Review of MJC², Planning Software Vendor

By Léon Levinas-Ménard

Last updated: December, 2025

MJC² (MJC2 Limited) is a UK-based software house founded in 1990 that develops highly specialised optimisation and scheduling systems for complex, operationally intensive environments such as parcel networks, bulk transport, multimodal freight, manufacturing plants and mobile workforces. Legally, it is a small private company with single-digit headcount and sub-£1m annual turnover, yet it has been repeatedly selected as an optimisation partner in large EU-funded projects and industry collaborations, where its role is to deliver fast, real-time algorithms that replan large networks in response to live disruptions. The product line is not a single monolithic platform but a family of optimisation engines and applications – notably DISC for distribution and vehicle routing, PIMSS for production scheduling and process control, SLIM for strategic transport planning, plus workforce and rostering modules – typically deployed as bespoke projects embedded into customer IT landscapes. Commercial collateral emphasises real-time rescheduling, synchromodal logistics, “physical Internet” concepts and AI-based optimisation, but there is almost no public detail about underlying technology stack, data model, or machine-learning methods; the strongest evidence concerns the company’s combinatorial optimisation capabilities and integration with SCADA/TMS/WMS rather than probabilistic forecasting or end-to-end SaaS architecture. In practice, MJC² should be understood as a niche optimisation boutique: a small R&D-heavy team selling project-style optimisation systems for transport, manufacturing and workforce scheduling, rather than a general-purpose, cloud-native supply chain planning suite.

MJC² overview

Corporate identity and size

MJC2 Limited is registered in England and Wales under company number 02531037. Companies House records show incorporation on 14 August 1990 (originally as Oysterlock Limited, renamed MJC2 Limited the same year), with its registered office in Crowthorne, Berkshire and SIC code 62012 (“Business and domestic software development”).¹ Alternative business directories confirm the same incorporation date, location and classification.² Independent financial aggregators (Endole, Craft, D&B) classify MJC² as a small private company: recent filings show annual turnover of roughly £0.6–0.7m, around 7 employees and several million pounds of net assets.³⁴⁵ EU Innovation Radar lists MJC² as an SME with about €2m of Horizon funding across projects.⁶

Public recruiting material describes MJC² as developing “advanced scheduling & optimization software for logistics, manufacturing and workforce planning”, recruiting mostly highly specialised optimisation developers rather than a large sales or implementation organisation.⁷ External profiles (Craft, AI4Europe, Innovation Radar) consistently characterise the company as a provider of algorithms for large, complex planning and scheduling problems, with emphasis on dynamic, real-time optimisation rather than generic business software.⁸⁹¹⁰ There is no evidence of venture funding rounds or acquisitions; the company appears to be organically grown, project-funded and independent.

Product family and functional scope

MJC²’s own website positions its offering as “planning & scheduling software” across three main domains: logistics, manufacturing and workforce.¹¹¹² Key branded components include:

DISC – distribution scheduling and vehicle routing software for large logistics networks (multi-depot, linehaul, backhauls, multi-drop).¹³¹⁴
SLIM – strategic transport planning and network optimisation (capacity, corridors, timetable design, freight logistics).¹⁵¹⁶
PIMSS – production planning and manufacturing process scheduling, integrated with SCADA/process control systems.¹⁷¹⁸¹⁹
Workforce / Mobile workforce / ROCS / MOBi – job allocation, rostering and field service optimisation.²⁰¹⁵¹⁷¹⁴
Multimodal & container logistics modules – optimisation for container freight, synchromodal planning, inland waterways, rail freight and “physical Internet” use cases.²¹²²²³⁶²⁴

Across pages, MJC² repeatedly stresses three functional themes:

Real-time re-planning at scale – ability to recompute schedules for thousands of vehicles, jobs or employees in seconds, responding to live GPS/SCADA or status updates.¹¹²⁵²⁶
Complex operational constraints – rules for driver hours, skills, rest periods, vehicle capacities, hazardous materials, multi-leg freight paths, crew skill mixes, etc.¹³¹⁷¹⁴
Synchromodal / multimodal logistics – dynamic choice of mode (road/rail/barge/sea/air) and path given network congestion, capacity and sustainability objectives.²¹²²²³⁷

