Review of ClearOps, Supply Chain Software Vendor

By Léon Levinas-Ménard

Last updated: November, 2025

ClearOps is a Munich-based software company focused on the aftersales supply chains of machinery manufacturers, operating a cloud platform that connects OEMs with thousands of dealers and their installed machine base to improve spare parts availability and machine uptime. Founded in the mid-2010s under CEO William Barkawi and incubated within the Barkawi supply chain group, ClearOps positions itself as an independent intermediary layer between OEMs, dealer management systems and, increasingly, process-mining and optimization partners. The platform has been adopted by industrial OEMs such as Jungheinrich, Terex and AGCO to orchestrate aftersales operations across 2,000+ dealers, 50+ dealer management systems and several million spare-part SKUs, with claims of planning on the order of 5 million parts per day and tens of thousands of orders per week. Commercially, ClearOps remains a relatively small but specialised vendor (roughly a few dozen employees) whose product footprint is tightly centered on networked aftersales ecosystems rather than general-purpose supply chain planning. Technically, publicly available evidence indicates a mature cloud integration and collaboration layer, with optimization and “AI” functionality partly delivered through partnerships (notably PTC’s Servigistics and Celonis) rather than through clearly documented, proprietary multi-echelon optimization engines of its own.

ClearOps overview

ClearOps presents itself as an “aftersales platform that enables collaboration between manufacturers, dealers and end customers”, aimed at ensuring spare-parts availability for OEMs and technician efficiency for dealers by uniting OEMs, dealers and machines on a single cloud platform.¹² Public customer-facing materials and third-party directories consistently describe it as holistic B2B aftersales software for the data-driven optimization of supply chains in the machine manufacturing industry.³⁴

Positioning and scale are reasonably well substantiated. EU-Startups and organisational directories report that ClearOps is a Munich-based SaaS startup led by CEO William Barkawi and that its solution has been in use at major industrial OEMs such as Jungheinrich, Terex and AGCO since around 2016.³⁴ The same sources state that ClearOps is already used to plan roughly 5 million spare parts per day and process about 30,000 orders per week across these networks.³⁴ Barkawi Group’s own portfolio description further claims that ClearOps has, over recent years, connected more than 2,000 dealers, integrated 50+ common dealer management systems (DMS) and manages over 5 million SKUs worth more than €1 billion of inventory in sectors including construction and agricultural machinery, material-handling equipment and power tools.⁵

Regional startup coverage from Munich Startup describes ClearOps’ core purpose as making supply chains more transparent to minimise downtime by digitally connecting machine manufacturers with their dealer and distribution networks.⁶⁷ A later “follow-up” interview confirms the same narrative and notes that ClearOps focuses on preventing outages through this connectivity rather than, for example, generic enterprise planning.⁷⁸ ClearOps’ own About page summarises its mission as “keeping the world of machinery moving” by harnessing data to transform how OEMs operate their aftermarket networks.⁹ Customer and case-study pages emphasise that the aftersales platform is already “trusted by the world’s leading manufacturers and over 8000 associated dealers”, though this 8000+ figure is a marketing claim without independent corroboration.¹⁰

Commercial maturity. Headcount listings such as The Org indicate a company size in the 11–50 employee range, headquartered in Munich.⁴ Combined with the presence of recent job postings for roles like Enterprise Account Executive and DevSecOps Engineer in 2024–2025, this suggests a small but actively hiring SaaS vendor still closer to scale-up than large-enterprise status.¹¹¹² There is no evidence of major venture capital rounds; instead, ClearOps appears to be incubated and funded within the Barkawi group, which specialises in seeding supply chain technology companies out of consulting and client work.¹¹ Public interviews mention “financing and challenges” but do not provide precise cap-table or round details.⁷⁸ Overall, ClearOps is best characterised as a focused, vertically specialised SaaS provider with several high-profile OEM references and meaningful production scale, but still modest organisational size.

ClearOps vs Lokad

ClearOps and Lokad both operate in the broad domain of supply chain software, but they occupy different layers of the stack and embody distinct philosophies.

