Bodhee Adaptive Scheduling FAQ: 119 answers for every buying-committee question.

Bodhee is the Adaptive Scheduling product family for process manufacturing. Three coordinated schedulers — Bodhee Production Scheduling, Bodhee Quality Control Scheduling, and Bodhee Maintenance Scheduling — work against a live Process Digital Twin (PDT) of your plant. Where a traditional plan is set weekly and re-worked manually, Bodhee re-plans continuously as orders, materials, assets, quality results, and labour change in the real world.

Plans that survive contact with reality. In most process plants, the schedule the planner builds on Monday is broken by Tuesday — a delayed material, a quality hold, an unplanned breakdown, a rush order. Today, planners and supervisors absorb the disruption with phone calls, whiteboards, and overtime. Bodhee replaces that firefighting with a schedule that re-adapts in minutes against the same constraints a human would apply, and shows the planner why.

Mid-to-large process manufacturers — pharma (API and formulation), food & beverage, specialty and bulk chemicals — and the contract manufacturers that serve them. Bodhee fits sites with multi-line, multi-asset, multi-product complexity where a single disruption ripples through orders, lab, and maintenance. It is not built for purely discrete-assembly environments.

No. Bodhee is a scheduling product. Scheduling is done using a Process Digital Twin, Constraint Programming, and Optimisation.

Yes. Bodhee orchestrates above MES and below planning. It consumes orders and master data from ERP (typically SAP S/4HANA or ECC, Oracle, or any other ERP), live execution events from MES and historian, quality results from LIMS, and asset events from CMMS. It then pushes back a feasible, optimised, re-adapting schedule that MES executes and ERP confirms.

No. Bodhee works in online, offline, and hybrid modes. Where a live connection is not possible, file uploads or manual inputs can supply the same data.

APS (Advanced Planning & Scheduling) is built to optimise a plan once — typically weekly — against a snapshot of demand and capacity. Adaptive Scheduling is built to re-plan continuously against the live state of the plant. APS answers "what is the best plan for next week?"; Adaptive Scheduling answers "given everything that has changed in the last hour, what is the best plan for the next 24 hours, and which orders are now at risk?"

Disruption is now the steady state, not the exception. Demand volatility, supply variability, labour churn, regulatory holds, and asset surprises arrive every shift. Static plans were always optimistic; today they are obsolete the moment they are published. Adaptive Scheduling assumes change and treats re-planning as a continuous operating discipline, not an escalation.

It is an emerging category, and Bodhee is one of the products defining it. Adjacent terms in the market — "dynamic scheduling", "autonomous planning", "real-time scheduling" — describe pieces of the same idea. We use "Adaptive Scheduling" because it is honest about what changes: not the planner, not the ERP, but the schedule itself, on a cadence the plant actually runs on.

The two co-exist by design. Your S&OP and master plan set the boundary conditions — what to produce, by when, with what priority. Bodhee adapts inside those boundaries: which line, which order first, when to clean, when to test, when to maintain. The planner stays in charge of the strategic plan; Bodhee handles the second-by-second feasibility.

No. It removes the firefighting from the planner's day and frees them for work that actually requires judgement — scenario evaluation, allocation decisions, exception escalation, customer trade-offs. Customers consistently tell us their planners report feeling more in control, not less, after Bodhee goes live.

It depends on the module and the process. Bodhee Production Scheduling adapts at the order, batch, or campaign level. Bodhee Quality Control Scheduling adapts at the sample, test, or analyst-slot level. Bodhee Maintenance Scheduling adapts at the work-order, crew-shift, or window level. The cadence is event-driven — the moment a constraint changes, a re-plan is triggered.

The Process Digital Twin (PDT) is the live representation of your plant that Bodhee reasons against — products, recipes, asset, materials, labour, quality holds, and the constraints that connect them. It is not a 3D model; it is a decision model. Every Bodhee recommendation traces back to a specific state of the PDT, which means every recommendation is explainable.

No. A 3D / CAD twin is built for visualisation, simulation of physical phenomena, or operator training. The Bodhee PDT is built for decisions — what to schedule next, in what order, under what constraints. It carries state, constraints, and dynamics, not geometry.

At minimum: products, process flows, material, recipes, assets, labour, and basic constraints. We do not require every source on day one — most deployments stand up a minimum-viable PDT first and enrich it as integrations land.

Yes — and that is the recommended pattern. Start with one production line, one asset class, or one lab; prove the value; then expand. The PDT is designed to extend without reshaping the data model already in place.

