Review of StockIQ Technologies, Supply Chain Planning Software Vendor

By Léon Levinas-Ménard

Last updated: December, 2025

StockIQ Technologies sells a supply chain planning suite aimed at distributors and manufacturers, centered on demand forecasting and replenishment decisions, with adjacent modules for SIOP, promotion planning, supplier performance monitoring, and inventory analysis. The company positions its product as a practitioner-built planning layer that integrates with existing ERPs/WMSs and is supported by an implementation process marketed as fast (often framed as weeks rather than months). Publicly available technical documentation emphasizes configurable time-series forecasting approaches (including algorithm “tournaments” and error-based model selection), operational planning workflows, and deployment prerequisites consistent with an enterprise web application that can be hosted or deployed on-premises.

StockIQ overview

StockIQ presents its software as an “intelligent supply chain planning suite” spanning demand planning/forecasting and replenishment planning, plus supporting functions such as supplier performance, promotion planning, inventory analysis dashboards, and SIOP rollups.¹² The emphasis in StockIQ’s own materials is on improving planners’ day-to-day decisions (e.g., order timing and quantities) rather than positioning the product as a transactional system of record.¹³

Commercially, StockIQ’s most visible recent milestone is a 2025 strategic partnership / recapitalization with Serent Capital (terms not publicly detailed in the announcement), which signals an intent to scale operations and product development.⁴⁵

Detailed introduction

StockIQ is best understood as a planning add-on designed to sit “next to” an ERP: it ingests historical demand and operational inputs, generates forecasts and planning signals, and exports order recommendations back into execution systems. StockIQ’s own help-center documentation describes an operational workflow where the system runs distinct processing steps and can publish order suggestions as structured files (commonly pipe-delimited) or as database outputs with matching fields, which downstream ERP integrations can consume.³⁶

On the forecasting side, the most concrete publicly inspectable evidence is StockIQ’s documentation of its forecasting engine and “tournament” functionality. StockIQ describes a “StockIQ Forecast Algorithm” that combines multiple component forecasts and uses backtesting-style evaluation to select configurations, paired with a “Tournament Forecast Algorithm” concept and UI surfaces that expose the “grand champion” (best-performing) configuration along with error metrics and comparisons across candidate algorithms.⁷⁸⁹ These descriptions align with classical statistical forecasting practice (multiple candidate models, parameter sweeps, and error-based selection), and do not, by themselves, substantiate modern ML architectures (e.g., deep learning) or probabilistic forecasting outputs (full distributions) in the way those terms are used in current research and advanced planning systems. StockIQ’s own product roadmap is notable here: it explicitly lists “Enhanced AI/ML” as a future item for forecasting, lead times, and safety stock—suggesting that, at least as of the roadmap snapshot, those capabilities may be viewed internally as areas for further development rather than already-established differentiators.¹⁰

In deployment terms, StockIQ markets short implementation cycles (often stated as “28-day implementation”), and also publishes an implementation scope document describing implementation as a single-phase deployment.¹¹¹² Technical support articles indicate that StockIQ supports both hosted and on-premises setups and (at least for the “Mt Huron” version) requires .NET 8.0 runtimes; additional support articles discuss SSO updates toward OIDC authorization flow with PKCE, and step-by-step Entra SSO configuration that references restarting the application in IIS for self-hosted instances.¹³¹⁴¹⁵

Client evidence is mixed in strength. StockIQ provides testimonials and quotes, but many are not fully attributable to a legally verifiable organization (e.g., first name + role only).¹⁶ Still, StockIQ’s own resources page includes named organizations in testimonials (e.g., BuildASign, CEPI, Hot Tub Club) albeit without independently verifiable case-study detail.¹⁷ Separately, a third-party marketplace listing (Acumatica) includes a named reviewer organization (“Silvertree Holdings”) and describes StockIQ as a planning solution for distributors and manufacturers, which provides at least one externally hosted, attributable customer mention (though still limited in technical depth).²

StockIQ Technologies vs Lokad

StockIQ and Lokad address overlapping planning problems (forecasting, replenishment/inventory decisions), but their approaches diverge sharply in (i) what constitutes the “core artifact” delivered to customers, and (ii) the technical form of uncertainty handling and optimization.