ClearOps is tightly focused on networked aftersales ecosystems for industrial machinery OEMs. Its declared value proposition is to connect OEMs, dealers, DMS/ERP systems and, increasingly, machine telemetry into a single aftersales platform to improve spare-parts availability and machine uptime across distributed dealer networks.¹²⁶ In practice, ClearOps functions as a hyper-connectivity and orchestration hub: it standardises and ingests data from 50+ dealer management systems and 2,000+ dealers, centralises parts, order and installed-base information, and exposes collaborative workflows and analytics back to OEMs and dealers.⁵¹⁰

Lokad, by contrast, positions itself as a programmatic quantitative optimization engine for supply chains across many industries (retail, manufacturing, aerospace, etc.), not just machinery aftersales. Its cloud platform is built around Envision, a domain-specific language (DSL) engineered specifically for the predictive optimization of supply chains.¹³¹⁴ Lokad emphasises probabilistic forecasting and numerical optimization: it models complete demand and lead-time distributions, then optimizes decisions (order quantities, allocations, production plans, pricing) to maximise expected economic outcomes.¹²¹⁵ The platform is explicitly code-driven: supply chain scientists express forecasting and optimization logic in Envision scripts, which run automatically on a multi-tenant SaaS infrastructure.¹³¹⁴¹²

From a technical-architecture perspective, ClearOps’ public materials highlight extensive connectivity to dealer DMS/ERP systems via an integration hub already connected to 80+ ERP systems, promising “swift and sustainable system integration” across the dealer network.¹⁶ This underlines its strength in data integration, workflow digitisation and role-specific UX for OEMs and dealers. However, ClearOps’ own documentation remains relatively high-level about internal algorithms; detailed descriptions of forecasting approaches, optimization solvers or probabilistic models are absent from public sources.

Lokad, conversely, openly documents the core of its stack: technical documentation describes Lokad as a programmatic SaaS platform where Envision scripts implement the full pipeline: data integration, probabilistic modelling and decision optimization.¹³¹² Lokad’s forecasting engine is described as delivering integrated probabilistic demand forecasts that incorporate probabilistic lead times, accounting for seasonality, product lifecycles and demand distortions such as stockouts and promotions.¹⁵ Lokad’s own explanations and case studies further explain that decisions (purchase orders, allocations, etc.) are derived via numerical solvers and stochastic optimization, not heuristic rules.¹²¹⁵¹⁷

Regarding “AI” and optimization, ClearOps’ most explicit ties to advanced optimization engines come via its partnership with PTC’s Servigistics: the joint PTC–ClearOps announcement frames ClearOps as providing data integration across the service supply chain network, while Servigistics contributes “service parts optimization capabilities” that rely on AI, machine learning and multi-echelon optimization.¹⁷¹⁸¹⁹ In other words, industrial-grade optimization in the ClearOps ecosystem is, as far as public evidence shows, largely delegated to a partner product (Servigistics). A second, more recent partnership with Celonis positions ClearOps as a specialist in “maximizing machine uptime by orchestrating connected service ecosystems”, with Celonis providing process mining and process-intelligence capabilities to surface process bottlenecks and drive proactive decisions.²⁰²¹¹⁶

Lokad, in comparison, embeds its own probabilistic forecasting and optimization stack directly into its platform. Its documentation and manifesto emphasise that uncertainty is modelled explicitly through probabilistic forecasts, and that numerical solvers consider and score all possible decisions in order to choose those that optimise economic objectives.¹²¹⁵²² Lokad’s participation in the M5 forecasting competition, where a Lokad team ranked 6th out of 909 teams overall (and later showcased #1 SKU-level performance), provides independent evidence of its forecasting capabilities.²³¹²⁴

