A.I.-Enabled Scheduling Assistants Can Save Months on Complex Projects

Last time we spoke about artificial intelligence, we looked at some pioneering digital quality and safety risk assistants, which surface insights into potentially critical project risks which might slip through the cracks, only to resurface as major issues later in the project.

 

In this article, we’ll look at another artificial intelligence application for construction which can assist project schedulers to generate a range of optimal project construction sequences that a human planner might miss. ALICE (ArtificiaL Intelligence Construction & Engineering) is able to generate millions of project schedule scenarios in a matter of minutes, then presents a handful of the most appealing alternatives to the project team for further consideration and validation, to guide them to select the best option.

 

So how does it work?

 

First, a project team produces production recipes. These are sequences of activities needed to produce a building component - for example a wall assembly, concrete slab, steel roof, or piping run, and are input by the project planner/schedulers and the site supervisors. Once codified, these parametric mini-schedules can be applied to all similar objects in the project. They can be easily adjusted and the results of changed parameters are quickly visible. Recipes can even be saved and passed on to other projects for future use and refinement.

 

Next, these recipes are linked to the objects and systems in a BIM model, which contains detailed information about location, quantities and types of materials. On complex projects where clients and contractors require designers to produce detailed models for coordination and asset management, these information rich 3D models are readily available and can easily have recipes applied to them. 

 Create a production recipe that can be applied to all similar objects in a model.  Image courtesy Alice Technologies

Create a production recipe that can be applied to all similar objects in a model. Image courtesy Alice Technologies

 

The team then asks Alice to run the schedule. She first determines which tasks have no predecessors and so can be done first, then analyses thousands of alternatives against spatial constraints, crew sizes, and other factors, repeating this process for each sequence until a schedule is built up. Being an intelligent assistant, Alice understands calendar nuances, for example that concrete cures over the weekend even if crews aren’t working, giving preference to schedules which plan for curing on weekends. 

 Explore different schedule alternatives: Each dot represents a different scenario based on variations in resources, methods and constraints.  Image courtesy Alice Technologies

Explore different schedule alternatives: Each dot represents a different scenario based on variations in resources, methods and constraints. Image courtesy Alice Technologies

 

Alice crunches the schedule numbers in a tiny fraction of the time that humans could, and presents a range of options for the project team in a simple to use web-interface, allowing human engineers and schedulers to spend more time fine tuning the viable sequencing options and optimizing the constructibility. In addition to saving hours of a scheduler’s time, Alice can also significantly optimize a project’s overall timetable (they estimate up to 15%) and surface insights into better production sequences that may have not been apparent previously. Then, as a project progresses, the schedule can be updated within minutes to take change orders and delays into account. If a client needs to take 2 months off a schedule, parameters like number of cranes or crew size can be adjusted and an updated schedule and budget projection is quickly visible.

 Adjust and manage the schedule with actual events, delays, changes and opportunities as they arise, and see updates reflected within minutes.  Image courtesy Alice Technologies

Adjust and manage the schedule with actual events, delays, changes and opportunities as they arise, and see updates reflected within minutes. Image courtesy Alice Technologies

 

Although its still very early days for AI scheduling applications like Alice, as more pilot projects validate the concept and provide positive results, we can expect to see much more use of digital planning assistants in complex construction projects in the coming years. 

If you're interested in learning more about A.I. and applications of machine learning in construction projects, you can see Michael Moran of Telos talking on the subject at Digital Construction Week at 16:30-17:00 on 17 October, 2018 at the ExCel Centre in London

How Cloud Tech Can Help Contractors Be More Profitable

Mobile, cloud based technology, which is readily vailable to every stakeholder on the construction site, offers contractors a powerful set of tools to stay competitive, save time, track workflow, analyze data, proactively manage projects and develop more efficient schedules. 

And the research is clear: All of those benefits add up to projects with sharply reduced rework and warranty claims, as well as improving margins for the company as a whole. 

Read more here (courtesy Construction Dive and Autodesk):

https://cdn2.hubspot.net/hubfs/86543/2017_Assets/bim360_2017/5_ways_avoid_construction_rework.pdf

Get Ready for A.I. In Construction

We're now seeing the first practical applications of of Artificial Intelligence (A.I.) in construction. As construction sites become increasingly digitally connected and as datasets grow, the question is not if AI will impact construction, but how an industry with relatively low levels of innovation can take advantage of a rapidly improving AI toolkit.

