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How Data Analytics Will Improve Your Bottom Line

Discover how data analytics and technology advancements are transforming the construction industry, improving productivity, profitability, and safety. Learn how companies can leverage data to optimize resource utilization, make better business decisions, and address common industry challenges.

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How Data Analytics Will Improve Your Bottom Line

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  1. How Data Analytics Will Improve Your Bottom Line Presenters: Trevor Robertson Construction Ryan Merryman Data Analytics & Forensic Services

  2. The Technology Disruptor One of the largest disruptors to any industry, especially construction in the near future, will be technological advancements. How are Companies using technology to make better business decisions? Do Companies have adequate systems in place? Lets look how data, and to a large extent systems, is disrupting succession planning.

  3. Common Issues in the Industry Productivity and Profitability Study by the Construction Owners Association of America! Underperforming Projects (complexity of jobs) Labor/Skills Shortage Sustainability - How do we create a greener footprint Safety Issues – 71% reported accidents in the past year Use of Technology – Construction is a SLOW adapter. Slight pickup with drone use, wearable technologies, and VR Lets look at some examples of Companies who failed to act!

  4. Productivity and Profitability How well are we are we TRULY measuring productivity and profitability? Lets assume the previously mentioned statistic is true, and that ~63% of direct worker time is spent on the following: a. Waiting for materials b. Traveling to location c. Deciding to take early breaks d. Choosing how to carry out the work What’s one way we can improve worker productivity?

  5. Smartphones & GPS Wearables • How about we place sensors on materials and equipment? • Let’s look at how individuals move around a job site and how they interact with the job site. • Solution: reorganize the placement of tools and even materials! • How about Travel Time?

  6. Data from Weather Both External & Internal • Import weather data from third parties • How could we use this to plan / bid our jobs? For example, if we had GPS coordinates for all of our jobs and are to pair that data with weather patterns / data from a third party, that could be an extremely powerful predictive tool: - Predict weather delays - Deployment of workforce - Use historical data to make future decisions - Record the actual effects of weather delays - Help bid jobs and write change orders

  7. Buy versus Rent Equipment / Replace Parts What If we had sensor input from machines / equipment? • Review utilization of equipment – buy vs lease? • Review historical data on replacement of parts in equipment based on use – have those parts available preemptive rather than waste time waiting for the parts to arrive.

  8. Utilization of Workforce / Materials / Equipment • What decisions can be made by tracking employee utilization? • For example, lets assume your workforce is 50% utilized – how does that change your subcontract decisions? • What decisions can be made by tracking materials and equipment utilization? • For example, you have a dozer and fill dirt 10 miles away – how does that change your onsite “import” decisions?

  9. Bids / Change Orders What decisions can data play related to bids & change orders? • Bid vs actual tracking • Disconnect between perceived margins and actual margin • How are we accounting for indirect costs such as depreciation, insurance premiums, and estimators salary? • What is our true margin on a job? • If we knew the true cost of a job, how would our bids change going forward?

  10. Bids / Change Orders • How about the bid process? • Does the Company look at historical bids, even the ones we didn’t win? • Are those bids compared to our competitors when that information was made available? • What if we had a data bank from the past 5 years of bid spreads against your competitors?

  11. Bids / Change Orders How about the change order process? • How well do we track all costs for change orders? • Not just the hard costs! • Overestimates and underestimates are common in traditional change order management because of the lack of real data!

  12. Skilled Labor Shortage How does the industry innovate to keep their high performing workforce engaged…..to the same extent, how do we keep them accountable to each other and management?

  13. Skilled Labor Shortage • Its a common theme to hear that good employees are hard to find, and are becoming even harder to keep. Which begs the question, how can data possible help? • Lets look at a study completed by CCI • 91% couldn’t find or keep their skilled workforce • How does the baby boomers play a role / how about generation Y (or the Millennials) • A few going in & allot retiring or leaving the industry • How about immigration?

  14. Skilled Labor Shortage • So how does data help with this issue? • Transparency! • A generation defined by transparency • Glassdoor provides real time information on a company’s management, pay, and philosophy • Millennials want to know what management is looking at, how they compare, and what they need to do • Build a system to alert employees when metrics are not being met! • Make compensation / bonuses more transparent

  15. Safety Concerns / Issues • How does the company measure safety – i.e. is there a time of day in which most accidents happen at a job site? • For example, if we knew that 90% of all plan crashes occur when there is a new crew assigned to a flight and flight prep, how would management change their decision making processing going forward)?

  16. Safety Concerns / Issues • There has been numerous studies on data and its predictability of safety in the workspace. • By using 4 years of real world safety data, a Company would be able to accurately predict workplace injuries at a rate up to 97%!

  17. Safety Concerns / Issues • There needs to be a commitment to safety! • Need to take action to prevent reputation loss, talent loss, and even financial loss • Lets look at an example from a publically traded company that was in the news about a month ago. • Deployment of a predicative analytic tool at 10 locations!

  18. Use of Technology • Data Analytics • AI and Machine Learning • Predictive Analytics • Virtually Reality / Augmented Reality • Autonomous Equipment • Drones

  19. Data is Everywhere! So how do we gather it and more importantly how do we us it.

  20. Data Model Delivery

  21. Data Model Considerations Data Sources – ERP, Production, Etc. Tables – that come from Queries Data Fields (Columns) Relationships Calculated Data Fields Measures

  22. Data Model Structure

  23. Practical Example – Cash Flow Concerns Lets assume the flowing: • The Company has a strong backlog • The Company has increased operational contract revenue by 10% year-over-year for the past 4 years • Margins on jobs, year-over-year, have increased • General & administrative costs have remained consistent • YET the company has consistently had cash flow issues and finds itself drawing on their line of credit to fund operations. HOW CAN DATA PROVIDE INSIGHT?

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