In terms of product surface and user model, ClearOps is closer to an application layer: OEM product teams, dealer operations and service managers work within a domain-specific UI designed around aftersales processes (dealer order workflows, service campaigns, parts availability dashboards). OEMs can then plug in heavy-duty optimization or process-mining engines (Servigistics, Celonis) beneath or beside ClearOps via partnerships.¹⁷²⁰²¹ Lokad is closer to an analytics engine and development environment: supply chain scientists write Envision code and expose the resulting dashboards and action lists to planners; there is no prebuilt “aftersales app” as such, but general-purpose building blocks to encode any supply chain model.¹³¹⁴¹²

For a machinery OEM, this translates into trade-offs:

If the main pain point is fragmented dealer data, inconsistent DMS integrations and a lack of unified visibility/standard workflows, ClearOps’ integration hub, dealer connectivity and OEM/dealer UX are directly aligned with the problem, with proven deployments in exactly that context.⁵⁶¹⁰²²²⁵
If the main pain point is mathematically optimising inventory, production and pricing decisions across many echelons under uncertainty, Lokad’s probabilistic modelling and optimization engine targets that problem more directly, but it expects the client (with Lokad’s help) to encode their business logic programmatically.¹³¹²¹⁵¹⁷

These approaches are not strictly mutually exclusive: an OEM could, in theory, use ClearOps as the aftersales data and workflow layer for its dealer network and feed cleansed, standardised data into a quantitative optimization engine like Lokad or Servigistics. However, as currently positioned in public materials, ClearOps emphasises connectivity and operational collaboration, while Lokad emphasises quantitative optimisation and decision automation.

Corporate background, ownership and history

ClearOps is repeatedly described as a Munich-based startup led by CEO and founder William Barkawi.³⁶⁷¹⁹ The company appears to have been founded in the mid-2010s: EU-Startups notes that its cloud-based SaaS solution has been in use “since 2016” at major industrial OEMs.³ Munich Startup’s early profile (2022) describes ClearOps as a young company aiming for “maximum supply chain visibility for minimal downtime” by connecting machine manufacturers with their dealer and distribution networks.⁶

The Barkawi Group, a long-standing supply chain consulting and technology group, lists ClearOps among its portfolio companies and states that Barkawi companies typically emerge in-house from demand research, client requirements or transformation projects, with seed and early-stage investment borne by Barkawi itself.¹¹⁵ Barkawi’s German-language page about ClearOps quantifies the platform’s reach (2,000+ dealers, 50+ DMS systems, >5m SKUs, >€1bn inventory), suggesting that ClearOps matured within Barkawi’s ecosystem before (or alongside) external marketing.⁵

There is no public evidence of ClearOps having been acquired or having acquired other companies. Available interviews talk about financing and challenges but do not mention external VC rounds, major strategic investors or M&A.⁷⁸ From this, the most cautious interpretation is:

ClearOps is privately held, likely majority owned within the Barkawi ecosystem plus the founder and early employees.
Growth appears organic and reference-driven, anchored in a small number of large OEM programmes rather than VC-driven hyper-growth.

This ownership structure mirrors other Barkawi-origin companies and is consistent with ClearOps’ narrow vertical focus and relatively small headcount.

Product scope and functional coverage

Functional scope: aftersales ecosystems and dealer networks

Across its website, ClearOps consistently frames its product as an aftersales platform for OEMs and dealers, not as a general supply chain planning suite.¹²¹⁰¹⁹ Key functional themes include:

Dealer/DMS integration and data unification. ClearOps advertises an “advanced integration hub” already connected to more than 80 different ERP systems, explicitly aiming to give OEMs “visibility and control” over fragmented dealer and distribution networks “in no time” via its hyper-connectivity suite.¹⁶ Barkawi quantifies this as 50+ dealer management systems integrated over time.⁵
Spare parts availability and uptime. ClearOps’ German homepage states that the platform ensures spare-parts availability for OEMs and technician efficiency for dealers by uniting OEMs, dealers and machines on a single platform.² Munich Startup coverage echoes that the goal is to prevent outages by making the supply chain more transparent.⁶⁷
Aftersales workflows and collaboration. Customer pages and case studies describe collaborative workflows between OEMs and dealers: for example, Terex states that leveraging the ClearOps Aftersales Hub allows dealers to save time on day-to-day tasks through process automation, improve inventory profiles by reducing obsolescence while increasing part availability, and ultimately increase sales and brand loyalty.²² This implies capabilities such as automated order proposals, exception handling and campaign management, though public documentation stops short of detailed workflow diagrams.
OEM-level analytics across the network. Case studies refer to OEMs using ClearOps to gain cross-network visibility, identify under- or over-stocked locations and coordinate actions across dealers. For instance, AGCO’s “Success Study” references a vast dealer network of over 2,000 dealers struggling with part shortages and reactive service, contextualising ClearOps as the digital layer to address these issues.²⁵

