Asset modelling framework for use in computerised physical asset management systems

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(1)COPYRIGHT AND CITATION CONSIDERATIONS FOR THIS THESIS/ DISSERTATION. o Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. o NonCommercial — You may not use the material for commercial purposes.. o ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.. How to cite this thesis Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/ M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved from: https://ujcontent.uj.ac.za/vital/access/manager/Index?site_name=Research%20Output (Accessed: Date)..

(2) UNIVERSITY OF JOHANNESBURG. MASTERS DEGREE IN ENGINEERING – ENGINEERING MANAGEMENT (MIN013). ASSET MODELLING FRAMEWORK FOR USE IN COMPUTERISED PHYSICAL ASSET MANAGEMENT SYSTEMS. A minor dissertation submitted in partial fulfilment of the requirements for the subject:. Engineering Management: Minor Dissertation (MEM4408). Student Name: CA Henry Pr Eng Student Number: 201186092 Date: October 2017. 1 University of Johannesburg, 2017.

(3) ABSTRACT The objective of the research is to formulate a simplified physical asset modelling framework that is universal to any industry or any asset type, for use in the chosen Computerised Physical Asset Management System (CPAMS) such that it provides the benefit of improved management of physical assets over its lifecycle. The research highlights that there are numerous CPAMS, whether branded as ERPS, CMMS, EAMS or CFMS as example, however the track record of providing organisational benefit given the significant initial and ongoing investment, is limited. The literature review highlights that the configuration of the CPAMS is one of the contributors to limited benefit, where configuration refers to the structuring and organisation of the CPAMS to do that which the user require. One aspect of the CPAMS configuration is the physical asset modelling framework within which the physical assets are recorded in the CPAMS along with its myriad of data, information and various settings. In addition to that which is entered by the user, the processing of various performances and reporting against the asset modelling framework is equally critical. The development of the physical asset modelling framework involved two independent approaches, firstly deriving a framework from literature review of reliability and logistics engineering theory, and secondly, a framework derived from the research of existing industry asset and asset hierarchy naming conventions, standards or frameworks. The theory-derived framework was validated by the existing industry frameworks with the difference that majority of the existing industry frameworks has an additional organisation level in the hierarchy. A single physical asset modelling framework was derived from the nine existing industry frameworks and since this also validated the theory-derived framework, the industry-derived framework was concluded as the universal simplified physical asset modelling framework. A CPAMS is a necessity for the optimised management of physical assets over its lifecycle to achieve levels of sustained performance required by the organisation’s long-term business strategy and real-time operations. A CPAMS has the potential to realise this optimised management, however, and this is to be emphasised, this potential is only realisable when the CPAMS is configured and maintained by skilled asset management professionals having detailed understanding of what is required for lifecycle asset management, and therefore how the CPAMS must enable this management. This research has developed one such critical requirement which is a universal simplified physical asset modelling framework.. 2 University of Johannesburg, 2017.

(4) CONTENTS ACKNOWLEDGEMENT............................................................................................................................. 5 LIST OF FIGURES ..................................................................................................................................... 6 LIST OF TABLES ....................................................................................................................................... 7 ABBREVIATIONS...................................................................................................................................... 8 CHAPTER 1: INTRODUCTION ................................................................................................................... 9 1.1.. Problem Statement .................................................................................................................. 11. 1.2.. Objective of the research ......................................................................................................... 15. 1.3.. Importance of the research ...................................................................................................... 16. 1.4.. Research questions .................................................................................................................. 17. 1.5.. Scope and organisation of the research .................................................................................... 17. 1.6.. Conclusion and introduction to Chapter 2 ................................................................................ 18. CHAPTER 2: LITERATURE REVIEW .......................................................................................................... 20 2.1.. Reliability and Logistics Engineering ......................................................................................... 20. 2.2.. Industry asset and asset hierarchy naming conventions, standards or frameworks ................... 35. 2.2.1.. Manufacturing industry framework for continuous, batch and discrete processes ................ 39. 2.2.2.. Industrial systems, installations, equipment and products .................................................... 43. 2.2.3.. Petroleum industry standard - NORSOK Coding System ........................................................ 50. 2.2.4.. Reference Designation System for Power Plants ................................................................... 52. 2.2.5.. Economic Infrastructure Asset Modelling Framework ........................................................... 55. 2.2.6.. Asset Management Part 2: Guide for implementation of PAS 55-1........................................ 58. 2.2.7.. South African National Standard: Asset Management........................................................... 59. 2.2.8.. International Accounting Standard (IAS) 16 – Property, Plant and Equipment ....................... 60. 2.2.9.. Elemental cost estimating for building works........................................................................ 60. 3 University of Johannesburg, 2017.

(5) 2.3.. Findings ................................................................................................................................... 61. 2.4.. Conclusion and introduction to chapter 3 ................................................................................. 67. CHAPTER 3: RESEARCH.......................................................................................................................... 68 3.1.. Approach and research methodology ....................................................................................... 68. 3.2.. Theory-derived asset modelling framework.............................................................................. 68. 3.3.. Industry-derived asset modelling framework............................................................................ 70. 3.3.1.. Common asset modelling framework characteristics ............................................................ 70. 3.3.2.. Industry-derived common physical asset modelling framework ............................................ 73. 3.4.. Comparison and validation of theory-derived with industry-derived frameworks...................... 74. 3.5.. Conclusion ............................................................................................................................... 75. CHAPTER 4: CONCLUSION ..................................................................................................................... 77. REFERENCES ............................................................................................................................86. 4 University of Johannesburg, 2017.

(6) ACKNOWLEDGEMENT The author acknowledges his God for the grace of life and health to undertake the research, his wife and daughter, Euleen and Rebecca Henry for their ongoing encouragement, and the University of Johannesburg for the given opportunity, and notably Professor Jan-Harm Pretorius and Dr Arie Wessels.. 5 University of Johannesburg, 2017.

