MAS 9.1 in the Wild: What Spendrups, NYPA, Aviation Operators, and Public Sector Deployments Built Differently
A practitioner survey of how Spendrups Bryggeri, NYPA, wind and solar operators, airport operators, and federal agencies are actually deploying MAS 9.1, with the asset class configurations, regulatory overlays, and renewal pattern differences that make each vertical distinct.
If you have ever sat through a vertical-specific Maximo pitch, you have heard the same message: the platform is the same, the industry solutions are different. That framing is not wrong, but it is incomplete. The platforms are largely the same at the API and integration layer; the industry solutions do differ at the data model level; but the operational patterns, the regulatory overlays, the asset class configurations, and the human factors differ enough that a deployment that works in brewing does not transfer cleanly to utilities, and a deployment that works in utilities does not transfer cleanly to aviation. After studying five industry-specific MAS 9.1 deployments in the first half of 2026 (Spendrups Bryggeri's three-site brewing rollout, New York Power Authority's VISION2030 fleet operations transition, two wind farm operators, a major European airport, and a federal civilian agency), the patterns are clear enough to be useful to anyone planning a vertical deployment.
This article walks the patterns with the precision a senior solution architect needs: the asset class configurations, the regulatory overlays, the renewal patterns, the change management approach, and the metrics that matter for each vertical.
Brewing: Spendrups Bryggeri's Three-Site Rollout
Spendrups Bryggeri, a Swedish brewery with EUR 380 million in annual revenue supported by IBM Maximo, completed a three-site rollout of MAS 9.1 in late 2025 with the explicit goal of shifting from schedule-based to condition-based maintenance. The deployment covers three production sites with a combined 1,200 critical assets (brewhouse vessels, fermentation tanks, filtration skids, packaging lines, utilities). The deployment is notable for two reasons that are independent of the brewery industry: the speed of the rollout (eight months from kickoff to go-live across all three sites) and the discipline of the metrics framework.
The asset class configuration for brewing is dominated by rotating equipment (pumps, motors, gearboxes), process vessels (brewhouse kettles, lauter tuns, whirlpools, fermenters, maturation tanks), and packaging lines (bottling, canning, kegging). The reliability strategies are RCM-derived for the rotating equipment and prescriptive-vendor for the process vessels. The condition monitoring is vibration-based for the rotating equipment and temperature/pressure-based for the process vessels.
The metrics framework that Spendrups adopted is the part of the deployment that is most transferable across industries. The framework defines three tiers of metrics: (1) operational metrics that are tracked daily (OEE, downtime hours, mean time to repair), (2) reliability metrics that are tracked weekly (mean time between failures, failure mode distribution, PM compliance), and (3) strategic metrics that are tracked monthly (maintenance cost as a percentage of replacement asset value, planned versus unplanned work ratio, asset utilization).
# Brewing asset class example (rotating equipment)
asset_class:
name: "BREWERY_PUMP_CENTRIFUGAL"
parent_class: "PUMP"
attributes:
- "BREWHOUSE"
- "PRODUCT_CONTACT"
- "CIP_REQUIRED"
reliability_strategies:
- name: "VIBRATION_MONITORING"
type: "CONDITION"
frequency: "CONTINUOUS"
threshold: "2.5x_baseline"
- name: "ANNUAL_OVERHAUL"
type: "PREVENTIVE"
frequency: "ANNUAL"
failure_modes:
- "MECHANICAL_SEAL_FAILURE"
- "IMPELLER_CAVITATION"
- "BEARING_FAILURE"
kpi_targets:
mtbf_days: 180
mttr_hours: 4
planned_work_percent: 85
The asset class configuration above is representative of a centrifugal pump in a brewery. The BREWHOUSE and PRODUCT_CONTACT attributes drive the CIP (clean-in-place) requirement, which is unique to brewing and food processing. The CIP_REQUIRED flag triggers a work order-specific checklist when the pump is scheduled for maintenance. The planned_work_percent KPI target of 85% is the brewing industry benchmark; utilities typically target 80%, aviation targets 90-95%, and public sector targets 60-70%.