 

What is AI anyways? A Very Brief Introduction

Every day we hear news about new applications of A.I., machine learning and deep learning, all related terms, often used interchangeably. They describe a series of technologies which are creating both excitement - and confusion. A.I. has often been used as an overhyped marketing buzzword, we hear about robots taking our jobs, and the dystopic risks of intelligent machines have even been touted as potential threats to civilization by high-profile technologists. But A.I. is also foundational to step-change improvements in how computers can interact with the world and solve complex challenges. So lets start by talking about what A.I. is - and what it isn't.

When we say A.I., we're using a blanket term to talk about algorithms that can be focused on problems which, up until recently, could only be solved by human intelligence - like speech and image recognition, language translation or how to win a chess game. Machine learning refers to a specific form of A.I. which can be trained to recognize patterns and make better decisions with more inputs - it can learn without being explicitly programmed to do so. Amazon's book recommendation engine is a well known example that looks at all of your past purchases and recognizes what you're likely to want to read next. Deep learning is a sub-category of machine learning using multiple levels of abstraction to organize inputs into accurate categories, for instance recognizing that an edge in an image is part of an eye, which belongs to a face, before deciding whom the face belongs to. 

The combination of 3 important technology trends have resulted in a drastic performance improvement - and an great expansion of the applications for AI:

1. The rapidly falling price of parallel computing which lets artificial neural networks (the mechanism behind machine learning) perform multiple operations in parallel. Graphics processing units (GPUs) are examples of neural networks. They can see a pixel of an image in relation to all the pixels around it, and can thus make classifications based on complex patterns. When running in the cloud, GPUs can recognize your friend's face in an image uploaded to Facebook and make millions of 'tag suggestions' a day.

 

2. Better algorithms, such as a tweak to hierarchical pattern recognition that enabled what we now call deep learning. When this algorithm was applied to Google's online translation service in 2016, its error rate fell by 60% overnight.

 

3. Big data and a widely available set of data sources. Think of a toddler - the more chairs he sees, the better he'll be at identifiying a chair next time she sees one. A.I. needs exposure to a very large quantity of data to accurately identify patterns, classify inputs and to solve the problem its being trained on.

We're not (yet) talking about super-intelligent builder-robots which execute designs generated by algorithms, but even some early explorations of this are underway and could be indicative of future engineering trends. But let's save that discussion for another day.

 

A.I. for Construction

Nowadays, a construction site manager with a mobile device can quickly capture photos, videos and updates to the latest project conditions (models, drawings, schedules). The latest media and data is uploaded to cloud-platforms, where it can be analyzed at a project or cross-project level. These data-sets will continue to grow exponentially as the cost of drones, laser scanners, wearable cameras and other data collection devices falls, as mobile-apps for construction add functionality, and as technology becomes adopted by more and more project team members.

Smartvid.io lets users upload photos, voice and video recordings to their platform, where their A.I. engine VINNIE then looks at and listens to the media to place smart tags on images and audio. Thanks to this automatic tagging, contractors can later accurately sift through gigabytes of media taken on a typical project to find exactly what they're looking for. In some cases builders can find documentation that protects them against a claim, saving of millions of dollars. It's not just precise, it's quick too. In 2016 Engineering News Record (ENR) hosted their annual photo competition, but this time they partnered with smartvid.io to make sure none of the pictures depicted hazardous safety situations on-site. Alongside human safety experts, VINNIE scanned through the more than 10,000 photos in under 10 minutes - the same task took the human judges over 4.5 hours. What's more, VINNIE correctly identified 10% more photos depicting people than its carbon-based competitors, flagging 32 pictures featuring people not wearing safety helmets and 106 without high-visibility clothing. The safety experts agreed that an AI like this could help them quickly sort and identify on-site safety hazards which require a more careful review. 

