Construction Ryan Merryman Data Analytics amp Forensic Services How Data Analytics Will Improve Your Bottom Line The Technology Disruptor 2 One of the largest disruptors to any industry especially construction in the near future will be technological advancements ID: 1028772
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1. Presenters: Trevor RobertsonConstruction Ryan MerrymanData Analytics & Forensic Services How Data Analytics Will Improve Your Bottom Line
2. The Technology Disruptor2One 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 IndustryProductivity and ProfitabilityStudy by the Construction Owners Association of America!Underperforming Projects (complexity of jobs)Labor/Skills ShortageSustainability - How do we create a greener footprintSafety Issues – 71% reported accidents in the past yearUse 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 Profitability4How 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 workWhat’s one way we can improve worker productivity?
5. Smartphones & GPS Wearables5How 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 & Internal6Import weather data from third partiesHow 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 Parts7What 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 / Equipment8What 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 Orders9What decisions can data play related to bids & change orders?Bid vs actual trackingDisconnect between perceived margins and actual marginHow 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 Orders10How 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 Orders11How 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 Shortage12How 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 Shortage13Its 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 CCI91% couldn’t find or keep their skilled workforceHow 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 Shortage14So 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 philosophyMillennials want to know what management is looking at, how they compare, and what they need to doBuild a system to alert employees when metrics are not being met!Make compensation / bonuses more transparent
15. Safety Concerns / Issues15How 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 / Issues16There 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 / Issues17There needs to be a commitment to safety!Need to take action to prevent reputation loss, talent loss, and even financial lossLets 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 Technology18Data Analytics AI and Machine LearningPredictive AnalyticsVirtually Reality / Augmented RealityAutonomous EquipmentDrones
19. Data is Everywhere!19So how do we gather it and more importantly how do we us it.
20. Data Model Delivery
21. Data Model ConsiderationsData Sources – ERP, Production, Etc. Tables – that come from QueriesData Fields (Columns)Calculated Data FieldsRelationshipsMeasures
22. Data Model Structure
23. Practical Example – Cash Flow Concerns23Lets assume the flowing:The Company has a strong backlogThe Company has increased operational contract revenue by 10% year-over-year for the past 4 yearsMargins on jobs, year-over-year, have increasedGeneral & 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?