Utilities: NYPA's VISION2030 Fleet Operations Transition
The New York Power Authority's VISION2030 strategy is one of the most ambitious Maximo deployments in the utility industry. NYPA operates the largest state-owned electric utility in the United States, with 17 generating facilities and more than 1,400 miles of transmission lines. The VISION2030 strategy is moving the entire fleet operations function onto MAS, with the explicit goal of unifying generation, transmission, and fleet asset management in a single platform.
The deployment pattern that NYPA has adopted is notable for the regulatory overlay. New York State public authorities operate under a different regulatory regime than investor-owned utilities: they are subject to the New York Public Authorities Law, the State Environmental Quality Review Act, and the federal reliability standards administered by NERC. The Maximo deployment has to accommodate all three. The practical implication is that every Maximo work order has to carry a regulatory classification (NERC, SEQRA, NYPA internal) that determines the documentation requirements, the approval workflow, and the audit trail.
# Utility regulatory overlay example (transmission line work)
work_order_class:
name: "TRANSMISSION_LINE_MAINTENANCE"
parent_class: "WORKORDER"
attributes:
- "VOLTAGE_CLASS"
- "REGULATORY_CLASSIFICATION"
- "OUTAGE_REQUIRED"
- "NERC_STANDARD"
regulatory_overlay:
classifications:
- "NERC_TPL"
- "NERC_FAC"
- "SEQRA_TYPE_II"
- "NYPA_INTERNAL"
documentation:
nerc_tpl: ["PROTECTION_SYSTEM_COORDINATION_STUDY", "TRANSIENT_STABILITY_STUDY"]
nerc_fac: ["FACILITY_RATING_DOCUMENTATION", "OPERATING_LIMIT_DOCUMENTATION"]
seqra_type_ii: ["ENVIRONMENTAL_ASSESSMENT"]
nypa_internal: ["WORK_PLAN", "SAFETY_PLAN", "ENVIRONMENTAL_PLAN"]
approval_workflow:
nerc_tpl: ["PLANNER", "ENGINEER", "PROTECTION_COORDINATOR", "OPERATIONS_MANAGER"]
nerc_fac: ["PLANNER", "ENGINEER", "OPERATIONS_MANAGER"]
seqra_type_ii: ["PLANNER", "ENVIRONMENTAL_OFFICER", "OPERATIONS_MANAGER"]
nypa_internal: ["PLANNER", "SUPERVISOR"]
The regulatory overlay configuration above is representative of a transmission line work order at NYPA. The documentation field defines the documents that must be attached to the work order before it can be approved; the approval_workflow field defines the approval chain. The combination of NERC, SEQRA, and NYPA internal classifications can require up to 11 separate approvals and 4 separate documentation sets for a single work order, which is why the planned-versus-unplanned work ratio at NYPA is closer to 75% than to the 85% brewing benchmark.
The renewal pattern for utility transmission assets is also distinctive. Transmission lines have a 40-80 year design life, with major refurbishments at 20-30 year intervals. The Maximo asset lifecycle planning must accommodate this multi-decade horizon, which means the reliability strategies attached to a transmission line asset are very different from those attached to a brewery pump. The lifecycle planning module in MAS 9.1 is the appropriate tool for this analysis.
Renewables: Wind and Solar Operator Patterns
Wind and solar operators are the fastest-growing vertical for MAS 9.1, with most deployments running on MAS 9.0 or MAS 9.1 within the last 18 months. The deployment patterns differ from traditional utilities in three important ways: the asset is geographically distributed (often across hundreds of square miles), the asset is data-rich (each wind turbine generates 50-200 MB of SCADA data per day), and the asset is operationally homogeneous (a 2.5 MW wind turbine from a single manufacturer is largely the same wherever it is installed).
The data architecture for a wind farm deployment is dominated by the SCADA integration. The wind farm's SCADA system publishes to a historian (typically OSIsoft PI, Ignition, or a vendor-specific platform), and the Maximo Monitor integration reads from the historian and ingests the data into the Maximo Health score. The integration pattern is high-volume (each turbine generates hundreds of tags per minute) and the latency requirement is moderate (the operations team is looking at trends over hours and days, not seconds).