The availability of software that can automatically sort, filter and extract pertinent features from images strengthens the case for increased onsite photo and video monitoring. Drones, already popular in construction as a way to perform inspections of inaccessible areas, may soon "see" sites as humans haven't been able to, not just from a different angle, but identifying patterns that lead to higher safety risks or impending schedule and cost overruns. And as "smart helmets" such as DAQRI go from prototypes to field-ready devices in the coming years, A.I. image and video recognition tools will assist site inspectors to identify similar hazardous behaviours or conditions in real time.

  Keeping your head safe - and the rest of your site too? Image courtesy DAQRI®

Keeping your head safe - and the rest of your site too? Image courtesy DAQRI®

Patterns don't just manifest themselves visually. Most construction management software applications are built on cloud-based platforms that allow users to connect real-time project data to an increasing number of external tools such as document management systems, scheduling, business intelligence and project reporting apps. Contractors will soon run machine learning algorithms on their ever-increasing cross-project datasets to identify patterns that humans simply can't - like the effect of model changes by a mechanical designers on schedule delays and cost overruns. On a project level, project managers may want to use AI to search through keywords in digitally documented reports or inspections, to flag potential high-risk observations for urgent resolution.

 

What next?

A.I. is starting to see, hear and understand our construction sites, and offer us better insights into how we should be managing them. Contractors can look to other industries to imagine what the next years might hold for the adoption of these tools in construction. The tech giants are already in an all-out A.I. Arms race. Google's CEO recently stated that the world is becoming A.I. first. As algorithms become more fine tuned, companies who successfully implement machine learning to assist project management may significantly reduce risks in key areas like site safety and schedule reliability. They will also inevitably gain key competitive advantages in labour efficiency - A.I., as any new technology, will replace some human workers whether we like it or not. But A.I. won't replace human judgement (yet). Instead it's beginning to assist project managers by uncovering insights that help them make better decisions.

 

If you want to dig deeper into the topic of A.I., machine learning and deep learning, the following are good places to start:

What the heck is… Machine Learning (5 mins read)

The wonderful and terrifying implications of computers that can learn (20 mins video)

The Great A.I. Awakening (45 min read)

How to Mine Construction Data to Measure Supply Chain Performance

Thanks to the growing digital data streams generated by construction, we're seeing an increase in practical applications of data analysis. One of those is in supply chain management. It's no surprise that price has traditionally been the sole factor considered when selecting construction contractors, it's easy to measure, can be exactly defined in a contract, and can be tracked during project execution (usually in the form of huge overruns!). But more complex projects are considering a variety of other performance indicators when awarding contracts, including:

  • Qualifications
  • Schedule
  • Quality
  • Safety

The goal of using this  selection process, sometimes referred to as the "Best Value Model" is to minimize project risks and to enhance long term performance and value of the built facility. A client will invite qualified vendors to bid. Once selected, the vendor(s) will create a project specific risk  plan. Then, during the project, the client will track performance according to the risk plan and require regular reports. This performance can be logged and consulted again when awarding future contracts.

 

Weighted multi-criteria subcontractor scoring

One big challenge when awarding contracts based on this approach is that the additional performance dimensions are much harder to objectively measure than price. How do you track past schedule compliance? What indicates a contractor’s relative safety record?  During execution, a client may doubt the reliability of progress reporting from the vendor. Luckily, the digitization of construction information is beginning to give us objective indicators of subcontractor performance. Lets consider some practical examples:

Example 1: Short term planning and coordination of trades. In projects using theLast Planner System of lean production planning,  subcontractors are required to commit to a detailed series of tasks which they will complete in the coming week. If they do not deliver the work by the promised date, the committed activity will be recorded as incomplete. Planned Percent Complete (PPC) is a score measuring the percentage of completed activities out of the total committed activities, and is tracked as a measure of planning reliability.

Example 2: Safety Compliance. As structured, safety inspections are recorded digitally, measures of compliant vs. non-compliant observations associated with particular subcontractors can be analysed. On a growing number of sites with biometric access control, this can be further analysed per man-hour per subcontractor.

