Capability maturity model pdf download

Log in with Facebook Log in with Google. Remember me on this computer. Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up. Download Free PDF. A short summary of this paper. It can be applied to broad areas in the industry. This section explains each of these four data types in more detail. It should be noted however that acquiring and even storing the correct data does not ensure it is utilized effectively, in essence this is the aim of adopting a DM CMM approach.

While configuration data may typically begin life as OEM related information, periodic enhancements, upgrades and other events such as hazard assessments may inform the configuration data over the life-cycle of the asset.

This data type typically involves simple, routine data collection activities carried out as part of planned maintenance routines. However asset condition data may be captured at different times by different populations within the engineering asset community e. This data is recognized as highly relevant and useful by those tasked with maintaining and operating the asset. By its nature event data can be highly variable in the quality and quantity of data provided, particularly if the data is acquired manually.

Typically this information is recorded as part of a maintenance work-instruction or work-package issued in response to a planned maintenance request or asset failure. High quality process data is more likely to be observed in those organizations with well developed predictive asset management regimes and a focus on organizational capability improvement.

However it is important to note that what data to collect should be dictated by a well articulated need. We suggest that not all assets require the same level of data management and the degree of investment for data management. The next section outlines a generic framework of desired outcomes that the use of engineering data might result in or facilitate. Engineering asset organizations collect asset management data from multiple sources for multiple reasons of which four are applicable across all industry types.

We represent these four data outcomes as a continuum of data use maturity from the most basic, superficial data collection and use, to the maximum exploitation of data for multiple uses, not just for current assets but the design, management and utilization of those in the future.

This typology of outcomes reflects the broader concerns of engineering asset management while incorporating the four typical maintenance methodologies described by Tsang evident within engineering asset intensive organizations. If not governed by regulatory compliance asset condition can also be driven by contractual obligations either by the OEM or the asset owner as the service provider.

Time Based Asset Management: Refers to institutionalized, reactive or planned maintenance where data collected is only used to maintain the current condition of the asset. Tsang refers to preventative maintenance, where items are replaced or returned to good condition before failure may occur. Time based or preventative maintenance is typically driven by OEM maintenance stipulations or established planned maintenance routines.

Organizations engaging in time based AM are either unable e. Data used during sustainment maintenance may be sourced from configuration data, asset condition or event data. Condition Based Asset Management: This asset management outcome largely relates to maintenance regimes such as Condition Based Maintenance CBM that rely on sophisticated predictive modeling to determine maintenance schedules.

This capability represents a high degree of data maturity as this type of predictive modeling requires accurate, timely, reliable longitudinal data to not only be collected, but used. We suggest that asset age, asset replacement cost and availability are likely determinants of whether an organization is willing to expend the resources to engage in performance based asset management.

While not an outcome desired by all organizations the use of data to inform future developments is considered a valuable use of engineering asset data and reflects a high degree of data utilization maturity. Therefore the CMM is underpinned by these two aspects of engineering data its type and desired outcome and the ability to collect quality data relevant to the needs as they may be dictated by the asset or the operations of the organization.

The degree to which an organization is able to achieve an equilibrium in terms of desired data management outcomes, asset performance and optimum levels of investment is represented by the four key stages in the DMCMM. These are described in detail in the next section. The elements determining the capability of an organization to utilize its engineering data depends on a range of factors, the discussion of which lie outside the scope of this paper.

Examples of those elements include; the level of IT systems investment, the level of systems and functional integration, the quality of the data being used, and the level of training and technical competence of those responsible for data acquisition. This acknowledged the CMM presented here has been developed with these external constraints in mind. The five maturity levels are situated within four key determinants of level maturity namely the level of resource investment commitment by the firm, the structural, institutional and cultural orientation towards data management and the nature of that asset itself.

The capability levels have been adapted to reflect the specific nature of engineering data types and desired outcomes, however at their core the continue to reflect the key accepted components of the Carnegie CMM approach. Consistent with our earlier comments we argue It should be noted that not all assets will dictate mature data outcomes such as condition-based maintenance or capacity development. Used appropriately it may also help to identify areas requiring additional focus or resource investment to ensure not only data quality, but maximum data utilisation.

