Optimisation framework for asset maintenance investment Allen S.B. Tam Department of Mechanical Engineering, Monash University
[email protected] Allen Tam is a PhD candidate in the Department of Mechanical Engineering, Monash University. He received his BE in Aerospace Engineering with 1st class honours from the University of New South Wales. His PhD scholarship is funded by the Co-Operative Research Centre for Integrated Engineering Asset Management.
John W.H. Price Department of Mechanical Engineering, Monash University
[email protected] Professor John W.H. Price is in the Department of Mechanical Engineering, Monash University. Prior to joining the university, he was a senior engineer in the electricity industry.
Maintenance is often viewed as a necessary expenditure in the business world. It is however, a critical investment needed for physical assets. The return on maintenance investment is not easily quantifiable in monetary value, thus often not valued as a return on investment in the business context. However, if invested and performed properly, maintenance can effectively reduce operational risks and potential losses (costs) due to unwanted breakdown failures. It is a more significant function when operating complex assets such as aircrafts and chemical plants. This paper aims to provide a view of maintenance investment optimisation from the engineering asset management prospective and proposes a framework for optimising maintenance investment decisions aligning with enterprise business objectives. Keywords – Maintenance investment, maintenance optimisation framework, enterprise asset maintenance
Introduction An asset management framework would be expected to be a tool which permits decisions making and optimisation. A particular concern is the optimisation of maintenance. It is the assumption of this research that the objective of an enterprise is to generate as much return on investment as possible. Return is a value delivered over a period of time. Thus two things have to be considered, the meaning of value and the meaning of period of time. Value can be in either monetary return or some other less easily measurable benefit in industries such as defence and health care. Serious asset managers are presumably interested in physical assets with a long life-cycle and realise that spending in one year may produce benefits in another year. It is needed to seek a meaningful way for optimising maintenance aligning with the enterprise business objectives. This research proposes a framework for optimising asset maintenance for capital intensive commercial enterprises where value is measured in monetary value over a long life-cycle.
© Monash Business Review Volume 2 Issue 3 – November 2006
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Literature review Jardine and Tsang (2006) proposed that for asset managers who wish to optimise life-cycle value of the organisation’s assets, four decision areas must be considered: component replacement, inspection procedures, capital equipment replacement and resource requirement. The framework presented outlines the key issues of maintenance and specific models are proposed for tackling particular problems. However, the research is focused on the maintenance function itself instead of how the maintenance function should be used to support the asset operation and the whole business. Nevertheless, the work by Jardine and Tsang (2006) is one of the seminal works in the area. Tsang et al. (2000) categorised businesses into three operational scenarios which impact on the strategies of maintenance, namely, cost-constrained, capacity constrained and compliance constrained. • • •
The cost constrained businesses are those where more products or services could be sold if prices are lowered. The focus in these businesses should be based on controlling cost, i.e. labour, materials, contractor costs and overheads. The business where everything it produces can be sold is said to be a capacity-constrained business. To achieve maximum profits, the focus of such businesses should be on maximising output through reliability, availability and maintainability of the asset. Compliance constrained businesses are dependent heavily on the compliance with regulations enforced by the government authorities or the customers.
Recently the research focus has shifted to asset management where maintenance is viewed as a critical function of physical asset management. The term “asset management” has been introduced in various research, including Platfoot (2001), Amadi-Echendu (2004), Schuman and Brent (2005) and Malano et al. (2005). One of the more significant initiatives in recent times is the Co-operative Research Centre for Integrated Engineering Asset Management (CIEAM). CIEAM (2006) defined Integrated asset management as “a continuous process covering the whole-of-life cycle of an asset from conceptual design through to construction/manufacture, operational use, maintenance, rehabilitation and/or disposal.” The asset management framework proposed by CIEAM (2006) is an asset management system that includes the following elements: risk management, budgeting and costing, data management, condition monitoring, tactical planning, human resources, asset usage life cycle, performance measures, information systems, financial management, strategic planning, asset ownership. This is however cannot be treated as a framework as there is no clear approach that indicates how the different elements should be integrated and used to solve the maintenance problem. Second, the concern of such a “framework” is its practically, as the framework covers too board an area. Integrated optimisation can only occur when all these elements are expressed in the same units. To implement such a framework the first question that arises is the needed to quantify all these elements.