Safety dashboard showing incidents per 1000 man-hours worked for a particular subcontractor across 5 projects

Example 3: Resolution of design review and on-site issues. When an issue such as a costly clash between a structural element and service duct is identified in a 3D model environment it can be assigned to a responsible party, communicated in a shared model and tracked through to resolution. Both the total number of such clashes, and average time taken for satisfactory close-out can be measured and analysed per consultant engineering firm in a project. Likewise, the number of issues and observations occurring onsite can be quantified per million $ of contract value and similar subcontractors can be ranked against each other, resulting in a measure of quality compliance.

     

 

 

Summary performance dashboard of drywall subcontractors in a geographic region, scored across key dimensions

The Importance of Data Quality. Remember the old adage: garbage in, garbage out. Slick, convincing looking business intelligence (BI) dashboards can support catastrophic failures in human decision making if they're backed up by erroneous or incomplete data. We need structured quality assurance at all phases of data collection and analysis to ensure reliable decision making. This includes:

Creating a Framework for Performance Evaluation. Once we can ensure that quality data is being reliably collected and analyzed, we need to agree on the dimensions of subcontractor performance evaluation. The importance of carefully defining these KPIs cannot be overstated – they affect the supply chain management, strategic and operational planning of a company.

There must also be a clear process in place for interpreting the results of KPIs at all levels within an organization. Consider, for example, a seasoned project manager who consults the results displayed on dashboards to conduct monthly reporting on supplier performance in adherence to quality, safety, schedule and cost. How can this PM’s report, which is after all an aggregation of insights offered by the BI tools combined with the knowledge derived from years of experience on multiple projects, be filtered up into a cross project scorecard that can be presented to a supply chain manager?

Some other important questions to consider: What is the consequence of a score below a certain threshold in a critical area? Do subcontractors have regular score reviews, after which they can are promoted to or demoted from a “top league” of vendors who can be invited to tender on high risk projects? Is the overall pool of potential vendors examined on a project specific basis, based on the performance criteria deemed critical for that project’s needs? 

Where Do We Go From Here? This post outlines a framework for data-driven supply chain selection in construction, outlining some of the KPIs currently available for analysis, and highlighting some of the data quality concerns that need to be addressed to surface truly useful to useful insights to contractors. It is still early days for contractors and clients as they incorporate data analysis and BI into their decision making processes. But as data sets grow larger and more comprehensive, we can expect that data-driven supply chain selection will be more commonplace in project procurement strategies. As BI analysis techniques become more sophisticated, we'll begin to develop techniques for identifying correlations of key risk factors based on machine learning and predictive analytics.

This article was originally posted on LinkedIn Pulse by Michael Moran on 2 Nov, 2016

A Wave of Digital Transformation Is About to Hit Construction

The construction site of the (near) future. Picture the scene: as you walk onto the construction site, a drone buzzes overhead. Lasers bounce off every surface.You pull a device out of your pocket to view the latest drawings and fully coordinated 3D models, and record a video to capture progress. As you upload it to the cloud, the objects in the video are automatically recognized and tagged to let other project members quickly find what they need. Nearby, an engineer wearing a smart helmet performs a complex task with the aid of real time information beamed straight onto his field of vision. A few years ago, this scene would have seemed straight out of science-fiction, but it's fast becoming a reality. 

 

And yet, as fantastic as some of this technological progress is, we've only begun to scratch the surface of digization in construction. The outwardly visible, tangible manifestations of gadgetry on jobsites represent the first distant rumblings of a tsunami of digital transformation which will hit the construction industry in the coming years. Let me explain:

Digitization vs Digital Transformation. Digitization refers to the conversion analog information into digital form. More broadly, it is the trend turning various aspects of our life into digital data, and transforming this data into new forms of value. 

 

The diagram above is an example of an ever increasing amount of real-world objects and activities for which a digital replica is recorded and stored. Throughout human history we've had complex webs of interpersonal connections. But in the last decade, by having hundreds of digitally recorded connections on online social networks like LinkedIn and Facebook, we can visually map the mutual links within our professional or social circles. Digital Transformation is when business activities, processes and models are restructured to fully leverage the opportunities of digital technologies and their impact across society in a strategic and prioritized way.

