Infor LN Batch Jobs: Scheduling and Optimization Guide
Batch jobs in Infor LN execute the heavyweight business processes that cannot run interactively—MRP planning runs, general ledger period closings, data replication between companies, mass price updates, and automated EDI processing. The LN Job Scheduler manages the execution timing, dependencies, resource allocation, and failure recovery for these critical processes. Poor batch job scheduling leads to missed processing windows, data inconsistencies between modules, and morning-after fire drills when overnight jobs fail silently.
Job Scheduler Architecture and Configuration
The LN Job Scheduler (ttadv8100m000) runs as a system-level service that evaluates job definitions against their scheduled execution times and available server resources. Each job definition specifies the session to execute, the input parameters, the execution schedule (CRON-style or calendar-based), the execution priority, and the dependency chain (which jobs must complete before this job starts). The scheduler supports parallel execution of independent jobs and serial execution of dependent chains.
- Define a scheduled job: navigate to Job Scheduler (ttadv8100m000); enter a job code, select the target session (e.g., cprrp0200m000 for MRP run), specify the parameter set (company, planning group, date range), and set the schedule expression
- Schedule expressions: LN supports CRON-style scheduling: '0 2 * * 1-5' = every weekday at 2:00 AM; '0 0 1 * *' = first day of each month at midnight; '0 */4 * * *' = every 4 hours; for calendar-based scheduling, reference a factory calendar that accounts for holidays
- Job dependencies: define chains using the Dependency tab: Job B depends on Job A means Job B will not start until Job A completes successfully; if Job A fails, Job B is held with status 'Waiting-Dependency'; use this for MRP → Planned Order Release → Purchase Requisition Generation chains
- Priority levels: assign priorities 1 (Critical) through 5 (Low); when server resources are constrained, the scheduler runs higher-priority jobs first; set MRP and financial closing jobs to priority 1, data archival and cleanup jobs to priority 4-5
- Execution slots: configure the maximum number of concurrent jobs in System Parameters > Job Scheduler Settings > Max Concurrent Jobs; set based on server CPU cores and memory: typically 2-4 concurrent jobs for a standard LN application server
Optimizing Critical Batch Processes
The three most resource-intensive batch jobs in any LN implementation are MRP (Material Requirements Planning), GL Period Close, and Data Replication. Each has unique optimization strategies that can reduce execution time by 50-80%. MRP optimization focuses on planning group segmentation and selective regeneration. GL Period Close optimization targets journal posting parallelism and balance validation. Data Replication optimization uses incremental change detection instead of full table copies.
- MRP optimization: split the full MRP run into planning group segments that execute in parallel; configure in MRP Parameters (cprrp0100m000): define planning groups by product family or warehouse, then schedule each group as a separate job that runs concurrently; a full MRP that takes 4 hours serially completes in 90 minutes with 4 parallel planning groups
- GL Period Close: pre-validate journal entries before the close run using the GL Validation Report (tfgld1400m000) to catch errors early; configure the close job to post journals in parallel batches of 1,000 entries using the Parallel Posting parameter in GL Parameters; this reduces close time from hours to minutes
- Data Replication: switch from full-table replication to incremental replication using the LN Change Data Capture (CDC) mechanism; only replicate records modified since the last replication run; configure in Replication Parameters (ttadv8200m000) > Replication Mode = Incremental; this reduces replication of a 1M-row table from 2 hours to 5 minutes
- Index maintenance: schedule database index rebuild/reorganize jobs immediately after large batch processes (MRP, data imports) that create significant index fragmentation; use the LN Database Maintenance session (ttadv9500m000) to configure automatic index maintenance
- Temporary table cleanup: batch jobs create temporary working tables that are not always cleaned up on failure; schedule a nightly cleanup job that drops orphaned temp tables older than 24 hours to prevent database bloat
Monitoring, Alerting, and Failure Recovery
Batch job failures must be detected immediately because downstream processes and business operations depend on successful completion. The LN Job Monitor (ttadv8120m000) provides real-time status for all scheduled jobs, but passive monitoring is insufficient—proactive alerting and automated failure recovery are essential for production reliability. The combination of status alerts, automatic retry, and conditional re-execution ensures that batch processing completes within the required business windows.
- Job status monitoring: navigate to Job Monitor (ttadv8120m000); view all jobs with status Running, Completed, Failed, or Waiting; the monitor shows start time, elapsed time, records processed, and error count for each execution; drill into failed jobs to see the error message and BShell stack trace
- Email alerting: configure job-level email notifications in Job Definition > Alerts tab: send email on Failure (always), on Success (for critical jobs), and on Long-Running (elapsed time > threshold); include the job log excerpt in the email body for immediate diagnosis
- Automatic retry: for transient failures (database lock timeout, network interruption), configure Retry Count = 3 and Retry Interval = 300 seconds in the job definition; the scheduler automatically re-executes the job up to 3 times before marking it as permanently failed
- Failure recovery: for MRP and GL Close jobs that fail mid-execution, the LN runtime writes a checkpoint to the job's state table; on retry, the job resumes from the last checkpoint instead of restarting from the beginning; verify checkpoint behavior is enabled in the session's job properties
- SLA tracking: define batch processing SLAs (e.g., MRP must complete by 6:00 AM, GL Close must complete by 8:00 AM) and configure the Job Monitor to flag any job that is at risk of missing its SLA based on current progress rate and remaining work volume
Optimize your Infor LN batch processing with Netray's AI-driven scheduling analysis—reduce batch windows by 50% and eliminate overnight failures.
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