Life Cycle Costing is rapidly gaining popularity as more organizations recognize its role in making long-term optimal decisions. The idea of “buying the cheapest” is losing its appeal as more managers realize that the cheapest acquisition costs rarely coincide with the least expensive buy in the long run. This workshop combines Life Cycle Costing decisions with many real-world examples in an interactive and hands-on setting. It will help those responsible for LCC related decisions learn when to buy a new asset, how to determine the best time to replace an existing one, and how to forecast the future life cycle costs of a fleet of assets.
The applications of the models discussed in the workshop will be demonstrated by means of real case studies. We will introduce software packages called AGE/CON for mobile equipment replacement decisions and PERDEC for fixed capital equipment decisions and demonstrate how to use them to solve LCC problems. We will also display a software package that can be used to predict future operation and maintenance costs.
- When to buy a new asset
- Identifying the best buy among several competing alternatives
- Why you should incorporate the time value of money when establishing the economic life of an asset
- How to arrive at the economic life of an asset where its utilization declines as it ages
- What approaches to use for monitoring the performance of an individual asset
- The answer to optimizing the repair-or-replace decision
- How to work with AGE/CON and PERDEC software packages to perform economic life calculations
- How tax considerations influence the economic life of an asset
- Whether or not to take advantage of a technologically improved asset
- How to predict future o & m costs when there are little data available
WHO SHOULD ATTEND?
Asset Managers | Maintenance Planners | Maintenance Managers | Asset Replacement Planners | Capital Planners, Project Managers |
Planning and Scheduling Department | Maintenance Supervisors and Superintendents| Capital Project Planners | those responsible for Asset Information Management | Reliability and Maintenance Managers and Engineers.
LIFE CYCLE COSTING MANAGEMENT PROGRAM CERTIFICATION
The Centre for Maintenance Optimization and Reliability Engineering is directed by Professor Andrew K. S. Jardine, the internationally recognized maintenance optimization expert, within the Department of Mechanical and Industrial Engineering at the University of Toronto. C-MORE’s research is driven by close interactions with industry, in particular with C-MORE consortium members and with researchers at universities worldwide. Our focus is on real-world research in engineering asset management in the areas of condition based maintenance, spares management, protective devices, maintenance and repair contracts, and failure-finding intervals. These strong industry connections not only benefit the companies we work with, but also our graduate students, who find work in the maintenance divisions of industry leaders after graduation. We apply our research with prototype software tools that obtain valuable information from data in corporate databases. Two of these tools are now commercially available through the Ontario-based C-MORE spin-off company OMDEC, and through Ivara an industry leader and innovator in asset reliability solutions. C-MORE is also the driving force behind IMEC: The Asset Management Conference, which brings together leaders in the global maintenance field.
For information about the conference, you can visit our site, or view Maintenance Technology’s article about the conference. C-MORE welcomes maintenance professionals as visitors and collaborators.
- What is Life Cycle Costing management and why it is important
- How to choose the best buy in long term
- How to calculate economic life of an asset
- How to calculate the best time to replace the current asset with a more technologically improved asset
- When stop repairing and buying a new asset
- How to predict future life cycle costs of a fleet of asset
- How to do LCC analysis when there is few data or no data available