Review of MJC², Planning Software Vendor
Go back to Market Research
MJC², formally known as MJC2 LIMITED and established in 1990 in the UK, has built a longstanding reputation developing decision‑support systems that address large‑scale, complex planning and scheduling challenges across logistics, manufacturing, supply chain, and workforce management. The vendor’s integrated suite encompasses modules for multi‑depot logistics, real‑time dispatch and routing, just‑in‑time production scheduling, and workforce rostering—all designed to streamline operational processes through deterministic, rule‑based optimization techniques. Despite the frequent use of contemporary AI buzzwords, MJC²’s technology appears rooted in advanced operations research and integer programming methods rather than in adaptive machine learning. This mature, operations‑research driven approach positions the company as a distinct alternative for organizations that prefer proven, real‑time optimization over experimental, highly programmable platforms.
1. Company Background and History
MJC² was incorporated in 1990, a fact verifiable through the Companies House record (1). Initially traded under the name OYSTERLOCK LIMITED, the company eventually rebranded to MJC² and has since maintained independent operations. Focused on real‑time optimization for sizable, complex operations, MJC² serves major global organizations facing intricate logistics and manufacturing challenges (2).
1.1 Founding and Market Focus
Founded over three decades ago, MJC² has concentrated on deploying sophisticated scheduling and planning solutions. Its heritage is anchored in solving complex decision problems by harnessing deterministic optimization methods that ensure highly responsive and precise operational planning.
2. Product Offerings and Underlying Technology
MJC²’s portfolio is segmented into specialized modules addressing distinct facets of operational planning:
• In logistics and distribution, the DISC module provides comprehensive support for multi‑depot logistics, route planning, driver scheduling, and warehouse integration (3). Complementing this, the REACT module enables dynamic, real‑time dispatch and routing—integrating data streams from GPS and telematics—to support same‑day delivery needs.
• In manufacturing, the PIMSS module delivers real‑time, just‑in‑time production planning. It automatically generates production schedules within seconds based on order details, raw material availability, resource capacities, and changeover constraints (4).
• The company also offers robust solutions for workforce scheduling, covering both employee rostering and mobile workforce management (5).
• Beyond these, MJC² invests in advanced research and algorithm innovation, developing proprietary scheduling algorithms and exploring concepts such as quantum computing for logistics (6). Several pages highlight “AI” initiatives—such as its work on the Physical Internet and the ePIcenter project—but detailed technical disclosures remain sparse.
3. Deployment and Integration
MJC²’s systems are deployed as comprehensive, integrated platforms that support both real‑time scheduling and strategic planning. The solutions are engineered for seamless integration with external systems such as ERP, SCADA, telematics, and production control systems, thereby enabling tailored deployments in diverse operational environments (2, 4). The vendor claims that its technology can yield significant operational benefits—including reductions in transport costs by up to 30%—by dynamically rescheduling complex logistics operations (6).
4. Critical Analysis of AI and Automation Claims
While MJC² frequently incorporates terms like “lightning‑fast algorithms” and “AI‑powered” solutions into its marketing narratives, a closer technical examination suggests that its methodologies lean toward deterministic optimization. The available documentation does not offer detailed accounts of modern machine learning techniques (for example, deep neural networks or probabilistic models); rather, the emphasis is on advanced operations research based on mixed‑integer programming and heuristic rule sets (7, 8). For technology decision-makers, this distinction is critical when assessing the vendor’s claims relative to truly adaptive, data‑driven learning systems.
5. Conclusion
MJC² delivers a comprehensive suite of planning and scheduling solutions spanning logistics, manufacturing, supply chain, and workforce management. Its decision‑support systems are engineered around advanced, real‑time optimization algorithms that tackle highly complex operational challenges. At the same time, the vendor’s reliance on established, deterministic methodologies—despite frequent AI-related buzzwords—suggests that potential adopters should carefully evaluate whether the promised “AI‑powered” benefits represent a genuine technological breakthrough or are largely a rebranding of conventional operations research practices.
MJC² vs Lokad
Both MJC² and Lokad offer innovative decision‑support platforms for supply chain optimization, yet they differ fundamentally in approach and technology. MJC²—established in 1990 in the UK—centers its solutions on deterministic, rule‑based optimization and mixed‑integer programming to drive real‑time scheduling and planning (as seen in its DISC, REACT, and PIMSS modules; 1, 3, 4). In contrast, Lokad, founded in 2008 in Paris, has evolved from cloud‑based forecasting into a programmable, end‑to‑end supply chain optimization platform that leverages probabilistic forecasting, deep learning, and differentiable programming (9, 10, 11). This juxtaposition reflects a choice between a mature, operations‑research-driven methodology embodied by MJC² and a more experimental, data‑intensive, and flexible platform approach as advanced by Lokad. For supply chain executives, the decision hinges on whether deterministic, proven techniques or highly programmable, ML‑integrated approaches better suit their operational priorities.
Conclusion
MJC² stands out as a veteran provider of decision‑support systems built on robust, deterministic optimization techniques. Its integrated suite covers a broad spectrum of planning and scheduling needs across various industries, providing significant value through real‑time responsiveness. However, while the vendor touts “AI‑powered” capabilities, its technical foundation remains closely tied to established operations research methods rather than to modern adaptive machine learning models. In contrast with newer platforms like Lokad, which embrace a flexible, programming‑oriented, data‑intensive approach, MJC² offers a more traditional yet proven set of tools. Prospective customers should carefully assess their own needs and technical readiness when evaluating these contrasting methodologies to ensure the chosen solution effectively underpins their supply chain goals.