1) Product shape: configurable suite vs programmable platform. StockIQ presents a suite of planning modules with configuration surfaces, workflows, and integration outputs intended to be consumed by ERPs (e.g., exporting order suggestion files or equivalent database outputs).¹³ Lokad positions a programmable optimization platform where the deliverable is typically a bespoke “decision pipeline” that produces prioritized actions (purchases, allocations, etc.) from probabilistic inputs, rather than a fixed set of screens and per-module configurations.¹⁸¹⁹

2) Forecasting posture: point-forecast model selection vs probabilistic forecasting as a first-class primitive. StockIQ’s published documentation emphasizes algorithm selection via error evaluation (“tournaments”) and model configuration “winners,” which is consistent with selecting the best point-forecast model (or best parameterization) for a series.⁸⁹ Lokad’s public positioning, by contrast, is explicit that probabilistic forecasting (i.e., producing distributions, not single values) is foundational, and that optimization consumes these distributions to compute risk-adjusted decisions.²⁰²¹ In other words, StockIQ’s public artifacts emphasize “best forecast accuracy” workflows, whereas Lokad frames “forecast distributions + downstream optimization” as the core architecture.²⁰¹⁸

3) Optimization semantics: replenishment recommendations vs end-to-end predictive optimization. StockIQ clearly exports replenishment/order recommendations and supports planning governance workflows (including service-level measurement configuration and operational processes like system recalculation steps).³²²⁶ However, StockIQ’s public documentation (in the sources reviewed) does not provide a detailed technical account of a stochastic optimization layer that explicitly optimizes an economic objective under uncertainty. By contrast, Lokad publicly documents a dedicated stochastic optimization approach (stochastic discrete descent) designed to consume probabilistic forecasts and return risk-adjusted decisions.²¹¹⁸

4) Evidence posture: roadmap signals vs publicly documented technical paradigms. StockIQ’s product roadmap explicitly flags “Enhanced AI/ML” as forward-looking for forecasting, lead times, and safety stock.¹⁰ Lokad’s public materials emphasize already-deployed probabilistic forecasting and decision-centric optimization, and Lokad points to performance in an open forecasting competition as a form of external validation (6th of 909 teams in M5).²³²⁴²⁵

In practical terms: if a buyer wants a planning suite with configurable forecasting workflows (including algorithm tournaments), established ERP export patterns, and optional on-prem deployment, StockIQ’s published materials align with that operating model.⁸³¹³ If a buyer wants a programmatic system centered on probabilistic forecasts and explicit stochastic optimization of decisions, Lokad’s public technical stance is oriented toward that paradigm.²⁰²¹

Technology and architecture signals from public documentation

Deployment model and prerequisites

StockIQ’s support documentation indicates:

Hosted vs on-prem support, with different runtime installation guidance depending on whether StockIQ is on-prem or hosted with a local sync agent.¹³
.NET 8.0 requirement (for “Mt Huron” version) and guidance to install ASP.NET Core runtimes/hosting bundle.¹³
SSO modernization toward OIDC authorization flow with PKCE, and Entra configuration steps that reference restarting the application in IIS for self-hosted deployments.¹⁴¹⁵

These are consistent with an enterprise Windows/web-stack deployment profile (at least for on-prem customers) and suggest typical enterprise IT integration touchpoints (SSO, runtime prerequisites, instance restarts).

Data processing and operational workflow

StockIQ documents an internal processing workflow where a “Calculate” step is one of the major steps and can block users from working while it runs.⁶ This is a meaningful architectural clue: it suggests batch-oriented computation (recomputation of planning state) rather than continuous, always-on incremental recomputation.