(7) LIST OF FIGURES Figure 1: 2012 Asset Health Management Report – Existence and effectiveness of software .. 12 Figure 2: 2012 Asset Health Management Report - Data integrity (quality) for decision-support ............................................................................................................................................... 13 Figure 3: 2012 Asset Health Management Report - Use of management tools ........................ 14 Figure 4: Asset information management system - Effectiveness to support asset management ............................................................................................................................................... 15 Figure 5: Physical asset carrying value of the South African industry ....................................... 17 Figure 6: System design and development process ................................................................. 21 Figure 7: Functional decomposition ........................................................................................ 23 Figure 8: Illustration of system partitioning............................................................................. 24 Figure 9: Reliability Block Diagram .......................................................................................... 25 Figure 10: Illustration of reliability allocation to system hierarchy........................................... 26 Figure 11: Illustration of a fault tree ....................................................................................... 28 Figure 12: System lifecycle data collection and analysis .......................................................... 30 Figure 13: Lifecycle cost analysis - Acquisition cost ................................................................. 34 Figure 14: Lifecycle cost analysis - Sustaining cost ................................................................... 34 Figure 16: Functional hierarchy within a manufacturing organisation ..................................... 40 Figure 17: Equipment hierarchy in manufacturing industry ..................................................... 40 Figure 18: Equipment hierarchy model for batch manufacturing ............................................ 42 Figure 19: BS 81346-2: 2009, Principle of objects' classification .............................................. 43 Figure 20: BS EN 81346-2:2009, Generic process model for object classification ..................... 44 Figure 21: BS EN 81346-2:2009, Infrastructure objects' classification ...................................... 48 Figure 22: NORSOK Petroleum industry standard - System, Function and Type code ............... 52 Figure 23: NORSOK Petroleum industry standard - Area location code .................................... 52 Figure 24: Reference Designation System for Power Plants ..................................................... 53 Figure 25: Reference Designation System for Power Plants - Major processes ........................ 54 Figure 26: Reference Designation System for Power Plants - Equipment designation code ..... 55 Figure 27: International Infrastructure Asset Management Manual - Asset hierarchy ............. 56 Figure 28: International Infrastructure Asset Management Manual - Asset and Cost Hierarchy ............................................................................................................................................... 57 Figure 29: IIAMM - Illustration of Waste Water Treatment Works .......................................... 57 Figure 30: PAS 55-2 - Asset Hierarchy ..................................................................................... 58 Figure 15: Asset modelling framework derived from reliability and logistics engineering theory ............................................................................................................................................... 70 Figure 31: Common asset modelling framework derived from industry frameworks ............... 74 Figure 32: Comparison of theory-derived and industry-derived frameworks ........................... 75 Figure 33: Physical asset modelling framework derived from reliability and logistics engineering theory .................................................................................................................................... 81 Figure 34: Physical asset modelling framework derived from existing industry frameworks .... 83 Figure 35: Comparison of theory-derived and industry-derived physical asset modelling frameworks ............................................................................................................................ 84. 6 University of Johannesburg, 2017.

(8) LIST OF TABLES Table 1: Physical asset carrying value per SA industry for 2014 financial year ......................... 36 Table 2: Physical asset carrying value per asset type for 2014 financial year ........................... 37 Table 3: Selected industry frameworks for research................................................................ 39 Table 4: BS EN 81346-2:2009, Extract of object classification .................................................. 45 Table 5: BS EN 81346-2:2009, Extract of object sub-classification codes ................................. 47 Table 6: BS EN 81346-2:2009, Extract of infrastructure objects' classification codes ............... 50 Table 7: Comparative table of industry asset modelling frameworks (No 1 of 3) ..................... 65 Table 8:Comparative table of industry asset modelling frameworks (No 2 of 3) ...................... 66 Table 9: Comparative table of industry asset modelling frameworks (No 3 of 3) ..................... 66 Table 10: Reliability and logistics engineering analysis techniques’ influence on the modelling framework.............................................................................................................................. 80. 7 University of Johannesburg, 2017.

(9) ABBREVIATIONS Abbreviation AAQS CAFM CMMS CPAMS EAMS ERPS FMECA FRACAS FTA HAZOPS IAMS IAS 16 IIAMM LCC LCCA LSA MMI MSI MTA PAS 55-2 PMP PPE QFD RBD RCM RDS-PP SA. Full description Africa Association of Quantity Surveyors Computerised Facility Management System Computerised Maintenance Management System Computerised Physical Asset Management System Enterprise Asset Management System Enterprise Resource Planning System Failure Modes, Effects and Criticality Analysis Failure Recording and Corrective Action System Fault Tree Analysis Hazard and Operability Studies Infrastructure Asset Management System International Accounting Standard 16 - Property, Plant and Equipment International Infrastructure Asset Management Manual Lifecycle cost Lifecycle cost analysis Load-Strength analysis Maintenance-Managed Item Maintenance Significant Items Maintenance Task Analysis Publically Accessible Standard, Part 2 Parts, materials and processes reviews Property, Plant and Equipment Quality Function Deployment Reliability Block Diagram Reliability-Centred Maintenance Reference Designation System for Power Plants Supportability Analysis. 8 University of Johannesburg, 2017.

(10) CHAPTER 1: INTRODUCTION Organisations exist to satisfy the needs of other organisations or people for diverse products and services. Management of organisations require varied data and information to support or enable decision making towards achieving the organisations objectives. This decision making is enabled by computerised tools or systems that capture, process and presents the data and information in the form of varied indicators and reports. These provide management with needed insight to make the right decisions and once executed, verify the degree of success and whether further decisions and actions are needed. Computerised tools and systems to aid the management of organisation processes and decisionsupport, goes by varied names, but one that is most common is Enterprise Resource Planning System (ERPS). An ERPS can incorporate all the functionality associated with enabling business process and decision-support as required by the various organisation functions. Examples of organisation functions include sales, operations and financial management. Each organisation, regardless of their product or service offering, requires varied physical resources or assets. These physical assets generally fall into two categories, firstly, production assets which are assets necessary for the production and delivery of the organisations products or services. An example of production assets are steam-generating boilers in an organisation that generates electricity for onward transmission and distribution. The second category is non-production, support or enabling assets, an example would be a workshop building or facility that houses maintenance facilities. Regardless of the category of physical assets, significant capital outlay is associated with its acquisition and further significant operating costs associated with its operations and maintenance over its lifecycle. Asset management is a management discipline that has risen in prominence with the emphasis that value or returns from assets can only be achieved with a holistic and integrated approach over the lifecycle of the asset. A notable milestone was the publication of the asset management series of standards in January 2014 by the International Standards Organisation, and this followed with the publication of the same series of standards adopted by South Africa and published by South African Bureau of Standards. The series comprises three standards being (South African Bureau of Standards, SANS 55000:2015, 2015) , Asset Management – Overview, principles and terminology; (South African Bureau of Standards, SANS 55001:2015, 2015) , Asset Management – Management systems – Requirements and (South African Bureau of Standards, SANS 55002:2015, 2015) , Asset Management Management systems – Guidelines for the application of ISO 55001. (South African Bureau of Standards, SANS 55000:2015, 2015) defines an asset as an item, thing or entity that has potential or actual value to an organisation; asset management is defined as the coordinated activity of an organisation to realise value from assets and the benefits of. 9 University of Johannesburg, 2017.

