Equipment Replacement Decisions Research Articles

Using Analytical Hierarchy Process for Medical Equipment Maintenance and Replacement Decision in Malaysia Private Hospitals

Medical equipment is any instrument, appliance, software, or material intended by the manufacturer to be used, alone or in combination for medical purpose. Further, medical equipment maintenance and replacement is a challenging procedure, especially for high-end equipment. This topic has been a long debate as the equipment will increase undesirable failure; where an early maintenance or replacement will result in high investment costs and premature disposal and late maintenance or replacement will lead to malfunctions that can pose serious risks to patient safety and healthcare operations. However, hospitals are unable to decide whether to maintain or replace the equipment especially when the equipment reaches its life and has a high repair cost. A few studies were conducted on the same research topic, but most of the findings emphasized maintenance and replacement methods rather than the criteria contributing to the decision. The criteria to maintain or replace medical equipment play an essential role to ensure the equipment is operating cost-effectively. Hence, the objective of this research is to apply Analytical Hierarchy Process (AHP) approach to prioritize important criteria that influence decision-makers to decide whether to maintain or replace medical equipment. Some of potential criteria for the decision-making include maintenance cost, support, and regulatory compliance. This research utilized a mixed method approach to gather preliminary data from medical equipment experts and surveys to prioritize the criteria. Findings suggested a list of criteria that are influenced in decision-making to maintain or replace the medical equipment. The prioritization of the criteria reveals the downtime, life cycle cost and beyond economical repair (BER) to be the most important criteria.

Markov-chain-driven optimization of inspection-based maintenance, Part I: Models and methods

System reliability and life-cycle cost are significantly influenced by preventive maintenance (PM). PM is commonly classified into two categories: time-based maintenance (TBM) and condition-based maintenance. Another PM approach referred to as inspection-based maintenance (IBM) incorporates condition-based activities within periodic inspections and tests. Determining the optimal maintenance rate for both TBM and IBM plans, and scheduling them effectively, poses a crucial challenge. Traditional methods assess equipment importance and condition within a reliability-centered maintenance framework. Mathematical models are commonly employed to devise robust plans, but accurate modeling based on practical conditions of the system equipment remains a key challenge. This two-part paper series introduces a novel model based on the Markov chain theory, that establishes the relationship between equipment status transition rates and the risk indices. The proposed model can be used alone or in combination with risk analysis outcomes to overcome data and budget limitations. To demonstrate its efficacy, the proposed method is implemented to optimize the PM rate of high-voltage equipment within a real-world sub-transmission substation, where the results can aid in cost estimation, equipment replacement decisions, and inventory management.

Analyzing the Impact of Depreciation-Estimating Methods on State Transportation Agencies’ Equipment Replacement Decisions Using Dynamic Programming

Replacing equipment at the most economical time not only helps to save state transportation agencies (STAs) costs for operating the fleet but also keeps the fleet’s level of service at an optimal level. Prior research studies focused on developing alternative economic-oriented equipment replacement models rather than the equivalent annual cost (EAC) model to achieve better economic decisions. In addition, various optimization techniques were applied to equipment replacement problems with different objectives, constraints, and contexts. However, few studies examined the impact of depreciation estimation on the equipment replacement decision within STAs by minimizing total equipment cost over a finite study period using the dynamic programming optimization method. This study performed a case study of two class codes of equipment [1.5 m3 (2-yd) diesel engine front-end loaders and 0.453 t (half-ton) fleetside pickup trucks] to analyze the impact of different depreciation calculations on equipment replacement decisions. Using real-world data provided by the Oklahoma Department of Transportation, the study showed that the double-declining balance depreciation method substantially reduces the number of pieces of equipment recommended for replacement compared with the result of the straight-line depreciation method. This study contributes to the understanding of the impact of depreciation methods on equipment replacement decisions as well as the importance of properly estimating equipment depreciation to minimize the equipment costs over a designated study period among STA communities. The demonstrated manual calculation procedures of dynamic programming for cost optimization potentially may facilitate STA’s adoption of dynamic programming for equipment economic decisions.

