Review of Optilon, Supply Chain Planning Software Vendor

By Léon Levinas-Ménard

Last updated: December, 2025

Optilon AB is a Stockholm-based supply chain consultancy and software reseller founded in 2005, with roughly 60–70 employees and operations across Sweden, Denmark and Finland; it positions itself as “founded by engineers” and focused on helping Nordic companies become more competitive by using their resources more efficiently, primarily through implementations of third-party planning software (notably ToolsGroup’s SO99+ suite) combined with Optilon-branded “advanced analytics” add-ons for predictive order monitoring, robotic data correction and network/route evaluation; public information portrays Optilon much more as an implementation/integration specialist with a few narrow AI-powered modules than as a vendor of a broad, proprietary supply chain planning platform, and technical disclosures about architecture, stack and algorithms are sparse, which makes it necessary to piece together its capabilities cautiously from registry filings, partner announcements, solution papers and marketing materials rather than from detailed product documentation.

Optilon overview

Swedish company registries describe Optilon AB as a privately held aktiebolag headquartered in Stockholm, registered in 2005 and reporting roughly 175 M SEK in net sales and 66 employees for the 2024 financial year.¹ Optilon’s own careers site and “Who we are” page repeat that it was founded in 2005, is active across the Nordic region with offices in Sweden, Denmark and Finland, has completed more than 1 000 projects and currently employs “close to 70 people”, serving more than 200 client companies over a 19-year history.²³ The company presents itself as “founded by engineers” that “combines world-leading technology with Nordic expertise in Supply Chain”, explicitly framing its value proposition as pairing off-the-shelf software with domain-specific consulting rather than providing an entirely home-grown planning system.²³

Optilon’s offering is organised into supply chain planning, supply chain design, “supply chain digitalization & AI” and “new technologies”.⁴ Across these lines it targets typical mid-to-large Nordic firms in retail, FMCG, manufacturing, distribution and process industries, promising better demand planning, inventory management, S&OP, distribution network design and data-driven decision making.³⁴ Its public case-study library highlights customers such as Orkla and Höganäs moving from “fragmented forecasting and manual processes” to integrated, more automated planning processes, and an “Unnecessary Report” claiming that large Nordic firms tie up over €23 billion in unnecessary inventory due to disconnected planning and outdated tools.⁵

A central structural fact is Optilon’s long-running partnership with ToolsGroup. ToolsGroup’s partner network lists Optilon as a partner headquartered in Stockholm covering Sweden, Denmark and Finland, specialising in supply chain planning and inventory optimisation.⁶ In December 2023 ToolsGroup named Optilon its “Global VAR of the Year”, explicitly citing a 15-year partnership and praising Optilon’s “exceptional skill in harnessing ToolsGroup’s solutions” and “Nordic expertise”.⁷ An earlier ToolsGroup announcement said the joint ToolsGroup-Optilon offering served “over 60 companies, including Absolut Vodka, Thule, Cloetta, Arvid Nordquist, Carlsberg, GANT, and Volvo Group” across the Nordics.⁸ Taken together, these sources show that Optilon’s primary planning engine is ToolsGroup’s SO99+ stack, with Optilon acting as a regional implementation and value-added reseller rather than a core engine vendor.

On top of ToolsGroup and other third-party engines, Optilon markets a small set of branded “New Technologies”: Predictive Order Monitoring (POM), Robotic Data Correction (RDC) and Network Route Evaluation (NRE). A “New Technologies” offering page describes these as AI-enabled solutions built on digital twins, advanced analytics and machine learning to improve delivery reliability, scalability/resilience and sustainability.⁹ A companion article summarises POM as predicting late supplier deliveries, RDC as using ML to clean master data without hand-crafted rules, and NRE as combining cost and CO₂ modelling to evaluate transport network designs.¹⁰ However, as detailed later, the underlying technology for POM and RDC appears to rely heavily on Rulex’s off-the-shelf machine-learning platform rather than on software uniquely developed by Optilon.

