Users can take advantage of the Genix advanced analytics capabilities that are provided with the iBPMS to optimise the event sequencing, manage asset related workflows, functional equipment groups, work teams and scheduling. The long-term critical path is mapped and activities are monitored with automatic alerts being issued to key men when sub-optimal management practices are identified.
Softchill is forging new channels to market in partnership with IBM and resellers to launch the LCP in the energy, utilities, resources and transport markets. Genix rolled the LCP into the IBM SAFE Framework for energy and utilities to enable these channels.
The Softchill team has rolled 40 years worth of domain expertise into software for markets that have previously relied upon high-cost consultants and short-term decision-making tools. The company offers expert professional services, software delivery, support capabilities and maximised, risk weighted ROI for a fraction of the cost of existing software tools.
Advancing Asset Management
Most organisations in the energy sector have moved with the times and installed systems that manage asset registers and provide computerised maintenance management (e.g. SAP and Maximo). Specialists have typically undertaken full asset lifecycle management manually with niche software often developed in-house providing limited functionality
While these systems have delivered savings in terms of maintenance costs, most of the existing maintenance and capital expenditure schedules are based on manufacturer nominated schedules rather than actual experience in the plant.
Given that global and environmental factors play a major role in determining the nature and frequency of maintenance and the timeline for capital replacement, a one-shoe-fits-all approach is sub-optimal.
Existing maintenance management systems facilitate the extraction of costs from day-to-day maintenance operations, however little or no headway has been made to optimise the use of existing assets and better manage maintenance schedules across the short-term or whole life of assets. They do not allow for:
- Scenario planning or long term (more than 2 year) forecasts
- Synchronising maintenance activities across equipment groups (e.g. components in a turbine) and are therefore, unable to actively influence the shutdown parameters dictated in the manufacturer’s handbook.
- Optimised prioritisation and selection of projects for implementation given a limited budget.
- Over-the-horizon views on costs that effectively support pricing decisions where assets and indeed organisations are being bought and sold.
These shortcomings are the result of a general lack of understanding of how the progression from maintenance management to risk-based asset optimisation actually works as well as the complex nature of the asset optimisation task and the lack of computerised programs that deliver scenario planning and risk management capabilities for asset optimisation.
That said, customers recognise that there’s a need for change:
- A need to move away from pure cost reduction and straight forward computerised scheduling to better alignment with business performance objectives
- A need to incorporate risk weighted asset management (leveraging financial and operational risk criteria)
- A need to move to fleet-based and perhaps portfolio-based asset optimisation.
Every asset-intensive industry has a need to establish, and, over time, review the framework and the sequencing of plant maintenance to reduce or at least, optimise, maintenance costs given changing business needs and potentially, asset purchases and divestments. In the Energy Sector, this is compounded by the emergence of carbon trading and environmental management imperatives, higher customer expectations, energy trading (opportunity cost for downtime), and increased government regulation. Given that existing financial systems (SAP, Mincom etc) and CMMS solutions (e.g. Maximo) do not provide this functionality, these optimisation activities are currently undertaken by extracting data from the financial and maintenance management systems and then manipulating them in spread sheets. However, the extent to which this spreadsheet-based analysis can be undertaken is limited and the skills required to conduct the analysis are even harder to find. As a result there is a compelling need for a solution like the LCP Optimiser.
The founders of Softchill have a background in providing utilities with advice on how to improve elements of their asset management operations and have had extensive experience in the set up and use of the spreadsheets – and their limitations. All this experience has been incorporated into the design of the LCP Optimiser making it a unique offering.
Transitioning to Asset Optimisation
The transition to asset optimisation typically occurs in four stages:
- Stage 1: The Short Loop – The first stage typically involves a focus on plant maintenance tasks. In this stage, schedules are typically driven by OEM suggested schedules and defects.
- Stage 2: Performance Driven Maintenance Outcomes – This second stage in the progression involves a move to aligning maintenance schedules with performance management objectives within the plant. This stage typically requires schedules developed by a CMMS to be overlaid with performance management objectives and since available CMMS platforms do not provide this functionality, where organisations have graduated to this level, this activity is undertaken using spreadsheets and other stand alone analytical tools. This is the first stage at which the LCP will add value by providing the opportunity to incorporate the performance management overlay.
- Stage 3: Alignment with Business Objectives – The third stage aligns the performance driven outcomes with broader business objectives (e.g. operational strategies and value based models). The LCP used advanced algorythms and decision support analytics to create scenarios that are weighted across risk, cost, operational and investment parameters.
- Stage 4: Risk Weighted Asset Management – This stage aligns asset management with long-term business strategy. By prioritising maintenance projects based upon the scenario modelling and optimising discreet asset management activities broader benefits are realised. Trading windows and investment decisions are better informed by the modelling and optimisation that provides long range insight into costs of optimally managing the assets.
Existing software solutions address Stage 1 and approximately 70% of what is required in Stage 2. The LCP starts to add value in Stage 2 and is the only commercially available application software that delivers functionality to support Stages 3 and 4.
The LCP Benefits
The LCP has the potential to facilitate a range of improvements in the utilisation of assets resulting in reduced costs and down time for plant and equipment. Mere alignment of the maintenance schedules by functional equipment group has the potential to reduce down time by 2-3 days per year.
The potential to overlay emission reductions and carbon trading information into the decision making process will ensure that decisions in the future can be taken after all relevant costs have been considered and optimised by LCP. This leads to significant improvements in operational efficiency, reduced capital costs and therefore reduced wholesale energy costs. In energy and power production alone at least a 15-20% reduction in capital and operational costs is expected.
Significantly, the LCP facilitates improvements in maintenance practices across the organisation, bringing best practice and refined techniques that drive efficiency, improve outcomes and reduce errors and breakdowns. “Asset optimisation” specialists can be trained and exported across global operations.
In general, LCP customers benefit from:
- An ability to generate long range expenditure plans and forecasts of resource needs (e.g., labour and materials), required to run the business.
- The ability to leverage output from risk weighted cost/benefit decision-making during investment planning through the project-ranking tool.
- Access to a framework that looks ahead and schedules all work on plant and equipment throughout the life cycle of the asset.
- Support for an optimisation process that allows for scenario analysis and final selection of the most appropriate solution.
- Availability of an “outage forecasting window” for communication between long term Trading Planning and the shorter term trading optimisation process.
- The ability to integrated with commercial asset management and financial systems (e.g. SAP and Maximo) to deliver a seamless asset optimisation capability.