Revenue‐Sharing Contracts Under Quality Uncertainty in Remanufacturing

Business users (suppliers) of information technology (IT) equipment often contract with third party remanufacturers (3PRs) for the end‐of‐use disposition of their used electronic equipment. Our goal in this research is to develop a model to analyze contracts consisting of an upfront payment ( t) and a revenue share ( r) between a supplier and 3PR, study their impact on disposition decisions, and explore the impact of 3PR risk‐aversion and quality uncertainty on optimum contracts. We model a Stackelberg game where the supplier offers risk protection from quality uncertainty to a 3PR. The 3PR faces a convex increasing remanufacturing cost and a sales revenue that is increasing and concave in the volume resold. We show general conditions under which a unique contract exists and can be expressed in closed form. We calibrate our model using a dataset on supplier contracts and equipment conditions provided by a 3PR. Our managerial insights are derived from the data and summarized as follows. First, there is a benefit to suppliers from characterizing the quality of used assets. Therefore, they should consider adjusting contracts to reflect asset quality. Second, suppliers can increase profit by contracting based on quality grade and using a portfolio of contracts. Third, we show that the asset age impacts contract terms, with suppliers with assets at a lower age keeping a higher revenue share and observing a greater fraction of equipment remanufactured. This suggests that suppliers should consider the benefits of disposition contracts when making equipment replacement decisions.

Markov chain optimization of repair and replacement decisions of medical equipment

• The study determines the optimal repair or replacement decision based on the product lifecycle data. • A Markov Decision Process (MDP) framework is proposed for optimizing repair decisions. • A dataset of medical device repair and maintenance is used to show the application of the model. • Product lifecycle data is essential for lowering repair and replacement costs. The cost of repair and maintenance of medical devices can be fairly burdensome to the healthcare industry. Healthcare providers often consider plans such as increasing in-house repairs, using multiple repair service providers, or timely replacement of devices to overcome this issue. This study aims to develop a data-driven Markov Decision Process (MDP) framework based on Discrete-Time Markov Chain (DTMC) model to optimize medical equipment repair and replacement decisions. Effective decision-making on whether to repair or replace is crucial to managing the product lifecycle cost and the costs to healthcare facilities. The study determines the optimal repair or replacement decision based on the product lifecycle data and current product failure status. It utilizes a net present value model to maximize expected value over an infinite time horizon. The study uses a dataset of 24,516 repair and maintenance records of 5,171 individual medical devices of a particular type to extract parameters needed for the optimization model. The dataset provides a rich baseline for analyzing different failures and event modes during product lifespan such as battery-related issues, random failure, preventive maintenance, and physical damage. It further quantifies the chance of moving from one product status to another. The model outcomes are discussed for a particular case. The findings reveal the most frequent reasons for failures and the most economically viable repair and replacement decision for each end-of-use device based on their current condition. Several sensitivity analyses are conducted to clarify the impact of operating revenue and warranty time on the repair or replacement.

Ensuring consistency between strategic plans and equipment replacement decisions

ABSTRACT Strategic business plans are based upon assumptions of future wood costs. Future wood costs are affected by future equipment cost inputs and technological change. Equipment replacement decisions react to future cost inputs and technological change, taking advantage of opportunities to reduce future wood costs. By explicitly defining future equipment cost inputs and technological change, and their effect on equipment replacement decisions, future wood costs used in strategic planning have a rational and consistent basis. To ensure consistency between strategic plans and equipment replacement decisions we developed a forward reaching dynamic programming algorithm that recognizes the special structure of the equipment replacement problem. The output is an equipment replacement schedule that can be used to project equipment investment needs, operational costs, and tree harvesting costs. The results show that when we compare the proposed algorithm with the classic infinite series machine replacement approach under constant costs, both of them presented an optimal life with 52 months and a discounted cost of 5.691 USD million. Considering a 1200 month planning horizon under technological changes, the economic life policy proved optimum with 80 months of use for the first machine, while the next generations varied between 69 and 71 months of use with a discounted cost value of 4.617 USD million. Under the estimates of future costs and technological improvements, production costs decline and production rises. The algorithm is intuitive, flexible, easy to calculate, and yields the exact solution. No restrictions are needed on cost inputs over time, technologies, or discount rates.