The digitization of construction. In construction, site management activities have relied heavily on paper until very recently. BIM and project management software have allowed designers and contractors to coordinate complex designs and simulate construction tasks. But there was always a gap in transferring digital information between site and office. Now, thanks to mobile tools which bridge this gap, a growing number of these tasks are being performed in a fully digital workflow, tracked in centralized cloud databases. Despite the exciting early signs of technological progress, construction is still in its digital infancy:

We've begun to realize some early benefits of digital construction through adoption of BIM, project management and mobile collaboration software. Change orders and design errors have been significantly reduced on many projects. But the change we've seen so far has been incremental, not transformational. If you know otherwise, please tell me in the comments below. Projects still overrun deadlines. Project managers still lose sleep. Site workers are still inefficient, and get injured and killed more than in just about any other industry. Contract structures are still combative and litigious. Waste and rework still standard, projects (when profitable) still have razor-thin margins, clients are still often unsatisfied, and the public still views construction as a wasteful, polluting nuisance. And it still is. 

The wave of transformation in construction will hit when we connect the digital dots. A real step-change in performance will happen once the exponentially-growing digital data streams generated during design, construction and operations start to become interconnected in a meaningful way. The terabytes of images and photogrammetric models captured by drones, mobile devices and scanners. The millions of data points on suppliers', designers' and subcontractors' performance, stored in 3D models and project management software. Growing data-streams from cheap sensors being embedded in building components and wearable technology.

Just as manufacturing was transformed when PLM, ERP and CRM created systems of integrated applications to manage production, logistics and demand, construction will see significant improvement as BIM becomes the true information backbone of the jobsite. Data will be analyzed in a way that reveals actionable insights. New types of contracts, procurement models and methods of managing supply chains will develop to facilitate, rather than hinder, adoption of collaborative construction technology and meet clients needs. Technology companies like Autodesk broadly call this Construction in the Era of Connection. In the UK, the term Level 3 BIM is used. In Germany, they refer to Building 4.0. 

This change will be good for some and painful for others. Many small, hitherto unknown contractors and construction managers who successfully recognize how to get value out of the "I" in BIM will have distinct early-mover advantages. Large, established companies who fail to react, rehire and restructure will be hurt. Whats more, other industries have already begun undergoing this revolution, which could accelerate the rate of change in construction, since we wont need to reinvent the wheel. How can we predict future needs to ship materials to site before they've even been ordered? Ask Amazon. How can humans and robots build something together? Ask Audi. 

This post was originally published by Michael Moran on LinkedIn Pulse, Oct 26, 2016

How to Measure Benefits and Calculate ROI of Construction Mobility Software

Contractors around the world are reporting significant gains through adopting construction mobility software, but most still struggle to measure improvements. A good way to assess whether a new technology is successful is by quantifying its benefits. And lucky for us, there are plenty of simple, proven methods to track improvements in worker productivity, project schedule and quality:

Productivity Improvements. Much of the low productivity in construction is a result of workers spending too much of their time in the field performing non-value adding tasks. 

Download.png

Site engineers, superintendents and other field workers are constantly transferring information, drawings and observations between the site and the office. Mobility tools can boost their productivity by massively cutting down the time they spend:  

  • Searching or waiting for the latest drawings, photos, statuses of issues, etc. 
  • Walking back to the office to get information or tools they forgot
  • Copying information in the office which was recorded on paper in the field
  • Reporting and sending site observations to the wider team for follow up

To measure time savings, we need to first set a historical performance baseline. For example, how many hours does it take the typical site supervisor to create his weekly report back in the office after walking the site using a paper clipboard, printed plans and a digital camera? Once we have an estimate, we can then survey workers on changes in weekly durations of tasks. One study conducted by a major US general contractor found that compared to paper-based methods, mobility tools cut out nearly 1 day of non-value adding work per week for site staff, making them 15-20% more productive!

Schedule Improvements. Instant access to the latest project information such as construction drawings or issue statuses also eliminates "information bottlenecks" and reduces waiting time. Getting rid of non-value adding tasks optimizes end-to-end workflows, and shortens the overall time needed for critical activities in a schedule, such as:

  • Inspections and rectification of quality issues
  • End of project punch-listing/snagging
  • End of project commissioning and owner handover 


Once we have a baseline duration for an activity on similar projects in the past, we can measure schedule improvements against it. For example, if the paper-based commissioning process of a data-center has historically taken 10 weeks, and quicker issue turnaround reduces that to 8 weeks, the task has been compressed by 20%. This means we can hand over the project 2 weeks early, or at least claw back half a month's worth of previous delays.


