Maintenance in heavy industry has entered a decisive new phase. As plants contend with rising production pressures, leaner staffing levels and increasingly complex equipment inventories, the difference between reactive, proactive and predictive maintenance becomes ever more critical.
At the centre of this shift is the Mitsubishi Adroit Process Suite (MAPS), an integrated engineering, control and supervisory platform developed jointly by Adroit Technologies and Mitsubishi Electric. After more than a decade of implementation in Europe, Southern Africa and other industrial regions, MAPS is demonstrating how AI-ready automation environments can unlock genuine predictive maintenance at scale.
Traditional automation architectures have always relied on a clear distinction between the programmable logic controller on the factory floor and the supervisory system that sits above it. The PLC controls the machinery, while the SCADA system visualises the process, logs the data and informs operators.
A seamless connection between PLC and SCADA
What has long been missing is a seamless connection between the two layers. Engineers have had to design and configure each environment separately, build tag databases manually and ensure that the plant documentation remains aligned with the equipment actually installed. This duplication is costly and makes it difficult to maintain a single source of truth throughout a plant’s life cycle.
The MAPS platform was created precisely to remove these inefficiencies. It allows engineers to work with objects rather than raw inputs and outputs. A pump, valve, tank or conveyor is defined once in MAPS, and the system automatically generates both the PLC control logic and the SCADA visualisation.
This standardisation ensures that the design, implementation and maintenance phases all draw on the same configuration, which significantly reduces errors and engineering hours. It also establishes the perfect foundation for long-term maintenance optimisation.
Much more than a commissioning tool
According to Johan Nieuwenhuizen, Sales Director and co-CEO, Adroit Technologies, the value of MAPS becomes clear when viewed through the lens of a plant’s entire operational life. He notes that MAPS has always been more than a commissioning tool.
“True innovation lies in creating a single point of configuration that accompanies an automation project from design to ongoing operations,” he says. This continuity is what makes meaningful predictive maintenance possible, because it ensures that the data feeding AI and analytics systems is structured, reliable and consistent.
The MAPS model is anchored in international engineering standards such as ISA S88 and S95, which means every asset is contextualised within a formal equipment hierarchy. This structure is immutable and it brings order to environments that often span decades of upgrades, retrofits and mixed vendor technologies.
Maintenance-related value
Once this structure is in place, the real maintenance-related value begins to emerge. Each object in MAPS can include predefined logic relating to operational behaviour and maintenance triggers. For example, a pump object can contain rules that track runtime hours, vibration, temperature and number of starts.
Similarly, a breaker can include a counter that flags the need for inspection once it has tripped a certain number of times. These rules form a built-in layer of embedded maintenance intelligence, long before an AI model is introduced.
The next step is to aggregate this equipment intelligence. MAPS is designed to feed plant data into modern data lakes via an edge gateway. This creates the conditions for machine learning and predictive modelling. Once the operational history of assets is centralised and combined with metadata such as manufacturer, model, kilowatt rating and operating environment, the platform can begin uncovering patterns that would otherwise remain invisible.
“These insights can reveal, for instance, whether a specific motor type fails more frequently in certain conditions or whether pumps from one supplier consistently outperform alternatives,” points out Dave Wibberley, Director, Adroit Technologies. “MAPS positions organisations to make maintenance decisions rooted in empirical evidence rather than anecdotal experience.”
Alarm Management and Analysis solution
One of the strongest supporting examples of how MAPS enables better maintenance is its decade-old Alarm Management and Analysis solution. Long before AI became a central talking point in industry, Adroit Technologies had already recognised that poor alarm discipline was undermining plant reliability.
“Many sites were drowning in alarms that operators simply acknowledged without investigation,” says Nieuwenhuizen. In one early client deployment, a cement plant had nearly nine thousand alarms on its system, most of which were misclassified events. By applying the formal definition of an alarm and removing everything that did not require operator intervention, the plant was able to reduce its alarm burden to manageable levels for the first time.
This principle is now carried forward into predictive maintenance. Clean alarm data and accurate equipment histories are essential for meaningful machine learning. When MAPS stores each alarm together with its start time, acknowledgement point and resolution time, the resulting dataset becomes an invaluable resource.
“AI models can identify chronic issues, track deteriorating performance and highlight bad actors that repeatedly cause downtime,” adds Wibberley. Instead of sifting through thousands of alarms, maintenance teams can focus on the few recurrent problems that are costing them the most.
Aligned with real-world industrial needs
The partnership between Adroit Technologies and Mitsubishi Electric has also ensured that MAPS is deeply aligned with real-world industrial needs. For over thirteen years the companies have implemented solutions across Europe and Africa, including bakeries, ferroalloys, chrome operations and general manufacturing facilities.
These deployments have highlighted the importance of equipment templates that reflect authentic plant requirements. Engineers can customise objects to reflect the logic that governs their specific environment. Once developed, these templates can be redeployed across multiple sites, creating major cost efficiencies and a unified maintenance approach across an organisation.
MAPS also brings practical organisational benefits. When maintenance teams have access to accurate runtime data, temperature profiles, vibration readings and equipment lineage, they can approach suppliers with concrete evidence. “Plants are often reliant on OEMs for root-cause analysis and support, yet without structured data they have limited leverage. MAPS changes this dynamic,” says Nieuwenhuizen.
Aggregated performance data feeds into procurement
A group operating several plants across the continent could, for example, aggregate all its performance data and identify that a particular motor brand has an unacceptable failure rate in its smaller kilowatt ranges.
Equipped with these insights, procurement departments can revise approved supplier lists, negotiate better terms or request customised equipment. “Predictive maintenance becomes a strategic lever rather than a technical afterthought,” notes Wibberley.
The system also supports standardisation across multi-site organisations. Once a hierarchy and object library have been established, new plants can be engineered far more rapidly. They inherit proven control strategies, maintenance logic and alarm philosophy from existing operations.
This reduces commissioning time and ensures that the data generated across all sites is compatible, comparable and ready for consolidated analytics. Many enterprises struggle with fragmented maintenance data that varies wildly between sites. MAPS solves this by enforcing structure and consistency from the outset.
Shift towards AI-enabled predictive maintenance
The shift towards AI-enabled predictive maintenance is gathering pace globally, but the foundations must be in place for it to succeed. MAPS provides those foundations. It unifies the PLC and SCADA layers, embeds maintenance intelligence inside the control objects themselves, structures all equipment data within a formal hierarchy and supports seamless integration into modern analytics environments.
For plants in heavy industry, where downtime is expensive and asset lifecycles are long, this approach offers a practical and measurable pathway to improved reliability. “As industrial operations continue to digitalise, the real differentiator will not be the ability to collect data, but the ability to translate that data into dependable, actionable maintenance insights,” stresses Nieuwenhuizen.
With MAPS, Adroit Technologies and Mitsubishi Electric have provided a system that does exactly this. “For organisations seeking to move beyond reactive repairs and into the realm of predictive intelligence, the MAPS platform offers both the technical tools and the long-term strategic advantage needed to thrive,” concludes Nieuwenhuizen.