The portfolio is broad but consistently centred on operational scheduling rather than high-level S&OP or classical demand planning. Supply chain optimisation is mentioned in SLIM and lean manufacturing pages, but mainly as network design and resource allocation rather than end-to-end probabilistic inventory planning.¹⁵²⁷²³

Research projects and external validation

A large fraction of publicly verifiable evidence about MJC² comes from EU and industry projects where the company is named as optimisation technology provider:

FLAGSHIP / FAST – EU maritime transport project where MJC²’s vehicle routing software FAST was made available to UK SMEs; CORDIS notes its role as “leading provider of real-time logistics scheduling software”.²¹
e-Freight – large multimodal logistics project; CORDIS reports that an MJC² real-time scheduling system for container logistics won an international IT innovation award for container ports.²⁸ MJC²’s own e-Freight pages emphasise conflict-free routing, green logistics optimisation and inland waterway logistics.²²²¹
CONTAIN – container security project; both CORDIS and industry press describe MJC²’s algorithms as a major component of the decision-support and real-time scheduling toolkit used to increase container security while improving efficiency.¹²⁹¹⁷
SYNCHRO-NET – an €8m EC-funded initiative on slow steaming and synchromodal logistics; MJC² states it acted as “leading innovation force”, responsible for synchromodal optimisation algorithms, in collaboration with ~20 supply chain organisations.²⁰¹¹⁷
PIONEERS & other ports projects – EU-funded work on green logistics optimisation in ports, where MJC² develops algorithms to optimise barge use and future autonomous e-barges/e-trucks.¹⁵²⁴¹¹

Third-party descriptions (Innovation Radar, AI4Europe, Synchro-NET partner pages, maritime logistics portals) broadly corroborate that MJC²’s speciality is advanced scheduling algorithms for large multimodal networks, and that major clients include large logistics operators, ports and industrial manufacturers.⁸⁹³⁰³¹ Some CORDIS articles explicitly name well-known logistics brands (e.g. TNT, B&Q, Pepsi) as users of MJC² technology within EU projects,³⁰ but there is limited detail on the scope and commercialisation beyond the research context.

MJC² vs Lokad

From a supply chain perspective, MJC² and Lokad occupy overlapping but structurally different niches.