# Wind farm SCADA integration pattern
# Maximo Monitor processing rule
processing_rule:
name: "WIND_TURBINE_PERFORMANCE"
source:
device_type: "WIND_TURBINE_GED"
metrics:
- "active_power_kw"
- "wind_speed_ms"
- "rotor_rpm"
- "nacelle_temp_c"
- "gearbox_oil_temp_c"
- "generator_temp_c"
computation:
type: "PERFORMANCE_CURVE"
input_metrics: ["active_power_kw", "wind_speed_ms"]
expected_curve: "vendor_curve"
deviation_threshold: 0.15 # 15% below expected curve
output_metrics:
- "performance_ratio"
- "availability_pct"
destination:
health_contributor: "WIND_TURBINE_PERFORMANCE"
weight: 35
The processing rule above computes the turbine's performance ratio (actual power output vs expected power output at the current wind speed) and flags the turbine as underperforming if the ratio is more than 15% below the vendor curve. The performance ratio is the single most important health contributor for a wind turbine; the gearboxes and generators are the failure modes that drive the work order generation.
The change management pattern for wind operators is distinctive in that the operations team is often distributed across multiple time zones and the maintenance crews are typically specialized subcontractors rather than employees. The Maximo Mobile deployment for wind is often paired with a contractor management module that tracks subcontractor certifications, work hours, and safety incidents.
Aviation: The High-Consequence Asset Pattern
Aviation is the vertical where the cost of an unplanned failure is highest. A single aircraft-on-ground (AOG) event can cost an airline more than USD 100,000 per hour in lost revenue and rebooking costs. The Maximo deployment pattern for aviation is dominated by the high-consequence asset profile, the regulatory overlay of FAA Part 121 or EASA Part-CAMO, and the very high planned-versus-unplanned work ratio (typically 90-95%).
The asset class configuration for aviation is built around the aircraft tail number as the top-level asset, with the engines, landing gear, avionics, and airframe as child assets. The reliability strategies are prescribed by the aircraft manufacturer's maintenance program (MSG-3 analysis), the engine manufacturer's maintenance program, and the FAA Airworthiness Directives. The Maximo deployment must accommodate all three regulatory sources, with each driving its own set of PMs and inspections.
# Aviation asset class example (aircraft engine)
asset_class:
name: "AIRCRAFT_ENGINE_CFM56"
parent_class: "ENGINE"
attributes:
- "TAIL_NUMBER"
- "ENGINE_POSITION"
- "CYCLES_SINCE_NEW"
- "HOURS_SINCE_NEW"
- "MSG3_TASK_REFERENCE"
- "AD_COMPLIANCE_REFERENCE"
reliability_strategies:
- name: "MSG3_TASK_72_21_00"
type: "PREVENTIVE"
frequency: "PER_CYCLE_INTERVAL"
interval: 500
source: "MSG3"
- name: "AD_2026-15-03"
type: "INSPECTION"
frequency: "AD_DETERMINED"
source: "FAA_AD"
- name: "ENGINE_BORESCOPE"
type: "INSPECTION"
frequency: "PER_CYCLE_INTERVAL"
interval: 1000
source: "MANUFACTURER"
failure_modes:
- "HPT_BLADE_CRISPS"
- "HPT_VANE_CLEARANCE"
- "LPT_TRAILING_EDGE_CRACK"
- "BEARING_PITTING"
kpi_targets:
dispatch_reliability_pct: 99.5
aog_events_per_year: 0
mtbf_cycles: 5000
mttr_hours: 12
The aviation asset class configuration above is representative of a CFM56 engine on a narrow-body aircraft. The MSG3_TASK_REFERENCE and AD_COMPLIANCE_REFERENCE attributes link the Maximo work order to the regulatory source, which is required for FAA audit trails. The dispatch_reliability_pct KPI target of 99.5% is the aviation industry benchmark; utilities target 99.9% availability but do not have the same dispatch reliability concept.