(11) pursuing asset management in a coordinated manner will include the following: ·. Improved financial performance: improving the return on investments and reducing costs can be achieved, while preserving asset value and without sacrificing the short or longterm realisation of organisational objectives;. ·. Informed asset investment decisions: enabling the organisation to improve its decision making and effectively balance costs, risks, opportunities and performance;. ·. Managed risk: reducing financial losses, improving health and safety, good will and reputation, minimizing environmental and social impact, can result in reduced liabilities such as insurance premiums, fines and penalties;. ·. Improved services and outputs: assuring the performance of assets can lead to improved services or products that consistently meet or exceed the expectations of customers and stakeholders;. ·. Demonstrated social responsibility: improving the organization’s ability to, for example, reduce emissions, conserve resources and adapt to climate change, enables it to demonstrate socially responsible and ethical business practices and stewardship;. ·. Demonstrated compliance: transparently conforming with legal, statutory and regulatory requirements, as well as adhering to asset management standards, policies and processes, can enable demonstration of compliance;. ·. Enhanced reputation: through improved customer satisfaction, stakeholder awareness and confidence;. ·. Improved organizational sustainability: effectively managing short and long-term effects, expenditures and performance, can improve the sustainability of operations and the organisation; and. ·. Improved efficiency and effectiveness: reviewing and improving processes, procedures and asset performance can improve efficiency and effectiveness, and the achievement of organisational objectives.. The coordinated organisation-wide management of assets requires establishment and management of elements or requirements of the asset management system as explained in (South African Bureau of Standards, SANS 55001:2015, 2015) and includes leadership, planning, support, operations, performance evaluation and improvement. The importance of this coordinated management applies both to a single organisation and industries. (Statistics South Africa, Statistical Release P0021, 2015) quantifies the South African industry asset carrying value for the 2014 financial year; excluding government institutions, educational institutions, agriculture and financial intermediation; at R2.427 trillion of which 86%, or R2.126 trillion, was physical or tangible assets. Further to the significance of the asset value is the annual repairs. 10 University of Johannesburg, 2017.

(12) and maintenance expenditure of physical assets at R1.062 trillion for the financial year. The business processes associated with the lifecycle activities and management of physical assets, and the decision-support, are typically enabled by computerised tools or systems. ERPS provide such functionality as an integrated package, or can be provided by specialised systems with naming that includes Computerised Maintenance Management System (CMMS), Enterprise Asset Management System (EAMS), Infrastructure Asset Management System (IAMS) and Computerised Facility Management System (CFMS). In either case, the goal of procuring and implementing a system is to enable the effective execution and integrative management of all activity associated with the lifecycle of physical assets towards achieving the required asset performance, which will include varied parameters associated with functional performance, cost and risk control. Any Computerised Physical Asset Management System, hereafter referred to as the CPAMS, that has the required functionality for lifecycle asset management has the potential for enabling effective asset management leading to asset performance. However, despite this potential, evidence of failed or poorly performing systems is readily available. This minor dissertation addresses a one important requirement for a successful implementation and use of the CPAMS, this being the physical asset modelling framework to which, or within which the physical assets and its associated data and information is connected.. 1.1. Problem Statement (Šochová, 2009) referenced the Robbins-Gioia Survey (2001) where 51 % of ERPS implementations were identified as unsuccessful, the Conference Board Survey (2001) identified that 40% of the projects failed to achieve their business case, the KPMG Canada Survey (1997) identified over 61 % of the projects that were analysed were deemed to have failed. (Šochová, 2009) suggests that an information technology project, which includes physical asset management systems, implementations is more likely to be unsuccessful than successful. (Labib A. , 2004) describes a CMMS and ERPS as “black-holes”, a term coined as a description of a system demanding considerable efforts to input data, but seldom provide outputs for decision support and ultimately questions the added value that these systems provide to the organisation. (Baxter, 2010) highlights that success of the ERPS is its configuration and the most important configuration is the modelling of the physical assets and against which the actual assets and their data and information is entered. (Baxter, 2010) has provided reasons for ERPS implementation failures but certainly the problem relevant to CMMS is the deficient method to configure the CMMS with the correct asset model against which the specific assets and their related data and information is recorded for later analysis and conversion to decision support outputs.. 11 University of Johannesburg, 2017.

(13) (Bradshaw, May 2004) has shown that there is no shortage of state of the art CMMSs, yet the 2012 Asset Health Management Report, (ReliabilityWeb.com, 2012), has proven that the majority are failing to realise the benefits of investing in CMMS. Figure 1: 2012 Asset Health Management Report – Existence and effectiveness of software illustrate the responses to the question of existence of software, and if it exists, the effectiveness in supporting collation of data necessary for assessment of the asset’s health. Only 33% of all respondents have the required software that delivers the required functionality, 33% has software but is lacking and the balance has none.. Figure 1: 2012 Asset Health Management Report – Existence and effectiveness of software (Source: Reliabilityweb.com, www.reliabilityweb.com, Asset Health Management Report, July 2012, Page 22). Figure 2: 2012 Asset Health Management Report - Data integrity (quality) for decision-support illustrate the responses to the question of data integrity for asset health management decisionsupport by asset type. Machinery asset type scored the highest with 50% responded that their data quality for decision-support is good to excellent, and infrastructure scored lowest at 25%.. 12 University of Johannesburg, 2017.

(14) Figure 2: 2012 Asset Health Management Report - Data integrity (quality) for decision-support (Source: Reliabilityweb.com, www.reliabilityweb.com, Asset Health Management Report, July 2012, Page 26). Figure 3: 2012 Asset Health Management Report - Use of management tools, illustrates the responses to the question of frequency of use of analysis tools in decision-support. For further clarification and to use Root Cause Analysis (RCA) as example, three options of frequency of use were provided, 0% – 25%, 25% to 75% and 75% to 100% of the time; the responses received were 44%, 35% and 21% respectively. As example, the interpretation is that 44% of the respondents uses RCA, 0% to 25% of the time. There would be many reasons for the performance, but one would certainly be the ready availability of data and information from the software in use to support the RCA. Observations from the responses to management tools used 75% to 100% of the time, include the most frequently used management tool is standard operating plans (Std Ops Plans) at 38%, while Failure Modes and Effect Analysis (FMEA) is the least frequently used at 14%.. 13 University of Johannesburg, 2017.

(15) Figure 3: 2012 Asset Health Management Report - Use of management tools (Source: Reliabilityweb.com, www.reliabilityweb.com, Asset Health Management Report, July 2012, Page 35). By observation of the responses, it can be concluded that despite the existence of software for the management of assets, the effective use for management decision-support does not correlate with the ready availability of software. The reasons for this are many, but one is certainly the quality of data and information appropriate for use by management tools and further decision-making. This reasons for this are also many but one foundational aspect is having the correct physical asset modelling framework incorporated into the software to facilitate right allocation of predefined data. A further reference to dominant lack of asset information management systems is illustrated in Figure 4: Asset information management system - Effectiveness to support asset management.. 14 University of Johannesburg, 2017.

(16) Figure 4: Asset information management system - Effectiveness to support asset management (Source: Reliabilityweb.com Research Report on Asset Management Practices, Investments and Challenges 20142019. www.reliabilityweb.com). (Mitchell, 2004) describes the relationship between management decision-support and data and information by using the analogy to the heart and blood in a human body. Mitchell suggests if management decision-support is considered analogous to the heart in the human body, then the data that feeds the decision-support is equivalent to the blood that circulates in the body. Taking the analogy further, the organisation, structure or framework for the attachment of the physical assets and its data and information, is the skeleton to which all is connected.. 1.2. Objective of the research The problem statement elaborated on the poor track record of ERPS, CMMS and other variants, collectively referred to as CPAMS, to enable effective decision-support for improved physical. 15 University of Johannesburg, 2017.