Scope of decisions: MJC² is primarily an operational scheduling and routing vendor. Its core engines compute daily or intra-day assignments: which truck or barge moves which loads when and along which route; which production line runs which job; which technician visits which site in which order.¹³¹⁷²¹²³ Lokad, by contrast, focuses on tactical and strategic planning decisions in supply chains: inventory targets, purchase orders, network stock allocation, and occasionally pricing, based on probabilistic demand and supply models.³²³¹²⁹
Optimization approach: MJC² emphasises combinatorial optimisation under operational constraints, often framed as vehicle routing and scheduling with complex rules. EU and vendor material describes “lightning-fast algorithms” for very large, NP-hard planning problems and refers to “AI-based” optimisation in projects like ePIcenter and rail freight.²¹⁸²⁸²³⁶ There is no public evidence of end-to-end probabilistic modelling of demand or lead times; uncertainty is mentioned mainly as real-time events (delays, disruptions) handled by re-planning. Lokad’s documented approach is probabilistic and decision-centric: it computes full demand and lead-time distributions and then applies stochastic optimisation (Monte Carlo plus custom algorithms) to maximise expected economic outcomes across the whole network.³²³¹²⁶¹⁰
Architecture & productisation: MJC² appears to deliver project-specific optimisation systems, often tightly integrated with SCADA, TMS or field-service infrastructures. PIMSS, for example, is designed to sit alongside SCADA at levels 2–3 of the control hierarchy, while logistics modules integrate with GPS and telematics systems.¹⁷²⁶ There is little public information about a multi-tenant SaaS platform or a customer-exposed programming layer. Lokad, by contrast, is explicitly a multi-tenant SaaS platform built around Envision, its domain-specific language (DSL) for supply chain optimisation; customers’ solutions are implemented as Envision scripts executed on Lokad’s cloud infrastructure.³²²⁴¹³²⁷
AI/ML claims: MJC² uses “AI algorithms” language in several project pages (e.g. rail freight, ePIcenter, physical internet) and “artificial intelligence based algorithms” for global logistics optimisation,²⁸²³⁶ but there is no public disclosure of model types (e.g. neural networks, gradient-boosted trees), training pipelines or validation benchmarks. The visible emphasis is on heuristic or OR-inspired real-time scheduling. Lokad openly documents use of deep learning for forecasting, differentiable programming for joint forecast–decision optimisation, and probabilistic models validated via external benchmarks (e.g. the M5 competition), with extensive technical documentation.³²³¹²⁶¹⁰
Forecasting vs. reactivity: MJC²’s materials largely treat demand/workload forecasting as one input among many to scheduling, and focus more on reactive re-planning in response to live status changes.¹¹¹⁵⁸ Lokad treats probabilistic forecasting as central, computing demand distributions even in the absence of real-time signals and optimising decisions over those distributions.²⁶¹⁰ Where MJC² mentions forecasting (e.g. demand/workload forecasting for workforce planning), it is not accompanied by technical detail.¹⁸⁹
Commercial model and maturity: Both firms have long histories (MJC² since 1990; Lokad since 2008). MJC² is structurally a very small optimisation boutique (single-digit staff, sub-£1m revenue), albeit with repeated roles in EU research programmes and specialised logistics deployments.³⁴⁸⁶ Lokad is a larger but still mid-sized vendor (dozens of staff), selling a standardised SaaS platform delivered with a consulting layer (“Supply Chain Scientists”).³²³¹ In practice, MJC²’s fit is highest where a client needs hard real-time scheduling embedded deep into ops systems (e.g. ports, rail freight, utilities field operations), while Lokad targets quantitative supply chain planning (multi-echelon inventory, purchasing, allocation) across retail, manufacturing, aerospace and similar sectors.

For a buyer, the critical distinction is that MJC² is not a generic demand planning or inventory optimisation suite; it is an optimisation R&D shop whose technology is best leveraged when the key business problem is complex real-time scheduling. Lokad, conversely, is not a TMS/SCADA replacement or dispatch engine; it sits above ERPs/WMS/TMS to determine economically optimal planning decisions.

Company history and commercial maturity

Founding, legal history and ownership

Official UK filings show that MJC2 Limited was incorporated on 14 August 1990, with a brief initial name “OYSTERLOCK LIMITED” and a rapid rename to MJC2 Limited.¹² The firm has remained privately held; no acquisition activity (as acquirer or acquired) appears in Companies House charge/filing history or mainstream M&A databases. Business registries and marine-industry profiles reiterate that the company focuses on advanced planning, scheduling and optimisation for logistics and manufacturing.³¹¹⁰

Several external profiles (Endole, D&B, Craft) show consistent trends over the last decade: modest but stable revenue, a small staff (around 7–10 employees), positive net assets and a low debt ratio.³⁴⁵ This is consistent with a small, specialised optimisation house rather than a scale-up or venture-backed vendor.

EU funding and project trajectory

CORDIS and Innovation Radar provide partial visibility on MJC²’s R&D trajectory:

In the late 2000s and early 2010s, MJC² appears in projects like FLAGSHIP (maritime) and e-Freight (multimodal ports), delivering vehicle routing and real-time scheduling components that are later re-used or commercialised (e.g. FAST routing for SMEs).²¹²⁸
In the early-to-mid 2010s, it participates in CONTAIN (container security with optimisation back-end) and develops optimisation components for container logistics tracking and routing.¹²⁹
From mid-2010s onwards, MJC² features in SYNCHRO-NET and related synchromodal rail/freight projects, focusing on smart streaming, modal shift to rail, and integrated green logistics optimisation.²⁰¹¹²³⁷
More recently, projects like PIONEERS and ePIcenter focus on sustainability in ports and global logistics; Innovation Radar lists around €2m in total H2020 funding awarded to MJC² for these and related initiatives.⁸²⁴²⁸

This pattern reinforces the view that MJC²’s R&D roadmap is strongly tied to EU and industry collaborative projects, with its products evolving as applied outputs of this research rather than as a fully productised, off-the-shelf suite.