Equipment replacement policy for forest machines in Brazil

ABSTRACT In this work, we present a model to assist in the equipment replacement decision process in the context of the Brazilian forestry sector using detailed equipment maintenance schedules. The approaches are based on the economic life (EL) method for three scenarios. In the first scenario, we considered buying a new machine during the first period. For the second scenario, we considered keeping the current machine for 24 months, and then buying a new one while for the third scenario, we considered selling the current machine, leasing a new one for 24 months and then buying a new machine. Our example draws from five tree harvesting machines that cut, delimb, and process the tree stem into logs in forest plantations of a large integrated Brazilian forestry company. The algorithms were coded in Visual Basic for Applications (VBA). The results show the new machine discounted cost (CNM) and rebuild machine discounted cost (CRM) scenarios presented higher values than the leased machine discounted cost (CRE) scenario, and those solutions should be rejected. The economic life policy proved optimum with 52 months for a new machine, while the best decision to make is sell the current machine, lease a new one for 24 months and then buy a new one. The solution and the algorithms used are described.

OPTIMIZATION MODEL FOR VEHICLE ROUTING AND EQUIPMENT REPLACEMENT IN FARM MACHINERY

ABSTRACT: An equipment replacement decision takes into account economic engineering models based on discounted cash flow (DCF) such as the Annual Equivalent Cost (AEC). Despite a large number of researches on industrial assets replacement, there is a lack of studies applied to farm goods. This study aimed at assessing an alternative model for economic decision analysis on farm machinery replacement, with no restrictions on the number of replacements and assessed goods during a defined timeline. The results of the hybrid model based on the combination of the vehicle routing problem and the equipment replacement problem (RVPSE) applied to three different farm tractors showed the model reliability, providing a wider range of decisions for management support.

Integrating mining loading and hauling equipment selection and replacement decisions using stochastic linear programming

Equipment selection is a key strategic decision in the design of a material handling system, because an improper one will lead to operational problems and unnecessary investment costs. It involves determining the number and combination of loaders and trucks which will move the material, fulfilling a specified production schedule. Previous works have addressed this problem with deterministic approaches, without considering the inter-dependent availability of trucks and loaders. In order to fill this gap, we developed a stochastic model that combines the selection and equipment replacement problems, subject to a stochastic production rate constraint. This is a new idea that will help decision-makers to decide faster and more reliable. The proposed model optimises the fleet by minimising the total life cycle costs. To solve it, we used a linearisation approach that reduces the computational effort. We tested our approach with a benchmark model, using a mining case study. Results indicate that the solutions ensure with a high probability a determined production target, producing good robust solutions compared to the deterministic counterpart.

Equipment Replacement Decision Making:Opportunities and Challenges

The primary function of equipment managers is to replace the right equipment at the right time and at the lowest overall cost. In this paper, the opportunities and challenges associated with equipment replacement optimization (ERO) are discussed in detail. First, a comprehensive review of the state-of-the art and state-of-the practice literature for the ERO problem is conducted. Second, a dynamic programming (DP) based optimization solution methodology is presented to solve the ERO problem. The Bellman’s formulation for the ERO deterministic (DDP) and stochastic dynamic programming (SDP) problems are discussed in detail. Finally, comprehensive ERO numerical results and implications are given.

