Current Practices

Review of current available systems for MM. This is NOT product comparison, but a survey of the existing MM architecture approaches.

In current practice, the total system model may be more or less well captured and managed in a cohesive fashion.  The following outlines different dimensions of storage and control as applied across industry, recognizing that any given project may have a mix of approaches, which may not be followed in an identical fashion across the multiple projects in work by a given enterprise.

Different technical/methodology approaches for MM, pros and cons  - Manas Bajaj, Lonnie VanZandt

Model Repository Approach 

1. Multiple Local Repository Approach - This is the default and most common approach in organizations for model management. In this approach team members in a single team or teams within a group/organization use file systems on their individual computers or shared drives to store models that they develop.

Often, there is no formal version control where the history of models can be tracked except for identifying changes in the timestamp on the model files; where formal version control is applied, the execution may be a highly manual process and subject to incomplete adherence. Sharing of models within and across teams is achieved by file transfers via emails or shared drives.  Notification of updates to constituent models may be ad hoc or non-existent.  There is limited to no tracking of design models to analysis models or to corresponding analysis results.

The multiple local repository approach may be combined with the other approaches for portions of the total system model that have not been considered significant to the model management.  For example, the design model may be fully controlled through a multiple version-controlled model repository, yet which may not encompass the analysis model (or more commonly the model analysis results).

2. Single Version-controlled Model Repository Approach - In this approach, a team or organization selects a single repository with formal version control (ability to track model history) to manage all of the different types of models - CAD, CAE, software, part libraries, and supplier information. This is not realistic in organizations that involve multi-disciplinary development, because each repository type is often designed to manage specific categories of models - PLM system for CAD, CAE, and BOM; SCM systems for software code; Databases for large collections of instance data; ERP systems for supply chain information.

3. Multiple Version-controlled Model Repository Approach - This approach provides a better mechanism for model management than the single version-controlled repository approach. It allows different engineering teams to select the best tools and repositories suited for the types of models they are managing, e.g. a PLM system for CAD,CAE,BOM; SCM system for software code; and databases for libraries. Each repository provides formal version control. Most organizations have multiple version-controlled repositories.

Governance and Security of Models

1. None, A Single User Owns All Rights and Content - For small projects of short duration this may be acceptable, but otherwise the single owner becomes a bottleneck to the update and management of the constituent models or model configuration items.

2. OS Authentication, A Multi-User OS restricts Access but Content is Non-Attributable - for small and static teams this may be acceptable if procedural model governance mechanisms are established and followed, but otherwise the incomplete tracking of change invariably lead to the desire to question the rationale for a change, or some other query of the change author is needed that can not be accomplished.  Absence of individually assigned access controls introduces challenges to the information assurance in the situation of changing team composition.

3. LDAP, An External Server manages Users and Access but Content is Non-Attributable - this approach is only marginally better than the prior alternative, inasmuch as it provides a better mechanism to limit access to authorized users.  However, the approach still requires procedural model governance mechanisms.

4. Change-Managed Content, A CM system tracks Who Made Commits but atomic modifications are Non-Attributable - this approach improves on the prior methods by virtue of greater granularity of records of change of the model configuration items, and is essential for large multidisciplinary teams that change over time periods that are considered short with respect to the product lifecycle.

5. Authenticated, Role-Based-Access with Atomic-Level Attributability (e.g. Quad-store Provenance per Triple) - Atomic-level access control, change control and attribution is the bees knees of model lifecycle management.  

As for the case of the Model Repository Approach, the various levels of model governance and security may be combined or mixed across the various dimensions of the total system model.

Communication with models

1. P2P connectivity
2. API/SOA based integration
3. Open Linked Data and standard based connectivity  - Amit Fisher

Do we need better classification here?

b.      Need –solution approach mapping matrix - Manas Bajaj, Lonnie VanZandt

c.       Include mapping table between different technical approaches and MM aspects in section #3 above

A Sampling of Approaches and Challenges in Various Industrial Settings

- Manufacturers of Large, Complex Consumer Product Lines with Significant Optional Variability and Software Driven Features (“Mass Customizable” Mechatronic Products)

In design and construction of complex product lines, models play an increasingly important role. Systems level models are becoming the “glue” that holds the software and hardware design artifacts together and provides the basis for the definition of the product line. In product line engineering, individual products can be more quickly designed and constructed from patterns that are composed of model elements and connections with variation settings, allowing them to meet a range of requirements and provide many “levels” of capability to individual products built from that product line. Models are used to describe the product lines themselves, their possible variation dimensions, the requirements they can satisfy and the rules governing their instantiation. Because the number of possible individual buildable product configurations can be huge, analysis of the models is increasingly important for verification. Deriving the correct configuration of all the models to perform large scale simulation of a particular individual variant, at a particular effective point during the life of the entire product line, is difficult without strong model management capabilities. Manual model variant configuration and effectivity methods have hit practical limits; by the time the analyst can get the models configured to do the simulation the design has already changed.

Increasingly, for building the software (and some hardware) parts of these products the models are transformed directly into the actual product components through autocode generation or model driven manufacturing systems. Since the models must comprehend many dimensions of variability, including option based variants, time-based, serialized or lot effectivities, model management systems must support these capabilities. In today’s practice, most Product Lifecycle Management (PLM) systems contain these capabilities, but their integrations to modeling tools are limited, mainly due to lack of standards and defined modeling language support for those concepts. In the rare cases where models are used to handle this complex variability they have been integrated into PLM systems with significant effort and custom code. Application Lifecycle Management systems are beginning to gain product line capabilities, but take a software centered view on the problem that does not always match the hardware or total system design approach. The modeling of the mechanical Computer Aided Design (CAD) elements is supported in these variation dimensions better than the software or systems level models, owing to the history of PLM systems predecessors, the Product Data Management (PDM) systems that evolved to handle the CAD data in complex variable assemblies.

Another challenge industry faces in integrating modeling tools is in the choice of investment. In order to make a good modeling investment decision, it’s important to accurately forecast future capability and future cost.  As standards become more comprehensive and more widely accepted, industry members will be able to increase investment based on increased confidence of future usefulness.  Standards supporting protection of intellectual property will help address the challenge of developing an agile environment with suppliers and other partners in a business environment where protection of intellectual property is important.

Collaboration depends on some level of standardization. The more standardization, the more capability exists for collaboration. Internal standards on MCAD modeling practices, model schemas, model quality check rules, etc., are the first step to collaborate internally. As standards are adopted by a company’s customers and supply chain, it builds capability for further collaboration. Understanding the standards is important as we must constantly evaluate the value of adopting a new standard relative to the cost of working with incomplete standards. Vendors tend to promote the standards that they adopt in order to capture a larger share of the market. They are not always ready for real world deployment .Model Management depends on specifying the model types that will be managed, the scope of control, and the method of governance.