Asset-intensive organisations hold years of work, failure, materials and operational data. Much of its value sits in the relationships between systems, records and the knowledge of the people who use them.
TEAM combines asset management and operational knowledge with data science, analytics and simulation. We clarify the decision, prepare the required data and apply the analytical method needed to produce usable evidence.
Operational context. Defensible analysis. Clearer decisions.
The work may begin with a maintenance backlog, recurring failure, workforce constraint, inventory position or future asset requirement.
TEAM defines the question to be answered and the decisions it needs to support. We identify the relevant data, operational constraints and measures of value.
Operators, planners, engineers and other subject matter experts provide the context behind the records. Their knowledge helps explain how information is created, what has changed over time and which assumptions need to be tested. The analytical response is then designed around the requirement. It is not limited to the information available in a single system or a predetermined technique.
TEAM profiles and restructures asset registers, functional locations, naming conventions, failure codes and work-order data. We develop taxonomies, mappings and quality rules that keep information consistent across systems and sites. Source information and transformation decisions remain traceable.
TEAM analyses backlog health, schedule compliance, work-order duration, risk-based prioritisation and planning workload. Patterns across structured fields, free text and system records can show how work moves through the operation. They can also identify where delays, variation and repeated activity warrant closer attention.
Failure histories can be used to identify recurring patterns and extract failure modes, causes and affected components from maintenance records. TEAM prepares evidence for RCM and FMEA activities. We can also assess maintenance strategy coverage and examine whether preventive maintenance intervals are supported by operational history.
TEAM connects consumption, procurement, inventory and asset data to examine the decisions affecting materials performance. Applications can include spare-parts classification, bills of material, obsolete inventory, supplier lead times and purchasing behaviour.
Forecasting and simulation allow teams to examine future maintenance demand, workforce capacity, component replacement timing and other operational scenarios. Different assumptions and constraints can be tested before decisions are carried into planning. The logic remains visible so results can be reviewed and challenged.
TEAM establishes the decision, available choices, operating constraints and required measures.
We also identify the people who hold relevant operational knowledge and the systems in which the supporting information resides.
Data is extracted and connected across relevant ERP, EAM, CMMS and operational systems.
TEAM profiles, cleanses, classifies and structures the information required for the analysis. This can include structured records, free-text descriptions, fault logs, technical documents and other operational sources.
The analytical method is selected according to the question and the available evidence.
Methods may include statistical and survival analysis, forecasting, classification, clustering, pattern recognition, network analysis, scenario modelling and simulation. Results are tested against operational knowledge. Assumptions, limitations and areas of uncertainty are made clear.
The outcome may be a prioritised backlog, forecast, simulation model, analytical tool, dashboard, structured dataset or set of recommendations. TEAM works with the relevant users to determine how the output should be incorporated into existing decisions and workflows.
Decision and data requirements
A clear definition of the decision, the information it requires and the measures used to assess the result.
Structured information assets
Depending on scope, this may include cleansed datasets, data dictionaries, classifications, taxonomies, hierarchy structures and transformation rules.
Analytical and simulation models
Models include documented assumptions, inputs, constraints and validation requirements. This allows the organisation to understand how the result was produced and when the model should be reviewed.
Decision-support outputs
Outputs may include forecasts, scenarios, risk rankings, prioritised actions, visualisations, dashboards or purpose-built tools. They are designed for the people responsible for making or carrying out the decision.
Governance and traceability
TEAM defines ownership, quality controls and review requirements where these are needed to sustain the information or analytical output.
Source data, transformation logic and key analytical decisions remain traceable.
TEAM applies technology according to the scale and nature of the requirement.
Axient supports the audit, structuring and transformation of complex asset and maintenance data. It gives teams a visual environment for reviewing hierarchies, classifications and proposed changes while maintaining traceability to the source.
GNOS supports simulation where asset, workforce or operational scenarios need to be configured and tested. It allows assumptions and constraints to be examined with the people who understand the operating environment.
Where the requirement falls outside these products, TEAM develops fit-for-purpose analytical models and decision-support tools.
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TEAM can begin with a defined operational question or examine available information to determine what it can reliably support. We identify the most useful analytical response and the level of effort warranted by the decision.