Integration outputs

StockIQ documents a common scenario in which the application publishes order suggestions as pipe-delimited text files, with an alternative database output option containing the same columns/fields.³ This reinforces the characterization of StockIQ as a planning engine designed to hand off decisions to execution systems rather than directly executing them.

Forecasting and “AI/ML” claims: what is substantiated publicly?

StockIQ’s most technically explicit public materials (help-center articles) describe:

A forecasting approach that combines multiple algorithms/configurations and evaluates them by error metrics (“tournament” workflows).⁷⁸⁹
UI-level evidence of backtesting-like comparisons (current vs backdated errors, grand champion selection, visual comparison of models against actuals).⁹

What is not clearly substantiated in the reviewed public technical materials is the use of modern ML model classes (e.g., deep neural forecasting architectures), or the generation of probabilistic demand distributions as a standard output artifact. The presence of “Enhanced AI/ML” on the product roadmap (for forecasting, lead times, safety stock) is a cautionary signal against over-interpreting “AI” branding as describing current, deeply technical ML implementations.¹⁰

This does not imply StockIQ’s forecasting is ineffective; rather, based on the publicly inspectable evidence, it appears closer to advanced classical forecasting practice (model families + parameter search + backtesting selection) than to “state-of-the-art ML” in the research sense.

Implementation and rollout methodology

StockIQ markets a rapid implementation process (commonly “28-day implementation”) in its marketing materials.¹¹²⁶ Separately, it publishes a “Standard Implementation Scope” describing implementation as a single-phase deployment under its subscription/services agreement structure.¹² These claims are supported primarily by StockIQ-authored sources and should be treated as vendor-reported process targets rather than independently verified timelines.

Clients and case evidence: named vs weakly attributable

Named or externally attributable evidence (stronger):

A third-party marketplace listing (Acumatica) contains at least one named reviewer organization (“Silvertree Holdings”) and describes how the product is used (replenishment, demand forecasting, production scheduling, etc.).²

Named but primarily vendor-hosted testimonials (moderate):

StockIQ’s resources pages include testimonials referencing organizations such as BuildASign, CEPI, and Hot Tub Club, but without detailed, independently verifiable case-study metrics in the reviewed materials.¹⁷
A StockIQ-hosted PDF of client quotes provides additional endorsements but frequently lacks sufficient company identifiers for independent corroboration.¹⁶

Overall, StockIQ does provide customer evidence, but much of it is not published in a form that enables robust third-party verification (e.g., detailed case studies with named entities, scope, baselines, and measured outcomes). Where decisions are high-stakes, a prospective buyer would likely need direct customer references under NDA rather than relying on public testimonials.

Commercial maturity assessment

StockIQ appears to be beyond an early prototype stage: it has a documented support knowledge base with detailed operational and configuration topics, a release naming/versioning practice (“Mt Huron”), and enterprise integration features (SSO, ERP export patterns, REST/API references in support navigation).²¹³¹⁴³² The 2025 Serent Capital partnership indicates external investor backing and scaling intent.⁴⁵ Based on these public signals, StockIQ is best characterized as a commercially established mid-market planning vendor rather than a nascent startup—while still smaller and less transparently documented (publicly) than the largest enterprise planning suites.

Conclusion

StockIQ Technologies’ publicly accessible technical materials support a concrete characterization: it delivers a supply chain planning suite focused on demand forecasting and replenishment planning with adjacent planning modules, implemented as a deployable (hosted or on-prem) enterprise application that integrates with ERPs through published outputs (files or database) and supports enterprise identity integrations (SSO). The most substantiated “algorithmic” capability is model-selection via forecasting tournaments and error evaluation, consistent with advanced classical forecasting practice. Public evidence for modern ML architectures or probabilistic forecasting as a first-class output is limited, and StockIQ’s own roadmap language suggests at least some “AI/ML” enhancements are future-facing. In comparison to Lokad, StockIQ’s public posture aligns more with configurable planning workflows and forecasting model selection, whereas Lokad emphasizes probabilistic forecasting and explicit stochastic optimization of decisions as the core technical paradigm.