Data to the Rescue: How Danos Applied Analytics to Deliver Customer Cost Savings
By applying data analytics, Danos has developed a process to streamline coatings operations, delivering cost savings to the customer. Corrosion maintenance of a pipeline from offshore, through marsh, over land and into a plant is expensive. Utilizing key performance indicators (KPIs) and project management techniques produces better results and saves the customer money.
This case study demonstrates how data analytics saved a customer money. By merging customer data on the life of paint systems with overall maintenance costs, Danos has created a healthy quality assurance and quality control program, which demonstrates that through data analytics, companies can drive better operations and customer cost savings.
Data Collection and Analysis
The following analysis, comprised of multiple atmospheric corrosion maintenance projects, at numerous pipeline locations, with a variety of owners, shows how data can help drive down a pipeline company’s ownership costs and is presented from the perspective of the coatings’ contractor. The expert analysis is from a corrosion technician with 30 years of experience with one pipeline company and a project superintendent with 20 years of experience executing pipeline maintenance projects offshore, through marsh lands, into plants and through midstream stations and facilities. Additionally, KPIs were gathered over a five-year period and helped to drive data analysis and trends.
Over the course of several years and numerous pipeline projects, data gathering can reveal patterns, trends and associations. The output is only as good as the input, and while the human factor in gathering data is often skewed from one person to the next, the subset of information collected begins to tell a powerful story.
Scenario 1: Inefficiencies in Platform Maintenance
This hypothetical project began on an offshore platform for Company X’s pipeline export and gathering asset. Company X has used the same subcontracted coatings crew and equipment from the same contractor, Contractor A, for many years. This year, Contractor B was offered the opportunity to provide the coatings maintenance services. Contractor B sent the same size crew and same equipment spread. It utilized the same vessel to mobilize and demobilize as Contractor A had previously used. Contractor B developed a way to track offshore project progress utilizing KPIs tied to their quality management system (QMS).
After two weeks of working at Company X’s platform, several efficiency indicators became clear regarding how maintenance money was being spent on the asset. Utilizing a daily man hour report, a cost performance index, schedule performance index and contractor efficiency norms, Contractor B sent Company X a report indicating the areas of inefficiencies and actions needed to increase contractor time on tools. The report provided cost-effective ways to perform and manage these types of projects.
- Inefficiency 1: Delays from permit timing: Because of platform space constraints, the crew lived on a boat. Every morning, Contractor B’s crew received permits later than ideal. After waiting for crane permits to be issued, the crane operator’s first task was to bring the crew to the platform. In the first few weeks, Company X was losing $2,000 per week due to schedule delays as a result of waiting for permits.
- Inefficiency 2: Shortened crew work time: Each night, the crew had to shut down early to prevent the operator from working overtime. The early shut down meant the crew did not have time to fill the compressor and sand pot at the end of each day which would have maximized time on tools the following morning. This resulted in additional inefficiencies and costs to Company X.
Further data showed the cost performance index outpaced the schedule performance index, which is a sign of an unhealthy project. Contractor B showed the customer the collected data along with an analysis of improved support operations for the paint project that would eliminate waste. Collectively, this demonstrated to Company X how data collection and analysis could save them time and money on projects with similar constraints.
Scenario 2: Sacrificing Time on Tools for Cost Savings
For this hypothetical job, Company X had a lead on a corrosion project during inspections on the pipeline into the marsh, where a riser came up from the sea floor into a metering station. KPIs utilized in the bidding process helped drive down the projected cost of the atmospheric corrosion maintenance. The biggest cost impacting this project was the need for specialized equipment along with the skilled labor required to operate it, i.e. aquatic vessels and trained captains.
With this in mind, Contractor B identified a more cost-effective solution. To provide the crew more time on tools, a jack-up barge with living accommodations could be used to allow the crew to stay in the field; however, Contractor B analyzed cost performance, schedule performance and time on tools KPIs to find a more cost-effective alternative. The solution was to have the crew stay in a hotel near the boat launch and travel to and from the metering station by crew boat. Once on location, a smaller, more cost efficient jack-up barge without living quarters was utilized for the crew to perform the necessary tasks. Sacrificing the time on tools to save on boat costs put money back in Company X’s pocket.
Scenario 3: No one strategy fits all
Company X’s pipeline continues into a plant where Contractor B regularly performs coatings maintenance work, and for a decade, has assigned the same crews, equipment and personnel to oversee the work. In the back of the plant, another company, Company Z, owns assets that Contractor B has maintained throughout the years. Both companies X and Z have the same coatings specifications, but each company has a different coatings strategy. Company X spends more money to perform maintenance on entire sections of pipe at one time. Company Z has a smaller budget and performs spot maintenance to mitigate corrosion as needed.
Over the past 10 years, Contractor B has seen the integrity of the coatings systems sustained on Company X’s side of the facility with little to no breakdown. While the upfront cost for Company X is more, the long-term cost is less when compared to Company Z. Alternatively, Company Z’s upfront costs are lower, but in the long run, they pay more to have repetitive maintenance work done to the same areas, because they are addressing only localized corrosion without ever covering a complete coatings job.
From the contractor’s point-of-view, a good quality control and quality assurance program drives the data analysis to determine which strategy best combats active corrosion. The answer is not black and white; neither strategy is right or wrong, as there are many influencers for every coatings project, as demonstrated below.
The above chart represents many of the outside forces that can impact decisions on every coatings project. The majority of factors are out of the control of the coatings contractor that was hired to perform the work. The domino effect of a single decision by one of the outside forces can directly or indirectly influence the final price and scope of a project. For example, while executive management is far from the actual project work execution, the decisions made at that level, regarding budgets, timing, etc., have a direct impact on the decisions of other departments. The corrosion manager and accounting department may be restricted to only addressing localized corrosion at the highest level of risk-inspection criteria, instead of addressing a more comprehensive issue.
For Company Z, the opportunity and timing may have been the right choice for the project; the short-term budget was met, and maintenance was completed. Fit-for-purpose is one of many benchmarks used to determine upkeep of a pipeline’s mechanical coatings integrity. Any number of external factors can drive this; however, in the long term, costs and maintenance accrue when a contractor returns to perform maintenance on an adjacent section of pipe year after year.
This study is not a complete guide to best practices. Each company and project is unique. For a paint program, logistics will almost always be a big factor. If a coatings contractor is the best steward of company expenditures, the small gesture of analyzing data and logistics will lead to a more cost-effective paint program. This enables funding allocations that can produce greater results than planned. It is a win-win for the contractor and the company.
Danos, Data Analytics, November December 2018 Print Issue
Clay Carter is a coatings operation manager at Danos, and Richard Mott is a construction project manager at Danos.
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