Inverted-pyramid view: what Optilon actually is

At the highest level, public evidence supports the following characterisation:

Business model: Optilon is primarily a specialised supply chain consultancy and systems integrator in the Nordics, with a strong commercial and technical dependency on ToolsGroup for core demand and inventory planning, and on other vendors (Optilogic, Rulex, etc.) for network design and ML-based data quality modules.⁴⁶⁷⁸
Product posture: The company sells projects and solutions (implementations, integrations, process design) more than it sells a single, unified software product. Its few Optilon-branded “AI” modules (POM, RDC, NRE) are relatively narrow and typically sit at the edge of existing ERP/WMS/TMS systems.⁹¹⁰¹¹¹²¹³
Technology disclosure: There is no substantial public technical documentation on architecture, internal data models, execution engine or programming stack for any Optilon-developed software. Where solution papers exist (notably for POM and RDC), they reveal that core ML capabilities are supplied by Rulex’s platform.¹²¹⁴¹⁵¹⁶
Commercial maturity: Registry and careers data point to a ~70-person firm with nearly two decades of history, >1 000 projects and ~175 M SEK in annual revenue, indicating a commercially established regional consulting house rather than an early-stage startup.¹²³

The rest of this report unpacks these points and evaluates, with as little speculation as possible, how technically substantive Optilon’s software contributions really are.

Optilon vs Lokad

Optilon and Lokad both position around “advanced” supply chain planning, but they occupy very different places in the technology stack and solve different parts of the problem. Optilon is fundamentally an implementation partner and reseller: its core planning capability is delivered through ToolsGroup’s SO99+ and similar third-party products, and its own software is limited to a few narrow, mostly ML-powered adjuncts (POM, RDC, NRE) that wrap or feed other systems.⁶⁷⁸⁹¹⁰¹¹¹²¹³¹⁷ Lokad, by contrast, is a vertically integrated SaaS vendor that has built its own forecasting and optimisation engine, including the Envision domain-specific language, probabilistic forecasting stack and custom optimisation algorithms, and delivers bespoke supply chain predictive optimisation apps directly as a cloud service.¹⁸¹⁹²⁰²¹²²

From an architectural standpoint, Optilon’s projects typically involve stitching together existing systems: ERP/WMS/TMS on one side, ToolsGroup SO99+ (and sometimes network design tools) on the other, plus optional Optilon-branded ML modules; each project is, in effect, an implementation of a third-party product plus consulting.⁴⁶⁷⁸⁹¹⁰¹⁷ Lokad’s approach instead is to ingest raw data and express the entire forecasting and optimisation logic in its own Envision DSL, executed by the Thunks distributed virtual machine on a multi-tenant Azure-based platform, with event-sourced storage and columnar content stores, producing daily probabilistic and financially optimised decisions.¹⁸¹⁹²⁰²³

On the AI side, Optilon’s claims are concentrated in very specific areas: POM (classification of purchase orders as at-risk vs on-time) and RDC (ML-based master-data cleaning) use models that, according to Optilon’s own solution papers and independent Rulex materials, are implemented on top of Rulex’s Robotic Data Correction and Learning & Composite Modelling environment; these are valuable but narrow point solutions.¹¹¹²¹³¹⁴¹⁵¹⁶²⁴ Lokad, by contrast, embeds probabilistic forecasting and stochastic optimisation across the whole decision pipeline (demand, inventory, production, pricing), using Envision to model uncertainties and constraints and then applying technologies such as quantile forecasting, probabilistic forecasting, stochastic discrete descent and latent optimisation to turn forecasts into decisions.²⁰²¹²²

Finally, in terms of degree of productisation, Optilon’s deliverable is mostly a configured instance of someone else’s product (e.g. SO99+) plus some custom scripts and ML models; support, upgrades and product roadmap for the underlying engines sit with ToolsGroup, Rulex, Optilogic and others.⁶⁷⁸¹⁴¹⁵ Lokad owns its full codebase and deploys it as a multi-tenant SaaS platform dedicated to quantitative supply chain, offered as “software + experts” where Supply Chain Scientists implement and maintain client-specific Envision apps on top of the shared engine.¹⁹²⁰²⁵ For a customer, this means that with Optilon the technical risk and dependency are spread across several vendors and an integrator, whereas with Lokad they are concentrated in one platform whose architecture and technological evolution are under a single vendor’s control.¹⁹²⁰²³

In short: Optilon is best seen as a well-established Nordic systems integrator with niche AI add-ons, while Lokad is a single, programmable optimisation platform vendor. This distinction matters when comparing long-term technical leverage and the ability to evolve the solution: Optilon will move as fast as its upstream vendors and project-specific ML work allow, whereas Lokad controls (and iterates) the full stack.⁶⁷⁸¹⁸¹⁹²⁰²¹