Optimal maintenance and replacement decisions under technological change with consideration of spare parts inventories

Classical spare parts inventory models assume that the same vintage of technology will be utilized throughout the planning horizon. However, asset replacement often occurs in the form of a new technology that renders existing spare parts inventories obsolete. This paper aims to study the impact of spare parts inventory on equipment maintenance and replacement decisions under technological change via a Markov decision process formulation. The replacement decision is complex in that one must decide with which technology available on the market to replace the current asset. Under technological change, it is shown that the do nothing and repair options have significantly more value as they allow waiting the appearance of even better technologies in the future.

Data mining and activity based costing for equipment replacement decisions Part 1 – establishing the information infrastructure

This paper discusses the first steps in a process which combines historical production and cost data with long term mine schedules to develop an equipment replacement analysis tool. The focus is on the enabling technology of data mining, used to define key physical and economic parameters for the proposed model, as well as model the relationships between key variables. This research considers two types of physical parameters for equipment replacement analysis purposes: operational and maintenance parameters. Activity based costing, an evolutionary extension of the two stage procedure in modern cost accounting, is applied in this research to determine full operating and maintenance costs by machine. This technique is mainly used to trace costs such as general expenses (e.g. overhead costs) from activities to products based on a representative cost driver.

Right-Sizing and Replacing the Right Equipment at the Right Time

Right-Sizing and Replacing the Right Equipment at the Right Time

Green renewal: incorporating environmental factors in equipment replacement decisions under technological change

Equipment replacement is a fact of life in every industrial setting, and this paper seeks to answer the question: How can firms and policy makers effectively balance environmental and economic concerns with respect to replacement decisions? A replacement model which includes both economic and environmental factors is presented. One must decide whether to keep the existing technology, upgrade to a newer technology which produces a smaller environmental burden, or wait for an even newer, cleaner technology which may be introduced soon. More than 25 000 test problems are solved, examining different objectives and covering a wide range of applications. Including environmental costs does not lead to a consistent increase in the adoption of cleaner technologies; however, including incentives to adopt newer technologies does. When one accounts for the environmental impact of producing new equipment and disposing of old equipment, earlier adoption of new technologies actually increases the total environmental burden in some cases.

Equipment Replacement at Departments of Transportation: Prioritization Measures, Software Tools, and Supplementary Data

In the United States, individual state departments of transportation (DOTs) maintain large fleets of equipment. An important and difficult challenge of managing such a large amount of equipment is deciding when and what equipment to replace. For example, the Oregon Department of Transportation (ODOT) Fleet Services manages a fleet that in 2008 included approximately 5,000 pieces of active equipment worth $340 to $390 million. Every two years, a fixed budget is available to replace a certain amount of this equipment. ODOT follows a decentralized approach for managing equipment in its fleet by assigning equipment to maintenance crews throughout the state. This project had three main objectives related to improving DOT equipment replacement decisions and processes. The first was to evaluate various measures (computed from available equipment data) that could be used to establish equipment replacement priorities. The second was to modernize and update a software tool used to compute equipment replacement priority rankings and to help manage the equipment replacement process. The third was to design an electronic system that could be used by equipment crews throughout the state to input qualitative equipment data based on actual use of specific equipment. Using simulation, a designed experiment was conducted, and results indicated that replacing the oldest equipment first minimized total costs over time. Additionally, a software tool was developed for ODOT Fleet Services to facilitate equipment replacement decisions, and an electronic process was designed to collect maintenance crew equipment knowledge for future integration into the equipment replacement model.

Data fusion/data mining-based architecture for condition-based maintenance

Condition-based maintenance (CBM) is a maintenance philosophy wherein equipment repair or replacement decisions are based on the current and projected future health of the equipment. The constituents and sub-processes within CBM include sensors and signal processing techniques that provide the mechanism for condition monitoring, and decision support models. Since past research has been dominated by condition monitoring techniques for specific applications, the maintenance community lacks a generic CBM architecture that would be relevant across different domains. This paper attempts to fulfil that need by proposing a combined data fusion/data mining-based architecture for CBM. Data fusion, which is extensively used in defence applications, is an automated process of combining information from several sources in order to make decisions regarding the state of an object. Data mining seeks unknown patterns and relationships in large data sets; the methodology is used to support data fusion and model generation at several levels. In the architecture, methods from both these domains analyse CBM data to determine the overall condition or health of a machine. This information is then used by a predictive maintenance model to determine the best course of action for maintaining critical equipment.