A mix of streaming pipelines for high-frequency sources (MES, historian, SCADA), scheduled syncs for slower-changing data (ERP master data, BOMs, recipes), and manual data entries. Events drive the re-plan: when an equipment is marked as down, a sample fails, a work order is closed, or a customer accelerates an order, Bodhee re-evaluates within minutes.

Both, jointly. Operations defines the semantics — what counts as an asset, a batch, a campaign, a hold. IT / OT operates the pipelines and the integration plane. Bodhee provides the PDT model, the connectors, and the ongoing model operations.

No. The PDT is what makes Bodhee's schedules explainable and constraint-grounded. The good news is the PDT does not have to be perfect or complete at go-live — a minimum-viable PDT, scoped to the first use case, is enough to start delivering value. It then grows with the deployment.

Bodhee Production Scheduling generates and continuously adapts a feasible, optimised production schedule against live constraints — orders, recipes, asset availability, materials, labour, and priority. When any of those change, the schedule re-plans within minutes with a goal to minimise the impact of that change on the schedule and the plant performance.

Demand / order data (campaigns, process orders, planned orders), products & recipes, assets, raw-material and intermediate availability, teams and operators and their skills, changeover and cleaning rules, and calendars (factory, shift, holiday). Most of these already exist in your ERP, MES, and master-data systems — Bodhee Production Scheduling reads them through standard connectors and the PDT layer.

All three, and the mixes of them that real plants run. Bodhee Production Scheduling treats each line on its own terms:

• Campaign-mode lines — orders are grouped and sequenced so similar products run back-to-back, amortising changeover and cleaning across the campaign and minimising cross-contamination risk.
• Batch-mode lines — orders are scheduled batch-by-batch against recipe, asset compatibility, and batch duration, with cleaning, validation, and hold-time windows respected between batches.
• Continuous-mode lines — production is scheduled as a throughput rate against asset constraints, run-length limits, and the upstream / downstream lines that feed or are fed by the continuous train.

A single plant can run all three side by side — the scheduler does not force one model onto every line. Where one mode's output feeds another (e.g., a continuous reactor feeding a batch crystalliser feeding a packaging campaign), Bodhee schedules the handoffs as constraints rather than treating each line in isolation.

Cleaning is modelled as a schedulable process in its own right — with its own tasks, durations, resources, and dependencies — not as a fixed overhead bolted onto production. Cleaning tasks link to production batches through the trigger patterns that real plants actually run on:

• Pre-production — cleaning before the first batch or campaign starts.
• Post-batch / post-campaign — cleaning at the end of a batch or at the end of a campaign.
• Mid-batch — cleaning triggered after a specific task inside a batch completes.
• Production-blocked-on-cleaning — a production task that cannot start until a specified cleaning process is complete.

Changeovers follow the same model — typed by product-pair, costed, and sequenced by recipe family — and validation holds (post-cleaning verification, pre-processing steps) are modelled as their own gated task class with their own duration and downstream blocking effect. Bodhee Production Scheduling respects every cleaning, changeover, and validation rule, and sequences orders to minimise their cumulative cost when the order book allows.

Yes — overrides are first-class, not exceptions. A planner can fix or freeze an order, lock an equipment to an order, exclude an asset, exclude a set of operators, set priority on certain materials, or force a window; the scheduler adapts the rest of the plan around the override.

Minutes for typical single-plant scope; configurable for larger horizons and multi-site scope. Re-plan cadence is event-driven by default — the system triggers when a constraint materially changes, not on a fixed clock. Typically granular rescheduling (with operator re-allocation) runs for the next 24–48 hours, and strategic re-planning runs for the next 2–4 weeks, depending on the level of disruption and the user-configured cadence.

Both. The default is a single recommended plan, ready to dispatch. The planner can also run what-if scenarios — "what if we lose Line 2 for six hours", "what if we accept the rush order", "what if we push the changeover to night shift" — and compare KPI impact side-by-side before committing.

Yes. The scheduler consumes material status from ERP and MES, detects shortages or late arrivals against the planned sequence, and re-sequences orders to absorb the disruption rather than break the plan. Where re-sequencing is not enough, it surfaces the OTIF risk early with the trade-offs spelled out. Material availability data from ERP is enriched using the active schedule for forecasted material availability (e.g., semi-finished-goods availability for finished goods).

