Review of OPTANO, Optimization Software Vendor

By Léon Levinas-Ménard

Last updated: December, 2025

OPTANO is a German software company founded in 2009 and headquartered in Paderborn that specializes in using mathematical optimization and operations research to support complex planning problems across supply chain and operations, offering a .NET-based modeling API (OPTANO Modeling) for building mixed-integer and linear optimization models, together with the OPTANO Platform and a portfolio of packaged applications for network optimization, flowpath control, inventory management, transportation, production planning, and employee capacity planning; since late 2022, the company has operated as “OPTANO, a Kearney company” following its acquisition by the management consulting firm Kearney, positioning OPTANO’s technology as the optimization engine behind Kearney-led operations and end-to-end supply chain transformation projects while continuing to serve its existing industrial client base.

OPTANO overview

From a technical standpoint, OPTANO is best understood as a specialist in mathematical optimization and operations research (OR) that has built a commercial platform and application layer around a .NET-based modeling framework. The company develops optimization models—primarily mixed-integer programming (MIP) and linear programming (LP)—for problems such as distribution network design, multi-echelon flow routing, inventory policies, vehicle and parcel routing, production scheduling, and workforce capacity planning. These models are implemented using OPTANO Modeling, a C#/.NET API that lets developers describe decision variables, constraints, and objective functions programmatically and then delegate the actual solving to third-party solvers such as Gurobi, CPLEX, or open-source alternatives.¹²³⁴⁵ On top of this, OPTANO provides the OPTANO Platform—a web-based front-end and application framework—and several domain-specific products (Network Optimization, Flowpath Optimization, Transport Management, Inventory Management, Production Planning, Employee Capacity Planning, and more) which package reusable model structures, data interfaces, and visualization layers for specific planning domains.⁶⁷⁸⁹¹⁰¹¹¹²¹³¹⁴

Commercially, OPTANO appears as a mid-sized niche vendor, with continuous growth since its founding in 2009 and a headcount of roughly 70 employees in Paderborn.¹¹⁵ The firm was acquired by Kearney in November 2022 and now operates as “OPTANO, a Kearney company,” explicitly positioned as an AI- and optimization-focused software capability that underpins Kearney’s operations engagements, including end-to-end supply chain design and large-scale operations transformations.¹⁶¹⁷¹⁸¹⁹²⁰¹² Public references and marketing material indicate deployments across automotive, chemical, consumer goods, logistics, energy, and mining industries, with named case studies including BMW Group and Holcim.²¹⁹¹⁰¹²²²²³ The technical narrative emphasizes predictive and prescriptive analytics, scenario planning, and “AI-powered” operations, but closer inspection reveals a core that is firmly rooted in classical mathematical programming and OR practice, with AI and machine learning elements used primarily for forecasting or demand modeling around those optimization cores rather than as end-to-end autonomous decision-making systems.⁶⁷²⁴¹⁰¹³

In terms of supply chain scope, OPTANO’s offerings span strategic (e.g., network footprint design), tactical (e.g., inventory policy selection, capacity planning), and operational questions (e.g., daily flow routing in parcel networks, short-term production schedules). The Flowpath Optimization and Network Optimization products focus on multi-echelon flow control and warehouse/route configuration, Transport Management targets courier/express/parcel networks, Inventory Management handles safety-stock and order policy decisions, and solution templates for production and employee capacity planning address utilization, shift scheduling, and regulatory constraints.⁷⁸²¹⁹¹⁰¹¹¹²¹³¹⁴²⁵ Overall, the technology stack is that of a classical OR vendor: C#/.NET modeling, third-party solvers, and domain-specific application shells, with the “AI” label largely corresponding to the use of advanced analytics, scenario analysis, and (in some cases) predictive modeling around otherwise deterministic optimization models.