Corporate history, structure and positioning

Swedish company register Allabolag lists Optilon AB under organisation number 556679-7337, with “registreringsår” 2005, headquarters in Stockholm and turnover of 174 987 000 SEK in 2024 alongside 66 employees.¹ Optilon’s own careers site states that the company “was founded in 2005 and is today active throughout the Nordic region with offices in Sweden, Denmark and Finland” and that it has completed “more than 1000 projects” with “close to 70 people” employed.² The “Who we are” page emphasises that Optilon “was founded by engineers”, that it “helps companies use their resources where they generate the most value”, and that it has “over 19 years” of experience across “200+ customers”.³

The same pages frame Optilon’s mission as making Nordic companies “the most competitive in the world” by enabling better use of their resources, explicitly tying this to supply chain planning and design.²³ Geographically, the firm is clearly Nordic-centric, and the case-study list (Orkla, Höganäs, Nordic companies featured in its “Unnecessary Report”) reinforces this regional focus.⁵ There is no evidence of significant operations outside the Nordics, nor of any acquisitions or major external funding events; Optilon appears to have grown organically as a mid-sized consulting firm.

ToolsGroup’s partner content provides an external view of Optilon’s positioning. The partner network lists Optilon as a regional value-added reseller and implementation partner for ToolsGroup’s planning suite in Sweden, Denmark and Finland.⁶ A ToolsGroup news article in December 2023 names Optilon “Global VAR of the Year” and quotes Optilon’s CEO calling their 15-year partnership “integral to the success of our customers, leveraging powerful ToolsGroup technology and our Nordic expertise”, which confirms that Optilon’s core planning solution is fundamentally ToolsGroup’s technology plus Optilon’s services.⁷ An earlier ToolsGroup announcement describes Optilon as helping “over 60 companies, including Absolut Vodka, Thule, Cloetta, Arvid Nordquist, Carlsberg, GANT, and Volvo Group” implement ToolsGroup’s solutions in the region.⁸

Taken together, registry data and partner communications depict Optilon as an established, mid-scale, regionally focused integrator with a strong reliance on ToolsGroup, rather than as a standalone, all-in-one supply chain software vendor.

Product and solution portfolio

Core offerings

Optilon’s “Our Offering” pages group its services into:

Supply chain planning – demand forecasting, inventory optimisation, replenishment planning, S&OP and production planning.⁴
Supply chain design – network design, footprint optimisation, and strategic scenario analysis.⁴
Supply chain digitalization & AI – projects around data platforms, advanced analytics and AI applications in the supply chain.⁴⁹
New technologies – a small group of packaged solutions (POM, RDC, NRE) marketed as AI-driven enhancements tackling late deliveries, poor data quality and cost/CO₂ trade-offs in transport networks.⁹¹⁰

The case-study section under “Knowledge & Events” illustrates how these come together in practice. For example, Orkla is said to have moved “from fragmented forecasting and manual processes to a more integrated supply chain” with a smoother planning process using Optilon’s solution, and Höganäs is described as having “revolutionized its global forecasting and supply chain with Optilon’s automated, collaborative planning solution”.⁵ In the absence of explicit technical details, it is reasonable (and consistent with partner information) to infer that these projects are built on ToolsGroup’s SO99+ planning suite configured and implemented by Optilon. Nothing in the public material suggests a separate, Optilon-developed, end-to-end planning engine.

“New Technologies” add-ons

The “New Technology” offering page describes Optilon’s newer solutions as helping to “respond faster and meet customer demand with confidence”, “quickly adapt to shifting market conditions and disruptions” and “build a supply chain that’s efficient and environmentally responsible” by leveraging “advanced analytics” and “AI”.⁹ A related article on “What is the next step in your supply chain?” introduces three concrete modules: Predictive Order Monitoring, Robotic Data Correction and Network Route Evaluation, framed as examples of using digital twins and AI to solve specific operational problems.¹⁰

These modules are important because they are the only places where Optilon appears to offer software beyond configuration of third-party planning engines.

