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Have you been wondering how the structure housing your office got to where it's standing today? Your next line of thought, then, would be about the skills used to meet expectations in compliance with quality. It'll interest you to know that even the simplest construction floor plans involve a great deal of effort, not to mention detailed feasibility and environmental impact studies!
The nature of construction is such that timelines tend to exceed the original estimate, which corresponds to costs rising by the hour. Given how critical it is to have people who know their way around a construction site, it's no surprise why construction project managers double up as site supervisors. After all, surveying your site analytically lets you mandate safety procedures that ensure your crew are taking all necessary precautions prior to commencing work.
Predictive analytics tools aid you in your efforts by giving you valuable data streams in real-time. With the right body of knowledge, skills and experience, you'd be able to optimize work schedules and utilize the right people at the right time.
While business intelligence (BI) gives you visibility over information, predictive analytics categorizes this by relevance and accuracy. It generates actionable insights specific to your role, enabling you to reach informed decisions. As such, when you know what you're working with, you're better equipped to inject confidence into your project estimates.
How Does Predictive Analytics Help?
A Work Breakdown Structure (WBS) lets you jot down tasks, adding more or removing some off individual work packages. With predictive analytics, you can simplify the task breakdown by the quantity and type of skills needed. Once you match the right people to the right tasks, expectations are clarified based on what needs doing and who will be doing it.
Predictive analytics scans previous project documentation, including the WBS, to assess what went wrong in executing these tasks. It informs you of
• The skills deployed that were relevant and accurate for the work carried out.
• The tasks that were time consuming yet mission-critical.
• Those learning programs that helped your staff upskill without disrupting regular BAU activity.
Consequently, when you replace outdated skills with true know-how, you'll scientifically cut your resourcing costs without compromising on potential. The staff you retain are not only qualified but have the bandwidth to be utilized optimally. Plus, you'll avoid overloading inexperienced staff with unfamiliar work. Predictive data analytics lets you discover cost-effective resource types who can pitch in alongside full-time staff, thereby increasing your project's ground speed. For example, if your team comprises of civil, electrical and structural engineers, you can recruit contractors with exposure to specialized lines of work. This way, your full-time staff aren't investing all of their working hours and efforts into a single project or phase alone. What's more, with data analytics recording modifications to previous work packages, you are rest assured that any design recommendations put forth are backed by experiential prowess!
In order to make sense of the information before you, the first step is take your paperwork online. By digitizing your records, you have a centralized block of data from which relevant information can be retrieved.
Descriptive analytics creates a master database which comprises of the failure points encountered on previous projects, their triggers and severity. Predictive analytics then analyzes the likelihood of their recurrence and their impact on ongoing tasks. With prescriptive analytics, you can explore technologies and options that have evolved specifically to prevent these risks from seeping into future work. For example, imagine a record of the contractors you've contacted in the past and the projects they've corroborated on.
In the event of their unavailability for the period you're looking at, predictive analytics can scan the skills inventory and notify you resources on the bench who possess these skills. Not only does this measure reduce the bench-time clunk drastically but also allows you to onboard new contractors if the quantity still doesn't add up.
No sector advocates for health and safety procedures more vociferously than the construction and engineering space. After all, if your crew gets injured on-site, it lowers productivity and affects team morale when a member is down.
With a combination of predictive and prescriptive analytics, you can divert your workforce away from site disasters. For example, predictive analytics pinpoints disaster zones to the nth degree accuracy. It makes use of pedometer analytics to measure the distance your crew covers during working hours. This way, you can position tools and heavy-duty equipment at various access points so that visibility is improved in low-lying areas. Shortening distances boosts your resources' productivity by one hour per working day!
Predictive analytics also alerts you to the type and quantity of technical resources that need servicing. It gives you an overview of past maintenance dates in order to inform your staff of those periods of time machines are not fit for use. You'll know when to schedule servicing of equipment to ensure outdated machines aren't allotted to your crew. You can even schedule site visitations to ensure both the project's progress and your crew's adherence to the mandated safety procedures.
Unplanned downtime is a term every project manager fear because it delays the production line. Consequently, your project activities are paused owing to the backlog in tasks that can't be executed till the machines are back in working order.
Relying on manual monitoring methods is not only laborious but fails to factor in the probability of the downtime rising again in the future. By taking the predictive route, you prevent your labor and production costs from increasing. In fact, GE's Kimberlite survey reported that operators who used a predictive, data-based approach experienced 36% less unplanned downtime than those with a run-to-failure approach. With machine life expectancy hanging in the balance, monitoring heavy-duty construction equipment digitally prepares you to schedule repair in real-time by:
• Retrofit sensors: Sensors that contain complex algorithms can be attached to the machines themselves. This way, different permutations about the data such as climatic conditions, usage and warranties can be analyzed. Companies can then save costs on manual support and maintenance activities.
• Cloud computing: By putting your information on the cloud and fortifying its security with access rights, you can ensure that only the right data is communicated from any corner of the globe. Simply put, even during times that you're not at your desk, you're made aware of upcoming issues detected, including an overview of machinery usage and their longevity.
Besides detecting faults in the line, Big data lets you optimize the performance and quality of your machines and equipment. It predicts and diagnoses issues early on in order to let you implement an analytics-based work management program to manage both human and technical resources.
The tertiary advantage of big data is that it centralizes reams of data to prevent any information from remaining unanalyzed. Predictive analytics converts dormant data into actionable insights. It collects information from upstream operations to see how your bottom line is being protected.
Based on your role and permissions, you can run multiple scenarios based on optimistic, likely and pessimistic estimates. Your reporting becomes naturally granular and lets you assess your construction readiness by their duration and feasibility. You can subsequently profile your project portfolio by those projects with a winning streak. You can simultaneously recognize and kill high-risk projects that are guaranteed to fizzle out.
Did these benefits clear any concerns you had about making the Big Data Move? Let us know in the comments below!
