EPC firms are facing increased competition in an industry disrupted by increasing regulation, environmental pressures, and new market players. To help navigate turbulent waters, established companies have the advantage of years of collected project data – intellectual property that paves the way to better risk management, more efficient builds, and lower costs. Drawing on the power of their accumulated data, EPC firms will outpace new competitors, win more bids, and protect margin. But to get there, firms need to organize and mine their data wealth. And for that, they need these 7 advanced knowledge management capabilities.
1. Data systems help people work together – not build walls between them.
Knowledge management applications and initiatives need to prioritize collaborative capabilities. If data is going to help engineers work smarter, they all need access to the same information and relevant analysis of that information. In addition to project specifications and designs, knowledge management must support team discussions, notations on shared documents, and workflow alerts.
2. Engineers can quickly find experts when needed.
It takes an A-team of experienced engineers with specialized skills to execute EPC megaprojects. These experts often consult with various project groups over different phases of the build, weighing in on planning or operational challenges as needed. But in a large project, people who need specialized advice don’t always know who to call on or how to find them.
That’s why expert locators are one of the first important capabilities any knowledge management system should have. They’re smart directories that allow teams to search for and connect with the right person. Many expert locators can also point people to documents or videos created by resident authorities to answer common questions, similar to a Google search, but customized for the EPC build.
On the more advanced end, expert locators are starting to include artificial intelligence (AI) that reads engineers’ resumes, employee profiles, emails, and publications to determine the range of issues they’re qualified to consult on. These systems save time and money by automatically keeping the directories up-to-date and accurate.
3. Engineers can quickly search data resources for needed information.
EPC megaprojects generate massive amounts of documents, conversations, emails, blueprints, and more, making it harder and harder for engineers to find the right document when they need it. According to a Deloitte workforce survey,
“Twenty-nine per cent of our survey respondents claimed it is difficult, or nearly impossible, to extract the knowledge needed for daily work from repositories.”
And it’s no wonder. Shared drive folder structures often run four or five levels deep with cryptic file names that make it impossible for people to find the document they need unless they already know where it is. A knowledge management strategy must include sophisticated search capabilities so engineers can easily locate any file. Unfortunately, advanced search is one of the most difficult tasks to automate, and it requires engineers to structure documents with searchable information such as tags, departments, authors, formats, and project names.
Advanced engines use strategies like contextualized search and federated search to help engineers find what they need faster. In contextualized search, software looks at the context of a search query to find the most relevant documents. For example, the system may narrow down search results based on the user’s geographic location, recent search history, or job role. Federated search involves looking across different data silos for results so people can access documents from many business units with a single search.
4. Data is available to the right people at the right times.
In any EPC project, people will work together from different business units and companies, and some will need access to higher-security information than others. knowledge management strategies need to handle permissions and keep proprietary information safe. Usually, permissions are role-based. So, people gain access based on their job titles, companies, and project stage. A robust permission structure also needs a way to extend data access beyond the corporate firewall and set different permission levels for document uploads and updates.
5. Engineers can evaluate trends in collected data to improve current and future project results.
As with any large, complex project, EPC builds often suffer from cost and schedule overruns caused by unexpected problems in supplies, worker availability, or design conflicts. Data analytics can help catch nascent problems, support more informed planning for future builds, and measure the effectiveness of new procedures.
Analytics technology searches project data for patterns or trends, which give engineers a detailed view of what’s happening in the work and some guidance toward improving outcomes. The information fed into analytics software must be collected and organized properly for the most accurate results.
6. Artificial intelligence automates data analysis.
One of the great challenges of knowledge management is digesting, tagging, categorizing, and indexing files of all formats for improved search, access, and security. But AI systems have stepped up to handle much of the work. AI software using natural language processing (NLP) can read documents for tagging and indexing. It handles written text, forms, materials specifications, emails, and chat. AI can analyze photos or videos for key information. And NLP powers advanced search, returning the most relevant results in response to naturally phrased queries.
7. Virtual models capture information in context.
Knowledge management programs need to include a way to represent information in relation to the overall project. Virtual models for EPC store data in the context of the build, representing the work status from requirements and planning to final inspections. The building information model (BIM) provides an industry standard for capturing and sharing data and progress with team members and stakeholders across the organization.
Bonus: Knowledge management capabilities are integrated with each other.
There’s no single application which delivers all the essential knowledge management capabilities at once. Different technology and software have to work together as a coordinated suite in a common data environment (CDE). When building out your strategy, make sure technology you deploy is designed to integrate well with data sources and other knowledge management applications. Where possible, use systems based on open APIs.
Find out how Rudy for Engineers supports your knowledge management strategy.
Rudy for Engineers is an AI-driven application that analyzes and manages the mountain of data generated during the technical bid evaluation (TBE) process. Rudy digests materials specifications and project documents, creates bid packages, reads vendor submissions, and completes the TBE templates and reports based on tendered bids. As part of your knowledge management program, Rudy saves TBE engineers from having to scour through dozens of documents, which lowers labor costs significantly. Rudy also provides the data analysis, contextualized search, status reporting, and collaborative capacity that supports your overall knowledge management program.
Learn how Rudy saves time and lowers the cost of your TBE process. Schedule a free demo today.