Technology stack and AI components

Predictive Order Monitoring (POM)

Optilon’s Predictive Order Monitoring (POM) solution is advertised as an AI model that predicts which purchase orders are likely to be delayed, allowing planners to act proactively. A short case-style page states that Optilon “delivered a solution for a global manufacturer who historically had experienced order delays from the suppliers” and that “the AI model delivered a result close to 90% precision compared to the reality”.¹¹

More technical detail comes from an older “POM Solution Paper v1.0” PDF hosted on an old.optilon.com domain. The document describes “Predictive Order Monitoring” as part of “Optilon Advanced Analytics” and notes that it is implemented using Rulex’s learning engine, even including an example “Rulex LCM” rule: IF Supplier Country = Spain AND Weight > 123.78 THEN Delay = Yes, illustrating that under the hood the system is a machine-learned rule-based classifier trained on historical purchase order data.¹² The paper outlines an architecture in which data is extracted from the ERP, processed by Rulex’s engine to assign a probability of delay to each order, and surfaced via dashboards and alerts to planners.

Crucially, there is no disclosure of independent Optilon-developed machine-learning libraries, model training pipelines or infrastructure; instead, Optilon configures a Rulex-based solution around the client’s ERP data, wraps it in reporting and process changes, and brands this as POM. Given that Rulex is an established third-party platform for rule-based ML, this is closer to solution engineering than to developing a new optimisation engine.

Robotic Data Correction (RDC)

Optilon’s Robotic Data Correction (RDC) is presented as an AI solution to improve master-data quality. The Optilon resource page states that “Robotic Data Correction (or RDC in short) uses Machine Learning models that automatically detect data inconsistencies without any human-defined rules and learns over time from user acceptance of corrections recommendations and new data values it has not seen before, ultimately being able to quickly correct any relational data values”.¹³ The same page mentions a manufacturing client that “corrected Supply Chain data with AI and saved 3%”, implying a reduction in costs tied to improved data quality.¹³

A companion PDF titled “Robotic Data Correction – A Solution for Improved Data Foundations in Supply Chain” exists on an optilon.se domain but is effectively a Rulex solution brochure with Optilon branding (“Optilon Advanced Analytics 1 SOLUTION PRETEXT 1.1 Organizational Context”).⁷ Independent vendor materials confirm that Rulex’s “Robotic Data Correction (RDC)” is a turn-key AI solution for automated correction of data entry errors in transactional systems, using ML to discover patterns in historical data and propose corrections.¹⁴ TDWI carries a Rulex press release describing RDC as automatically finding, fixing and preventing data entry errors in operational systems.¹⁵ An article in ElectronicSpecifier likewise presents Rulex Robotic Data Correction as an AI-based solution for correcting human data entry errors on forms, already in production at a Fortune 50 manufacturer.¹⁶

Textual and conceptual overlap between Optilon’s RDC materials and Rulex’s RDC documentation is strong: same product name, identical positioning (“turn-key AI-based solution for automatic correction of data”), and explicit mention of Rulex in the Optilon PDF.⁷¹⁴¹⁵¹⁶ The most cautious interpretation is that Optilon’s RDC is Rulex RDC deployed and integrated by Optilon for supply chain use cases, rather than a novel AI engine. Again, Optilon’s contribution is primarily in configuration, integration and process design, not in developing the underlying ML algorithms.

Network Route Evaluation (NRE) and CO₂ trade-offs

For network route evaluation, Optilon promotes a solution that balances logistics cost and CO₂ emissions. A case summary titled “Balance cost and CO2 emissions with statistical and AI modelling” describes using “advanced statistical and AI models” to evaluate many combinations of shipping modes and routes (e.g. 10 vs 50 different route options), assessing both total cost and emissions to identify optimal trade-offs.¹⁷

Another Optilon article under the “New technologies” umbrella explains that these solutions rely on building a “digital twin” of the supply chain, then running scenario analyses with AI-enhanced models to understand the impact of different configurations on service levels, cost and emissions.¹⁰ However, no technical details are disclosed: there is no description of the mathematical form of the models, whether they are classical optimisation (linear programming, mixed-integer programming) or ML approximations, nor what solver or platform is used. Unlike POM and RDC, there is no explicit mention of Rulex or any other specific engine.

Given the lack of hard information, the safe conclusion is that NRE is a model-based scenario evaluation service, probably implemented using a combination of statistical modelling, spreadsheet/BI tooling and possibly a third-party optimisation engine, but not a clearly defined Optilon software product in the sense of a reusable, general-purpose solver.

“Supply chain digitalization & AI” and digital twins

Optilon’s “Supply chain digitalization & AI” messaging is high-level. The site talks about building digital twins of the supply chain, leveraging machine learning and even generative AI to simulate scenarios, improve forecasting and support decision-making, with claims of better resilience and sustainability.⁹¹⁰ However, these statements are not tied to a specific platform or clearly named product; instead they appear to be a narrative wrapper on top of the combination of ToolsGroup implementations, the New Technologies modules and bespoke analytics work.