Market presence and customer references

MJC²’s website and EU project pages mention several well-known brands as collaborators or users of its technology in specific projects, including TNT, B&Q, Pepsi (in multi-modal logistics contexts), as well as ports (e.g. Valencia) and logistics providers (e.g. Schenker, DSV, Jan de Rijk, Stena Line).²²²⁸³⁰ However:

The majority of these references are project-level, tied to EU-funded initiatives, not stand-alone commercial case studies.
There is little granularity on deployment scope (e.g. which business units, data volumes, commercial terms) or before/after KPIs.
Public case descriptions emphasise technology innovation (e.g. international IT award for container scheduling) more than long-term production use.²⁸²⁹

Commercial directories like Datanyze and Craft list customers across logistics, manufacturing and utilities sectors in general terms but do not provide detailed reference names. Overall, evidence suggests credible niche adoption in highly specialised logistics and industrial contexts, but not broad penetration as a mainstream supply chain planning suite.

Product portfolio in detail

MJC²’s logistics stack is the most clearly described part of its portfolio.

The DISC product (DIstribution SCheduling) is marketed as a high-speed optimisation engine for large, complex logistics operations covering parcel, bulk, building materials, retail and similar flows. It supports multi-depot routing, backhauls, multi-drop rounds, trunking/linehaul integration and driver rostering; vendor collateral claims it can schedule thousands of movements in seconds and replan in real time.¹³¹⁴¹⁶ DISC can integrate with ROCS roster creation to simultaneously plan vehicle schedules and driver rotas based on workload forecasts and working-time rules.¹³

Complementing DISC, SLIM is described as a strategic transport planning tool for freight logistics optimisation, network design, intermodal routing and timetable creation. Use cases include transport systems planning, supply chain optimisation and capacity planning.¹⁵¹⁶ SLIM’s functional scope overlaps with typical strategic network design tools but with an emphasis on multi-modal routing and corridor utilisation rather than SKU-level inventory placement.

Additional pages describe:

Distribution management: integrated planning of warehouse/hub and transport operations, with what-if analysis for shift patterns, sortation capacity and scenario modelling.²⁸⁶
Real-time vehicle scheduling and dispatch: automatic dispatch, GPS-based tracking, maps and Gantt chart visualisations for control room staff, with KPI dashboards for strategic analysis.²⁶
Bulk tanker scheduling: real-time scheduling for bulk liquids and dairy, optimising farm collection and inter-plant transfers.³⁰
Passenger transport and TMS: similar scheduling algorithms applied to passenger networks and general transport management systems.²⁷³³

From a technical standpoint, these modules all revolve around variants of vehicle routing and resource allocation under constraints. They appear well aligned with VRP-like formulations (VRP with time windows, multi-depot, multi-leg, driver rules, etc.), solved via proprietary heuristics that emphasise speed and re-optimisability.

Multimodal, synchromodal and “physical Internet” logistics

A distinctive area for MJC² is multimodal freight and synchromodal optimisation:

The e-Freight module focuses on integrated planning across road, rail, barge and sea, with conflict-free routing, green logistics optimisation and inland waterway scheduling; pages emphasise standardised “transport execution plans” and multi-leg movement planning.²²²¹
Container logistics software provides both strategic and real-time optimisation across global forwarding, hinterland logistics, port terminal operations and depot management, including driver/haulage assignment and container repositioning.²¹²²
The Rail Freight Optimisation module applies AI-based algorithms and real-time scheduling to increase utilisation and efficiency on TEN-T corridors; success stories cite deployments with major European rail freight operators.²³
Control tower material describes real-time dashboards showing projected inventory levels at terminals, shipment status and the effect of disruptions on service levels, with automatic updates as events occur.²²³²
“Physical Internet” and ePIcenter pages discuss AI-based optimisation for future synchromodal networks, autonomous barges, and sustainable global supply chains.²⁸⁶²⁴

Independent sources (CORDIS, SupplyChainDigital, Synchro-NET, Innovation Radar) reinforce that MJC²’s role in these projects is to supply optimisation engines that can dynamically select modes and paths given capacity, emissions and cost constraints.¹¹⁷²⁰⁸³⁰ This is advanced but still firmly in the realm of deterministic or scenario-based combinatorial optimisation; there is no explicit probabilistic modelling of demand distributions or lead time distributions in the public material.