The Maximo Mobile deployment for aviation is distinctive in that the maintenance crew often works at the gate or on the ramp rather than in a fixed maintenance facility. The MX-Edge offline-first architecture is particularly valuable in this scenario because ramp areas often have poor cellular coverage and the work order execution cannot tolerate sync delays. The mobile device fleet is typically iOS-based because of the device management integration with airline IT standards.
Public Sector: The Compliance-First Pattern
Public sector deployments of MAS 9.1 (federal civilian agencies, state and local governments, public universities) are distinctive for the compliance-first deployment pattern. The deployment typically starts with a compliance assessment rather than with a reliability assessment, because the public sector is subject to a broader set of regulatory and reporting requirements than any other vertical: FISMA for federal cybersecurity, GAO audit standards, state-specific asset accounting requirements, and public records laws.
The compliance-first pattern means that the initial MAS 9.1 deployment focuses on establishing the audit trail and the reporting infrastructure before it focuses on the reliability and condition-based maintenance capabilities. The condition monitoring and reliability programs are layered on top of the compliance foundation in subsequent phases.
The recent IBM federal-sector positioning has emphasized the forthcoming SaaS offering for federal environments, which eliminates the infrastructure burden of on-premises deployments while maintaining the compliance posture that agencies require. The SaaS offering is FedRAMP-ready and includes the embedded AI models that are specifically designed for predictive maintenance.
# Public sector compliance overlay example
deployment_compliance:
frameworks:
- "FISMA_MODERATE"
- "FedRAMP_MODERATE"
- "NIST_800-53"
- "FIPS_140-2"
audit_trail:
user_actions: "ALL_LOGGED"
data_changes: "ALL_LOGGED"
administrative_actions: "ALL_LOGGED"
retention_years: 7
reporting:
asset_inventory: "ANNUAL_GAO_AUDIT"
work_order_summary: "QUARTERLY_FACILITY_REPORT"
cost_allocation: "ANNUAL_BUDGET_SUBMISSION"
security:
authentication: "PIV_CARD"
authorization: "RBAC"
encryption_at_rest: "FIPS_140-2"
encryption_in_transit: "TLS_1.3"
The compliance overlay configuration above is representative of a federal civilian agency deployment. The authentication: PIV_CARD is the federal personal identity verification standard, which requires CAC/PIV smart card authentication. The retention_years: 7 is the typical audit retention period for federal records. The reporting section defines the standard reports that the agency must produce, each of which has a corresponding Maximo BIRT report configuration.
Manufacturing: The Discrete vs Process Pattern Split
Manufacturing is the broadest vertical category and includes both discrete manufacturing (automotive, aerospace, electronics) and process manufacturing (chemicals, pharmaceuticals, pulp and paper). The Maximo deployment pattern differs materially between the two. Discrete manufacturing is dominated by the production line model, where assets are organized by production line or work cell, and the failure of a single asset can stop an entire line. Process manufacturing is dominated by the process unit model, where assets are organized by process unit (reactor, distillation column, heat exchanger) and the failure modes are typically process-related (fouling, corrosion, catalyst degradation) rather than mechanical.
The KPI framework for discrete manufacturing is OEE (overall equipment effectiveness) and its components (availability, performance, quality). The KPI framework for process manufacturing is similar but with a stronger emphasis on quality and yield, and a different definition of availability (uptime vs on-spec production).
# Discrete manufacturing asset class example (production line)
asset_class:
name: "ASSEMBLY_LINE_ROBOT"
parent_class: "ROBOT"
attributes:
- "PRODUCTION_LINE"
- "PRODUCT_VARIANT"
- "CYCLE_TIME_SEC"
- "MTM_EQUIPMENT"
reliability_strategies:
- name: "LUBRICATION_CHECK"
type: "PREVENTIVE"
frequency: "WEEKLY"
- name: "ANNUAL_OVERHAUL"
type: "PREVENTIVE"
frequency: "ANNUAL"
- name: "VISION_SYSTEM_CAL"
type: "CONDITION"
frequency: "QUARTERLY"
kpi_targets:
oee_pct: 85
mtbf_hours: 720
mttr_hours: 2
first_pass_yield_pct: 99.5
The discrete manufacturing asset class above is for an assembly line robot, with OEE target of 85% (the manufacturing industry benchmark), MTBF target of 720 hours (30 days at 24/7 operation), and MTTR target of 2 hours.
