Review of PlanetTogether, Advanced Planning and Scheduling Software Vendor

By Léon Levinas-Ménard

Last updated: December, 2025

PlanetTogether is a US-based software vendor focused on Advanced Planning & Scheduling (APS) for manufacturing, i.e., finite-capacity production planning and detailed scheduling under machine, labor, material, and sequencing constraints. Its public product materials emphasize scenario-driven schedules, capacity planning, bottleneck identification, analytics dashboards, and integrations with ERP/MES ecosystems (including partners presented on its site such as Kinaxis and John Galt).¹² Available technical PDFs portray the core “optimization” as constraint-driven schedule construction plus heuristic search/rule-based improvements, with extensive user control over priorities and manual adjustments—rather than a documented machine-learning pipeline or probabilistic decision optimization.³⁴ PlanetTogether also publishes deployment/integration documentation consistent with a Windows/.NET enterprise application footprint (e.g., Windows server components and client deployment via ClickOnce in at least some published guides), and positions typical implementation as measured in weeks in at least one integration guide.⁵⁶ On company maturity, PlanetTogether’s own materials repeatedly state “since 2004” and reference Cornell University scheduling research heritage, but public corporate registry footprints show multiple entities/registrations across years, making the exact corporate lineage harder to establish from public records alone.²⁷⁸ The vendor publicly displays customer logos and publishes case studies (e.g., Bema; New Belgium), but third-party corroboration of specific client outcomes is uneven, so logos and vendor-authored narratives should be treated as weaker evidence than independently verified references.¹⁹¹⁰

PlanetTogether overview

PlanetTogether APS is primarily a production planning and shop-floor scheduling system: it ingests demand/orders, inventories/material availability, routings/recipes/BOMs, and resource calendars, then generates feasible schedules that attempt to improve objectives such as on-time delivery, throughput, and reduced changeovers.¹¹¹ Its own materials foreground “schedule optimization” and “scenario planning,” but the most concrete technical descriptions available publicly depict a deterministic scheduling engine that (1) evaluates constraints, (2) builds or repairs schedules, and (3) searches for improvements via rules/heuristics (including priority/weighting choices and what-if comparisons), with planners retaining interactive control.³⁴ The deployment documentation available publicly aligns with a conventional enterprise footprint (server services + database + client installations) and emphasizes integration patterns, upgrades, and support processes, suggesting a commercially mature product line in the APS niche rather than a research-grade “AI-native” system.⁵⁶¹²

PlanetTogether vs Lokad

PlanetTogether and Lokad address “planning” from materially different starting points:

Scope and decision layer: PlanetTogether is centered on finite-capacity production scheduling and manufacturing planning, producing executable schedules under explicit shop-floor constraints.¹¹¹ Lokad positions itself as a predictive optimization platform for broader supply chain decisions (inventory, purchasing, allocation, production planning, pricing) rather than a dedicated shop-floor APS product.¹³
Modeling philosophy (deterministic scheduling vs uncertainty-first): PlanetTogether’s publicly available technical descriptions present scheduling as constraint satisfaction + heuristic optimization over a schedule timeline (deterministic feasibility and improvement rules), with no comparable public emphasis on probabilistic demand/lead-time modeling as the primary abstraction.³⁴ Lokad explicitly centers probabilistic forecasting and decision optimization under uncertainty (distributions rather than point forecasts), and documents this orientation in its technical and product narratives.¹⁴
Extensibility mechanism: PlanetTogether’s public deployment/integration documentation reads like a configured APS application integrated into ERP/MES data flows (connectors, deployment guides, upgrades, client installers).⁵⁶¹² Lokad’s core extensibility claim is a programmable platform organized around a domain-specific language (Envision) and an internal execution architecture described as a multi-tenant SaaS stack (front-end, persistence, execution layers).¹⁵¹³
Operational workflow: PlanetTogether foregrounds planner interaction (dashboards, optimization settings, manual interventions) typical of APS environments.¹¹⁴ Lokad’s documented approach emphasizes automated pipelines that produce decision recommendations from probabilistic models, with “white-box” inspection framed around the platform’s architecture and method descriptions.¹⁵

In short: PlanetTogether looks like a comparatively classic APS pattern—deterministic, constraint-heavy scheduling with heuristic optimization—while Lokad positions itself as uncertainty-first predictive optimization delivered via a programmable SaaS platform.³¹⁵¹⁴

Corporate history, legal footprint, and commercial maturity

PlanetTogether’s own “Company Overview” states it has operated “since 2004,” ties its origin story to Cornell scheduling research, and names founders Jim Cerra and Larry Hargis in that narrative.² Vendor collateral likewise repeats a 2004 founding claim.¹⁶

However, public corporate registry footprints suggest the corporate structure has changed over time (e.g., a Florida Sunbiz entry for “PLANETTOGETHER, INC.” and a separate California-oriented listing via CorporationWiki), which complicates the task of cleanly tracing a single continuous legal entity back to 2004 using public records alone.⁷⁸ This does not refute product longevity, but it does weaken high-confidence statements about legal continuity without additional filings.

On market presence, PlanetTogether’s own pages assert international reach (e.g., “more than 15 countries,” “34+ partners,” “100+ global support team”), but these are first-party claims and not independently audited in the sources reviewed.² No well-corroborated public record of venture rounds or acquisition events was found in the sources reviewed for this report (absence of evidence is not evidence of absence; it simply remains unverified based on the surveyed public materials).