OPTANO vs Lokad

Both OPTANO and Lokad target complex supply chain and operations planning problems, but they do so with fundamentally different architectures and philosophies. OPTANO follows a traditional operations-research-centric model: domain experts and developers encode planning problems as mathematical programs (MIP/LP and related formulations) in C# using the OPTANO Modeling API, then send those models to general-purpose solvers such as Gurobi or other MIP engines.³⁴⁵²⁶²⁷ The OPTANO Platform and its packaged applications (Network Optimization, Flowpath Optimization, Inventory Management, etc.) are essentially structured interfaces and workflows around these optimization models—users configure data, constraints, and scenarios, and the system computes optimal or near-optimal plans under deterministic or scenario-based assumptions.⁶⁷⁸²¹⁹¹⁰¹¹¹²¹³¹⁴ Lokad, by contrast, is a multi-tenant SaaS platform built around a proprietary domain-specific language (Envision) designed specifically for probabilistic forecasting and decision optimization in supply chains; instead of exposing a general MIP modeling layer, Lokad exposes a programmable environment where users describe data flows, probabilistic models, and economic objective functions, and Lokad’s engine computes decisions (orders, allocations, prices, schedules) under full uncertainty distributions.²⁸²⁹³⁰

Technically, OPTANO leans on third-party solvers and a C# modeling library: the emphasis is on structuring decision variables and constraints in a way that classical optimization solvers can handle, occasionally enhanced with scenario generation or analytics but fundamentally rooted in deterministic or scenario-based mathematical programming.³⁴⁵²⁶²⁷ Lokad instead emphasizes probabilistic forecasting and custom stochastic optimization algorithms (e.g., stochastic discrete descent, latent optimization) embedded directly into its platform, with Envision providing a higher-level language for expressing random variables, quantile forecasts, and economic drivers; decisions are optimized against full probability distributions rather than a small set of deterministic scenarios.²⁸²⁹³⁰³¹³²³³ Where OPTANO applications typically present planners with optimized plans or scenarios built around user-defined constraints and KPIs, Lokad produces financially-ranked action lists (e.g., purchase order lines) that explicitly quantify expected costs and benefits under uncertainty, aligning decisions with economic objectives such as profit, service level, or cash utilization.²⁸²⁹³³

In deployment and operating model, OPTANO’s offering often appears embedded in consulting projects—now explicitly via Kearney—where OR specialists design models and scenario structures for specific clients, then operationalize them through the OPTANO Platform.¹⁶¹⁷¹⁸¹⁹¹² Lokad also operates with “supply chain scientists,” but in a pure SaaS setting: the platform is multi-tenant, web-based, and projects are delivered as Envision scripts running on Lokad’s own infrastructure, with no local installation and relatively minimal reliance on external solvers.²⁸²⁹³⁰ Put simply, OPTANO is closer to a flexible OR workbench with packaged templates for classical planning problems, while Lokad is a probabilistic, event-sourced SaaS engine specifically optimized for supply chain forecasting and decision-making—OPTANO prioritizes general optimization modeling with .NET and solvers, whereas Lokad prioritizes probabilistic demand modeling, economic drivers, and programmatic supply chain decision optimization.

History, ownership and funding

OPTANO GmbH was founded in 2009 in Paderborn, Germany, and positions itself as a specialist in operations research and mathematical optimization for planning and decision support.¹ Public registry and company information services confirm the 2009 founding date and continuous operation since then, with annual reports filed regularly and commercial registry documents available for the company.²¹⁵ The firm’s headquarters are located in the Technologiepark area of Paderborn, and its team has grown to around 70 employees according to its own “About” page and contact information.¹¹⁴

On 9 November 2022, Kearney announced that it had acquired OPTANO as part of a strategy to add “AI-powered operations optimization” capabilities to its consulting portfolio.¹⁷¹⁸ OPTANO simultaneously published its own news release confirming the acquisition and rebranding as “OPTANO, a Kearney company,” stating that it would support Kearney primarily on AI-powered end-to-end supply chain projects and large-scale operations transformations while continuing to serve existing customers.¹⁶ Independent deal commentary from Oaklins, a mid-market M&A advisory firm, describes OPTANO as a provider of AI-powered operations optimization solutions for multiple industries—including automotive, chemical, consumer goods, logistics, energy, and mining—and highlights its role in network, employee capacity, production, and supply chain planning.¹⁹ Additional coverage from Boardroom Insight and other consulting news outlets repeats the acquisition narrative, emphasizing Kearney’s interest in artificial intelligence and optimization software for operations.²⁰

No evidence of large venture funding rounds or multi-stage VC backing is visible in public sources; instead, OPTANO appears to have grown as a privately held company before being acquired by Kearney. Corporate information services such as Dun & Bradstreet list the firm with standard credit and corporate family data but do not indicate external ownership until the acquisition by Kearney.¹⁵ Taken together, the picture is of a niche optimization software vendor that matured over more than a decade and then became part of a large management consulting firm as a specialized technology arm for operations and supply chain projects.