Quality Improvements. Quicker communication, access to the latest information and more engineering time spent in the field doing value adding activities (like more inspections and subcontractor coordination) all result in faster detection and resolution of:

  • Design coordination issues like errors, clashes or omissions
  • Issues found during structured quality or safety checklist inspections onsite
  • End of project punch/snag/defect lists 
Download (1).png

Above is the The MacLeamy Effort Curve: this illustrates the benefits of implementing BIM during the design phase, but its key principle applies equally well to construction: more rigorous, proactive inspections in earlier phases of a project ensure that we ‘nip problems in the bud’ and result in less costly rework issues. In the curve illustrated above, the baseline is $X - the average cost to resolve issues using a traditional process. The reduced total cost is $Y - thanks to earlier issue detection.

AAEAAQAAAAAAAAk3AAAAJDIyMTQwYjgyLWRlMzMtNDA4My1iMjkxLTc1YmEwYTU0NzdhOA.png

To see the effects of earlier issue detection in practice, let's consider the example above. We estimate the average cost to resolve an issue (such as a defective masonry joint) by calculating the associated labor, material and rework costs which accumulate over time. The longer an issue goes undetected, the more costly it is to resolve. Thanks to cloud-based mobility tools, subcontractors now have immediate access to up-to date lists of all outstanding items on site. They are notified the issue earlier, and can fix it with less time, effort and cost. By solving issues more quickly, we're also reducing the risk that other trades will install work on top of defects, which would otherwise need to be removed, resulting in costly rework (column furthest to right).

By analysing completed project data, we can measure the number of defective items which get escalated to an owners representative. We then multiply this number by the average cost of each overdue issue, to work out the total cost of late issue resolution. Over the course of a live project, we can then track a reduction in the number of these overdue issues, avoiding the costly involvement of more parties as time goes on. Some companies may also keep track of rework as a percentage of total costs, but this information is often sensitive and never openly broadcast!

 

Quantifying Savings and Return on Investment (ROI)

So far, we've talked about how to measure quality, schedule and productivity improvements achieved through using construction mobility software. Now, let's talk about how these benefits can be translated into cost savings, and how these savings can fit into an overall return on investment (ROI) calculation for construction mobility. Contractors and construction managers are now developing strategies for implementing software in a standard way across their business, but doing so without a strategy to track and maximize cost savings leads to sub-optimal results. 

Translating Improvements into Cost Savings. Good news – once we've measured efficiency gains, schedule acceleration and rework reduction, the hardest part is over! We can now take the values for improvements, and multiply them by various project factors to estimate total cost savings.

AAEAAQAAAAAAAAhDAAAAJDI2NjE3YjE3LTc1MzctNGM5ZS04ZWZkLWQxMGVhNjI1YzIwYw.png

 

There are generally 2 approaches to calculating cost savings of construction mobility:

1. Cost savings through reduced site supervision personnel needs.  A contractor decides that they can do the same amount of site supervision work with a smaller team. For example, if our overall team has 20% more time available for value-adding tasks, a team of 5 could be reduced to a team of 4 and achieve the same as before. In this case, the cost savings calculation would be:

% Efficiency gain * Hourly labour rate * users * hours worked per week * weeks in use

2. Cost savings through earlier delivery and less rework. On the same project reporting 20% efficiency gains per user, a contractor may decide that rather than reducing the size of the site management team, they will invest the thousands of hours saved into value-adding tasks which optimize schedule and quality. In this case, we first need to subdivide the overall project costs, to understand what we stand to gain if we deliver early and reduce rework. To help visualize these cost breakdowns, lets consider a typical project. On a commercial development project with an overall contract value of £50 million , the costs might be broken down as follows:

 

*Areas for improvement

The two areas in which we can expect to reduce costs through focusing more construction site personnel time in value adding activities are: 

  • Preliminaries (general conditions) due to less overhead costs as total time on-site to deliver a project becomes shorter, reducing time based variable costs (eg. site offices, security, crane rental).
  • Rework as a percentage of direct costs due to improvements in quality, through better coordination, and earlier issue detection and resolution. A study by a major US general contractor estimated that a project team could avoid up to 1/3 of all rework with an additional 50 hours/week of field supervision & planning, hours which mobility tools can make available by eliminating non-value adding activities.

