BIGCHANGE + MULTI-DEPOT OPTIMIZATION
BigChange’s scheduler treats depot as a per-engineer starting point. That works for single-depot operations and for small networks where the planner can hold the cross-depot picture in their head. Regional contractors with three or four depots, distribution arms with branch networks, and recurring programs across regions need something different: an optimizer that treats all depots as one problem and rebalances work between them under skill, capacity, time-window and SLA constraints. eLogii adds that layer on top, custom-integrated against BigChange’s RestAPI module.
Calculate the fastest route between jobs while accounting for traffic, engineer skills, and vehicle equipment.
From bigchange.com/features/job-scheduling/. The route calculation runs per-engineer once the planner has placed the jobs. Cross-depot rebalancing as a first-class input is not described on BigChange’s scheduling pages. Verified June 2026.
BigChange’s product pages cover the per-engineer depot model clearly: engineers have a base depot, vehicles are tracked from that base, the calendar shows the day’s assigned work, and the scheduler calculates the fastest route between the jobs the planner has placed for that engineer. What the pages do not describe is cross-depot optimization as a first-class input: a single run that takes all depots as inputs, rebalances jobs across them under shared constraints, and outputs assignments that may move work between depots. That layer is a different shape of product, and BigChange’s scheduler is not it.
BigChange’s reported planning outcomes (11% less drive time, 10% time saved per job, 10% less fuel) reflect the workflow product as designed. For operations whose planning problem is dominated by cross-depot rebalancing, the optimization layer underneath the scheduler is where the next step change lives.
Three concrete patterns make the case for multi-depot as a single optimization input:
In each case, the right answer is decided by the optimizer, not the planner. The planner steers the rules and signs off on the run.
A mechanical and electrical supply distributor with four depots across a UK region. Seventy engineers and drivers in the field. Daily delivery routes for trade counters; service and installation routes for accounts; parts pickup between depots when a route needs a SKU from elsewhere in the network. BigChange handles the workflow cleanly: orders, JobWatch on the van, vehicle tracking, daily reporting.
The friction sits at the depot interface. Depot A runs hot most weeks while depot D runs underused; capacity drift accumulates because no one has time to look across depots in real time. Routes that should start at depot A, pick up at depot B, then continue to customers run as two separate trips because the planner can only see one depot view at a time. Drive time creeps. The operations director can see the problem on the BI dashboard at end-of-week, but no one is solving it on Tuesday morning. A cross-depot optimization run takes all four depots as one input, drops drive time around 15–20% across the network, and rebalances capacity within roughly +/- 5%. BigChange continues to own the workflow; what changes is which depot does which job.
The workaround is the planner. The planner assigns work to depots based on rules of thumb (closest, skill match, current capacity), then BigChange calculates routes within each depot. At small numbers of depots and stable work mix, this is fine. The friction shows up at scale: capacity at one depot stays underused while another runs hot; drive time grows because work isn’t reassigned to the depot that should actually do it; SLA hits depend on the planner spotting the constraint in time. The longer the operation runs without a cross-depot optimization step, the more drift accumulates.
Multi-depot is a first-class input. Each depot has a location, opening hours, vehicle pool, skill mix, capacity and (where it matters) shift patterns. Engineers are associated with home depots but the optimizer can reassign work between depots when constraints allow. A single run produces one consistent plan.
BigChange stays in place as the system of record. The connector is custom-built against both products’ REST APIs; there is no published eLogii-BigChange integration on either side.
Most teams complete the connector build in 3 to 5 weeks. Typical first wave: the regional book where cross-depot rebalancing is the dominant planning task, or a distribution arm running parallel to the field service business.
30-minute custom simulation with your actual depots, engineers, vehicles and recurring programs. Projected savings in drive time, capacity utilisation and SLA hit rate.
BigChange supports multi-depot operations at the workflow level: engineers can be associated with home depots, vehicles can be tied to specific depots, and the scheduler shows assigned work on the calendar. What BigChange’s published docs do not describe is cross-depot optimization as a first-class input: a single optimization run that considers all depots together, rebalances jobs across depots under skill and capacity constraints, and produces assignments that may move jobs between depots when the math says they should move. That is the layer eLogii adds on top.
Three concrete patterns. First, regional contractors with three or four depots: the optimizer needs to decide which depot a job goes to based on skill availability, drive time, current capacity and SLA, not just which depot is closest. Second, distribution arms with branch networks: routes start and end at different depots in the same plan, and consolidation across branches drops drive time materially. Third, hub-and-spoke programs where engineers can start from one depot, pick up parts at another, then continue to jobs. None of these are solvable as the sum of single-depot optimizations.
The workaround is operator-driven: the planner assigns work to each depot based on rules of thumb, then BigChange calculates routes within each depot. At small numbers of depots and stable work mix, this is fine. At more depots, fluctuating work mix, or interacting skills and SLAs, the planner has to hold the cross-depot picture in their head. Capacity at one depot stays underused while another runs hot. Drive time grows because work isn’t reassigned to the depot that should actually do it. The longer the operation runs, the more this drift accumulates.
Multi-depot is a first-class input to the optimizer. Each depot has a location, opening hours, vehicle pool, skill mix and capacity. Engineers are associated with home depots but the optimizer can reassign work between depots when constraints allow. A single optimization run produces routes that may start at one depot, end at another, or pick up at a third. The output is one consistent plan, not the sum of per-depot plans. Both the Default engine and the Advanced engine handle multi-depot inputs.
Custom integration against BigChange’s RestAPI module and eLogii’s REST API. eLogii reads jobs, customers, engineers, vehicles and depots from BigChange. The optimization run treats all depots as a single problem. Routes and ETAs are written back to BigChange, mapped to the appropriate depots and engineers; JobWatch picks up the assignments unchanged. Completion data flows back to BigChange for invoicing and BI. Typical connector build: 3 to 5 weeks.
Last updated: June 2026. BigChange scope is taken verbatim from bigchange.com/features/job-scheduling/ and bigchange.com/features/. 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.