A Medical Equipment Replacement Score System

In Brief What is the best way to prioritize the replacement of equipment in a health facility or system? Most experienced biomedical engineers have qualitative mental process, which addresses most of the pressing medical technology replacement needs and ensures future system development. Of course they also have learned to accept that user "pet" projects, the most recent failure events that users are aware of, and politics will drive many equipment replacement decisions. A rule base, which can quantify the critical factors that medical technology managers, users, and healthcare executive feel are necessary to make decisions, could be developed. A Medical Equipment Replacement Score system can be designed and easily programmed to access information from most computerized maintenance management systems and produce real-time equipment replacement prioritization results. What is the best way to prioritize the replacement of equipment in a health facility or system? Most experienced biomedical engineers have qualitative mental process, which addresses most of the pressing medical technology replacement needs and ensures future system development. Of course they also have learned to accept that user "pet" projects, the most recent failure events that users are aware of, and politics will drive many equipment replacement decisions. A rule base, which can quantify the critical factors that medical technology managers, users, and healthcare executive feel are necessary to make decisions, could be developed. A Medical Equipment Replacement Score system can be designed and easily programmed to access information from most computerized maintenance management systems and produce real-time equipment replacement prioritization results.

A complex method of equipment replacement planning. An advanced plan for the replacement of medical equipment.

This complex method of equipment replacement planning is a methodology; it is a means to an end, a process that focuses on equipment most in need of replacement, rather than the end itself. It uses data available from the maintenance management database, and attempts to quantify those subjective items important [figure: see text] in making equipment replacement decisions. Like the simple method of the last issue, it is a starting point--albeit an advanced starting point--which the user can modify to fit their particular organization, but the complex method leaves room for expansion. It is based on sound logic, documented facts, and is fully defensible during the decision-making process and will serve your organization well as provide a structure for your equipment replacement planning decisions.

Equipment replacement decisions and lean manufacturing

Traditional manufacturing systems are built on the principle of economies of scale. Here, the large fixed costs of production are depreciation-intensive because of huge capital investments made in high-volume operations. These fixed costs are spread over large production batch sizes in an effort to minimize the total unit costs of owning and operating the manufacturing system. As an alternative to “batch-and-queue,” high-volume, and inflexible operations, the principles of the Toyota Production System (TPS) and lean manufacturing have been widely adopted in recent years in the US [1–4]. In this paper, we illustrate an equipment replacement decision problem within the context of lean manufacturing implementation. In particular, we demonstrate how the value stream mapping (VSM) suite of tools can be used to map the current state of a production line and design a desired future state. Further, we provide a roadmap for how VSM can provide necessary information for analysis of equipment replacement decision problems encountered in lean manufacturing implementation.

Equipment Replacement in Agriculture: The Case of a 110‐HP Tractor with an Overhauling Option

Smith, and subsequently others, have argued that equipment replacement decisions concern the minimization of cost in producing a given output. Faris and Perrin were the first to delineate a point-input, point-continuous, outputreplacement model for farm machinery. This model was later extended by incorporating specific expressions for taxes and inflation (Chisholm; Kay and Rister; Bates, Rayner, and Custance; Perry and Nixon; Perry, Bayaner, and Nixon). Reid and Bradford used a multiperiod, mixedinteger programming model to determine replacement intervals within the overall strategies for a farm. Chisholm; Leatham and Baker; and Perrin recast the replacement decision as one where the entrepreneur maximizes the present value of the future stream of residual earnings from the productive process associated with the asset.