(17) asset management and consequently improved asset performance. One of the most critical requirements is the modelling of physical assets within the CPAMS, where modelling refers to the structuring of the assets such that it mirrors the as-designed and as-installed assets. The objective of the research is to formulate a universal simplified physical asset modelling framework that has applicability regardless of industry, organisation and its form and use of physical assets; hence use of the words “universal” and “simplified”. The framework will be beneficial for the implementation and use of any CPAMS and contribute to realising the potential of the CPAMS for improved management of physical assets over its lifecycle.. 1.3. Importance of the research Physical assets comprise the most significant component of public and private sector tangible and intangible asset value. Since these assets are acquired with the sole purpose of enabling the production and delivery of organisations’ products and services, their performance is a key determinant of those organisations’ overall performance. A key determinant in turn for physical asset performance is having the necessary decision-support enablers to make right decisions regards the asset performance and this in turn dependent on well-structured CPAMS, leading to the necessity for a physical asset modelling framework. (Statistics South Africa, Statistical Release P0021, 2015) quantifies the South African industry asset value and Figure 5: Physical asset carrying value of the South African industry illustrates the composition of the industry tangible and intangible assets. These statistics summates the asset carrying value, being the acquisition cost less accumulated depreciation and impairments, of all South African industry excluding government institutions, educational institutions, agriculture and financial intermediation. The total carrying value of tangible and intangible assets for the 2014 financial year was R2.427 trillion of which 86%, or R2.126 trillion, was physical or tangible assets. Further to the significance of the asset value, (Statistics South Africa, Statistical Release P0021, 2015) reports the annual repairs and maintenance expenditure of physical assets at R1.062 trillion for the financial year. The significance of investment and operating expenditure of physical assets makes this research relevant for its contribution to improved physical asset management and subsequent performance.. 16 University of Johannesburg, 2017.

(18) Figure 5: Physical asset carrying value of the South African industry (Source: Statistics South Africa, Statistical release P0021, 2014 Annual Financial Statements). 1.4. Research questions The following research questions will be answered towards achieving the research objective: a.. What physical asset modelling framework can be derived from the literature review of reliability and logistics engineering theory?. b.. What common physical asset modelling framework can be derived from existing industry physical asset or equipment naming conventions, standards or frameworks?. c.. Is the theory-derived physical asset modelling framework validated by the industryderived framework.. d.. Can a single simplified universal physical asset modelling framework be formulated satisfying both the theory-derived and industry-derived modelling frameworks?. 1.5. Scope and organisation of the research The scope and organisation of the research is outlined as follows: a.. Reliability engineering and logistics engineering theory-derived physical asset modelling framework The literature review is primarily limited or focused on the work of two authors, (O'. 17 University of Johannesburg, 2017.

(19) Connor, 2001) and (Blanchard, 1998) who presents theory and practice in reliability engineering and logistics engineering and management respectively. Capital intensive industry invests in physical assets that has life expectancy of years to decades, the achievement of the asset performance over its lifecycle requires understanding of the initial design, such that actual performance can be compared against designed-in performance. If there is a disconnect between the asset as-design configuration and the way the assets are modelled or configured in a CPAMS, then the data recorded and performances calculated cannot correlate with the designed performances, and hence limits effective decision-support and the feedback loop to asset design. An essential consideration then to formulating a physical asset modelling framework is to review the literature on reliability and logistic engineering theory as presented by (O' Connor, 2001) and (Blanchard, 1998), and derive a physical asset modelling framework therefrom. The framework will be validated through a comparative analysis with the existing industry-derived framework.. b.. Existing industry-derived physical asset modelling framework Research will be conducted on identifying and evaluating existing industry asset and asset hierarchy naming conventions, standards or frameworks; establishing the common characteristics and deriving a modelling framework that best describes all researched industry frameworks.. c.. Comparing and validating the theory-derived with the industry-derived physical asset modelling frameworks and concluding on a single framework that incorporates both derivations The physical asset modelling framework derived from reliability and logistics engineering theory will be compared and validated with the industry-derived framework and a single framework will be formulated.. 1.6. Conclusion and introduction to Chapter 2 Public and private organisations exists to provide broad spectrum of products and services required by other organisations and people as final consumers. All organisations has processes that converts inputs to output products and services and all organisations has physical assets that performs or contributes to production and delivery of products and services. The complexities of managing business processes and resources necessitate the use of computerised management systems, however evidence exist that the track record of successful. 18 University of Johannesburg, 2017.

(20) implementations and use is limited. An important aspect in the management of physical assets is the effective modelling of the assets within the computerised management system to enable effective management and provision of decision-support. Chapter 2 will review literature of reliability and logistics engineering theory and the literature of existing industry physical asset naming conventions, standards or frameworks. The purpose of the literature review is to determine existing theory and practices for physical asset modelling.. 19 University of Johannesburg, 2017.

(21) CHAPTER 2: LITERATURE REVIEW The research objective is to formulate a universal simplified physical asset modelling framework that can be derived from the theory of reliability and logistics engineering and existing industry asset modelling naming conventions, standards or frameworks. The theory element of the literature review will focus reliability engineering and logistics engineering as presented by (O' Connor, 2001) and (Blanchard, 1998) respectively. The literature review of existing industry asset and asset hierarchy frameworks will be guided by the dominant industries of South Africa by asset carrying value.. 2.1. Reliability and Logistics Engineering Existing physical assets regardless of their setting, whether a power plant, manufacturing facility or water supply infrastructure as example, were all outcomes of design. The design defined its current configuration in terms of structure, performance attributes and its support requirements over its utilisation life. The design in turn was an outcome of iterative procedures that utilised various analysis techniques. The literature review aims to identify pertinent methods and techniques that led to the as-designed physical asset configuration and therefore influential in the formulation of a physical asset modelling framework for use in the utilisation or in-service phase of its lifecycle. (O' Connor, 2001) emphasises that reliability effort must be part of an integrated, holistic or lifecycle approach to systems/product development, production and in-service use. Lack in any one of the phases will compromise the reliability of the product or system regardless of how well the preceding phase was executed. An integrated reliability programme is a disciplined process that must be tightly controlled while still encouraging creativity and innovation. The disciplines of design analysis, test, failure recording, analysis and corrective action must be controlled and structured as any lack will impact on the asset system’s reliability. Achieving high reliability does come at a cost, but there is adequate evidence that well executed reliability programme delivers returns. This logic is based on the principle that it is considerably cheaper to solve issues at design stage than to carry it through manufacturing to in-service use where the cost of correction will be factors higher than design effort. Similarly, it is less costly to correct production defects whilst in production that to live with the consequences afterwards. (Blanchard, 1998) states that it is not unrealistic to expect adjustments to the operations and support of the system in the utilisation or in-service phase because of under or over-estimation of required level and depth of support. It is therefore essential that the systems support capability is evaluated on an on-going basis and continuously improved upon through the incorporation of changes. This on-going evaluation requires the establishment and management of data collection, analysis and system evaluation management system. The data collection,. 20 University of Johannesburg, 2017.

