Factors that influence savings

The following key factors are instrumental in evaluating the savings potential of various engineering projects. These factors highlight the benefits of a data-centric, holistic, multidisciplinary approach in optimizing design efficiency and cost management.

Engineering company firm size

Engineering company firms can be small, medium, and large. The size of the engineering company influences the project execution strategies and resource allocation. While savings percentages may be consistent across sizes for similar project types, larger firms may have greater access to resources for implementing data-centric approaches.

Across small, medium, and large engineering firms, savings percentages remain relatively consistent for similar project types, indicating that the effectiveness of a data-centric approach is largely independent of company size. Savings attributed to small engineering companies executing one-off designs typically hover around 33.9%, which is similar to their medium-sized and large counterparts. However, larger firms may benefit from increased resources and more sophisticated data management solutions, potentially enhancing their efficiency and leading to marginally better savings outcomes in complex projects.

Project type

The project type can be a one-off or a repeat design. The nature of the design being executed significantly impacts potential savings. One-off designs generally incur higher costs and lower savings as compared to repeat designs, which benefit from established templates and historical data.

The type of project significantly influences the savings potential, particularly distinguishing between repeat and one-off designs. For repeat designs, organizations can achieve savings of up to 61.2% due to the reuse of established templates and accumulated knowledge, which streamlines the design process. In contrast, one-off designs typically yield lower savings, ranging from 33.9% to 34.0%, reflecting the higher costs and inefficiencies associated with unique projects that lack historical data to draw upon.

Implementing a data-centric strategy is crucial for mitigating these costs by ensuring that relevant data is readily available for informed decision-making.

Impact of the Front-End Engineering Design (FEED) phase

The organization and quality of data produced during the Front-End Engineering Design (FEED) phase play a crucial role in a project's success.

Scenarios compared the efficiency and savings potential of projects executed with a structured FEED phase against those without, emphasizing the importance of producing structured data for automation and improved project outcomes.

The structure and quality of data produced during the FEED phase significantly impact overall project savings. Projects executed with a well-structured FEED phase can enhance savings potential by 5% to 10%, compared to those that lack a structured approach. For instance, one-off designs executed with a structured FEED may show savings of 34.0%, while those without a structured FEED may only achieve 33.9%. The prevalence of unstructured data in projects without a structured FEED phase can lead to inefficiencies that hinder automation, resulting in lower savings.

Ensuring a well-structured FEED is therefore crucial for maximizing cost efficiency.

Utilize AVEVA Asset Information Management

The use of a centralized data repository enhances access to structured data, which facilitates better design accuracy, decision-making, and overall efficiency.

Scenarios compared the impact of having an AVEVA Information Management data warehouse against operating without one.

The utilization of a data warehouse, particularly an AVEVA Information Management, plays a pivotal role in enhancing savings during the design process. Projects that leverage a centralized data repository can report savings improvements that range from 1% to 5%, compared to those without a data warehouse. For example, one-off designs utilizing a data warehouse may see savings of 34.0%, compared to 33.9% for similar designs without one. This improvement highlights how access to structured data enhances design accuracy, reduces redundancy, and facilitates more effective decision-making, ultimately leading to lower project costs.

Current company systems maturity level

The maturity of the tools used in the design process, which can be paper, electronic, or integrated tools, affects project efficiency and savings potential. Projects at lower maturity levels, such as paper-based tools, may see higher initial savings, while those using advanced electronic or integrated tools tend to achieve consistent and predictable savings.

The maturity level of the tools used in the design process is a critical factor influencing savings. Projects that utilize paper-based tools can achieve higher initial savings, especially for repeat designs, with percentages up to 61.2%. However, as organizations transition to more advanced electronic or integrated tools, they often experience a decrease in savings to levels around 49.9% to 56%. This transition reflects the learning curve and the initial investment associated with adopting new technologies, but it ultimately leads to consistent and predictable savings over time as efficiency improves.