Asset Lifecycle Management
Overview
An asset lifecycle is the series of stages involved in the management of an asset. It starts with the planning stages when the need for an asset is identified and continues all the way through its useful life and eventual disposal. The basic premise of asset lifecycle management is to extend your assets’ usability as far as you can, without losing any functionality, thereby decreasing total lifetime costs and increasing the economic value-add of the asset. For example, when maintenance is neglected, companies have to struggle with the resulting unexpected breakdowns, long delays, and costly emergency maintenance. Proper asset lifecycle management can improve the process of maintaining and managing valuable assets.
Applicable Industries
- Transportation
Applicable Functions
- Maintenance
- Quality Assurance
Market Size
The Application Lifecycle Management (ALM) market is expected to grow from USD 2.58 Billion in 2017 to USD 3.63 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 7.0% during the forecast period.
Source: PRNewswire
Case Studies.
Case Study
Coca Cola Swaziland Conco Case Study
Coco Cola Swaziland, South Africa would like to find a solution that would enable the following results: - Reduce energy consumption by 20% in one year. - Formulate a series of strategic initiatives that would enlist the commitment of corporate management and create employee awareness while helping meet departmental targets and investing in tools that assist with energy management. - Formulate a series of tactical initiatives that would optimize energy usage on the shop floor. These would include charging forklifts and running cold rooms only during off-peak periods, running the dust extractors only during working hours and basing lights and air-conditioning on someone’s presence. - Increase visibility into the factory and other processes. - Enable limited, non-intrusive control functions for certain processes.
Case Study
LumenData Delivers Real-time Predictive Analytics through IoT
In 2013, LumenData found itself in need of adding new real-time predictive analytics capabilities to its suite of services. To meet this need, LumenData acquired a state-of-the-art streaming data, capture and real-time predictive analytics company. This solved the pure predictive analytics end, but left LumenData with a need to be able to build IoT-targeted services.From an IoT perspective, LumenData was still missing the means to create suitable applications and dashboards that would make it easy for its customers to effortlessly make sense of whatever predictive analysis they might require.