(22) analysis and evaluation system must enable answering the following basic questions but also to identify reasons and causes of any performance deficiency: · · · ·. The actual performance and effectiveness of the system versus its designed goals; The actual effectiveness of the logistic support capability; Extent to which initial requirement are met; and The actual system cost-effectiveness relative to design.. 2.1.1.Reliability in design Design should be pursued with the ideal of eliminating all forms and causes of failure, i.e. a failure-free design. It may be argued that it is not financially viable to eliminate all forms of failure; however, it is significantly less costly to design out failure than to correct during manufacturing or in-service use. O’Connor elaborates on all phases of design and development and which is illustrated in Figure 6: System design and development process.. Figure 6: System design and development process (Source: Practical Reliability Engineering, O’Connor D.T). The process is triggered with submission of the specification, several design techniques are applied as part of an iterative process to identify potential failure risks, each of these risks are analysed and solutions incorporated to improve the design. Where uncertainty exist between the theoretical design and its physical capability, tests are required which are either experimental tests or stress-based test. The test results provide information and data for failure analysis and corrective action which is used to further improve the design. The pre-final stage is a design review which may result in further improvements. Having specified the reliability,. 21 University of Johannesburg, 2017.

(23) several design analysis techniques have been developed to identify critical design aspects and to focus attention on possible shortfalls and risks, these are briefly explained: a.. Quality Function Deployment (QFD) QFD is a technique to identify all factors that affect the ability of the design to satisfy client requirements. QFD as a series of ‘houses of quality’ where the top-level details characteristics of the customer requirements and lower levels used to evaluate more detailed aspects including detail design and component characteristics, production processes and tolerances, always against the same customer requirements.. b.. Reliability modelling and prediction The reliability modelling and prediction process is a key determinant of the eventual system structure and includes several analysis procedures to be briefly explained. i.. Functional analysis Conceptual design of a system requires a functional description of that system to identify the resources necessary to enable the system to achieve its objectives. A function is defined as a series of actions that is necessary to achieve a given objective. Functional analysis is an iterative process of decomposing the system requirements to sub-system level and as far down the hierarchical structure as necessary to identify input design criteria or constraints. (Blanchard, 1998) illustrates the successive levels of decomposing the system function into sub-functions in Figure 7: Functional decomposition.. 22 University of Johannesburg, 2017.

(24) Figure 7: Functional decomposition (Source: Logistics Engineering and Management, Blanchard B.S). Functional analysis constitutes a critical step in further design development as it forms the baseline of subsequent design activities. ii.. Partitioning the system into component elements Given the top-level definition of the system through the preceding functional analysis, (Blanchard, 1998) defined the next steps as breaking the system through a process of partitioning into sub-systems and lower-level components. The system is partitioned in a manner that closely group’s related functions identified in the preceding functional analysis. The partitioning of the system is an evolutionary process but should be directed by the following objectives: Sub-systems should be grouped by geographic location, common environment or by. 23 University of Johannesburg, 2017.

(25) similar type of equipment; Sub-systems should be as independent as possible from each other, i.e. the removal of one element should not require extensive changes to other interfacing elements and Sub-systems should be configured such that communications between sub-systems is minimised, though internal complexity of sub-systems may be high, the interfacing and communications between sub-systems should be simplified as far as possible. (Blanchard, 1998) illustrates an example of partitioning a system in Figure 8: Illustration of system partitioning.. Figure 8: Illustration of system partitioning (Source: Logistics Engineering and Management, Blanchard B.S). (O' Connor, 2001) refers to the concept of modular design which is an outcome of system partitioning and is used to improve maintainability and therefore availability, it enables a failed module to be removed and replaced by a functional module without having to replace the complete unit. The necessity for partitioning a system is to allocate or apportion system qualitative and quantitative performance requirements to various levels which provides the design performance criteria for these respective elements. iii.. Reliability block diagrams Blanchard further explains the allocation of reliability performance measures through the development of Reliability Block Diagrams (RBDs). RBDs follow from the functional analysis and system partitioning and each block in the diagram represents. 24 University of Johannesburg, 2017.

(26) a necessary element that must function for the system to successfully function, the RBD thus represents the failure logic of the system. (Blanchard, 1998) illustrates an example of a 5-level RBD in Figure 9: Reliability Block Diagram, level I and II are generally determined during concept design, level III during preliminary design and success levels during detail design.. Figure 9: Reliability Block Diagram (Source: Logistics Engineering and Management, Blanchard B.S). The RBD will comprise various combinations of series and parallel configurations. (O' Connor, 2001) briefly explains the combination of the basic reliability models which are used in the construction of RBD: · Series reliability: A series model comprises two or more independent components in series, failure of any one component will lead to a system failure; · Active redundancy model: A simplest active redundancy comprises two sindependent components’ in parallel and the system will function if either or both components are operational; · m-out-of-n parallel redundancy: This reliability configuration requires m out of total of n s-independent components to function for the system to function and · Standby redundancy: Standby redundancy involves two components in parallel of which one does not operate continuously but is switched-on when the other component fails.. 25 University of Johannesburg, 2017.

(27) The RBD is used together with reliability mathematics to allocate reliability performance requirements and through an iterative process to finalise the reliability allocation. (Blanchard, 1998) illustrates the allocation of reliability targets Figure 10: Illustration of reliability allocation to system hierarchy.. Figure 10: Illustration of reliability allocation to system hierarchy (Source: Logistics Engineering and Management, Blanchard B.S). An important consideration in the reliability modelling of redundant configurations is the identification of potential common mode failures which if materialises, will result in the failure of all paths in a redundant configuration. Common mode failure potential should be eliminated, or at least assure the probability of occurrence is considerably lower than other failures. (O' Connor, 2001) identifies several examples of common mode failures: · Changeover systems to activate standby redundant units; · Sensor and indicator systems to detect failure and alert failure respectively; · Power or fuel supplies which are common to system paths and · Software which is common to all paths c.. Load-Strength analysis (LSA) The fundamental cause of failure is load exceeding product or components strength. LSA identifies risks and potential problems for initiation and propagation of failure.. 26 University of Johannesburg, 2017.