Complexity of maintenance and the need for a framework Capital intensive complex assets often consist of various systems and components manufactured by different Original Equipments Manufacturers (OEMs) and vendors. Since it is undesirable to stop operation for maintenance, asset operators plan maintenance to minimise disruption to normal operation and at the same time seek not to jeopardise the overall asset’s safety, reliability and operational risk. How often maintenance is carried out are normally governed by the maintenance intervals which are normally determined by the OEMs during the design stage. Since the physical operating conditions and design specifications of each piece of equipments are diverse, deterioration due to usage and operating incidents will be different. As a result the maintenance specification as well as maintenance intervals of each system may not be the same. On top of that, for industries that have strict regulations on safety, the respective maintenance requirements are even tighter. Adding on to the complexity, the maintenance function requires support of other resources such as facilities, equipment, human resources and spare parts. The management of physical assets maintenance is a complex problem and a meaningful approach to optimise maintenance decisions aligning with enterprise objectives is needed. This research proposes an integrated and generalised framework which aims to optimise the
maintenance program at any period of time within the asset’s life-cycle. The proposed framework can assists in formulating optimisation models for optimising the operation and maintenance. In search for an asset maintenance optimisation framework, CIEAM (2006) and Tsang et al. (2000) work has been useful. As discussed in the literature review, Tsang et al. (2000) research proposed that businesses are categorised into three main business operational scenarios which affect the focus and strategies used in maintenance. This research however, disagrees with the above suggestion regarding dividing businesses into categories of cost, capacity and compliance constrained. All businesses engaged in physical asset management must consider all of the above constraints. The importance of these constraints varies from system to system. The following examples categorises businesses into different business operational scenarios according to Tsang et al. (2000)’s definition. However, it is shown that each of these businesses also ‘constrained’ by the other two dimensions. a) Compliance constrained: •
Based on Tsang et al. (2000)’s definition of a compliance constrained business operational scenario, the aircraft industry will be categorised as compliance constrained business. However, operational aspects and economics aspects are also definitely part of the decision framework. The operational aspects in the aircraft industry are concerned with meeting flight schedules and the economic aspects are to lower support costs (such as maintenance and inventories cost) to make the price of tickets competitive. Safety (the compliance dimension) is definitely of prime concern, but one can assume that the airlines will not want to run over budget or be delayed because they have been over-maintaining the aircraft.
b) Cost constrained: •
Manufacturing industry will be categorised as the cost constrained business scenario according to Tsang et al. (2000)’s definition, where more products could be sold if prices are lowered. However, the safety of the product and the plant as well as meeting operational targets can also be significant decision dimensions. Some production plant must meet seasonal demands where a target production rate has to be met. The quality of the product as well as the safety of the plant is also critical where failures can result in serious financial and legal liability.
c) Capacity constrained: •
The utility industry will be categorised as the capacity constrained business scenario according to Tsang et al. (2000)’s definition. Industries such as power supply, water supply and gas supply are some examples. Tsang et al. (2000) suggests that to increase profit, this type of business should focus on maximising output through reliability, availability and maintainability of the asset. Improving reliability through maintenance and capital investment costs money. Since there is always a budgetary constraint, the question is how to utilise this money wisely to obtain the best performance. These industries are also required to fulfil many regulatory and safety requirements.
In general, all businesses engaging in asset management are constrained by cost, capacity and compliance. By separating businesses into categories in decision making will shift the focus of analysis to a particular aspect. It is therefore important in the decision making processes that consider all three dimensions simultaneously. The overall framework proposed in this work is given in Figure 1.