Consistent with a CMM approach we identify 4 key outcomes with increasing levels of maturity that engineering asset organizations may wish to achieve as a result of acquiring engineering data.

Further, we consider the types of data required in order to achieve those outcomes. Finally we consider the contextual factors at both an organizational and asset level, that may impact on the ability of an organization to achieve improvements in their data management maturity. In doing so we provide some indicators for engineering personnel as to under what conditions may an appropriate maturity level may be desired - depending on asset type, organizational strategy and risks associated with the asset class for example.

Organisations invest increasing amounts of resources in an attempt to extract meaning out of data that often fails to live up to its operational requirements. It is suggested that while many organizations recognize in broad terms the critical nature of data and its link to organizational performance few have institutionalized the practice of data management in a way that ensures successful informational outcomes.

This scenario is compounded when considering the essential nature of data relating to engineering assets. Asset managers are faced with the continued lack of adequate investment in the collection and utilisation of engineering data into information able to be translated into improved asset reliability, safety, availability, utilisation and overall increased return on that investment.

In this brief paper we outline the fundamentals of a Data Management Capability Maturity Model DM CMM specifically orientated to the capture, management and utilisation of engineering assent data. Following the commonly understood CMM approach we articulate the five stages of data utilisation maturity and discuss key elements of each stage in relation to the critical elements that an organization may wish to demonstrate or achieve.

We argue that effective utilisation of data requires a comprehensive, integrated approach throughout the organization to the significant value represented by quality engineering data. We provide support for the idea that data maturity is more about the ability of the organization from an institutional, cultural and structural perspective to enable quality raw data to be captured, analyzed and most importantly, used in a way that allows the organization competitive advantage.

In simple terms a CMM aims to establish a continuum of repeatable and measurable set of processes, business rules, goals and activities each represented by an increasing maturity level and capacity to manage a particular event or activity.

Rather than begin developing in detail a series of key outcomes and goals this paper aims to articulate the desired maturity levels in data utilisation and discuss some of the key activities that may be intrinsic to each maturity level.

However a number of key factors have to be considered when developing a CMM relating to the management of engineering data. An essential consideration is the nature of the data being considered and its desired end use. To our knowledge there is no uniformly accepted typology of engineering data types and their uses within an organization. We assert that engineering asset data comprises of four main categorizations of data relating to a Configuration and baseline data, b Asset condition, c Event or incident data and d Process data.

It is suggested that each of these data types can potentially relate to a number of generic engineering outcomes including a regulatory compliance, b time based asset management, c condition based asset management and d capability development. We make a fundamental assumption in the DM CMM in that these asset management outcomes are functions not only of the data types collected by an organization, but its data utilization maturity - its capacity to use data at their disposal in an increasingly sophisticated manner when and where required.

Once these two critical aspects are documented and understood then we are able to then consider the various maturity levels of data utilisation in a more comprehensive and sophisticated manner.

The next two sections of the paper briefly outline with examples the data types and desired outcomes of engineering data that form the baseline for the maturity levels represented in the CMM presented here. While the specific kinds of data that might be collected by any one organization for any range of assets might number in their thousands each of these can be classified based on the function they are intended to serve.

When considering the form and function of engineering data we argue there are four key types of data that engineering organizations are interested in acquiring. This section explains each of these four data types in more detail. It should be noted however that acquiring and even storing the correct data does not ensure it is utilized effectively, in essence this is the aim of adopting a DM CMM approach.

While configuration data may typically begin life as OEM related information, periodic enhancements, upgrades and other events such as hazard assessments may inform the configuration data over the life-cycle of the asset. This data type typically involves simple, routine data collection activities carried out as part of planned maintenance routines.

However asset condition data may be captured at different times by different populations within the engineering asset community e. This data is recognized as highly relevant and useful by those tasked with maintaining and operating the asset. By its nature event data can be highly variable in the quality and quantity of data provided, particularly if the data is acquired manually.

Typically this information is recorded as part of a maintenance work-instruction or work-package issued in response to a planned maintenance request or asset failure. High quality process data is more likely to be observed in those organizations with well developed predictive asset management regimes and a focus on organizational capability improvement. If you are knowledgeable about any fact, resource or experience related to this topic - please add your views using the reply box below.

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