Yes. Multi-site deployments are supported with site-level autonomy as the default — each plant runs its own schedule with its own constraints — and cross-site orchestration where the order book genuinely flows between sites. Bodhee supports both topologies.

Both, plus the production batches awaiting release. Bodhee Quality Control Scheduling plans sample arrival, test execution, analyst allocation, instrument capacity, and release sequencing for batches blocked by pending results. It treats the lab as a constrained shop floor — because that is what it is.

By pulling three levers at once:

• Priority sequencing — release-blocking and deadline-critical samples are sequenced first.
• Equipment-level optimisation — the sample mix on each instrument is optimised against sample arrival times, required-by dates, and instrument capacity.
• Lab–production coordination — the lab schedule is aligned with the production release plan, so finished batches are not waiting on tests that could have run earlier.

The scheduler knows which finished batches are blocked, by which test, on which instrument, and by which analyst — and sequences accordingly. The visible outcomes are shorter lab turnaround time and fewer batches waiting on release.

Yes — with major LIMS vendors including LabWare, STARLIMS, and LabVantage. Bodhee Quality Control Scheduling reads sample registration and test status from the LIMS. Custom LIMS deployments are supported through the standard integration adapter. Writing the test schedule back from Bodhee into LIMS is technically feasible, but the recommended approach is to use Bodhee's data exports / API connectivity to integrate into LIMS outside the Bodhee environment.

As constrained queues alongside release testing, with their own priority and pull-date logic. The scheduler respects pull-date windows for stability protocols and ensures regulated tests are not starved by higher-volume release tests.

Yes. Each discipline is modelled as a distinct resource pool with its own instruments, analysts, certifications, and SOPs. A single deployment can manage a microbiology lab, a chemistry lab, and a physical-testing lab on different floors or sites under one schedule.

Customer outcomes show consistent reductions in sample backlog and lab turnaround time. Named results are published in the case-study library. Reference customers are made available after qualification stage.

Yes. The scheduler schedules around analyst skill, certification, shift, and current workload. Senior analysts are not over-allocated to routine work, and certifications are honoured automatically. Using the Bodhee side-task functionality, the lab can keep a buffer of pre-planned work (for example, a daily 9 AM status call or ad-hoc training for an analyst) ready to slot into analyst downtime without needing a new approval or re-prioritisation.

All three. Bodhee Maintenance Scheduling plans the work-order backlog, technician and crew capacity, planned shutdowns, and opportunistic maintenance windows that open up when production is paused or running below capacity. The output is a maintenance plan that fits the actual production calendar instead of fighting it.

The maintenance scheduler reads the production schedule directly from Bodhee Production Scheduling — or from your existing scheduler if Bodhee Production Scheduling is not yet deployed — and finds the least-cost maintenance windows. When the two run together, the system co-plans production and maintenance jointly, which routinely surfaces "free" maintenance opportunities the manual process misses.

No. Bodhee Maintenance Scheduling is a scheduling product; it does not provide condition-based or predictive maintenance models and features.

Yes — bidirectionally with SAP PM, IBM Maximo, Infor EAM, and eMaint. Bodhee Maintenance Scheduling reads work-order data, asset hierarchies, and technician availability from the CMMS, and writes scheduled execution windows and completion confirmations back.

As a hard constraint. The scheduler will not schedule a work order without confirmed parts availability, and it surfaces parts-driven schedule delays explicitly so procurement can act. Spare-parts availability is read as a flag from the CMMS. When parts arrive, opportunistic re-plan brings the work order forward.

No. APS systems plan at weekly / monthly cadence against forecast and capacity assumptions. Bodhee adapts at daily / shift cadence against live plant state. They are complementary: APS sets the boundary conditions and the production targets; Bodhee adapts the schedule inside those boundaries as the plant runs.

No. MES executes and records work on the shop floor; Bodhee schedules what the MES will execute. Most deployments leave MES untouched and integrate Bodhee above it.

For most customers, Bodhee fills a gap that ERP scheduling modules do not — sub-daily, task / operator-level, event-driven, constraint-rich re-planning. ERP scheduling continues to handle master data, capacity definitions, and high-level planning; Bodhee handles the operating cadence the ERP was never designed for.

SAP DS / PP-DS is built for static optimisation, typically on a weekly cadence, and is a heavy lift to keep current with live plant state. Bodhee is built for continuous adaptation against a live PDT. Several Bodhee customers run SAP DS or PP-DS at the planning layer and Bodhee at the operating layer — the two coexist.

