Review of RELEX Solutions, Supply Chain Planning Software Vendor

By Léon Levinas-Ménard

Last updated: April, 2025

RELEX Solutions, founded in 2005 in Helsinki by supply chain scientists Mikko Kärkkäinen, Johanna Småros, and Michael Falck, offers a unified supply chain and retail planning platform designed to diminish waste, optimize inventory and production management, and improve retail efficiency through data‐driven decision making. Built on a highly scalable, cloud‐native architecture with cutting‐edge in‐memory and in‐database processing, the platform integrates diverse planning processes into a digital twin of the supply chain. It leverages advanced AI and machine learning—ranging from specialized forecasting and mathematical optimization to emerging generative AI tools like Rebot—to deliver actionable insights and real‐time analytics. Emphasizing a “configure, don’t code” philosophy, RELEX Solutions provides a highly configurable, microservices‐based solution that efficiently handles vast data volumes while ensuring fast rollouts and measurable sustainability improvements such as reduced food waste and improved CO₂ metrics.

1. Product Overview and Functionality

1.1 What the Solution Delivers

RELEX Solutions positions its platform as a unified solution that:

Integrates Supply Chain Processes: Covering demand forecasting, inventory planning, automatic replenishment, promotional and price optimization, production scheduling, and distribution planning (¹, ²).
Improves Operational Efficiency: By creating a “digital twin” of the supply chain, the platform optimizes capacity, reduces stockouts, and lowers waste (³,⁴).
Enhances Sustainability: Demonstrated by measurable reductions in food waste—up to 10–40% along with improved environmental metrics (¹).

1.2 How the Solution Works

The RELEX platform employs a sophisticated technical approach:

Advanced Data Processing and Analytics: In-memory computing and in-database processing enable rapid analysis of terabytes of data daily, supporting fast, responsive planning decisions (³).
AI and Machine Learning: The system combines machine learning models with mathematical optimization (including linear programming, heuristics, and rule-based adjustments) to forecast demand and plan production schedules, as detailed in its ML-based demand forecasting and digital twin modeling (⁵,⁶).
Generative AI Integration: The recent launch of Rebot—powered by GPT-4 alongside proprietary algorithms—provides a conversational, insight-driven interface to assist users in troubleshooting and decision making (⁷).
Configurable and Scalable Architecture: Following a “configure, don’t code” philosophy, the cloud-native platform utilizes microservices and container orchestration (Docker, Kubernetes, Microsoft Azure) to ensure rapid rollouts and scalability, as reflected in insights from its tech stack (⁸).

2. Underlying Technology and Deployment Approach

2.1 Technical Underpinnings and Tech Stack

RELEX’s technical fabric is modern and diverse:

Backend and Infrastructure: The system is built on a combination of Java, Kotlin, Ruby, and even functional programming tools, with deployment references citing technologies such as Haskell for deployment utilities, all hosted on Microsoft Azure (⁹,¹⁰).
Data Management: A proprietary in-memory and in-database processing approach ensures that massive data streams are handled efficiently for real-time analytics (³).
Collaboration and Integration: Continuous integration and agile development are supported through tools like GitLab, Slack, and Jira, underpinning the company’s modern work culture (¹⁰).

2.2 Deployment and Roll-out Strategy

RELEX emphasizes a rapid, phased deployment strategy:

Phased Implementations: Clients typically experience a staged rollout that minimizes disruption while ensuring swift returns on investment, as demonstrated by examples such as the Bed Bath & Beyond rollout (¹¹).
Subscription-based SaaS Model: Delivered entirely as a cloud-based service, the platform provides continuous updates and evolving functionalities with minimal downtime.

3. Evaluation of the Technology and Claims

3.1 Strengths and Innovations

Comprehensive Unified Platform: Few vendors match RELEX’s breadth of integrated planning functionalities presented as a single digital twin, ensuring operational transparency.
Advanced Use of AI and ML: RELEX’s blend of specialized AI for demand forecasting, optimization techniques, and the integration of generative AI (Rebot) underscores its forward-looking investment in R&D—reportedly reinvesting 25–30% of revenue into continuous innovation (⁵).
Cloud-Native Scalability: The platform’s microservices architecture and container orchestration enable robust scalability and fast rollout capabilities, essential for rapidly changing supply chain environments.

3.2 Points of Skepticism and Caution

Buzzword Overlap: While RELEX frequently highlights “AI-powered” and “agile” capabilities, some aspects may rely on heavily tuned rule-based systems that require independent verification.
Customization versus Off-the-Shelf Claims: Promises of swift full-value rollout (e.g., ROI in as little as three months) could mask significant customization challenges within diverse supply chain contexts.
Transparency of Algorithms: The exact methods by which machine learning, optimization, and heuristics are combined remain proprietary, necessitating careful due diligence by potential clients.

RELEX Solutions vs Lokad

When comparing RELEX Solutions with Lokad, several key distinctions emerge. RELEX Solutions, established in 2005 in Helsinki, emphasizes a “configure, don’t code” approach—offering a highly integrated, out-of-the-box platform for retail and supply chain planning that focuses on delivering a digital twin of the entire supply chain with measurable sustainability benefits. Its strength lies in rapid deployment, robust scalability via microservices, and the incorporation of both traditional optimization and emerging generative AI tools like Rebot to provide conversational insights. In contrast, Lokad—founded in 2008 in Paris—adopts a more programmable, code-driven methodology centered on its proprietary Envision domain-specific language. Lokad’s platform is designed for deep quantitative supply chain optimization through probabilistic forecasting and predictive optimization, often requiring supply chain scientists to develop custom “numerical recipes.” While both vendors harness advanced AI and cloud-native architectures, RELEX prioritizes a preconfigured solution that minimizes coding requirements and emphasizes integrated retail applications, whereas Lokad offers a flexible, highly technical platform that trades simplicity for extreme customization and programmability.

Conclusion

RELEX Solutions presents a robust, integrated platform that leverages advanced data analytics, AI/ML, and cloud-native design to optimize supply chain and retail planning. Its unified “digital twin” approach offers tangible benefits in forecasting accuracy, inventory management, and sustainability, while the incorporation of generative AI enhances user interaction and decision support. However, as with any leading-edge technology, potential customers are advised to balance the attractive promises of rapid deployment and high ROI against the complexities inherent in customization and algorithmic transparency. In essence, RELEX Solutions stands as a compelling option for organizations ready to embrace a data-driven, configurable platform that streamlines supply chain operations without necessitating deep coding expertise.