Notably, ClearOps does not present modules like “demand planning”, “S&OP” or “network design” in the way that classical APS vendors do. Its scope is narrower but deeper in the aftersales context: it targets the specific pain of fragmented dealer networks and installed bases in industries where machine downtime is expensive (construction equipment, agricultural machinery, material handling).

Named customers and reference use-cases

Named references are relatively strong for a company of ClearOps’ size:

EU-Startups and company profiles state that ClearOps has been in use at Jungheinrich, Terex and AGCO since 2016.³⁴
ClearOps’ own case-study section lists multiple OEMs, including Jungheinrich, Terex and others, though not all case studies are fully open without registration.¹⁰²⁴²²
The Terex case study explicitly attributes improved dealer efficiency and inventory profiles to the ClearOps Aftersales Hub.²²
AGCO’s success study focuses on the challenge of maintaining parts availability across a 2,000+ dealer network to protect farmers’ uptime, implicitly positioning ClearOps as the enabling platform.²⁵

These are verifiable, named clients in the target verticals (material-handling, construction equipment, agricultural machinery) with global dealer networks, which supports ClearOps’ claims of operating at scale. However, publicly available case studies remain primarily qualitative; they do not disclose quantitative KPIs (e.g., specific percentage reductions in inventory or downtime attributable solely to ClearOps), nor do they detail the internal algorithms.

By contrast, ClearOps’ marketing claims about being “trusted by the world’s leading manufacturers and over 8000 associated dealers” should be treated as marketing-level: the specific identities of OEMs beyond the named examples and the precise 8000+ dealer count are not independently corroborated.¹⁰

Architecture, technology stack and integrations

Integration hub and data model

Public product pages and the DMS-integration subpage paint a consistent architectural picture: ClearOps operates an integration hub designed to rapidly onboard OEMs and their dealers by connecting to existing ERP/DMS systems.¹⁶ The hub is already connected to over 80 different ERP systems and 50+ dealer management solutions, enabling ingestion of orders, inventory, machine and customer data into a central cloud platform.⁵¹⁶

Based on this, we can infer a few characteristics:

The platform almost certainly relies on multi-tenant cloud infrastructure, given the diversity of dealer systems it connects to and its positioning as SaaS; EU-Startups explicitly describes ClearOps as a cloud-based SaaS solution.³
The core logical data model appears to revolve around entities such as dealer location, OEM, machine, part, order and installed base, enabling network-wide analytics and workflows.
The integration layer must deal with differing data qualities and schemas across each DMS – a nontrivial engineering task – but there is no public technical documentation on how schema harmonisation, data latency or error handling are implemented. For example, there is no mention of whether ClearOps uses an event-driven architecture, message queues or specific ETL tooling.

While this architecture is plausible and consistent with the problem being solved, ClearOps publishes very limited detail about its internal stack: there is no public mention of the primary cloud provider (AWS/Azure/GCP), programming languages, data stores (relational vs document vs columnar) or specific frameworks. Occasional job ads for DevSecOps engineers suggest a modern CI/CD and cloud-security posture, but they do not enumerate the stack.¹¹ Consequently, any deeper technical assessment of scalability or fault tolerance would be speculative.