Generic optimisers are math toolkits — they require a model, a data layer, an integration layer, an operations layer, and a UI before they deliver a schedule. Bodhee is a productised system that ships with the PDT model, the connectors, the operations runtime, the planner UI, and the ML feedback loop. You buy a working scheduler, not a toolkit.

Some customers consider it. The usual conclusion is that build cost — discovery, PDT, connectors, scheduler engine, UI, MLOps, and the ongoing model-operations burden across asset replacements and recipe changes — exceeds the cost of buying a system already in production at peer manufacturers. We share an honest build-vs-buy worksheet during evaluation.

No. Generative-AI tools are built for language and content generation. Adaptive Scheduling is structured constrained optimisation against a live PDT — a different problem class, with different math, different data, and different accountability. The two can coexist (an LLM may sit on top to explain a Bodhee recommendation in natural language), but one does not replace the other.

That conversation is best had specifically — every competitor has strengths and gaps, and the right answer depends on your process, your data maturity, and the gap you are feeling today. Common reasons customers move to Bodhee: continuous adaptation rather than one-shot optimisation, native process-manufacturing modelling rather than retrofitted discrete-manufacturing models, and explainable recommendations rather than black-box outputs.

Process manufacturing — pharma (API and formulation), food & beverage, specialty and bulk chemicals — and the contract manufacturers serving those industries. Inside each industry, Bodhee is deployed at sites with multi-line, multi-product, multi-constraint complexity.

All three, including hybrid sites that combine modes. The Process Digital Twin models each line on its own terms — campaign, batch, or continuous — and Bodhee schedules accordingly.

Not as the primary fit. Bodhee is built around the constraint patterns of process manufacturing — recipes, cleaning, validation, lab release, condition-based maintenance, regulatory holds. Discrete-assembly sites are better served by tools designed around BOM explosion and assembly-line balancing.

Yes. Bodhee is designed for regulated environments with full audit trail, data-integrity controls, and validation-friendly release patterns. Based on prior implementations, Bodhee Scheduling falls under non-GMP implementation in customer landscapes.

Yes — through phased data uplift. Most customers do not start with the data quality they would like; the first deployment wave is scoped to a minimum-viable PDT and enriched as integrations land. The first measurable outcomes are usually visible before the data foundation is "finished".

Mid-to-large running plants. The sweet spot is multi-line, multi-asset, multi-product sites — running 24/7 or close to it — where manual scheduling routinely breaks under the combined load of orders, lab releases, and asset events. Smaller or single-line running plants rarely justify Bodhee on operating ROI alone.

There is a separate, growing use case at the greenfield end: design teams planning a new plant or a major capacity expansion can use Bodhee's scheduler to simulate proposed layouts, asset mixes, recipe portfolios, and throughput targets before the plant is built. That engagement is structured as a design-phase deployment, not a running-plant rollout.

Yes — and CMOs benefit disproportionately. Multi-tenant order books, customer-specific recipes, customer-specific cleaning rules, and customer-specific reporting are designed into Bodhee's data model from day one, not bolted on. A single deployment can manage multiple brand-owner customers under one schedule, with the data-isolation and reporting boundaries each contract requires.

Yes — natively. Bodhee handles MTS, MTO, and ATO order patterns in the same schedule, with the priority and inventory-target logic each pattern requires.

Bodhee runs in Google Cloud Platform (GCP) regions, with options including India, EU, and US zones. Specific region is selected per customer to meet data-residency requirements and to keep latency manageable from the customer's plant. Once a customer is provisioned in a region, customer data does not leave that region.

Multi-zone deployment across GCP zones within the customer's selected region, with documented RTO and RPO targets in the support documentation (provided as part of commercial discussions).

Bodhee does not support air-gapped or fully-isolated deployments. The SaaS architecture requires the plant to reach Bodhee's GCP environment over an authenticated, encrypted network path — typically via a VPN or private interconnect. For customers with a hard air-gap requirement, please contact us — we will tell you honestly whether Bodhee is the right fit rather than commit to a deployment model we do not support.

Bodhee is historian-vendor-agnostic by design. Telemetry ingestion uses the integration pattern the customer's historian already supports — OPC UA / OPC HDA, vendor REST APIs, ODBC / JDBC, MQTT, or file import / export — through Bodhee-managed connectors.

Bodhee Quality Control Scheduling is LIMS-vendor-agnostic by design. Data exchange uses the integration pattern the customer's LIMS already supports — REST, SOAP, file import / export, or direct database read — through Bodhee-managed connectors. The scheduler reads sample registration and test status from the LIMS (and ERP for raw-material goods receipts).