Product scope and functional claims (what the software delivers)

Public product pages describe PlanetTogether APS as delivering:

Capacity planning (matching workload to machine/labor/resource capacity),
Schedule optimization (sequencing/dispatching under constraints to reduce downtime/changeovers),
Bottleneck management (identifying constraints and their impact),
Analytics dashboards for operational monitoring,
Inventory planning integrated with scheduling, and
Integration to ERP/MES and other planning systems.¹

AVEVA’s PlanetTogether APS page (a partner channel) similarly frames the product around schedule optimization, capacity/material constraint validation, what-if analysis, and user-driven optimization parameters, while also listing technical prerequisites such as Microsoft SQL Server and Windows environments.¹¹

These claims describe a fairly standard APS value proposition: produce feasible, improved manufacturing schedules and enable planners to adapt quickly to disruptions through scenarios and manual intervention.¹¹¹

Architecture, deployment model, and technology stack signals

PlanetTogether publishes several operational/technical PDFs that, taken together, indicate a traditional enterprise application stack:

A published deployment planning guide describes an APS deployment with multiple components and explicitly references Microsoft technologies (e.g., Windows services and related communication frameworks in that guide).⁵
A published Dynamics NAV integration guide (older but explicit) specifies Windows client and .NET-era requirements and describes client deployment/updates in a manner consistent with ClickOnce-style distribution in that document.⁶
A published service & support guide describes upgrades and operational support mechanics (processes, responsibilities), consistent with an installed enterprise product lifecycle rather than a purely self-serve SaaS footprint.¹²

As additional (but weaker) triangulation, third-party job listing aggregators frequently associate PlanetTogether roles with C#/.NET/SQL Server skillsets; aggregator data can be stale or incomplete, so it should only be treated as a supporting signal.¹⁷

Roll-out and implementation methodology (as evidenced)

One integration guide explicitly frames implementation timelines in the range of weeks (in that document’s context), and the broader documentation emphasizes deployment planning, upgrades, and support workflows.⁶¹² Separately, vendor collateral highlights proof-of-concept style approaches (e.g., “POC” positioning in marketing materials), but these are not the same as independently verified delivery timelines.¹⁶

Overall, the published materials best support the interpretation that PlanetTogether deployments are structured as classic APS rollouts: integrate data from ERP/MES, configure constraints and objectives, validate schedules with planners, and iterate until schedules are trusted for execution.¹⁵¹²

Optimization, “AI” claims, and what is technically substantiated

PlanetTogether’s most technical publicly available description of its scheduling logic appears in its constraints/algorithm PDF, which frames optimization as schedule construction and improvement under constraints using rules/heuristics (and related evaluation/selection mechanisms) rather than a disclosed machine-learning architecture.³ A separate schedule optimization document likewise emphasizes the mechanics of optimization priorities, parameters, and planner control rather than reproducible ML details (models, features, training loops, benchmarks).⁴

PlanetTogether’s marketing pages use phrases like “data-driven” and “automate complex scheduling,” but in the sources reviewed, the “automation” is best evidenced as algorithmic scheduling/heuristics (a legitimate form of optimization) rather than ML in the modern sense (trained statistical models with documented evaluation).¹³ In particular, the sources reviewed did not provide:

model cards / ML benchmarks,
published architecture diagrams for ML components,
references to specific ML frameworks used in production,
or reproducible demonstrations of learning-based optimization.

Therefore, any interpretation of PlanetTogether as delivering state-of-the-art ML/AI should be considered unsubstantiated based on the publicly accessible technical evidence reviewed here.

Clients, references, and strength of evidence

PlanetTogether’s public product page displays recognizable logos (e.g., Colgate-Palmolive, Saint-Gobain, Caterpillar, Graphic Packaging, among others).¹ Logos suggest commercial usage but are not proof of scope, deployment depth, or measurable outcomes.

PlanetTogether also publishes detailed case studies, including:

Bema (flexible packaging manufacturer) as a vendor-authored PDF case study,⁹
New Belgium Brewing as another vendor-authored PDF case study,¹⁰ and at least one partner-hosted case study referencing PlanetTogether (e.g., Westlake Global Compounds via an OnTimeEdge PDF).¹⁸

Additionally, a PlanetTogether/AVEVA brochure includes a named customer quote (Johnson & Johnson) and broad impact claims, but this remains marketing collateral and should be treated cautiously unless corroborated by independent sources.¹⁶

Net assessment: PlanetTogether provides named logos and multiple case studies, which supports commercial activity, but independent verification of outcomes is limited in the sources reviewed; evidence quality varies by reference (partner-hosted third-party PDFs are somewhat stronger than purely vendor-authored narratives, but still not equivalent to audited public results).¹⁹¹⁸

Conclusion

PlanetTogether appears to be a commercially established APS vendor specialized in finite-capacity production planning and scheduling for manufacturers, delivering feasible schedules and schedule improvements under constraints, supported by dashboards, scenarios, and integrations into ERP/MES ecosystems.¹¹¹ The most technical public documents reviewed characterize the optimization core as constraint-based scheduling with heuristic/rule-driven improvement, with significant planner configurability and intervention—credible APS engineering, but not demonstrably “state-of-the-art AI” in the modern ML sense based on the evidence available publicly.³⁴ Corporate history claims (“since 2004”) are repeated across first-party materials, but public legal footprints suggest multiple registrations/entities across years; precise legal continuity and funding history remain insufficiently documented in the public sources reviewed.²⁷⁸ Overall, PlanetTogether looks best understood as a mature APS product line with conventional enterprise deployment characteristics, rather than a transparently evidenced ML-centric optimization platform.⁵¹²