Optimization and analytics: ClearOps vs partner engines

The most concrete technical information on optimization comes not from ClearOps’ own site but from partner PTC’s materials on Servigistics:

PTC positions Servigistics as providing true multi-echelon inventory optimization for service parts, using advanced algorithms, AI and machine learning to optimise stocking levels across complex service networks.¹⁸¹⁹
The joint PTC–ClearOps blog frames ClearOps’ contribution as “cutting-edge data integration technology” and Servigistics’ as “best-of-breed service parts optimization capabilities”, with the goal of blending these technologies to deliver greater visibility and communication across the service supply chain.¹⁷

This strongly suggests that for customers using both Servigistics and ClearOps, ClearOps is primarily the data and collaboration fabric, with Servigistics acting as the optimization brain for service parts inventory. In those cases, ClearOps’ role is critical (without good data, optimization is meaningless), but the heavy mathematical lifting is outsourced.

ClearOps’ own pages refer to automation, recommendation and analytics but do not describe algorithms in formal terms (e.g., no mention of probabilistic forecasting, multi-echelon models, stochastic optimization, or specific machine-learning techniques). In the absence of such documentation, the safest interpretation is:

ClearOps likely includes rule-based automation and basic analytics for dealer workflows (e.g., reorder suggestions, ABC classification, alerts), as most modern SaaS platforms in this niche do.
For sophisticated multi-echelon optimization and AI-heavy service parts planning, ClearOps leans on partner engines such as Servigistics, which are explicitly marketed as providing these capabilities.¹⁷¹⁸¹⁹

The newer partnership with Celonis fits this pattern: Celonis provides a process-mining and process-intelligence platform that ingests event data, discovers process bottlenecks and recommends process improvements.²⁰²¹¹¹¹⁵ ClearOps contributes network data and domain context, while Celonis contributes a mature process-intelligence engine. Again, ClearOps is the orchestrator and connector rather than the process-mining engine itself.²⁰²¹¹⁶

From a state-of-the-art perspective, this ecosystem approach is reasonable and often pragmatic, but it means that ClearOps’ own proprietary algorithms (if any) are largely opaque from public materials. Prospective buyers should therefore treat claims about “optimization” or “AI” in ClearOps marketing as contingent on which partner products are actually deployed in a given project.

Assessment of AI, optimization and “state-of-the-art” claims

Evidence for scale and industrialisation

On the scale and industrialisation front, ClearOps’ claims are broadly credible and cross-validated by multiple independent sources:

5 million parts planned per day and 30,000 orders processed per week are quoted by EU-Startups, The Org and multiple company-related profiles, not just ClearOps marketing.³⁴⁷⁴
Barkawi’s portfolio description quantifies >2,000 dealers, 50+ DMS integrations and 5m+ SKUs worth >€1bn running through the platform.⁵
Named OEMs (Jungheinrich, Terex, AGCO) with global dealer networks and published case studies confirm that ClearOps is used in genuine production environments, not just pilots.³¹⁰²⁴²²²⁵

These facts support the conclusion that ClearOps is commercially battle-tested as a connectivity and collaboration layer in aftersales contexts.

Gaps in algorithmic transparency

When it comes to algorithmic sophistication and AI, however, ClearOps’ public documentation is significantly thinner:

There is no detailed exposition of forecasting methods, e.g. whether ClearOps uses classical time-series models, machine-learning models, probabilistic forecasts, or simple historical heuristics.
There is no description of inventory-optimization logic: no mention of multi-echelon optimisation, stochastic models, Monte Carlo simulation, or even traditional safety-stock formulas. Where such terms appear (e.g. “multi-echelon optimization” or “industrial AI”), they are in the context of PTC Servigistics, not ClearOps’ own code.¹⁷¹⁸¹⁹
No academic collaborations, open-source code or technical whitepapers from ClearOps are publicly visible that would allow independent scrutiny of its algorithms.