Figure 1: Optimisation Framework
Asset Database The availability of useful data is the paramount to making the best decision in asset management. Modelling and decision making processes commence with investigating the availability of data and its quality. The asset database provides the basic reference of the physical properties and functions of the assets for strategic decisions at senior managerial level. The strategic directions provide a common goal that all the decision dimensions will need to fulfil. In Table 1, a summary of data which is required for asset management optimisation are provided. The data is categorised into decision dimension relevance in this research. The uses and importance of each data source is discussed. In the final column, a priority ranking is given. This ranking (A to C) categorises the level of significance of that particular data to this optimisation framework. A data source has a ranking C only means the level of significance for enterprise optimisation in this proposed decision making framework is of lesser significance than those having the ranking A. Detailed planning of outages is not regarded as an enterprise significant factor, but total cost of an outage is enterprise significant. The table outlines data which are needed in this framework and where is normally known to operators. To-date, the authors have not come across precedent format of this table in the literature. The table was developed with experiences of the authors as well as interviews, workshops and internal reports from the following companies representing a wide range of industries including defence, aviation, power, manufacturing and research centre. Please refer the Appendix.
Table 1: Asset Database *The IT systems have been given generic names: MRP Materials and resource planning, CMMS Computerised Maintenance Management System. (See descriptions in text)
Decision Dimension Relevance Operation
Data
Importance
Priority for optimisation at an enterprise level A
Where it is captured?
A
Management strategy Control room in hardcopy, CMMS
Production/operation Rate
Calculate deterioration rate
To determine when a component is due for maintenance
Required availability
Calculate permitted shutdown time Condition monitoring
To determine if there is enough time for maintenance To monitor systems condition to determine maintenance intervals
Profit per unit time or per unit production
Calculate outages cost
Parameter to quantify breakdowns
Maintenance manual
Specify maintenance tasks and schedule
Detailed day to day operation
B
OEM suggested maintenance intervals Human resource (Skills level and training)
Schedule maintenance and shutdown Need to ensure personnel are qualified and trained for a given job Evaluate asset performance and the asset response to maintenance program
The basic guide to formulate maintenance program Maintenance work quality assurance
A C
Human Resource system
To check maintenance program effectiveness, Evaluation of costs and life expectancy
B
CMMS / hardcopy
Human resource costs
Budgeting
To quantify maintenance cost
A
Spare part unit cost
Budgeting
To quantify maintenance cost
Management accounting system
Spare part holding cost
Budgeting
To quantify parts holding cost
Spare part lead time Turn around time (for repairable)
Determine stock level and ordering point Determine stock level and maintenance schedule
To justify amount of parts in stock (mostly for non-repairable) To justify amount of parts in stock (for repairable)
Equipment and facility availability
Planning of heavy maintenance
To determine the outage schedule
B
MRP system
Operational Data (vibration, pressure, temperature, noise)
Assurance
Uses
Historical asset data
Maintenance costing
Economic
© Monash Business Review Volume 2 Issue 3 – November 2006
B (used for detailed maintenance planning) A
MRP system* Individual system
Management strategy OEM and operators manuals (soft or hard copies) MMS or hardcopy
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Maintenance manuals Maintenance is intended to prevent failures from occurring and in the case where failure occurs, to correct the respective failure. Much equipment is provided with OEM manual which specifies maintenance tasks and schedules. The basis of these instructions is often obscure. The OEM may perform Failure Mode, Effect and Critically Analysis (FMECA) before the system enters service, and tasks are designed based on that analysis. The in-house manual (as opposed to the OEM manuals), contains tasks that are specified by the operators. This document normally consists of OEM data but may include experiences accumulated by the operators during operation of the systems. Tasks described in maintenance manuals can be designed based on three basic maintenance policies: preventive maintenance, breakdown maintenance and condition based maintenance. a) Preventive maintenance is carried out to prevent failures from occurring. This type of policy includes scheduled replacement, repair, services (like cleaning and lubrication) and inspections. b) Breakdown maintenance responds to machine breakdown, some literature calls it corrective maintenance. Breakdown maintenance policy replaces failed components and to restore the system back to operation. c) Condition based maintenance is normally trigged by inspection or monitoring. It is a type of preventive maintenance. The component or system is monitored or inspected at regular intervals to check its condition to survive the operation until the next maintenance intervals. If the condition of the component or system is below standard, maintenance action is carried out. Maintenance manuals contain information about the actions to be carried out to the system. Information such as the intervals, time needed, spare parts, facilities and equipment, personnel skill levels, the procedures and the setting up of the job as well as appropriate references should be described fully in the maintenance manual.
