Analytics Technologies Being Employed

This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Business Intelligence for Healthcare Insurers." The speaker is Mark Flaherty, CMO at InetSoft.

And I think from our perspective, we recognize that that our a very unique BI platform, that you have an ability to start with a very cost effective core but then layer incremental value on it overtime, and that’s the way we have tried to go to the market. We will continue to challenge ourselves to go to market with solutions that have a very short return on investment per today’s measurements.

But also, I think it's even more important that these BI solutions are driving very attractive total cost of ownership over a three to five year period. So our clients should expect that. They should demand that when a BI vendor is coming to them that there is an effective price point. There is an effective return on investment for the next 12 months. But also, how does this position need to continue to drive incremental cost out of my operation and add incremental value to my constituents over the course of the next three to five years?

Is this kind of analytics solution just for large enterprises, or are mid-tier companies and smaller ones also adopting it? I think that’s a question that we need to get out there. I think that’s a message that we need to get to the industry that this does not have to be something that’s only affordable for the large national players, the super-regional health plans, those that have 4-5 million members in their covered base, that have $6 billion to $8 billion in revenues flowing and that are spending, that have an IT budget of $150 million to $300 million per year. That’s certainly a market.

But I think InetSoft has done a very nice job of not only offering a reasonable entry level from a price point standpoint, but also from a solution perspective. That I think makes the analytic solutions very relevant for local health plans, regional health plans, all the way up through the major national players.

So I think that’s something that I think we need to continue to get the word out on, and we are focused on that. And I think if you look at our client base today, it's significantly more diverse than it was three years ago. I think that will continue to take place. But because we serve with one of the largest health plans in the U.S., as well as very modestly sized local and state-based health plans, we think that their mission is every bit as relevant at the local level as some of the major players are.

Their interest in changing the lives and changing the wellness of their population whether it be for 600,000 members in the state community or 6 million members or 16 million members on a local basis, they deserve the best technology and the best consulting expertise and professional services to bring to their constituents again cost effectiveness as well as medical innovation. I think that’s where we like to continue to focus.

Case Study: Optimizing Operations and Maximizing Efficiency at GreenLNG with Analytics Software

GreenLNG is a major player in the Liquefied Natural Gas (LNG) industry, known for its commitment to sustainability and efficiency. Founded in 2005, the company has grown rapidly, operating multiple LNG production facilities and serving a global market. With increasing competition and the need to optimize operations, GreenLNG sought a comprehensive analytics software solution to enhance decision-making, improve operational efficiency, and maintain its competitive edge.

Challenges

1. Complex Operations: The LNG production process involves complex operations, including extraction, liquefaction, storage, and transportation. Managing these processes efficiently required robust data analytics.
2. Data Silos: Data was spread across various systems, including production databases, maintenance logs, and financial systems, leading to inefficiencies and difficulties in gaining a holistic view of operations.
3. Operational Efficiency: GreenLNG needed to minimize downtime, optimize energy consumption, and maximize production efficiency to remain competitive.
4. Market Volatility: The LNG market is highly volatile, with fluctuating prices and demand. Real-time market analysis was essential for strategic planning and decision-making.
5. Regulatory Compliance: Ensuring compliance with stringent environmental and safety regulations required meticulous monitoring and reporting.

Solution

GreenLNG decided to implement an advanced analytics software solution to address these challenges. The chosen platform offered comprehensive data integration, real-time analytics, and advanced reporting capabilities, making it ideal for the complex needs of an LNG producer.

Implementation

The implementation process involved several key steps:

1. Data Integration: Data from various sources, including SCADA systems, ERP systems, and market data feeds, was integrated into the analytics platform. This provided a unified view of all operations.
2. Dashboard Development: Custom dashboards were developed to monitor key performance indicators (KPIs) such as production efficiency, energy consumption, equipment performance, and market trends. These dashboards were tailored for different user groups, including operations, maintenance, and management.
3. Training and Adoption: Employees across departments were trained on how to use the new analytics tools. This included hands-on workshops and ongoing support to ensure effective utilization of the platform.
4. Predictive Analytics: The software's predictive analytics capabilities were leveraged to forecast maintenance needs, optimize production schedules, and predict market trends.
5. Compliance Reporting: Automated reporting features were set up to ensure timely and accurate compliance reporting, reducing the administrative burden on staff.

Benefits

The implementation of the analytics software brought significant improvements to GreenLNG:

1. Enhanced Operational Efficiency: Real-time monitoring and analytics enabled GreenLNG to optimize production processes, reduce energy consumption, and minimize downtime. This led to a 15% increase in overall production efficiency.
2. Integrated Data Management: By integrating data from various sources, GreenLNG achieved a unified view of operations, facilitating better decision-making and more effective management of resources.
3. Predictive Maintenance: Predictive analytics helped forecast equipment failures and schedule maintenance proactively, reducing unexpected downtime by 20%.
4. Market Responsiveness: Real-time market analysis allowed GreenLNG to respond quickly to market changes, optimizing pricing and sales strategies. This increased revenue by 10%.
5. Regulatory Compliance: Automated compliance reporting ensured that GreenLNG met all regulatory requirements efficiently, reducing the risk of non-compliance and associated penalties.
6. Improved Safety and Sustainability: Enhanced monitoring and predictive analytics improved safety protocols and optimized energy usage, supporting GreenLNG's commitment to sustainability.

Results

Within the first year of implementing the analytics software, GreenLNG experienced measurable improvements:

• Production Efficiency: Increased by 15%, due to optimized operations and reduced downtime.
• Energy Consumption: Reduced by 12%, thanks to better monitoring and optimization of energy usage.
• Downtime: Decreased by 20%, through predictive maintenance and proactive management.
• Revenue: Increased by 10%, as a result of improved market responsiveness and optimized sales strategies.
• Compliance: Achieved 100% compliance with regulatory requirements, reducing administrative workload and mitigating risks.
• Safety Incidents: Reduced by 15%, due to enhanced safety monitoring and predictive analytics.

Previous: Savings From Good Customer Analytics