Manufacturing and process control (PIMSS, lean manufacturing)

On the manufacturing side, PIMSS is presented as a production scheduler and process control optimisation system:

It optimises the allocation of production runs to lines and machines, while also scheduling associated labour, warehouse activities and cleaning/maintenance tasks.¹⁷²²
It integrates with SCADA architectures, occupying the production scheduling/control levels above PLCs and sensors; materials explicitly describe the mapping to SCADA levels 0–4.¹⁸
Lean manufacturing pages state that PIMSS integrates with DISC (logistics) and SLIM (strategic optimisation) to provide a holistic supply chain perspective.²³¹⁹

External profiles (EFFRA portal, EngNet) mirror this description, positioning PIMSS as a tool for complex manufacturing planning, process automation scheduling, capacity planning and MRP/line scheduling.²⁵¹⁹

Overall, PIMSS appears to be an operations-level scheduler tightly coupled to plant control systems, not a generic APS suite for all planning layers. It likely uses similar combinatorial heuristics to the logistics modules, but applied to job-shop or flow-shop scheduling.

Workforce, mobile workforce and rostering

MJC² offers several modules for workforce optimisation:

Employee Scheduling / job allocation – real-time algorithms to assign jobs, visits or calls to field personnel, following business rules and constraints, with the claim of scheduling hundreds or thousands of employees in seconds.¹⁷
Just-in-time workforce planning (MOBi, ROCS) – dynamic assignment of jobs based on skills, availability and location, with mobile apps for real-time dispatch; ROCS provides demand-led rostering and automatic shift scheduling.¹⁵
Crew and team scheduling – specialised modules for allocating large teams with diverse skills and interdependent tasks, modelling rules like rest periods, skill mix and crew preferences.¹⁴
Utilities & energy field service – mobile workforce optimisation for utilities, scheduling thousands of jobs and updating plans as field events occur.³⁴

These solutions share the same constraint-based scheduling flavour as logistics modules, but applied to human resources instead of vehicles.

Technology and architecture

Technology stack visibility

MJC² provides very little public information on its underlying technology stack:

No explicit mention of programming languages, databases or cloud providers appears on main product pages.
The recruitment page speaks generically of “advanced scheduling & optimization software” without listing specific technologies.⁷
Job portals (Glassdoor and similar) provide only high-level descriptions of working at MJC2, with no technical stack detail.⁴²⁹

Given this, any statement about specific languages or frameworks would be speculative. The only clear architectural hints are:

PIMSS integrates at SCADA levels 2–3, implying on-premise or closely coupled plant-level deployments.¹⁸
Several logistics modules integrate with GPS/telematics, TMS and mobile apps, implying a mix of server-side optimisation engines and real-time data feeds.²⁶³⁴
There is no visible multi-tenant SaaS control plane akin to a self-service web platform; deployments appear project-specific, often within the IT landscape of logistics operators, ports or utilities.

From an assessment standpoint, this strongly suggests a custom-solution / project-based architecture: an optimisation engine (or set of engines) is integrated via interfaces (files, APIs, SCADA connectors) into client systems, with bespoke configuration per project.