Product and solution portfolio

Platform and modeling stack

At the core of OPTANO’s offering is the OPTANO Platform, described as a “multitool for the optimization of structures and processes” that applies mathematical optimization, predictive analytics, and prescriptive analytics to planning problems.⁶ The platform is presented as capable of supporting network planning, logistics planning, end-to-end planning, employee planning, production planning, and supply chain planning, with users able to run what-if scenarios and evaluate different planning configurations.⁶¹⁴ From a technical perspective, the platform is a container for domain-specific applications and optimization workflows rather than a generic low-level modeling environment.

The low-level modeling environment is provided by OPTANO Modeling, a .NET-based API that supports the construction of mathematical programs in C#.³⁴ The documentation states that OPTANO Modeling “is a .NET API that helps to create mathematical programs and to send them to solvers,” and that it is “full-featured” while remaining lightweight.³ The product page emphasizes that OPTANO Modeling is “the best API for mathematical programming in .NET” and allows developers to translate sophisticated mathematics into enterprise-ready software, built on the .NET architecture.⁴ The NuGet package listing further clarifies that the library allows C# to be used as a modeling language for mathematical optimization, including mixed-integer and linear programming, and that it supports connections to several solvers.⁵ User documentation for OPTANO Modeling goes into some detail on creating model classes, configuring solvers, and running models, including examples for solving models, configuring optimization settings, and multi-objective optimization (hierarchical and weighted), which emulate the multi-objective features of Gurobi.²⁶²⁷

A notable aspect is that OPTANO Modeling is available free of charge as a standalone API (with professional support available), while the OPTANO Platform itself is commercial software.⁴⁵ This suggests a deliberate separation between the core modeling technology (usable by developers independently of the full platform) and the higher-level application and UI layer that OPTANO sells as part of larger projects or solutions.

Supply chain and operations solutions

On top of the OPTANO Platform and Modeling API, the company offers a range of domain-focused solutions, several of which are directly relevant to supply chain and logistics:

Network Optimization / Network Planning: OPTANO’s network optimization product targets strategic and tactical distribution network design. It claims to determine optimal warehouse locations, transportation lanes, and modes (e.g., FTL vs LTL), considering transport costs, capacities, lead times, storage costs, duties, and routing restrictions, as well as multi-echelon flow paths from central distribution centers to regional hubs and customers.¹⁰ The network planning solution emphasizes transparency, efficiency, and the ability to respond quickly to disruptions, positioning manual planning as insufficient once network complexity grows.¹¹
Flowpath Optimization: Flowpath Optimization focuses on dynamic product flow control across multi-echelon networks. Marketing material describes it as enabling dynamic steering of product flows from plants to hubs to customers, connecting strategic, tactical, and operational planning levels and automatically finding the “best route for every product, every time.”⁸ An associated insight article explains that Flowpath Optimization uses dynamic flow control and scenario planning, with mathematical optimization at its core, to improve distribution planning and adapt to demand and constraint changes.²¹ An interview piece cites industry benchmarks suggesting that optimized flow control can reduce transport costs by 10–15%, increase service levels by 5–10 percentage points, and cut CO₂ emissions by 8–15%, although these figures are presented as typical ranges rather than guaranteed results.³⁴
Transport Management (parcel / CEP networks): The Transport Management product is described as network optimization software for courier, express, and parcel (CEP) service providers, using mathematical decision support and AI to improve parcel network performance. It highlights rapid scenario calculations, visualizations, and scenario management to reveal untapped potential in networks, considering full-week schedules, door constraints, multi-stop routes, shift windows, and capacity constraints.⁹¹² The emphasis is on comprehensive modeling of operational details in parcel networks, with optimization algorithms recommending network configurations and routing decisions.
Inventory Management: OPTANO Inventory Management is positioned as a tool for optimizing safety stock and order policies. The product page describes support for periodic review and continuous review policies, risk period calculation, and incorporation of material categorizations; the safety stock is calculated not just from uncertainty but also from the risk period that must be covered.⁷ Release notes for version 1.0 mention multiple statistical-analytical models, scenario comparisons, and reporting features that allow distinguishing baseline and optimization scenarios and capturing the current state versus alternative configurations.³⁵ A blog article on optimized inventory management states that OPTANO uses predictive and prescriptive analytics to analyze alternative scenarios, map entire supply chains, and account for multiple objectives, variables, and constraints, with what-if scenarios used to forecast long- and short-term demand and adjust inventory and production accordingly.²⁴¹³¹⁵
Production Planning and Production Network: Production planning solutions are marketed as tools for creating optimal production plans that increase delivery reliability, optimize machine utilization, reduce setup times, and shorten lead times.¹²²⁵ For production networks, OPTANO advertises solutions that use mathematical decision support and AI to optimize costs, capacities, transport, and sustainability in complex multi-site production systems.³⁵
Employee Capacity Planning: The employee capacity planning solution focuses on cost-optimized assignment of employees with the right skills under labor regulations. It highlights the ability to create optimized schedules at the touch of a button, accounting for skills, preferred working hours, legal requirements, and short-notice changes such as postponed delivery dates or sick leave.¹³¹² Multiple industry pages (e.g., automotive, logistics, consumer goods) reference this capability as a way to meet production requirements while respecting employee preferences and constraints.²⁴¹²²²

