Infor CloudSuite and Jira integration

What moves between them

Purchase orders and projects in CloudSuite are polled and converted to Jira issues, creating a work tracking record for each financial commitment. Work logs and issue status changes in Jira are read via webhooks or polling and summarized into labor cost records posted back to CloudSuite's GL accounts and cost centers, mapped to the original purchase order. Cost center and project dimensions are mapped in both directions so Jira issues align with CloudSuite financial hierarchies.

How ml-connector handles it

ml-connector authenticates to CloudSuite with OAuth 2.0 Resource Owner Password Credentials and stores the tenant-specific API base URL and credentials encrypted. It polls CloudSuite purchase orders on a configurable schedule, converts each to a Jira issue in the mapped project, and tracks the Jira issue key in CloudSuite custom fields. On the return path, ml-connector registers a Jira webhook for issue updates and worklog entries, refreshes the webhook before the 30-day expiry, and reads the work logs to calculate labor hours and rates. Those records are posted back to CloudSuite as GL transactions allocated to the cost center and GL account specified on the original purchase order. CloudSuite rate limits (typically 100-500 requests per minute) are respected with backoff on HTTP 429 responses. The mapping between Jira issue types and CloudSuite project types is defined per customer.

A real-world example

A professional services firm runs Infor CloudSuite for project budgeting and accounting, and uses Jira for internal project tracking and team coordination. Before the integration, the project manager entered each CloudSuite purchase order into Jira manually, then the finance team exported time sheets from Jira and re-entered labor hours into CloudSuite to post cost actuals. With CloudSuite and Jira connected, each project-tied purchase order becomes a Jira issue automatically, assigned to the correct Jira project and linked to the budget. As the team logs work in Jira, those hours flow back into CloudSuite as GL postings to the correct cost center, and the project manager can see budget remaining in CloudSuite without re-keying.

What you can do

• •Poll Infor CloudSuite for purchase orders and projects on a schedule, then create or update matching Jira issues in the correct project and issue type.
• •Read Jira work logs and issue status changes via webhooks, then post labor cost transactions back to CloudSuite GL accounts allocated to the correct cost center.
• •Map CloudSuite project dimensions, cost centers, and GL accounts to Jira projects and custom fields so financial and project data stay synchronized.
• •Refresh Jira webhooks before their 30-day expiry and handle CloudSuite's OAuth token lifetime and rate limits automatically.
• •Validate CloudSuite purchase orders and Jira issue creation against configured mappings and business rules before posting transactions.

Questions

What records move between Infor CloudSuite and Jira, and in which direction?

Purchase orders and projects move from CloudSuite into Jira as issues. Work logs and issue status changes move from Jira back to CloudSuite as labor cost GL transactions. Cost centers and project dimensions are aligned in both directions so Jira issues reference valid CloudSuite GL accounts.

Why must Jira webhooks be refreshed, and how does ml-connector handle it?

Jira webhooks expire 30 days after creation or last refresh. ml-connector tracks webhook creation dates, refreshes them before expiry via the Jira API, and monitors for 401 responses that signal an expired webhook so it can re-register immediately.

How does ml-connector handle Infor CloudSuite's OAuth tokens and rate limits?

CloudSuite token lifetimes are configurable per tenant (1-24 hours). ml-connector caches the OAuth token and refreshes it proactively before expiry. Rate limits are typically 100-500 requests per minute; ml-connector backs off and retries on HTTP 429 responses with exponential jitter.