(28) d.. Failure Modes, Effects and Criticality Analysis (FMECA) FMECA is the most widely used design analysis technique and is equally well used by operational engineers for in-service problem analysis and maintenance strategy development. FMECA develops a clear connectedness from the system performance and objectives requirements to the likely failure modes and their effects on the system performance. It facilitates the assessment of design adequacy regards the prevention of probable failure modes. Should there be uncertainty whether all failure modes have been exhausted, then reliability testing will be conducted to stimulate in-service failure modes. The outcome of FMECA is to improve the design’s adequacy to prevent failures and because it develops an intimate understanding of the failure character, it is also used in the design of built-in test systems and development of maintenance strategies such as Reliability Centred Maintenance methodologies. (Blanchard, 1998) defines the following general approach to conducting a FMECA: · · · · · · · ·. e.. Failure modes are defined as the way the system fails to perform its function; Failure causes should be exhausted by applying the Ishikawa fishbone, as example, which prompts causes from raw materials, equipment, environment and procedures; Failure effects describe the effect on subsequent processes or next higher level functional entity; Failure detection entails identifying mechanisms, procedures or systems which can identify and detect potential failure; Failure severity is the seriousness of the impact or the effect of the failure; Frequency of occurrence should be determined for each failure mode and the probability of failure; Probability of failure detection is the probability that the design aids and verifications procedures will detect the failure and Failure mode criticality is the product of failure severity, frequency of occurrence and probability of failure detection to prevent a failure scenario from realising.. Fault Tree Analysis (FTA) A fault tree is a structure by which a system failure mode or event can be expressed in terms of combinations of component failure modes and operator actions. The system failure mode to be considered is termed the 'top event' and the fault tree is developed in branches below this event, showing its causes. In this way events represented in the tree are continually redefined in terms of lower resolution events. This development process is terminated when component failure events, termed basic events, are encountered. Figure 11: Illustration of a fault tree, provides an example of a fault tree.. 27 University of Johannesburg, 2017.

(29) Figure 11: Illustration of a fault tree (Source: Logistics Engineering and Management, Blanchard B.S). f.. Hazard and Operability Studies (HAZOPS) The intent with HAZOPS is similar FMECA whereby potential hazards generated by the system must be identified, the causes thereof and methods to be applied to remove or minimise the hazards.. g.. Human reliability Human reliability relates to the probability of inducing failures through incorrect human operation and maintenance of systems. Design should consider the how humans will interact with a system and ensure it is designed for ease of operability and maintainability and provision of aids to solve problems or adjust operations as may be necessary.. h.. Critical items list Critical items can be viewed as exceptions which require focus to resolve the associated. 28 University of Johannesburg, 2017.

(30) issues and risks. The list can start from the earliest phase of design and items are added as the design progresses. i.. Parts, materials and processes reviews (PMP) Typically, product designs may use parts, materials and apply production processes that are the same or similar in nature to other products. The organisation therefore has proven experience of their behaviour and performance under varying conditions in respect of reliability and quality as example. Whenever a new part, material or process is recommended for a design it should trigger critical assessment as the experience may not be readily transferrable to the ‘new’.. 2.1.2.Failure Recording and Corrective Action System (FRACAS) Despite rigorous design development processes, a product or system can still have in-service reliability or other performance problems. In-service reliability data must be collected, analysed and improvements designed and implemented. Most systems having long lifecycle are constantly undergoing evolutionary developments justifying continuous reliability improvements. In-service FRACAS is critical for the collection and analysis of reliability data, determination of in-service corrective action and feedback into the evolutionary design development processes. The objective of analysing in-service reliability data includes the following: · ·. Identifications of failure trends; Establishing the best-fit failure distribution which will provide a number insights and these include: o The nature of failure or failure modes; o The calculation of reliability parameters such as mean time between failures; o Improving the prediction of reliability and to the appropriate confidence levels and o Demonstration of achieved reliability.. (O' Connor, 2001) illustrates the typical lifecycle of raw data capture, conversion to knowledge and insight from analysis and finally, implementation actions which will improve design, manufacturing and in-service performance in Figure 12: System lifecycle data collection and analysis. The recording and capturing of reliability data occurs throughout the product or asset system lifecycle from design, through development testing, manufacturing and in-service use. This wealth of raw data is written to electronic or hard-copy media and later accessed for analysis. When analysis is required, the raw data is accessed and analysed into various forms of information which provides insight into underlying trends, failure distributions and causes of unreliability as examples. The figure illustrates that FRACAS and reliability prediction and modelling technique can be used as the analysis engines from which insight is drawn and improvement actions can be implemented.. 29 University of Johannesburg, 2017.

(31) Figure 12: System lifecycle data collection and analysis (Source: Practical Reliability Engineering, O’Connor D.T). 2.1.3.Maintenance concept The maintenance concept evolves from the system operational requirements at the concept design phase. Too often the main emphases on the prime mission or operating performance as opposed to an equal focus to the support of the prime mission equipment to assure sustainable cost effective performance. (Blanchard, 1998) is referenced in summarising the maintenance concept. a.. Levels of maintenance Levels of maintenance refer to the division of preventive and corrective maintenance tasks and activities to several geographic areas or zones. Blanchard proposes three levels; namely organisational, intermediate and depot maintenance. The division and levels of maintenance work is influenced by the mission of the system and the level of maintenance complexity and frequency as example. i.. Organisational level maintenance Maintenance is performed on the operational site and is generally performed by the organisation’s own maintenance resources and generally limited to routine preventive maintenance inspections and functional checks, first-line fault-finding and remove and replace repair actions. Once repaired items are returned to the operational site it is placed in position and restore to operational state.. 30 University of Johannesburg, 2017.

(32) ii.. Intermediate level maintenance Generally, items for repair are removed and transported to the more specialised personnel and facilities at intermediate level maintenance where typically disassembly, more detailed test and trouble-shooting is required and re-assembly and possible further specialised testing routines before returning to organisational maintenance.. iii.. Depot level maintenance Maintenance tasks beyond the capability of the intermediate level are forwarded to the depot level where the highest level of specialisation in skills, test equipment and system are available. Completed work from the Depot is generally returned to the Intermediate and onward to the organisational level maintenance.. b.. Repair policies Repair policy relates to extent to which any item or equipment is repairable, this is once again a conscious design decision for an item to be repairable, partially and fully repairable. The repair policy would set design boundaries within which detail design will progress.. c.. Organisational responsibility Organisational responsibility as it relates to the level of maintenance and more specifically defines the responsibility for execution of maintenance. Execution can be assigned to the client or user organisation, producer or supplier and/or third party contractors and any combination of the three. The responsibility can vary with different components of the system and as the system evolves through the operational and support use.. d.. Maintenance support elements The maintenance concept comprises various elements of maintenance support: i.. Supply support Supply support include the supply and maintenance of necessary inventories of spares, repair parts, consumables, specialised supplies to maintain the primemission equipment as well as support equipment which can include test, transportation, system and training equipment.. ii.. Maintenance and support personnel Maintenance and support personnel include all personnel at all levels of maintenance associated with preventive and corrective maintenance of prime-. 31 University of Johannesburg, 2017.