Bodhee Maintenance Scheduling is CMMS-vendor-agnostic by design. Data exchange uses the integration pattern the customer's CMMS already supports — REST, SOAP, file import / export, or direct database read — through Bodhee-managed connectors. The scheduler reads work-order data, asset hierarchies, and technician availability, and writes scheduled execution windows and completion confirmations back.

Yes — Bodhee ships OPC UA as a productised adapter, used for sensor data, asset state, and historian fallback where a dedicated historian is not deployed.

Yes. Bodhee exposes a documented OpenAPI for synchronous integration and webhook events for asynchronous flows. Both are versioned and backwards-compatible across releases per the published deprecation policy.

Banded by integration class:

• ERP and MES integrations: 4–8 weeks.
• LIMS and CMMS: 4–6 weeks.
• Historian and OPC UA: 8–12 weeks.
• Custom integrations: scoped during discovery.

SAML 2.0 and OpenID Connect for SSO. Bodhee integrates with the major identity providers — Azure AD / Entra ID, Okta, Ping, Google Workspace.

Role-based access control at module, site, and action level. Roles can be scoped per module (Bodhee Production Scheduling, Bodhee Quality Control Scheduling, Bodhee Maintenance Scheduling), per site, and per action class (read, schedule, override, admin, audit).

Annual third-party penetration tests, plus targeted tests on major releases. The latest pen-test summary is available under NDA.

Immutable, time-stamped, and exportable. Every scheduling action — recommendation, override, configuration change, integration event — is recorded with user identity, timestamp, and pre / post state. Audit data is retained per customer contract and can be exported on demand.

Yes. Bodhee's standard DPA covers GDPR, India DPDP, and other major regional regimes. Customer-specific addenda are accommodated through the contracting process.

Single line, single asset class, or single lab — scoped to deliver a measurable KPI shift inside the pilot window. The pilot validates the PDT model, the integration pattern, and the planner workflow before broader rollout. Pilot success criteria are agreed before kickoff, with a baseline measurement up front.

Both. Neewee leads first deployments at a customer and trains the customer's chosen SI partner for subsequent sites. Customers with an existing implementation partner (consulting firms, SIs) can run deployment through that partner with Neewee in a senior-advisory role.

Data access (ERP, MES, historian, LIMS, CMMS as relevant); SMEs for production, quality, and maintenance; a senior change sponsor; and integration owners for each system in scope. A typical customer-side team is 4–8 people across functions, working part-time on the deployment.

Five phases — discovery, PDT build, integration, pilot, scale. Each phase has explicit entry and exit criteria, agreed deliverables, and named owners on both sides. Deployment governance includes weekly working sessions and a steering committee at fortnightly cadence. The detailed governance plan is shared during commercial discussions.

A KPI baseline is captured at kickoff — OTIF, OEE, FPY, MTTR, lab turnaround time, planner cycle time, whichever apply. Target movements are agreed before pilot. Checkpoint reviews compare actual against baseline at pilot end, single-site rollout end, and quarterly thereafter.

That is the expected starting condition, not a blocker. Bodhee deployments treat data uplift as the first wave of work — clean enough to support the pilot scope, not perfect across the whole plant. Bodhee is designed to be minimum-viable at go-live and to enrich over time as data sources mature.

Trust is earned, not assumed. The pattern that works: explain every recommendation transparently, make overrides easy and audited, run shadow-mode before go-live so planners can compare Bodhee's recommendation against their own decisions, and celebrate small wins early. Customers consistently report planner trust building over the first 60–90 days of live operation.

Role-based curricula — planner, shift leader, foreman, production manager, operator, analyst, admin — delivered in a mix of classroom, on-the-job, and self-serve formats. Train-the-trainer is offered for customers with multiple sites. Refresher modules are included in the support tier and updated with each major release.

That is expected and planned for. The pattern that works: co-design the planner workflow with the planners themselves, give them explicit override authority, run shadow-mode before go-live, and let small wins build credibility.

Helpful, but not required for V1. For multi-site rollouts, a CoE is the most reliable way to standardise the deployment pattern, share lessons across sites, and maintain the PDT model over time. Most customers stand a CoE up after the first site goes live.

Roles evolve, not disappear. The day-to-day rhythm shifts from "re-planning when reality breaks" to "scenario evaluation, exception escalation, customer trade-offs, and continuous improvement". Planners consistently report the work becoming more strategic and less reactive.