 

Translating Cost Savings into ROI. In the first approach mentioned above, a contractor may decide to reduce site supervision personnel headcount, thus achieving the same performance with less staffing requirements, saving labor costs. In that case, our ROI calculation would be: 

 

Another contractor may take the second approach and decide to achieve better schedule and quality by reinvesting the time of the same-sized site team, in which case, our ROI calculation looks like this:

 

This means that ROI can be calculated differently, depending on how we decide to interpret the cost savings which result from performance improvements. 

Additional Benefits of Adopting Construction Mobility Software. Our discussion so far focuses on the three most commonly quantified benefits – productivity, schedule and quality - but these are not the only benefits of construction mobility software! There are two main reasons for our focus on these benefits: they are key project performance areas, and there is a proven methodology for quantifying savings in these areas. But our discussion of benefits and ROI would not be complete without considering that project-level improvements can also have knock-on benefits which improve a contractor's profitability. These include: 

  • Analysis of project data to identify trends, supply chain performance, problem areas and opportunities for continuous improvement. A future post will be dedicated to discussing the benefits of this analysis.
  • Increased client satisfaction due to reduced waste, improved quality, earlier delivery and more complete information handover creating cost savings later in maintenance and operation. This results in increased repeat business, higher rate of successful project bids, and a lower annual cost of project pursuit.
  • Insurance against claims after project completion due to clear and complete construction documentation.
  • Improved site safety through more rigorous inspections, and the ability to drive down insurance costs by KPIs such as EMR (Experience Modification Rate). 

For a detailed discussion of how a major US general contractor calculated ROI of construction mobility, watch this video.

This post was originally published as 2 articles by Michael Moran on Linkedin Pulse in Sept 2016

How to Get the Most out of Construction Mobility Software

Construction contractors have begun widespread adoption cloud-based, mobile collaboration software on devices such as iPads to manage information flow on their projects. Most contractors have reported significant improvements. But its hard to quantify these improvements, and there's lack of agreement on how to maximize the benefits. Over the next few weeks, we'll be discussing the details of some practical, proven methods to ensure successful implementation and optimize ROI of these powerful tools. 

Get support from the right people. Any successful implementation of a new technology within a business needs buy-in from both management and the main user base. To get support, put yourself in the shoes of anyone affected by it, and ask yourself: what's in it for me? How does this make the daily life of a site supervisor easier? How can we prove a positive ROI to a company's directors after 2 years? Then, make sure to:

Set goals and track progress. By mapping out each of the the workflows which we want to support and improve using mobility tools, establishing performance baselines and then measuring improvements against them, we're not only tracking the success of the implementation, but can also identifying what's not working, so that we can know what to fix if needed. 

View a project as a communication network and involve all relevant members. Sure, a tablet can serve as a camera, map, DJ mixer and Pokemon catcher, but mobile handsets were invented as a means to communicate. Yet general contractors who invest in mobility software tend to focus primarily on documenting site conditions, or on internal communication within the construction supervision and management team. They often to neglect the wider network of 3rd party project members such as designers, client representatives and trade subcontractors,who also need to exchange up to date information on a daily basis. The more members of an project team who can access to the latest info when they need it, the greater the benefit of the tool. And by engaging members of an extended supply chain with cloud based software, we collect valuable datasets that can give us insights into performance that were previously not available. 

Don't forget about staffing, training and IT infrastructure. BIM managers reading this will no doubt agree that their daily routines are busy enough. Don't expect a successful implementation if you just add rolling-out enterprise construction mobility software to a BIM manager's already groaning to-do list. To fully capture the potential benefits of this extremely strategic shift in how projects manage information, assemble a knowledgeable, specialized team who understands business needs, project specific requirements, and can support projects in a standardized way. And don't let a lack of hardware, internet connectivity or user training block adoption. 

This post was originally published by Michael Moran on Linkedin Pulse, Sept 7, 2016