Optimisation methods and “AI” claims

Across its site and project material, MJC² uses language such as:

“lightning-fast algorithms” for very large, complex planning problems;⁸⁹
“AI-based logistics optimization software and real-time scheduling techniques” for rail freight;²³
“artificial intelligence based algorithms for sustainable optimization of global logistics operations” in the ePIcenter context;²⁸
“one of the few companies in the world that can offer real-time scheduling solutions”, in a Synchro-NET partner description.⁸

However, none of the public materials provides formal algorithmic descriptions, pseudo-code, model equations, or training/validation procedures. In particular:

There is no evidence of published research papers authored by MJC² on deep learning, probabilistic forecasting or differentiable programming for supply chains.
EU project documents (e.g. Cargo Flow Optimisation slides, CORDIS summaries) describe “new multi-layered optimisation architectures” but stay at the level of high-level architecture diagrams, not formal methods.¹¹¹²
The models are consistently framed as optimisation under complex constraints and real-time events, not as end-to-end statistical models of uncertainty.

Given this, the most conservative and evidence-based interpretation is that MJC² uses advanced heuristic and metaheuristic methods for combinatorial optimisation (e.g. variants of local search, tabu search, genetic algorithms, or bespoke heuristics) combined with rules engines for constraints. The “AI” label is likely used in the broad, industry sense of “advanced algorithmic optimisation”, not necessarily implying deep neural networks or probabilistic graphical models.

By contrast, Lokad’s documentation provides detail about its use of deep learning, probabilistic forecasting and differentiable programming on relational data for supply chain planning, including technical workshops and language-level primitives in Envision for random variables and stochastic optimisation.³²³¹²⁶²⁴²⁷ This difference in transparency is significant: Lokad’s AI claims are technically substantiated; MJC²’s are not, at least in public.

Data and uncertainty treatment

MJC²’s pages implicitly handle uncertainty via real-time re-planning: as delays, disruptions or demand changes occur, schedules are recomputed using latest data.¹¹²²²³ Some project descriptions mention “de-risking complex networks” and “agile supply chains”, but not how uncertainty is modelled ex ante.¹¹⁸

By contrast, there is no discussion of:

probabilistic demand or lead-time distributions;
safety stock calculations;
Monte Carlo or scenario sampling methods.

In synchromodal and physical Internet contexts, MJC²’s algorithms appear to manage operational uncertainty by recalculating in real time, rather than by modelling probabilistic distributions ahead of time.²³⁶²⁴ This is entirely appropriate for dispatch and routing, but it means MJC² is not, on present evidence, a probabilistic supply chain planning platform.

Deployment, integration and rollout

Although MJC² does not publish formal implementation methodologies, several patterns emerge from product and project descriptions:

Embedded deployments: PIMSS sits within plant control architectures; logistics modules integrate with TMS, SCADA, GPS devices and smartphone apps.¹⁷¹⁸²⁶³⁴
Real-time data feeds: logistics control tower and real-time vehicle scheduling pages emphasise continuous data streams about shipments, vehicles and inventory levels; optimisation runs are triggered when deviations occur.²²³²²⁶
Scenario and strategic modes: distribution management and SLIM include strategic planning modes where planners can explore what-if scenarios (e.g. adding capacity, new volumes, shift pattern changes) using the same optimisation engines.¹⁵²⁸⁶
Project-style configuration: synchromodal and PIONEERS material indicates iterative collaboration with infrastructure managers and logistics companies to adapt algorithms to specific corridors, ports or networks.¹¹¹⁵⁷²⁴

There is no visible notion of an end-user programming interface or DSL; configuration appears to be done through parameterisation and custom development by MJC²’s team. Compared to Lokad’s “Supply Chain Scientist + DSL” model, MJC² looks closer to a traditional engineering consultancy plus proprietary solver: the vendor’s team adapts algorithms and integration for each client.

Assessment of technical merit and state-of-the-art

What MJC²’s solution delivers

Based on public evidence, MJC² delivers:

High-performance combinatorial optimisation engines for:
- vehicle routing and transport planning (multi-depot, bulk, parcel, passenger);¹³¹⁵³⁰²⁶
- multimodal and synchromodal freight routing and scheduling;²¹²²²³²⁴
- production scheduling and manufacturing process control;¹⁷¹⁸¹⁹
- workforce, crew and mobile field-service scheduling.¹⁵¹⁷¹⁴³⁴
Real-time re-planning capabilities that integrate with GPS, SCADA and other live data sources to reschedule operations when disruptions occur.¹¹²²²³²⁶
Custom optimisation architectures used in EU flagship projects, recognised via awards (e.g. e-Freight IT innovation award) and repeated selection as optimisation partner.²⁸²⁹³⁰

Technically, this constitutes a solid, specialised optimisation toolset for operational scheduling. It is particularly relevant where:

the problem size is large (thousands of jobs/vehicles/resources),
constraints are intricate (legal, safety, skill, capacity),
and real-time responsiveness is crucial (ports, rail freight, utilities field service).