(33) mission and support equipment and operators of test and calibration equipment as examples. iii.. Training and training support Training and training support includes all personnel, equipment and facilities necessary for the initial and on-going training of operational and maintenance personnel. Training support includes all documentation and equipment such as simulators and mock-up devices.. iv.. Test, measurement, handling and support equipment The equipment includes the hand-tools, calibration, condition monitoring and diagnostic equipment and maintenance jigs and specialised handling equipment to facilitate execution of preventive and corrective maintenance at the different levels of maintenance.. v.. Package, handling, storage and transportation Includes the packaging, preservation, storage, handling and transportation of primemission related equipment and materials and personnel associated with logistic support.. vi.. Maintenance facilities Maintenance facilities includes all necessary for the execution of preventive and corrective maintenance at all levels, and includes intermediate and depot level maintenance shops, calibration laboratories and plant and machinery. All utilities necessary for the functioning of the maintenance facilities are included, as example electricity, water, environmental controls and telecommunications.. vii.. Computer and software resources Include all computer hardware, software, interfaces and networks necessary for the maintenance execution and support at all levels.. viii.. Technical data, documentation and information systems Data and documentation includes all electronic and physical documentation that describes the physical configuration, operations and support and includes: · · · · ·. Engineering design data and specifications, inclusive of drawings, parts and materials data sheets; Chronology and history of modifications; Operating procedures; Maintenance inspection, repair and overhaul procedures and Test and calibration procedures.. 32 University of Johannesburg, 2017.

(34) 2.1.4.Supportability Analysis (SA) The preceding maintenance concept provided the requirements for the sustainable support of the asset system. Its present state or outcome was the result of an iterative supportability analysis commencing at the concept design phase, through detailed design, production and present utilisation phase. The supportability analysis process uses a variety of analysis methods of which the most common are further explained. a.. Lifecycle Cost Analysis (LCCA) Lifecycle costing is the total cost of the asset system and its support activities throughout its lifecycle. LCCA is essential to assess whether a system can be operated and supported in the most effective and efficient manner. LCCA can be used both in evaluating the design against a given cost ceiling and to evaluate alternative solutions against their respective LCC. (Barringer, 2003) considers the application of LCCA enhances the economic competitiveness by working for the lowest long term cost of ownership, several conflicting cost interests typically over the life cycle of the product: · Project Engineering wants to minimize capital costs as the only criteria; · Maintenance Engineering wants to minimize repair hours as the only criteria; · Production wants to maximize uptime hours as the only criteria; · Reliability Engineering wants to avoid failures as the only criteria; · Accounting wants to maximize project net present value as the only criteria; and · Shareholders want to increase stockholder wealth as the only criteria. LCCA is an analysis and decision making technique that facilitates the resolution of these conflicts by focusing on the minimum sustainable lowest LCC. (Barringer, 2003) identifies the major components of LCC in two tree-configurations, acquisition cost tree in Figure 13: Lifecycle cost analysis - Acquisition cost and operations or sustaining cost tree in Figure 14: Lifecycle cost analysis - Sustaining cost.. 33 University of Johannesburg, 2017.

(35) Figure 13: Lifecycle cost analysis - Acquisition cost (Source: International Conference of Maintenance Societies, A lifecycle cost summary, Barringer H). Figure 14: Lifecycle cost analysis - Sustaining cost (Source: International Conference of Maintenance Societies, A lifecycle cost summary, Barringer H). 34 University of Johannesburg, 2017.

(36) b.. Failure Mode Effects and Criticality Analysis (FMECA) FMECA is also applied in supportability analysis and has been discussed.. c.. Fault-tree Analysis (FTA) FTA is also applied in supportability analysis and has been discussed.. d.. Maintenance Task Analysis (MTA) Maintenance task analysis provides a detailed engineering package addressing all preventive and corrective maintenance functions and tasks and associated logistic support requirements for the equipment being analysed. MTA is an iterative process and is refined throughout the definition and design phases. MTA supports design decisions and provides the necessary inputs for subsequent provisioning of logistics support requirements. e.. Reliability-Centred Maintenance (RCM) RCM is a systematic approach to develop effective and cost efficient preventive maintenance programme. RCM should be an iterative procedure that is initiated in early system design phase and evolves as the system design develops. Important inputs are the functional analysis and the FMECA analysis.. 2.2. Industry asset and asset hierarchy naming conventions, standards or frameworks The literature review of existing industry frameworks was guided by the dominant industry by asset value as reported through Statistics South Africa. Statistics South Africa is a public sector organisation that produces numerous statistical products and reports on diverse socioeconomic matters. One of these reports is the industry annual financial statements which consolidates all industry financial results, with clearly recorded exclusions. These financial results include income, expenditure and the carrying value of different types of tangible or physical assets for the major industries of South Africa. Table 1: Physical asset carrying value per SA industry for 2014 financial year presents the total carrying value of physical assets in each industry at the close of the 2014 financial year and highlights South Africa’s top four industries that account for 80% of the summated industry asset base. The top four industries also provide direction for identifying the standardised asset frameworks for these industries.. 35 University of Johannesburg, 2017.

(37) Industry. Carrying value (R-million). %. Mining and quarrying. R 475 666. 22,4%. Electricity, gas and water supply. R 436 488. 20,5%. Transport, storage and communication. R 416 526. 19,6%. Manufacturing. R 409 376. 19,3%. Trade. R 158 700. 7,5%. Activities auxiliary to financial intermediation, real estate and other business services. R 124 539. Community, social and personal services. R 49 501. 2,3%. Construction. R 41 880. 2,0%. Forestry and fishing. R 13 638. 0,6%. Total. R 2 126 314. 5,9%. Table 1: Physical asset carrying value per SA industry for 2014 financial year (Source: Statistics South Africa, Statistical release P0021, 2014 Annual Financial Statements). Table 2: Physical asset carrying value per asset type for 2014 financial year, presents the carrying value by asset type for all industry. By observation; plant and machinery, buildings, motorised transport vehicles and equipment and civil construction works; are the dominant asset types and provides direction for identification of industry asset and asset hierarchy naming conventions, standards or frameworks. Industry. Carrying value (R-million). %. Plant, machinery and other office equipment. R 820 912. 38,6%. Capital work in progress. R 390 460. 18,4%. Non-residential buildings. R 235 927. 11,1%. 36 University of Johannesburg, 2017.

(38) Motor vehicles and other transport equipment. R 190 404. 9,0%. Other property, plant and equipment. R 165 840. 7,8%. Construction works, roads and parking areas. R 153 188. 7,2%. Network equipment. R 68 413. 3,2%. Land. R 62 707. 2,9%. Residential buildings. R 19 614. 0,9%. Computers and other IT equipment. R 17 082. 0,8%. Land improvements. R. 0,1%. Total. R 2 126 314. 1 767. Table 2: Physical asset carrying value per asset type for 2014 financial year (Source: Statistics South Africa, Statistical release P0021, 2014 Annual Financial Statements). The findings of the dominant industry and major asset types by asset carrying value, directed literature scan to identify industry standards that describe asset naming and hierarchy for the dominant industry and asset types. Table 3: Selected industry frameworks for research, presents the chosen industry asset naming and hierarchy conventions. Industry or asset type. Manufacturing industry Manufacturing industry. Organisation. Industry asset and asset hierarchy naming conventions, standard or framework. American National Enterprise-Control System Integration, Part Standards Institute 1: Models and Terminology (ANSI/ISA– 95.00.01–2000) American National Batch Control, Part 1: Models and Standards Institute Terminology (ANSI/ISA–88.01–1995). Norwegian Manufacturing Technology industry, and more Standards Institution specifically oil and gas International Electrical energy Standards generation, Organisation. 37 University of Johannesburg, 2017. Design Principles - Coding System (Z-DP002) Technical product documentation – Reference designation system – Part 10: Power plants (ISO/TS 16952-10.

