensive IT infrastructure and business applications and reduce dependency on manual issue resolution.

The Problem

The customer has a complex IT estate, which made it difficult to conduct continuous monitoring of workload processes and remediation or maintenance activities. It not only needed extensive efforts and resources but was also fraught with limitations as processes could not be monitored during non-business hours. Manual monitoring often resulted in lags in identifying anomalies, or in diagnosing and resolving the issues, resulting in serious delays in business operations.

For instance, delays in accurate digital capture of meter readings hampered the process of updating the record against the customer’s account. This also caused slippage in calculating charges and sending out bills, which led to late revenue realization. Additionally, it often resulted in inaccurate charges, or generation of duplicate copies of bills, and incorrect communication with customers, leading to increases in customer complaints as well as potential revenue losses of up to €5 million a day.

The existing Linux-based scheduler couldn’t store historical data in a usable format and was not able to integrate with external systems for real-time data. This was a major obstacle in generating analytics that could have been used to identify, predict, and avoid such IT failures.

The Solution

Digitate collaborated with the customer to provide a layered solution for monitoring workload processes. Digitate understood that clear visibility into the IT estate is the key to better monitoring. Digitate leveraged ignio AI.Workload Management to create a “blueprint” of the entire batch system that reflects all the jobs, their schedules, interdependencies with each other, and their historical behavior. This was collected from a variety of sources including execution logs, databases, and email reports, to ensure the platform has access to both real-time and historical information.

This blueprint helped the team understand the normal behavior of any job by accurately capturing process flows in depth, identifying focal areas of concern, and pinpointing issues in progress. Now the The customer team has to spend less time and energy on manual monitoring because ignio™ AI.Workload Monitoring provides always-on autonomous monitoring of the entire workload ecosystem and timely notifications of any potential breach. In fact, ignio™ offers a closed-loop solution by diagnosing the probable root cause of anomalies, analyzing the impact on critical business operations, and recommending the right solution to rectify it. This helps The customer ensure business continuity.

The Problem

The customer had a reactive mode of operation for workload management, where it relied on pre-defined rules to generate alerts in case of a process failure. Also, the utility did not have any defined SLAs (service level agreements) for any batch job or process, which usually help define the optimal timelines for process start time, execution time, and completion time. Lack of foresight and SLAs often prevented timely process completion, leading to escalations and expensive manual efforts, as well as business process violations or poor customer experience.

For example, when receiving payment information from third-party payment gateways, customer payments are routed through middleware to The customer’s SAP ERP system for processing, and then the amount is credited to corresponding accounts. This entire process is done by batch jobs that execute at the close of each business day. In case the process doesn’t complete in a timely manner, it can lead to loss of data or inconsistency in data, leading to faulty dunning (payment reminder) processes. This can cause unnecessary inconvenience for the customers, often resulting in complaints.

To prevent such issues from occurring, The customer needed a solution that could define SLAs, proactively identify potential SLA misses, and curb any impact on business operations

The Solution

The customer leveraged ignio™ AI.Workload Management to transform its workload management processes from reactive to proactive. With clear knowledge of the existing workload ecosystem and historical run data, ignio is now able to identify performance trends and patterns and thereby derive the optimum thresholds for every job run.

Leveraging ignio’s AI capabilities, the Digitate team provided dynamic recommendations for SLAs for the execution and completion of business-critical operations; the SLAs can now adjust themselves as per the changes in the environment in order to meet day-to-day requirements. This capability helps prioritize and focus on alerts that are important and meaningful, reducing alert noise.

One of the biggest advantages ignio provided was real-time predictions that could detect potential SLA breaches two to three hours ahead of time, diagnose and localize their probable causes, and recommend fixes, preventing many outages and minimizing the impact of others. This is helping The customer become more proactive in its ITOps process.

For instance, for customer payments, ignio analyzes the data captured from the payment gateways and payment files in real time to predict if the payment will be successfully reflected or not, as per the defined SLAs. In case of any discrepancies from normal behavior, it assesses the impact on the subsequent business operations, and accordingly informs the downstream applications to send or hold dunning letters and reminders to the customer accounts. This both increases customer satisfaction (because it protects users from getting unnecessary or inaccurate reminders) and The customer’s profitability (because payments are being reflected in a timelier way).

• 80% reduction in impacts to downstream processes like billing or payment communications

• >95% reduction in customer complaint tickets

• €5 million per day revenue loss prevented

• ~90% accuracy in predicting SLA misses

• 2-3 hours of look-ahead time for any business SLA failure, substantially improving The customer’s ability to predict and prevent failures