Model Driven Equipment Management (MDEM)

[mjeasleaSAP]

Summary

• Operators of assets need a way to en-masse represent similar equipment while still being able to individually maintain highly critical or specialized assets


• A significant reduction in the volume and variability of master data required can be achieved by using the model concept

Body

SAP led the way with the transformation of simple finance 10 years ago – which now allows finance users to analyse and dissect their financial data from different perspectives in real time. 5 years ago SAP started the same journey for asset management, focusing on how to simplify the SAP Technical Object structure to allow greater flexibility and better modelling of both simple and complex assets.

Asset information is needed in many different departments and asset information is fundamental for many business processes. An enterprise must have a common system of reference to identify all of its assets. This is done by creating unique master records for each technical object and is the base for structuring asset data and information.

However, operators of industrial, commercial, utilities, computer, fleet and other types of assets often have many thousands or millions of individual equipment units (items). In a perfect world the operator would have infinite knowledge and capacity to develop and keep live individualized strategies and performance plans for each equipment unit. Realistically, Asset management departments have a limited capacity and as such need to manage a substantial proportion of their equipment base “en-mass”. SAP began investing in Model Driven Equipment Management in 2014 to greatly improve the management of similar equipment and also substantially reduce the amount of master data required.

This means the first step for any new SAP implementation (or business improvement / upgrade project) is to identify commonly used equipment in your organization and to represent these as “models”. Each equipment unit then inherits significant amounts of information from this reference model including (but not limited to):

• Parts and Consumable lists


• Structure of expected child components and maintainable items


• Documentation (pdfs, videos, pictures)


• Failure modes


• Class / Subclass and Technical Attributes


• Alerts / alarms


• Announcements (including policies, parts change documents, service bulletins)


• Obsolescence information (including when the item will no longer be supported by the manufacturer)

The goal is to define and manage the majority of asset information at the model level, and to only have to provide equipment specific (i.e. serial number) and location specific details (geo location) at the equipment level.



The model object supports a hierarchical reference structure, which defines which and how many child classes / models including if they are optional or mandatory. This is then used to compare the actual equipment structure compared to the expected or allowed structure on the model – for instance to identify if an expected component is missing.



In addition predictive algorithms learn about failures and asset health at the “model” level. We do this because failures at individual equipment level are too infrequent which does not result in a large enough information set for learning. By learning from fleets of very similar equipment (and ignoring non similar equipment) we are able to automatically learn about how a product / model is potentially going to fail. In addition we can calculate key metrics automatically such as MTBF.

The Model most often will one to one match the manufacturers Make (i.e. Caterpillar) and Model (CAT787D). Optionally with the Asset Intelligence Network all of the model information can be automatically provided by the manufacturer and subscribed to for future updates.

EXAMPLE 1

Example	Before MDEM	After MDEM
A mining company has 200 trucks. 80 of the trucks are Model A and 120 of the trucks are Model B. The trucks are grouped into 5 fleets. Each truck has roughly 400 maintainable components and has a lower level BOM for parts list selection.	Create a functional location to represent each fleet of trucks Create a functional location hierarchy to represent the structure of each truck Create a construction type for Model A and for Model B – assign BOM items Create a reference functional location for Model A and Model B Create equipment for all components of the truck (motor, gearbox, 4 x wheels) Install all components into the functional location hierarchies Data Volume Required: 5 functional locations for fleets 200 x 400 Functional Locations 200 x 400 Equipment 2 reference functional locations 2 construction types Total: 160,009 master data objects	Create a group to organize trucks into fleets Create truck model hierarchies for Model A and B Assign spare parts lists to models Create equipment for all components of the trucks using the model as the top level element Data Volume Required: 5 group objects to represent fleets 2 x 400 model objects for the model hierarchies 200 x 400 equipment objects for the trucks Total: 80,805 master data objects

For certain types of assets there may not be a manufacturer. In this case the model can still be used as a reference structure to simplify similar equipment groups or compatible units.

EXAMPLE 2

Example	Before MDEM	After MDEM
A utility company with 2 million electrical poles. The poles are roughly categorized into 100 different types (i.e. wooden with steel cross arm).	Create a functional location to represent the location of each pole Create a functional location hierarchy to represent the structure of each pole Create equipment for all components of the pole (pole, cross arms, 4 x insulators) Install all components into the functional location hierarchy Data Volume Required: 6 x 2 million Equipment 6 x 2 million Functional Locations 2 million locations Total: 26 million master data objects	Create a Location for each address / geo coordinate details Create a model for common types of pole assemblies Create equipment for all components of the pole (pole, cross arms, 4 x insulators) by using the model as the top level (optional) Create groups to organize sets of poles into reusable elements for searching Data Volume Required: 6 x 2 million equipment assigned to 100 models 2 million location objects Total: 14 million master data objects

Example 1 and Example 2 show clearly that utilizing the model concept for fleet or highly repetitive assets delivers significant benefits including substantial reductions in master data volumes and increased consistency and sharing of insights across fleets of assets.

Technical notes:

• An equipment can have a model – it is not mandatory – but it is recommended.


• the Model object is a consolidation of the legacy reference functional location and construction type with significant additional functionality for asset performance and reliability modelling.


• This functionality is delivered in the new Intelligent Asset Management suite as a core piece of Asset Central and works with both ERP6 and S/4HANA Plant Maintenance.


• All model information can be managed via public APIs as defined here


• A general document for creation of equipment in Asset Central is defined here

 

 

This the first from an ongoing series of blogs covering top topics for asset management practitioners and experts. The other blogs in the series are:

• Standardizing Equipment Classification… (and other data)


• An Indicator of the Future


• Spare Parts information exchange for asset operators