The OPTANO website’s download section and success stories reveal that these solutions are used across industries, with public references including BMW Group (anticipatory network planning for an international automotive production and distribution network) and Holcim (cost-optimized production and delivery of cement across a complex international network).¹⁴²²²³ However, detailed quantitative case-study metrics are often gated behind downloadable factsheets that require registration, limiting the amount of independently verifiable detail available without direct engagement.¹⁶¹⁰¹⁴²³¹⁵

Technology stack and architecture

Modeling and solver layer

Technically, the most concrete part of OPTANO’s stack is the OPTANO Modeling library. It is a .NET API used from C# (and other .NET languages) to define variables, constraints, and objective functions and then submit models to solvers.³⁴⁵ The NuGet package description explicitly states that it allows the use of C# as a modeling language for mathematical optimization (MIP and LP) and connects to several solvers, while maintaining a lightweight footprint.⁵ The documentation includes examples on model creation, setting up model scopes, and solving, and it explains that a configuration object (OPTANO.Modeling.Optimization.Configuration) is used to populate a model scope and influence model generation, e.g., enabling or disabling full names to save memory.²⁷

Advanced documentation on hierarchical and weighted optimization shows that OPTANO Modeling emulates the multi-objective optimization features of Gurobi 7.x, including lexicographic (hierarchical) and weighted objective handling, giving users access to multi-criteria optimization without having to implement such behavior from scratch.²⁶ This indicates that the modeling framework is tightly integrated with commercial solvers and exposes their more advanced features within a C#-friendly API. There is no indication that OPTANO builds its own solver; instead, it appears to rely entirely on external optimization engines, focusing its own engineering efforts on the modeling layer and on application-level logic.

Application and UI layer

The higher-level OPTANO Platform and specific products provide user interfaces, visualization, and data management on top of the modeling layer. Marketing materials and product descriptions consistently mention:

Web-based interfaces with dashboards, maps, tables, and visual reports for analyzing scenarios and results.⁶⁷⁹¹⁰¹⁴
Scenario management facilities, enabling users to define baseline and alternative scenarios, compare their performance, and explore what-if analyses.³⁵²⁴²¹¹³
Industry-specific views (e.g., automotive, logistics, consumer goods) with tailored terminology and workflows while reusing underlying optimization modules for network, inventory, and capacity planning.²⁴¹²²²

While the documentation is sparse on internal architecture details (e.g., database choices, hosting models), public information strongly suggests a standard enterprise architecture: .NET-based backend components, a web front-end, and integration with corporate IT environments via data imports, exports, and possibly APIs. There is no evidence of a multi-tenant SaaS model comparable to cloud-native vendors; instead, OPTANO appears to deploy its software within specific customer contexts, often coupled with consulting projects, with deployment and integration details likely tuned per client.