IT system required Most IT systems have a role in asset management but normally not well integrated for this purpose. This section discusses some examples key IT systems. The key systems are: •
• •
Material Requirements Planning (MRP) System -
The Material Requirements Planning (MRP) is an IT systems developed to management the flow of materiel within an enterprise. The system is to ensure materials flow smoothly within the enterprise, to reduce level of inventory and to assist in planning schedules and purchases.
-
Recently, the Strategic Enterprise Management (SEM) and Enterprise Resource Management Systems (ERPS) are developed.
Management Accounting System - Management accounting is accounting software that handles payments and accounts in an enterprise. Computerised Maintenance Management System (CMMS) -
The computerised maintenance management system (CMMS) is a database that captures information of an enterprise’s maintenance. The intention of CMMS is to increase maintenance professionals’ effectiveness and help management in making decision. Different packages offer wide ranges of features and most of them include the followings:
•
Issuing work orders
Keeping track of preventive maintenance
Controlling maintenance inventories (spare parts and consumables)
Recording equipment data, historical machine data, machine specifications, warranty
Human Resource System -
•
The Human Resource system is to keep record of all personnel’s skills levels, salary, rosters and backgrounds. In-date training and other specific technical skills, individual training levels may not be stored.
Management Strategy -
Normally board level decisions, often only issued on paper format
Asset database – conclusion Previous research pointed out the importance of comprehensive data collection (Sherwin (2004)). To store and analyse data, IT systems are needed. However, as shown in Table 1, there is little integration in IT systems which resulted in multiple IT systems within an enterprise doing jobs without communicating to each other. With limited integration, vast amount of data is stored and sometimes duplicated with little being used fully. It was found often that several sources are needed to compile a file for a particular exercise as inconsistency in various systems needed to be resolved before useful analysis can begin [Sherwin (2005)]. As pointed out by Al-Najjar (1996) and Al-Najjar and Alsyouf (2000) a common database and integrated IT system is needed for managing maintenance of physical asset. For the development of this integrated system, this research argues that one need to first identify what is needed to be collected for optimisation and this is the purpose of Table 1. The development of the proposed framework identifies the processes and tools needed for optimisation and also points out what data it will be necessary to capture. The success of asset management requires a properly developed and integrated enterprise wide IT system, which will enhance the speed and accuracy of asset maintenance optimisation but such do not yet exist. The research on data collection and database and IT system development are beyond the scope of this research. This research recognises their importance in the support of the optimisation framework and believes that this work will help in the development of an integrated enterprise wide IT system.
Strategic decisions Strategic decisions are board level decisions concerning the future use or assets and expenditure on assets. The strategic decisions can be long or short term and are made at senior managerial level, normally involving the Chief Executive Officer and a team of directors. In making strategic decisions, input from enterprise’s visions, and demand from customers as well as study of enterprise historical performance are required. The resulting enterprise strategy formulates the different requirements and objectives which are to be met by implementing the strategies.
Decision dimensions Any enterprise that engages in physical asset management needs to consider various critical decision dimensions as shown in Figure 1. In this research the following division are used: operation, assurance and economic. These are based in part on Tsang et al. (2000), but they
have also been independently developed after wide consultation. The three decision dimensions for asset management considers in this work are defined as follows: 1. Operation dimension is achieving organisation output objectives for the asset. 2. Assurance dimension is achieving compliance with regulations, safety and company quality requirements. 3. Economic dimension is achieving most return without exceeding enterprise monetary allocation. Since the decision dimensions have direct or indirect relationships to one another, the tradeoffs between these dimensions are to be analysed such that the enterprise return on investment is optimised. This research takes the view that each decision dimensions consists of a family of parameters which can eventually be quantified to monetary values. Through quantifying these decision dimensions, models can be formulated and optimisation can be carried out simultaneously using a consistent unit which we term as the “value per unit time” approach.
