BIGCHANGE PPM + OPTIMIZATION ENGINE
BigChange’s scheduler generates recurring PPM schedules: the product page documents “set up recurring service schedules for PPM work” with automatic generation at the specified intervals. That covers a wide band of recurring service work cleanly. At thousands of recurring stops, with SLAs that vary by customer or contract, with cadence drift to keep capacity balanced, and where the recurring program interacts with reactive work for the same engineers, schedule generation is no longer the same thing as optimization. eLogii’s engine models task and route template groups as constraint inputs to the optimizer.
Set up recurring service schedules for PPM work.
From bigchange.com/features/job-scheduling/. BigChange generates the schedule at the right cadence. Constraint-aware optimization across thousands of generated stops with interacting SLAs is its own layer. Verified June 2026.
BigChange’s scheduling page documents recurring PPM cleanly: “Set up recurring service schedules for PPM work” with automatic generation at the specified intervals. The platform tracks the parent program and the child appointments, holds time on the calendar, and runs the standard route calculation between assigned stops. The vocabulary is generation-first: produce the stops, hold them on the calendar, route between them.
What the docs do not describe is constraint-aware optimization across the generated stops. The recurring program at scale isn’t just a calendar problem – it’s an assignment problem where SLAs interact, cadences drift, skill requirements pin specific engineers to specific stops, and capacity has to balance across the recurring program and the daily reactive work.
The recurring programs that outgrow BigChange’s scheduler are concrete:
In each case, the engine doesn’t replace BigChange’s recurring-schedule generation. It optimizes what the schedule generation produces.
A facilities maintenance contractor running monthly, quarterly and annual PMs across a corporate property portfolio. Fifty-five engineers in the field. Around 3,500 recurring stops per month across the PPM book, plus daily reactive work for the same engineers. SLA windows vary by customer contract: gas safety pinned to anniversary dates, lift inspections on a hard 6-month cycle, fire-safety checks within calendar months, water-hygiene programs at four-week intervals.
BigChange generates the recurring schedule cleanly: stops appear on the calendar at the right cadence, JobWatch carries the job sheets and PM checklists, completion data flows back to BI. The planning task that grows past the scheduler is the balancing: reactive work for the same engineers landing on top of recurring stops; cadence drift to keep capacity even across the month; SLA windows that need protection from the optimizer rather than from a planner spotting them in time. Template-group optimization absorbs cadence drift within rules, protects SLA-locked stops, balances against reactive, and outputs one plan that’s already reconciled across the book. The PPM data model stays in BigChange; the optimization across it runs underneath.
The workaround is the same as for the wider planning problem: the planner carries it. The generated PPM stops appear on the calendar; the planner assigns them to engineers, balances against reactive work, allows cadence drift to keep capacity even, and signs off on the day. At small recurring books, this is straightforward. At several thousand recurring stops a month, with SLAs and cadences interacting, the planner becomes the optimizer. The friction shows up as SLA misses on specific contracts, drive-time creep on the recurring book, and over-reliance on the one or two planners who know the recurring rules best.
Recurring programs are modeled as task and route template groups feeding the optimizer. Each template carries the cadence, skill requirements, SLA windows and depot rules; the optimizer treats them as first-class inputs alongside the daily flexible work.
BigChange stays in place. The recurring schedule generation continues to run in BigChange. eLogii reads the generated stops, plus the reactive work, plus the engineer/vehicle/depot model, runs the optimization across them, and writes back optimized routes and ETAs.
Most teams complete the connector build in 3 to 5 weeks. Typical first wave: the recurring program that is leaking the most against SLAs today, or the compliance book where cadence interactions are hardest.
30-minute custom simulation with your actual recurring PPM book, engineers and SLAs. Projected savings in drive time, SLA hit rate and planner hours.
Yes, at the workflow layer. BigChange’s Job Scheduling page documents: “Set up recurring service schedules for PPM work” with automatic generation at specified intervals. The schedule for the recurring program is generated on time; the assignment and route between the generated stops is then handled by the standard scheduler. What BigChange does not describe is constraint-aware optimization across thousands of recurring stops with interacting SLAs, cadences and skill requirements. That layer is where eLogii adds value.
Schedule generation: produce a list of stops at the right cadence (monthly PMs, quarterly compliance visits, weekly commercial maintenance). Recurring program optimization: take the generated stops, plus the daily flexible work, plus the engineer pool, plus the SLAs and cadences, and decide assignments and routes that respect all of them at once. BigChange does the first inside the scheduler. eLogii does the second, modeled directly through task and route template groups that feed the optimizer as constraint inputs.
When the recurring program runs to hundreds or thousands of stops per month, when SLAs vary by customer or contract, when cadence drift (a stop shifting from week 1 to week 2 to keep capacity balanced) becomes a planning task, and when the recurring program interacts with daily reactive work for the same engineers. Inspection programs across a campus, multi-site PPM for facilities, recurring commercial maintenance contracts, compliance visits across regions: each of these hits the optimization layer rather than the schedule generation layer.
Through task and route template groups: weekly, monthly, quarterly and bespoke cadences are modeled directly as inputs to the optimizer. Each template carries skill requirements, time-window constraints, SLA targets and cadence rules. When the run happens, the optimizer balances the recurring program against the daily flexible work, protects SLA-locked stops, allows cadence drift within rules, and outputs one consistent plan. Bristow & Sutor routes 200,000+ recurring case visits per year on eLogii.
Custom integration against BigChange’s RestAPI module and eLogii’s REST API. eLogii reads recurring PPM templates and the generated stops from BigChange, plus the daily reactive work and engineer/vehicle/depot model. The optimization run considers them together. Routes and ETAs are written back to BigChange; JobWatch picks up the assignments. Completion data flows back. Typical connector build: 3 to 5 weeks.
Last updated: June 2026. BigChange scope is taken verbatim from bigchange.com/features/job-scheduling/. eLogii capabilities documented at elogiiapidocs.apidog.io.
Custom simulation
A 30-minute working session with our solutions team. We take a sample of your real jobs, depots, vehicles and SLAs, run them through the eLogii engine, and show you the projected delta against how you plan today. No slides, no generic benchmarks.