Data, forecasting, and analytics

OPTANO frequently cites the use of predictive and prescriptive analytics in its materials, especially for inventory management and network planning.⁶²⁴¹⁰¹³ The inventory management blog explicitly mentions using predictive analytics to forecast demand and prescriptive analytics to derive inventory and production plans from those forecasts.¹³ However, the technical documentation and product pages do not provide much detail on which forecasting techniques are used (e.g., specific time-series models, machine learning algorithms, or probabilistic approaches) or how uncertainty is represented in the optimization models.

Third-party reviews such as SoftwareWorld describe the OPTANO Platform as comprehensive demand planning software that uses advanced algorithms and machine learning techniques to analyze historical data and predict future demand, enabling optimized production schedules and inventory management.²⁵ This suggests that machine learning-based forecasting is present in at least some configurations, but the lack of technical documentation or published benchmarks makes it difficult to independently assess the sophistication or robustness of these forecasting components. What is clear is that optimization remains the central pillar of the technology; forecasting and analytics are positioned as enablers that feed data into optimization models rather than as stand-alone AI decision engines.

AI, machine learning and optimization claims

OPTANO and, more recently, Kearney, frequently use the language of “AI-powered operations optimization” in describing the joint offering.¹⁶¹⁷¹⁸¹⁹⁶¹⁰¹² Product pages for network optimization, production networks, and transport management mention artificial intelligence alongside mathematical decision support, and inventory management materials reference predictive and prescriptive analytics.³⁵²⁴⁹¹⁰¹³ Third-party acquisition coverage explicitly labels OPTANO as an AI specialist in operations and supply chain.¹⁸¹⁹²⁰

Technically, the most verifiable AI/ML components are:

The use of machine learning or statistical-analytical models in inventory management, as indicated by the v1.0 release notes referencing multiple “statistical-analytical models,” though specific algorithms are not named.³⁵
The broader invocation of predictive analytics for demand and supply forecasts, particularly in inventory and capacity planning contexts.²⁴¹³²⁶
The use of multi-objective optimization features imported from solvers like Gurobi, which, while not AI in the machine-learning sense, do fall into advanced optimization techniques.²⁶²⁷

There is no evidence that OPTANO provides its own deep learning framework, probabilistic forecasting engine, or reinforcement-learning-based decision-making; instead, the technology appears grounded in classical OR, with AI and machine learning used in support of those optimization models. This is consistent with the modeling documentation and solver integration patterns, which emphasize deterministic MIP/LP models and scenarios rather than probabilistic distributions or end-to-end differentiable training loops.³⁴⁵²⁶²⁷

In practice, therefore, the “AI-powered” label should be interpreted cautiously: the core differentiator is the combination of optimization modeling, scenario management, and advanced solver usage, rather than novel AI algorithms. The lack of independent benchmarks or academic publications from OPTANO on forecasting or ML techniques further limits the ability to validate AI claims beyond marketing language.

Deployment and roll-out methodology

OPTANO’s public materials and download section suggest a project-centric deployment model. The website offers success stories (e.g., BMW Group, Holcim) and factsheets on “successful optimization projects,” which describe lessons learned on how to complete optimization projects, though the detailed documents are gated behind registration.¹⁰¹⁴²²²³ This implies a typical pattern where OPTANO (and now Kearney) engage with clients in consulting-style projects: analyzing existing data and processes, formulating optimization models, configuring the OPTANO Platform for the specific use case, and then iterating based on client feedback.

The network of industry-specific pages (automotive, logistics, consumer goods, energy, mining) and domain solutions (network planning, supply chain planning, employee capacity, etc.) also indicate that OPTANO often starts from a pre-existing template or solution package and then customizes it for the client’s data, constraints, and objectives.²⁴¹¹¹²²² There is no evidence of a pure self-service model where customers independently assemble models from scratch; instead, the presence of these templates and success stories points to a consulting-led configuration and rollout process.

Technically, integration with existing IT landscapes appears to rely on data imports/exports and potentially APIs, but public sources do not detail the exact mechanisms. The strong emphasis on scenario management, what-if analyses, and baselines suggests that deployments are typically used in planning cycles where planners explore alternative configurations and then export or manually implement chosen plans in ERP, WMS, or TMS systems rather than fully automating execution.