Usage analytics (logins, schedule acceptances, scenario runs), planner override-rate trajectory (high at go-live, declining as trust builds), and KPI movement against baseline. Adoption signals are reviewed in monthly success reviews during the first year.

English, French, German, and Hungarian are supported today. Other languages are added based on customer demand.

Both. Customers can license Bodhee Production Scheduling, Bodhee Quality Control Scheduling, or Bodhee Maintenance Scheduling individually, or take the family bundle. Most customers start with one module and expand based on outcomes.

Implementation services (Neewee or partner-led), data integrations, advanced support tiers, and customisations. Pricing for these is scoped during evaluation.

Yes — multi-site and multi-module discounts are standard. Bodhee's commercial model is designed to reward expansion across sites and modules rather than penalise it.

Both are available. Multi-year terms (typically three years) carry better commercial terms and are the more common choice for enterprise customers. Pilot agreements are typically time-boxed and convert to full licence on success criteria.

Standard payment terms with regional flexibility. Bodhee invoices in major currencies — USD, EUR, INR — depending on the customer entity. Specific terms are agreed in the MSA.

Yes — a defined pilot package scoped to a single line, asset class, or lab, with success criteria agreed up front. The pilot price is structured to be a low-risk on-ramp, not a profit centre.

Banded by module and customer profile. Most customers see payback inside 8–12 months; multi-module deployments amortise faster.

Pre / post against a baseline agreed at kickoff. The baseline captures the KPIs in scope, the measurement method, and the target movement; quarterly reviews compare actual against baseline and adjust the model where needed. Methodology is shared with the customer up front — no surprises.

Yes — an ROI worksheet is provided during evaluation, populated from a structured discovery session with the customer's operations and finance teams. It is not a marketing artefact; it is the same document the customer's CFO will see during procurement review.

Named and anonymised case studies are published in the case-study library. Reference customers in similar process types are made available after qualification stage.

Bodhee does not move KPIs that depend on factors outside the scheduling decision — raw-material cost inflation, demand collapse, energy prices, regulatory changes. We are explicit about this at evaluation so the business case is defensible.

Named customers with their permission; anonymised case studies otherwise. Customers in pharma, food & beverage, specialty chemicals, and cement are in production today. A list relevant to your industry is shared during evaluation.

Yes — after qualification stage. Reference conversations are scheduled with customers in similar industry, similar process, and similar scale, with their consent.

The case-study library is filtered by industry and process type — pharma API, pharma formulation, food, beverage, specialty chemicals, bulk chemicals, cement, paper. If your process type is not yet represented in a published case study, we share anonymised outcomes under NDA.

Yes. Bodhee's policy is that logos shown on www.bodhee.com represent production deployments with the customer's documented permission to display. Pilot-only or evaluation-only customers are not represented as production logos.

Roadmap themes — not feature dates — are shared during evaluation under NDA. Current themes centre on deeper Adaptive Scheduling capabilities across the three modules, broader integration coverage in regulated industries, and expansion of the explainability surface for end-user trust.

Customer data is portable. Bodhee's exit clause in the MSA includes documented export tools, a defined transition window, and certification of data destruction post-exit. No customer is ever locked in by data hostage.

Major capability deprecations are announced with a minimum notice period (defined in the support documentation) and backwards-compatibility guarantees for API consumers across at least one major version. Breaking changes are reserved for major releases and never shipped silently.

The planner logs in to a dashboard that already reflects everything that happened overnight — new orders, completed work, quality results, asset events, material arrivals. The recommended schedule is ready, with exceptions and trade-offs flagged. The planner reviews, adjusts where they want to, and dispatches. Most customers report the morning routine compressing from hours to minutes after go-live.

Seconds to minutes, depending on scope. A single-order override is seconds. A full re-plan across a multi-line plant is minutes.

An event-driven re-plan is triggered automatically when a material constraint changes — a sample fails, an asset goes down, a material arrives late, a customer accelerates.

Bodhee is compatible with tablets for supervisors and operators — schedule visibility and basic status updates. Full planning workflows remain on desktop, where the screen real estate matches the work.

Yes — through operator-scoped views and optional shop-floor displays. Operators see what is scheduled, in what sequence, against which asset, with the next-task context they need. Edit rights are role-scoped — operators see, planners and supervisors edit.

Bodhee can run unattended with policy guardrails — auto-acceptance of recommendations inside a defined envelope.