By contrast, partners like PTC and Celonis publish relatively extensive technical narratives about their engines (multi-echelon optimisation, AI-powered process intelligence), even though those narratives remain marketing-coloured.¹⁸¹⁹²¹

Given this, a cautious, sceptical assessment is:

ClearOps clearly operates at scale and has industrialised its connectivity and data-model layer.
Claims of advanced optimization or AI should be interpreted as primarily referring to partner products (Servigistics, Celonis) rather than to proprietary ClearOps algorithms, unless ClearOps can furnish technical documentation to the contrary.
From a state-of-the-art standpoint, ClearOps is closer to a modern, cloud-native integration and collaboration platform for aftersales – arguably state-of-the-art in its niche connectivity – than to a self-contained optimisation engine.

This does not diminish ClearOps’ value in projects (good data and workflows are often the hardest part), but it clarifies where the “intelligence” lives: mostly in partner engines and in human analysts using the harmonised data.

Deployment model and roll-out

Public sources outline a deployment pattern consistent with other B2B SaaS platforms in industrial contexts:

The integration hub allows relatively rapid connection to existing ERP/DMS systems; however, ClearOps itself notes that the timeline depends on the size of the dealer network and underlying systems, suggesting non-trivial integration work.¹⁶
Munich Startup interviews indicate that connecting numerous dealers and systems and convincing stakeholders to adopt new digital workflows has been a major practical challenge.⁶⁷
Case studies imply a phased roll-out, with an initial group of dealers/pilots, followed by wider network adoption once benefits are proven (e.g. in Terex’s case).²²

There is no detailed public documentation on implementation methodology (e.g. project phases, typical months-to-go-live, change-management playbooks). For prospective buyers, this means implementation risk and effort should be evaluated via direct reference calls rather than assuming turnkey deployment.

Conclusion

ClearOps is best understood as a vertically specialised, mid-stage SaaS vendor focused on the aftersales ecosystems of industrial machinery OEMs. Its core strength, as evidenced by independent and company sources, lies in:

Hyper-connectivity and data unification across fragmented dealer and DMS/ERP landscapes.¹⁵¹⁶
Operational collaboration and workflow digitisation between OEMs and thousands of dealers, with a focus on spare-parts availability and machine uptime.²⁶¹⁰²²²⁵
Demonstrated production scale at named OEMs such as Jungheinrich, Terex and AGCO, processing millions of parts and tens of thousands of orders weekly.³⁵⁴²²²⁵

From a technical-depth perspective, ClearOps’ publicly documented capabilities are more modest:

Optimization and AI claims appear largely tied to partner engines (PTC Servigistics for multi-echelon optimisation and service-parts AI; Celonis for process intelligence) rather than to clearly specified, in-house algorithms.¹⁷¹⁸¹⁹²⁰²¹¹⁶
Algorithmic transparency is low: there are no publicly described probabilistic models, solvers, or technical whitepapers that would allow a rigorous external assessment of ClearOps-native optimisation.
Commercial maturity is solid but not massive: a small team, incubated under Barkawi, with several flagship clients but far from the scale of global APS vendors.⁵⁷⁴¹¹

Compared to Lokad, which positions itself as a programmatic quantitative optimization engine with publicly documented probabilistic forecasting and DSL-based modelling, ClearOps occupies a different niche: it is closer to a networked aftersales application layer than to a generic optimisation engine. For OEMs whose main bottleneck is dealer network digitisation and data quality, ClearOps is a credible and field-tested option. For organisations primarily seeking state-of-the-art probabilistic optimisation across their entire supply chain, ClearOps would typically need to be complemented by a dedicated optimisation platform (whether Servigistics, Lokad or another engine).

In any due-diligence process, a technically sceptical buyer should therefore:

Separate connectivity from optimisation and explicitly ask which decisions are optimised by ClearOps itself versus by partner engines.
Request concrete, quantitative case data (inventory reductions, uptime improvements, lead-time reductions) with clear attribution of which component delivered which benefit.
Clarify the long-term architecture: will ClearOps be the data backbone feeding optimisation engines, or is it expected to evolve into a full optimisation layer?

As of late 2025, publicly available evidence supports the view that ClearOps is a serious, specialised vendor for aftersales network digitisation, but that its state-of-the-art optimisation capabilities are primarily accessed through ecosystem partnerships rather than via transparent, in-house algorithms.