Clients and market presence

OPTANO publicly names several clients across industries, including:

BMW Group: A success story on anticipatory network planning in the automotive sector, focusing on planning an international production and distribution network with complex dependencies and high vertical integration.²²
Holcim: A success story on cost-optimized production and delivery of cement to end customers in a complex international network.¹⁰
Additional references on the downloads and podcast pages include B&O Service, Daimler Truck AG, Lavazza Professional, Lufthansa Aviation Training, Storag Etzel, and others as success stories, though details are often behind gated content.¹⁰¹⁴²⁷

Industry pages for automotive, logistics, consumer goods, and others describe typical use cases such as network planning, production planning, employee capacity planning, and supply chain planning, reinforcing the picture of a vendor whose technology is applied in multiple capital-intensive and logistics-intensive industries.²⁴¹²²² Kearney’s dedicated OPTANO page further claims that the combined Kearney + OPTANO offering has already delivered improvements for automotive, consumer, energy, and retail clients, again without naming most customers but indicating sector breadth.¹²

Independent software review sites like SoftwareWorld list OPTANO Platform as a demand planning and supply chain optimization product that uses advanced algorithms and machine learning to forecast demand and help manage production and inventory, but these summaries largely restate vendor claims and do not provide independent benchmarks.²⁵ Corporate information services confirm that OPTANO has been operating continuously and filing annual reports, but they do not disclose revenue or detailed customer counts.²¹⁵ On balance, the firm appears commercially mature as a niche optimization vendor with notable enterprise clients, but public evidence remains limited in depth and specificity, especially regarding quantitative impact and deployment scale.

Conclusion

In precise technical terms, OPTANO delivers a combination of:

A .NET-based modeling framework (OPTANO Modeling) that allows developers to express MIP/LP and multi-objective optimization models in C#, delegating solving to third-party solvers.³⁴⁵²⁶²⁷
An application platform (OPTANO Platform) that wraps these models in web-based user interfaces, scenario management, and domain-specific templates for network, transport, inventory, production, and workforce planning.⁶⁷⁸⁹¹⁰¹¹¹²¹³¹⁴
A set of industry- and domain-focused solution packages (Network Optimization, Flowpath Optimization, Transport Management, Inventory Management, Production Planning, Employee Capacity Planning) that encode common model structures and planning workflows for typical supply chain and operations problems.⁷⁸²¹⁹¹⁰¹¹¹²¹³²²²³

The mechanisms and architectures through which OPTANO achieves these outcomes are largely classical: mathematical optimization models built in C# and solved via commercial solvers; scenario management and visualization layers for exploring alternatives; and, in some cases, forecasting and analytics components feeding data into those optimization models. The AI and machine learning claims likely correspond to these forecasting and analytics components and to the broader use of optimization in complex planning problems, rather than to fundamentally new AI architectures. The absence of detailed public technical documentation on forecasting algorithms, as well as the reliance on standard solver features (including multi-objective optimization), supports a cautious interpretation of “AI-powered”—competent and modern, but not necessarily state-of-the-art in AI research terms.⁵³⁵²⁴¹³²⁵²⁶

Commercially, OPTANO is an established, if focused, player: founded in 2009, operating from Paderborn with an OR/optimization-centered team, and now integrated into Kearney as its operations optimization software arm.¹²¹⁶¹⁷¹⁸¹⁹²⁰¹⁵ Named clients and sector pages indicate credible adoption in automotive, cement, logistics, and consumer goods, though detailed quantitative case studies are often gated and independent assessments are scarce.⁹¹⁰¹¹¹²²²²³²⁵ Compared with vendors that market broad “AI” solutions but offer limited transparency, OPTANO at least exposes a concrete modeling stack and leverages well-understood solver technology, which is a strength in terms of reproducibility and auditability.

At the same time, the platform’s reliance on traditional MIP/LP modeling and scenario analysis may limit its ability to natively handle fully probabilistic representations of uncertainty, end-to-end differentiable models, or advanced stochastic optimization techniques—areas where specialized quantitative supply chain platforms (such as Lokad) deliberately differentiate themselves by embedding probabilistic forecasting and custom stochastic optimization engines into their core.²⁸²⁹³⁰³¹³²³³ Organizations considering OPTANO should therefore view it as a robust OR-based optimization toolkit with strong consulting backing (via Kearney), well-suited to structured planning problems with clear constraints and objectives, rather than as a turnkey AI system that autonomously learns and optimizes supply chain decisions without careful model design and configuration.