(39) Industry or asset type transmission distribution. Organisation. Industry asset and asset hierarchy naming conventions, standard or framework. and. Association of Local Economic and public Government works infrastructure, Engineering New which typically is Zealand provided by the national or local government and includes roads, water and sanitation, electrical energy transmission and distribution, solid waste collection and treatment. Africa Association of Land improvements Quantity Surveyors and buildings South African Bureau Applicable to all asset of Standards types South African Bureau Applicable to all asset of Standards types. International Infrastructure Management Manual. Institute of Asset Applicable to all asset Management types (United Kingdom) International Applicable to all asset Accounting types Standards British Standards Applicable to all asset Institute types. Asset Management - Part 2: Guidelines for the application of PAS 55-1 (PAS 55-2: 2008). 38 University of Johannesburg, 2017. Asset. Elemental Cost Estimating & Analysis for Building Works 2016 Asset Management - Management systems - Requirements (SANS 55001: 2015) Asset Management - Management systems - Guidelines for application of ISO 55001 (SANS 55002:2015). IAS 16 - Property, Plant and Equipment. Industrial Systems, Installations and Equipment and Industrial Products Structuring Principles and Reference Designations (BS EN 81346-2:2009).

(40) Industry or asset type. Organisation. British Applicable to all asset Institute types. Industry asset and asset hierarchy naming conventions, standard or framework. Standards Physical Asset Management, Part 1: Specification for the optimised management of physical assets. Table 3: Selected industry frameworks for research The following sections describes each of the selected industry naming convention, standard or framework.. 2.2.1.Manufacturing industry framework for continuous, batch and discrete processes (American National Standard, 2000), Enterprise-Control System Integration, Part 1: Models and Terminology is an American National Standard (ANSI/ISA-95.00.01: 2000), whose objective is to provide standard terminology, concepts and models to enable the integration of manufacturing enterprise systems and manufacturing control systems. The goal is to have enterprise and control systems to inter-operate and integrate and thereby reduce the cost, risk and errors associated with integrating these systems. The need for this standard was motivated by amongst others, reduction in life-cycle engineering costs, optimisation of supply chains and reduction in the cost of automating manufacturing processes. The scope of the standard is broader than the scope of this research but it addresses one aspect namely the definition of the organisation of physical assets in a manufacturing enterprise. Prior to defining the equipment or physical asset hierarchy or model, the standard defines the functional hierarchy which relates to the levels of decision making in Figure 15: Functional hierarchy within a manufacturing organisationFigure 15: Functional hierarchy.. 39 University of Johannesburg, 2017.

(41) Figure 15: Functional hierarchy within a manufacturing organisation (Source: American National Standard, Enterprise-Control System Integration, Part 1: Models and Terminology (ANSI/ISA-95.00.01: 2000). The levels which relate and influence the physical asset hierarchy are levels defined by the type of production strategy namely batch, continuous or discrete manufacturing and referred to as functional levels 2, 1 and 0 respectively. The physical asset hierarchy, or as referred to the equipment hierarchy is typically organised in a manner illustrated in Figure 16: Equipment hierarchy in manufacturing industry.. Figure 16: Equipment hierarchy in manufacturing industry (Source: American National Standard, Enterprise-Control System Integration, Part 1: Models and Terminology (ANSI/ISA-95.00.01: 2000). 40 University of Johannesburg, 2017.

(42) The enterprise is the highest level of the model which can comprise a collection of one or more sites and the decision as to what product(s) is manufactured at each site is taken at enterprise level. A site is a physical, geographic and logical grouping determined by the enterprise, has a defined manufacturing capability and will contain one or more areas. An Area can be a physical, geographic or logical grouping comprising one or more cells, production units or production lines. An area is made up of elements that perform manufacturing functions and the types of elements correspond to the three manufacturing models of batch, continuous and discrete manufacturing. It is possible that an area can contain a combination of manufacturing models with their respective elements, i.e. cells, production units and production lines. An example referenced is a beverage manufacturer, it may have an area with continuous mixing in a production unit, which feeds a batch process cell for batch processing, feeding a bottling line for a discrete bottling process. Process cells and units are typically associated with batch manufacturing and is usually the lowest level of scheduling by level 3 and 4 functions where there is flexibility in routing differing products. A process cell or unit has a major processing capability for one product or family of products. Examples referenced have identifiers such as “Mixing Line #5,” “West Side Glue Line,” and “Detergent Line 13”. Production units are typically associated with continuous manufacturing models and has welldefined process capabilities and throughputs and generally associated with conversion of raw materials or feedstocks into intermediate products. Examples referenced have identifiers such as “Catalytic Cracker #1,” “Steam Cracker #59,” and “Alkylation Unit 2." Production lines and work cells typically relate to discrete manufacturing and cells have welldefined manufacturing capabilities and throughput capacities and comprise equipment modules, sensors, and actuators as examples. Examples referenced have identifiers such as “Bottling Line #1,” “Capping Line #15 and “Water Pump Assembly Line #4.” The asset modelling framework of Figure 16: Equipment hierarchy in manufacturing industry is further expanded by (American National Standard, 1995), ANSI/ISA-88.01-1995, The Batch Control: Part 1 Models and Terminology. Batch manufacturing processes lead to the production of finite quantities of material (batches) by subjecting quantities of input materials to a defined order of processing. The batch control standard expands from the production cell or unit described in the Enterprise-Control System integration standard. The asset model has seven levels of which the first three correspond to the asset modelling framework described earlier and is illustrated in Figure 17: Equipment hierarchy model for batch manufacturing.. 41 University of Johannesburg, 2017.

(43) Figure 17: Equipment hierarchy model for batch manufacturing (Source: American National Standard, Batch Control: Part 1 Models and Terminology (ANSI/ISA-88.01-1995). The first three levels are grouped by function or purpose whereas the last four levels are grouped by engineering activities. At the lowest level the equipment is physically connected or grouped together to form higher level of equipment which simplifies its operations, however the lower level equipment cannot be split without a re-engineering process. Levels 4 to 7 of the asset modelling framework are briefly explained: a.. A process cell A process cell contains all the units, equipment modules and control modules required to make one or more batches. A process cell can contain several sub-divisions known as trains which comprises all the units and equipment necessary to produce a batch, however it does not necessarily imply that all units or equipment will be used simultaneously. It is possible for more than one batch to use the train simultaneously. The order or sequence of equipment used in a train is a termed a path.. b.. Unit level A unit is made up of equipment and equipment control modules and performs all the necessary manufacturing activities as an independent equipment grouping. It is usually centred on a major piece of equipment. 42 University of Johannesburg, 2017.

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