Operation dimension The operation dimension is concerned with the operation of the assets to create the outputs required. In the maintenance management context, the operation dimension contributes several variables and constraints to the maintenance problem including the required output rate, the ageing rate of the equipment the ageing rate and the time available for maintenance. Required output rate The operation schedule is determined by company business department which become a target to be met by asset managers in achieving zero unplanned breakdowns. This is only achievable by evaluating the tradeoffs between operation targets and resources available to support the restoration of asset through asset maintenance. Ageing rate The frequency of operation, the operating condition and environment as well as how it is being operated are factors that affect the deterioration rate of the assets. Deterioration reduces the reliability of the system and increases the operational risk, which both will result in undesirable penalty cost. As failures due to deterioration can be restored and some can be prevented by maintenance, the deterioration rate will be a paramount parameter in determining the optimal planned maintenance schedule.
Assurance dimension The assurance dimension is concerned achieving compliance with regulations, safety and company quality requirement. Operation deteriorates the assets, and this deterioration reduces the reliability and increases the risk of failure. The consequences associated with some failures can be catastrophic and the respective penalty cost is in magnitude of millions dollars and some damages are not measurable. This is especially true for industries like civil aviation and nuclear power station, where failures during operation can be deadly. There are strict regulations requiring asset owners to have detailed procedures in maintenance and risk analysis to ensure the operation of these assets is adequately safe. Reliability and risk are the two measures of the assurance dimension. Reliability R (t ) is defined as the probability that a unit is working properly for its designed functions and specification at a given time, t. Unreliability F (t ) is therefore the probability that the unit is unable to function at a given time. The associated cost is the expected breakdown failure cost (EBDC). Risk relates to potential hazardous events caused by failures. For example: explosions in chemical plant, airplane crashes and railways accidents. The consequences of these incidents
are normally catastrophic which includes lost of human life, severe damage to the environment and serious financial penalty in the magnitude of millions of dollars. In this research, quantifiable risk is used, which is defined as the product of consequence and frequency. Consequence is quantified to a monetary value and frequency is the conditional probability that has a unit of failure per unit time. The increase of probability of failures and the risk can be controlled and reduced through proper maintenance.
Economic dimension The economic dimension is concerned with achieving most return without exceeding enterprise monetary budget allocation. The maintenance function required supporting resources such as maintenance personnel, facilities, spare parts and equipments. All these resources incur a cost and the economic dimension is concerned with whether the allocated budget is able to support these resources and to ensure that they are fully optimised for the best outcome. The budget allocated for asset maintenance is prepared annually, normally determined by the financial department without much consideration to the engineering aspect. This amount of money will be the maximum allowable actual spending the enterprise can afford in maintenance. Maintenance action needs maintenance personnel, facilities, equipments and spare parts. The costs associated with these resources are grouped into two parts, the maintenance cost and the resources cost. Maintenance costs are the direct expenses related to a maintenance action, which is the manhour, spare parts, rent of facility and special equipment (some enterprises own these recourses). Resources costs are the cost of indirect expenses such as holding cost of spares (which can be very significant for some repairable), running cost of facilities, training of personnel. The budget, if spent wisely and properly, can directly reduce the potential losses (due to failure) that are being categorised in this research as the assurance dimension. The challenge is to: 1. Determine what is the budget required for optimal performance. 2. Determine how to spend a given budget to achieve the best outcome. The above are two approaches where the first is when the objective is to determine the budget and the second is when a budget is already prepared. In most cases, the second approach will be more popular and realistic as the budget is normally predetermined by financial department. The main question is then to seek for an optimal between all these costs as shown in Figure 2. Optimise
Total Expected Cost
Cost ($/time)
Maintenance + Spares holding cost Planned downtime cost Expected unplanned downtime cost Risk cost
Actual Spending ($/time) Figure 2: Optimisation
Conclusion This paper presents a framework for optimising maintenance investment decisions aligning with enterprise business objectives. This framework provides a better understanding of the inherent relationship between maintenance costs and the returns on investment. The framework will be applied in future research for developing optimisation models for solving various asset management problems.
Appendix List of companies and organisations where the authors have gained access to information in the format of internal reports, workshops as well as interviews when developing Table 1. Defence Science and Technology Organisation (DSTO) Defence Materiel Organisation (DMO) Royal Australian Navy Hong Kong Aircraft Engineering Company Loy Yang Power An Australia based Manufacturing Company in heating and ventilations
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