How to Reduce Long Lead Times in Engineering Projects

When it comes to engineering project management, timing is everything. Especially in complex engineering projects such as large-scale infrastructure improvements, commercial builds or custom engineered systems like an industrial crane, marine vessel or even the wing of an airplane.

Engineers and project leads are constantly searching for ways to improve project planning and management processes.

For customers, long lead times mean lost revenue and other opportunities. For vendors, delayed projects turn into wasted internal resources, lost margin and irrecoverable time lost, sometimes even years if they’re truly unlucky. Needless to say, there is no bigger unifying desire across all fields of modern engineering than the need to complete projects at or above expectations, and most certainly on time.

Issues with Long Lead Times


Modern engineering relies heavily on contract text, especially in the procurement stages. And as one would guess, particularly in large-scale engineering projects, evaluating an array of suppliers is a thorough and lengthy process. When multiple offers are submitted, deciding quickly which supplier to go with in order to keep the project moving forward, becomes somewhat daunting.





From a supplier’s point of view, how quickly you respond to a proposal can signal your interest in the project or business and demonstrate how experienced your company is in providing the solution. However, responding swiftly to an RFP can be challenging when tender documents are hundreds of pages long and include regulations, standards and a large amount of technical requirement text. These documents need to be processed and understood quickly, but carefully, in order to gain a competitive edge.

In order to keep up with the demands of today’s markets – an ever-increasing speed of project delivery – engineers and project leads are constantly searching for ways to improve project planning and management processes.

Thanks to advancements in technology and the introduction of automation and digitalization, project managers have the ability to drive down the timelines of projects and ultimately reduce their own project lead times.

Solutions to Help Process Technical Project Data


The complexities of large new engineering projects involve tracking and balancing many moving parts and information. Companies create so much data, yet do not analyze or implement it, creating a lost opportunity to fill the gaps within their project workflows. The value their text data may contain can reveal how to deliver excellent project results and improve their overall efficiency. However, organizing and analyzing all of the previous project data and technical text is a huge task. Project managers can turn these formidable endeavors into opportunities to harness new technologies, thanks to digital advancements like robotics, artificial intelligence, and the internet of things to name a few.

For example, most timeline forecasting done by project managers is based on studying historical data on performed tasks. They’re able to identify how much time should be allocated to each task for successful completion. However, recording and analyzing such data can become an unwieldy and time-consuming assignment. While many project management tools are proficient in storing and collecting historical data, sometimes they fall short with aiding in analytics. Powering the tool with machine learning can not only help automate data collection and analysis but the entire process of predicting realistic timelines can be digitized, which helps project managers achieve gains from the start.





Another application of artificial intelligence (AI) in engineering projects is resource engagement. To ensure projects remain on track, it’s essential that each group in the supplier chain gets the sections relevant to them – but when the written specifications are hundreds of pages long, trying to extract those sections accurately and quickly does not come easily. AI can categorize the requirements based on group function and channel the right information to the relevant engineers, facilitating a more efficient RFP process.

Predictive analytics is another way that artificial intelligence is transforming project management. AI will comb through the specifics of past projects to find out what worked and what didn’t. With this information, predictions about the project can be made that either validates the future outcomes or identifies potential risks and shortages, useful for new project managers or engineers who may be unfamiliar with previous projects.

Of course, AI is not all about automation – deriving actionable insights and finding connections in disparate data is something that even the most trained project manager or engineer could miss. AI structures the data while finding its patterns and inconsistencies, which allows teams to collect insights from dense masses of technical text data, which can be used to improve their project workflows and processes.

Real-World AI Project Management Tools


With technical text data being the heart of large-scale engineering projects, there are a few technologies that can provide some relief from the complexity of long tender documents and safety-critical systems.



Lili.ai
Lili.ai is an artificial intelligence project management tool that automates repetitive tasks such as following up on meeting minutes, or updating risk registers. It also monitors task prioritisation impacts to improve awareness and organisational agility, and makes sure any built up knowledge can be reused in later projects.



Selko Analytics
Selko Analytics has developed Artificial Intelligence software that can automate the processing and categorizing of text data documents. The deep learning platform for engineering companies can help categorize technical specifications into any function groups and makes sure the right engineers get the relevant information.

This type of technology is useful in modern engineering and can reduce the amount of time and people needed to read through lengthy technical text documents or large projects where stakeholders need to communicate their system needs in long tender documents for custom engineered products.

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Cortical.io
The Cortical.io contract intelligence engine analyzes the meaning of not just keywords but of whole sentences, paragraphs, and long text so that the problems of language ambiguity and vocabulary mismatch within and across documents are overcome.



Reduce Long Lead Times with AI


Modern engineering is more technically complex than ever before, and the face of project management is another function that is seeing positive changes thanks to advancing technologies like artificial intelligence and machine learning. In order to meet the market’s demands of delivering engineering projects faster, accurately and efficiently, project teams and leads would do well to leverage emerging AI project management solutions to help them achieve gains from the very beginning.