How it achieves outcomes – mechanisms and limitations

The mechanisms are less transparent than the outcomes. The recurring elements are:

Custom optimisation heuristics: referenced loosely as AI-based algorithms or lightning-fast optimisation, but not described formally.⁸²⁸²³
Tight integration with operational systems (SCADA, TMS, mobile apps), enabling real-time event capture.¹⁷¹⁸³⁴²⁶
Scenario engines: allowing planners to run strategic or what-if scenarios using the same optimisation kernels.¹⁵²⁸⁶¹⁶

On the other hand:

There is no explicit probabilistic modelling of demand, lead times or failure distributions.
No mention of generic optimisation solvers (e.g. CPLEX, Gurobi) suggests highly bespoke code; this can be a strength (fine-tuned) but also a long-term maintenance risk.
The absence of language about SaaS, multi-tenancy, or customer-exposed configuration layers suggests limited self-service; solutions are likely heavily vendor-driven.

Relative to the state of the art in supply chain planning:

In real-time scheduling under complex constraints, MJC² appears competitive and technically credible, given repeated EU selection and awards.
In probabilistic planning, machine learning and integrated forecast–decision optimisation, MJC² is essentially silent; there is no evidence of techniques comparable to Lokad’s probabilistic forecasting, differentiable programming or stochastic optimisation pipelines.³²³¹²⁶¹⁰

Commercial maturity

Commercially, MJC² is:

Mature in age (35 years) and stable as an SME, with positive net assets and a history of delivering project work for large logistics and industrial players.¹³⁴⁶
Niche in scale – a very small team with revenue far below mainstream APS or supply chain vendors.
Strongly tied to EU R&D – much of its public track record is framed as EU or industry projects, not productised SaaS rollouts.

This combination positions MJC² as a highly specialised niche vendor rather than a full-spectrum supply chain software provider.

Conclusion

MJC² is best understood as a small but technically capable optimisation boutique, specialising in real-time combinatorial scheduling for logistics, multimodal freight, manufacturing and workforce operations. Its strengths are evidenced by decades of involvement in EU-funded research projects and repeated deployment of optimisation engines in demanding environments such as container ports, rail corridors, bulk tanker fleets and utilities field services. The firm’s algorithms appear well suited to large, constraint-rich operational problems where rapid re-planning is essential.

However, public information also highlights clear limits. MJC² does not present itself as a probabilistic, end-to-end supply chain planning platform. There is no visible support for multi-echelon inventory optimisation driven by probabilistic demand and lead-time distributions, nor for integrating pricing, demand forecasting and network planning into a unified economic optimisation framework. Its “AI” claims, while plausible in the sense of advanced heuristics, lack the technical transparency now expected from vendors claiming state-of-the-art machine learning.

Compared with Lokad, MJC² occupies a different point in the design space: where Lokad focuses on quantitative, probabilistic planning decisions across the supply chain, MJC² focuses on operational scheduling and dispatch under complex constraints. For organisations whose primary bottleneck is vehicle/crew/plant scheduling, MJC²’s technology may be attractive, provided they accept a project-style integration model and the opacity of the underlying algorithms. For organisations seeking a probabilistic inventory optimisation and demand planning engine, MJC²’s public footprint does not indicate that it plays in that category at all.

In short, MJC² is a specialised optimisation engine supplier, not a general-purpose supply chain planning suite. Its technical merit in operational scheduling appears strong and validated by independent projects, but its role in broader supply chain optimisation should be assessed cautiously and with a clear understanding that much of the modern probabilistic and ML-based planning stack lies outside its published scope.