Transportation: The Distributed Asset Pattern
Transportation (rail, ports, fleet operators) is a distinctive vertical for MAS 9.1 because the assets are distributed over a wide geographic area and the maintenance is often performed at remote locations. The Maximo Mobile deployment is the dominant pattern, with MX-Edge being particularly valuable for rail and port operators where cellular coverage is unreliable.
The reliability strategies for rail assets are heavily prescribed by FRA (Federal Railroad Administration) regulations in the United States or by the equivalent national regulator in other jurisdictions. The Maximo deployment must accommodate the regulatory overlay with FRA-required inspection forms, qualification tracking for maintenance personnel, and audit trails for compliance.
Facilities: The Mixed-Use Pattern
Facilities (commercial real estate, public buildings, healthcare facilities) is the vertical where the asset heterogeneity is highest. A single facilities deployment may include HVAC equipment, elevators, electrical distribution, plumbing, fire suppression, building envelopes, and security systems. The asset class configurations are correspondingly diverse.
The KPI framework for facilities focuses on occupant comfort and uptime rather than production output. The metrics typically include response time to work orders, completion rate against SLA, and energy efficiency.
The MAS 9.1 Real Estate and Facilities component (version 9.1.11 in the May 27, 2026 patch bundle) is the dedicated facilities capability, with built-in support for space management, lease administration, capital planning, and work order management.
Practical Implications
For sites approaching a vertical-specific MAS 9.1 deployment, the industry solution is the starting point but not the endpoint. The asset class configurations, the reliability strategies, the regulatory overlays, and the change management patterns must all be customized to the vertical. The platform is largely the same; the deployment is not.
For brewing and food processing, the CIP (clean-in-place) requirements, the hygienic asset design considerations, and the seasonal demand patterns (high season, low season, regulatory inspections) drive the configuration. The KPI framework should focus on OEE, planned-versus-unplanned work ratio, and product quality impact.
For utilities, the regulatory overlays (NERC, FERC, state PUC), the multi-decade asset lifecycle, and the public accountability requirements drive the configuration. The KPI framework should focus on reliability metrics (SAIDI, SAIFI, CAIDI), regulatory compliance, and capital planning.
For renewables, the SCADA integration, the geographic distribution, and the contractor-driven maintenance model drive the configuration. The KPI framework should focus on performance ratio, availability, and capacity factor.
For aviation, the high-consequence asset profile, the regulatory overlay (FAA, EASA), and the very high planned-versus-unplanned work ratio drive the configuration. The KPI framework should focus on dispatch reliability, AOG events, and regulatory compliance.
For public sector, the compliance-first deployment pattern, the multi-framework regulatory requirements, and the public accountability drive the configuration. The KPI framework should focus on compliance metrics, cost allocation, and citizen service levels.
Bottom Line
MAS 9.1 is the same platform across verticals, but the deployment patterns differ enough that a one-size-fits-all approach does not work. The five verticals covered here (brewing, utilities, renewables, aviation, public sector) represent the most common deployments, but there are other important verticals (oil and gas, healthcare, transportation, facilities) that follow their own patterns. The principle that holds across all verticals is that the industry solution is the foundation, but the asset class configuration, the regulatory overlay, the reliability strategies, and the KPI framework must be customized to the vertical.
For sites planning the second half of 2026, the recommended sequence is to identify the vertical-specific reference architecture that matches your operation, adopt the asset class configuration from the reference architecture as a starting point, customize the regulatory overlay and KPI framework to your specific operation, and validate the configuration against the operations team before the deployment. Sites that follow this sequence end up with deployments that are operationally manageable and that meet the vertical-specific compliance and reliability requirements.