There is no public documentation of Optilon having its own general digital-twin platform, time-series database or simulation engine. Nor are there technical blogs, open-source libraries or patents that would indicate original algorithmic work in digital twinning or AI beyond the Rulex-based components already discussed. In that sense, “digital twin” and “AI” are mostly marketing labels for project-level combinations of third-party tools and analytics, not evidence of a proprietary AI platform.

Technology stack and developer signals

Public job postings specific enough to reveal a tech stack are sparse. The departments page indicates an “IT & Tech Consulting” unit alongside “Supply Chain Consulting”, but does not spell out languages, databases or cloud platforms.² A profile of Optilon’s Head of Advanced Analytics (later Senior Head of Technology) notes that the role is part of “IT & Tech Consulting”, but again gives no details on the underlying tools.²⁴ Glassdoor job listings for “Optilon AB” list open roles but without visible job descriptions in the static HTML.¹⁸

Given the absence of explicit tech-stack mentions, the only concrete stack inference is via the solution papers: POM and RDC rely on Rulex’s Learning and Composite Modelling and Robotic Data Correction products,¹²¹⁴¹⁵¹⁶ while core planning depends on ToolsGroup’s stack as implemented by Optilon.⁶⁷⁸ There is no evidence that Optilon maintains its own large, general-purpose forecasting or optimisation codebase.

Deployment and roll-out methodology

Optilon does not publish detailed implementation methodologies, but some elements can be inferred. The case-study teasers for Orkla and Höganäs both refer to transformations from “fragmented forecasting” and “manual processes” to more integrated, automated planning with Optilon’s solution, describing outcomes such as smoother planning processes and more collaborative forecasting.⁵ These are classical outcomes of a ToolsGroup SO99+ implementation rather than of a bespoke, from-scratch software deployment.

The New Technologies articles emphasise starting from a specific pain point (e.g. late supplier deliveries, poor master data, unclear network CO₂ impact), then building a model around existing data, testing it on historicals, and integrating it into the client’s workflow.⁹¹⁰¹¹¹³¹⁷ The POM and RDC solution papers describe fairly standard analytics project stages: data extraction and cleaning from ERP, feature engineering, model training and validation (in Rulex), deployment in a production environment with user feedback loops, and gradual expansion of scope.¹²¹³¹⁴¹⁵¹⁶

Overall, the roll-out pattern looks like:

Assessment and scoping – analyse current planning or data-quality problems.
Data integration – connect to ERP and other systems.
Engine configuration – configure ToolsGroup / Rulex / other engines.
Pilot and refinement – run in parallel, tune thresholds and parameters.
Roll-out – expand to more products or regions.

There is no indication of a standardised, proprietary deployment framework; instead, execution seems project-specific and tool-specific, which is typical for an integrator.

Clients, sectors and market presence

Optilon’s site and the ToolsGroup partnership announcements give a reasonably consistent picture of its client base. ToolsGroup’s 2015-era Nordic press release listed more than 60 joint customers, including Absolut Vodka, Thule, Cloetta, Arvid Nordquist, Carlsberg, GANT and Volvo Group, spanning FMCG, beverages, apparel and industrial manufacturing.⁸ Optilon’s own case section highlights Orkla (consumer goods), Höganäs (metal powders), and broader research on large Nordic companies’ inventories (The Unnecessary Report).⁵

The regional focus is clear: headquarters in Stockholm, offices in the Nordics, and most named clients being Nordic or operating substantial Nordic operations.¹²³⁵⁸ Optilon’s market presence is therefore best characterised as established in the Nordic mid-market and large-company segment, but not a global software vendor.

In terms of verifiable references, named customers and co-branded ToolsGroup content carry more weight than generic statements like “we work with major manufacturers and retailers”. Generic claims appear throughout the marketing copy, but only the specific co-branded cases and partner announcements can be treated as strong evidence.

