Review of StockIQ Technologies, Supply Chain Planning Software Vendor

By Léon Levinas-Ménard

Last updated: April, 2025

StockIQ Technologies, founded in 2015 by industry veterans Jake Latham and Curtis Glesmann, presents itself as an end‐to‐end supply chain planning software provider. The company’s suite promises comprehensive capabilities—from demand forecasting and inventory replenishment to supplier performance management, promotion planning, and integrated Sales, Inventory & Operations Planning (SIOP). Built to aggregate both historical sales data and real‐time inputs from ERP and IoT systems, StockIQ positions its platform to deliver actionable dashboards and recommendations aimed at reducing stockouts and overstock situations. Emphasizing a rapid 28‑day implementation process and offering flexible deployment options both in the cloud and on-premise, the solution is marketed as “AI‑driven” and “data‑powered,” although the technical underpinnings of its proclaimed advanced algorithms remain ambiguous. Overall, StockIQ Technologies appeals to organizations looking for a tightly integrated planning suite that leverages robust ERP connectivity and strong customer support to drive operational efficiency.

Company Background and History

Founded in 2015 by Jake Latham and Curtis Glesmann, StockIQ Technologies builds on decades of industry experience to offer a supply chain planning suite. The company’s official About page emphasizes its mission to deliver efficient, user-friendly solutions augmented with strong customer support¹. Independent profiles on LinkedIn and other third‑party resources confirm its focus on supply chain planning and verify the details of its founding and operational approach, with no notable acquisitions or external investments disclosed².

Product Overview and What It Delivers

StockIQ’s product suite is designed as a comprehensive supply chain planning solution featuring several key modules:

– Demand and Replenishment Planning: The system leverages historical sales data and market signals to forecast demand and recommend inventory replenishment strategies to mitigate the risk of both overstocking and stockouts³.
– Supplier Performance Management: It tracks supplier lead times and various performance metrics, allowing users to monitor and manage supplier reliability efficiently³.
– Promotion Planning & SIOP: Tools for planning promotions and aligning cross-functional initiatives via Sales, Inventory & Operations Planning enable a coordinated approach across departments³.
– Inventory Analysis: Real-time dashboards provide detailed insights into inventory levels and turnover, offering a snapshot of operational health.
– Flexible Deployment: With both cloud-based and on-premise options available, StockIQ touts a rapid 28‑day implementation process that includes prevalidation, technical checks, and extensive parameter customization—ensuring that the system is closely tailored to customer needs⁴⁵.

How the Solution Claims to Work

Data‑Driven Forecasting and Optimization

StockIQ asserts that its “advanced algorithms” and “AI‑driven” technology transform raw historical data and live inputs into actionable forecasts and inventory recommendations. Public materials highlight that the system ingests data from ERP systems (such as Dynamics GP, Oracle NetSuite, and Epicor ERP) via dedicated ERP connectors. Although the company claims to employ machine learning and big data integration for demand forecasting, the specifics—such as the nature of the statistical models, programming languages, or ML frameworks—remain described in broad, high‑level marketing terms³⁴.

Implementation and Deployment

A key differentiator for StockIQ is its rapid deployment process, with the company promising a complete implementation in just 28 days. This process involves rigorous prevalidation and technical checks followed by a customization phase where inventory thresholds, order policies, and other planning parameters are set. The platform is touted as “error‑tolerant,” emphasizing efficient data feed management through finely tuned SQL queries and robust synchronization between ERP systems and the supply chain module⁵. The emphasis is on operational efficiency, with process rigor and a high level of customer support playing central roles in ensuring the solution integrates seamlessly into existing business systems.

Skeptical Analysis of Vendor Claims

A close scrutiny of StockIQ’s claims reveals several points for consideration:

– Vague AI/ML Explanations: Despite repeated assertions of “advanced AI and ML,” public materials provide few technical details about the underlying algorithms. It remains unclear whether the solution uses state‑of‑the‑art deep learning techniques or if the “advanced algorithm” label covers well‑known statistical forecasting methods rebranded with new terminology.
– Lack of Technical Stack Disclosure: Information regarding the programming languages, libraries, or modern frameworks underlying the platform is sparse. This opacity leaves questions about the robustness and modernity of the technology.
– Emphasis on Customer Support: Frequent references to rapid implementation and strong customer support suggest that a significant portion of the solution’s value may come from process management and integration services, rather than solely from breakthrough technical innovation.
– Variations in External Profiles: Third‑party profiles, such as those on Tracxn, present slight discrepancies regarding founding dates and operational focus. These variances highlight the necessity for prospective customers to verify that they are evaluating the correct “StockIQ” entity before proceeding⁶.

StockIQ Technologies vs Lokad

While StockIQ Technologies presents a solution anchored in rapid deployment, extensive ERP integration, and strong customer support—with both cloud and on‑premise options—the approach taken by Lokad differs markedly. Lokad’s platform is built exclusively as a multi‑tenant SaaS solution that leverages advanced machine learning, deep learning, and a domain‑specific programming language (Envision) to deliver probabilistic forecasting and predictive optimization. Unlike StockIQ’s high‑level, less‑transparent descriptions of its “AI‑driven” capabilities, Lokad demonstrates a clear commitment to technical innovation through its custom execution environment, minimal reliance on third‑party components, and continuous evolution toward differentiable programming. In short, StockIQ represents a more traditional, integration‑focused approach to supply chain planning, whereas Lokad emphasizes deep algorithmic sophistication and a tightly integrated, purpose‑built tech stack⁷.

Conclusion

StockIQ Technologies offers a comprehensive supply chain planning suite designed to improve inventory efficiency through integrated demand forecasting, replenishment planning, supplier management, and SIOP functionality. Its rapid 28‑day implementation process and availability in both cloud and on‑premise environments make it an attractive option for organizations seeking quick integration with existing ERP systems. However, potential customers should note that the technical details behind its proclaimed “advanced AI and ML” remain vague, suggesting that the platform relies heavily on established integration practices rather than cutting‑edge algorithmic innovation. In contrast with the highly specialized and transparently innovative approach of Lokad, StockIQ’s solution appears to prioritize operational efficiency and customer support over deep technological differentiation. Prospective buyers are advised to request further technical documentation to ensure alignment with their supply chain optimization ambitions.