Discrepancies, ambiguities and open questions

A few points deserve explicit flagging:

Nature of “Optilon software” vs integration work: The marketing sometimes blurs the line between Optilon as a vendor and Optilon as an integrator. For example, phrases like “Optilon’s automated, collaborative planning solution” in case studies could be read as suggesting a proprietary planning engine, but partner information clearly indicates that ToolsGroup provides the core engine.⁵⁶⁷⁸
True ownership of AI modules: POM and RDC are branded as Optilon solutions but rely heavily on Rulex’s platform, as confirmed by Optilon’s own solution papers and independent Rulex materials.¹²¹³¹⁴¹⁵¹⁶ Customers evaluating “Optilon’s AI” should understand that they are effectively adopting Rulex technology configured by Optilon.
Lack of technical transparency: There is no public information on architecture, scalability limits, latency, failure modes or security for any Optilon-developed component. Without such documentation, it is impossible to rigorously assess whether these modules are state-of-the-art or simply wrappers around generic ML engines.
Digital twin and generative AI claims: Optilon’s “digital twin” and “generative AI” language is conceptually plausible (and moderately standard in current supply chain marketing), but unsupported by technical detail. No specific generative models, simulation frameworks or evaluation results are disclosed.⁹¹⁰

These gaps do not imply that the solutions are ineffective, but they do mean that a technically rigorous buyer should treat the AI/digital-twin narratives as unverified until supported by deeper vendor documentation or proof-of-concept results.

Assessment of technical and commercial maturity

Commercial maturity: Optilon is clearly not an early-stage startup. It has nearly 20 years of operating history, stable revenue in the mid-hundreds of millions SEK, ~70 staff and a large body of client projects.¹²³⁵⁸ Its long-standing partnership with ToolsGroup and the Global VAR award further indicate a mature role in the Nordic planning ecosystem.⁷⁸

Technical maturity (own software): For its own branded modules (POM, RDC, NRE), Optilon demonstrates competence in applying existing ML and optimisation technology to specific supply chain use cases, particularly when leveraging Rulex for pattern discovery and data correction.¹²¹³¹⁴¹⁵¹⁶¹⁷²⁴ However, there is no evidence that Optilon is pushing the state of the art in algorithms or architecture. POM and RDC are essentially projectised deployments of Rulex’s platform; NRE appears to be an application of standard statistical and optimisation techniques wrapped in consulting.

Technical maturity (overall solution delivered): When combined with ToolsGroup’s SO99+ suite, Optilon can deliver a fairly sophisticated end-to-end planning solution, but the underlying technical depth belongs largely to ToolsGroup. Optilon’s value is in domain expertise, change management and integration rather than in owning an advanced planning platform.

From a state-of-the-art perspective, Optilon’s offering is thus mixed:

The overall solution stack (ToolsGroup + Rulex + Optilogic + Optilon consulting) can be competitive and technically solid.
Optilon’s own software contributions are narrow and mostly based on configuring external AI engines; they are not comparable in scope or depth to platforms that have built their own forecasting and optimisation engines from scratch.

Conclusion

Optilon is best understood as a Nordic supply chain consultancy and systems integrator with a small portfolio of AI-flavoured add-ons, not as a full-stack supply chain software platform vendor. Its commercial maturity is solid: nearly two decades in business, dozens of named Nordic clients, a long-term partnership with ToolsGroup and a footprint across Sweden, Denmark and Finland.¹²³⁵⁷⁸ For organisations seeking to implement ToolsGroup’s planning software or to deploy Rulex-based ML solutions in the Nordics, Optilon is a credible partner with clear regional experience.

Technically, however, buyers should be clear-eyed about what is and is not being purchased. The core planning intelligence—multi-echelon inventory optimisation, probabilistic forecasting, constraint-based replenishment—comes from ToolsGroup. The AI components Optilon brands as POM and RDC rely heavily on Rulex’s off-the-shelf ML technology. Network Route Evaluation appears to be a project-specific application of statistical and optimisation techniques rather than a well-documented, reusable engine. Claims around digital twins and generative AI are currently marketing-level, lacking detailed architectural or algorithmic backing.

If the evaluation criterion is “Does Optilon own a state-of-the-art supply chain optimisation platform?”, the answer is no. If the criterion is “Can Optilon implement and configure state-of-the-art third-party platforms (ToolsGroup, Rulex, etc.) and wrap them in domain-specific projects?”, the answer is yes, with the caveat that the technical core of those platforms resides with their original vendors. Prospective customers should therefore treat Optilon primarily as an implementation partner and weigh its strengths—regional expertise, project experience, and the convenience of a single point of contact—against the architectural reality that key algorithms and system evolution are controlled elsewhere.