Data seen as cost saver – TheMontrealGazette(QC)

Data seen as cost-saver

We need to use information to become more efficient and less wasteful. There are all sorts of levels you can do that at, from design to planning to construction to manufacturing.

Construction industry, tagged as being particularly wasteful, could see billions in savings through analytics, engineer says Construction industry leaders think the sector needs to cut waste, and that analytics will make it happen.

“We are a wasteful industry,” said Darlene La Truce, executive vice-president of the Edmonton Construction Association, pointing to the amount of wood a typical framer throws away while building a house.

“We’re not productive. We need all the help we can get.”

Building a single-family home can create as much as 50 tonnes of
carbon-dioxide emissions, says Mohamed Al-Hussein, a professor of
construction and engineering management at the University of Alberta,
who agrees with La Truce: “This industry is the most wasteful industry you could have.”

 

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Building Productivity by Darlene a Trace, Klaas Rodenburg, Allan Partridge, Mohamed Al-Husseiin

Estimates identify the waste in the construction industry at over 50 percent…much of waste comes from inaccurate or untrusted information causing information to have to be regathered multiple times throughout the life of a project. -Dianne Davis, BuildingSmart Alliance

Can Analytics reduce the cost of buildings by 50%?

  • Reduce duplication
  • Reduce Risk
  • Reduce Waste
  • Lean Construction
  • Lean Manufacturing
  • Building Automation
  • Life Cycle

The construction industry is changing at a dizzying rapid pace!

  • Slow growing uncertain markets
  • Pressure on traditional business models
  • Geographic Irrelevance
  • Shortage of Talent
  • Drowning in a sea of data
  • Disruptive technologies
  • Sustainability

 

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Building Industry to Cut Waste by Lewis Kelly

“We are a wasteful industry’” said Darlene La Truce, executive vice president of the Edmonton Construction, pointing to the amount of wood a typical framer throws away while building  a house. “We’re not productive. We need al the help we can get.”

Environmental Monitoring Data by Ray Keller

The Story So Far – Current Challenges

  •  Background
  • Monitoring Program – Business Drivers
  •  Data management challenges
  •  Data vs. information vs. knowledge, etc.
  •  Where is the data? What is the issue?

Implementing the Data Management Framework – Future

  • Addressing the data and data management challenges
  • Steps towards an integrated data management system
  • Development of an Enterprise wide Data Management Strategy

 What does all this mean to data and data analytics for the environment?

  • Common data framework is required to:
    • Move data from the unique to the shared – concept of core data
    • Must be collaborative
    • Must deal with complexity of security and traceability – science
    • Data quality issues
    • Data needs to be available in the open domain
  • Large change in culture will be required
    • Scientific focus
    • Tension will be between being “open” and “exclusive”

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Disturbance and Recovery Trajectories (DART) by Tim Vinge


DART is an acronym that stands for Disturbance and Recovery Trajectories

It’s time we face reality my friends… We’re not exactly rocket scientists.”

Let’s get started anyways

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Presentation Outline

  1. Introduction to restoration planning.
  2. The concept of DART
  3. Building the DART board
  4. Strategic opportunities for DART modeling
  5. Tactical opportunities for DART
  6. Summary comments and questions

Summary Comments

  1. DART will be used at the tactical and strategic planning levels in combination with other land use considerations (recreation, energy, forestry, wildlife) to formulate restoration plans.
  2. Vegetation recovery is based on the original disturbance, subsequent line usage and the ecosite.
  3. Some lines will come back fairly quickly and some very slowly. We have to sort out the wheat from the chaff so to speak.
  4. Models will be very useful for making strategic decisions on where to focus efforts for line restoration (caribou).
  5. Treatments will vary by ecosite. DART will help ensure that the correct treatments are being used.
  6. We have enough knowledge and experience to build DART.
  7. DART will form an important part of future landscape assessments.
  8. Dart is only a part of a more comprehensive landscape evaluation and planning program.

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Forest Industry Perspective – Land management and analytics by Gordon Whitmore

Our goal is to operationalize ILM and  work together to reduce our footprint, promote sustainability and remain profitable.

Today’s Discussion

  • Forest Resource Management
  • Multiple Use
  • Integrated Land Management
  • Opportunities

 Guiding Principles

  • Maintain Biological Diversity
  • Maintain Ecosystem Productivity
  • Protect Soil and Water
  • Consider Global Ecological Cycles
  • Multiple Benefits to Society
  • Accepting Society’s Responsibility for Sustainable Development

Challenges

  • Cumulative effects
  • Different degrees of planning between industry sectors & between government departments
  • Clarification of public priorities for forest lands
  • Local forest companies continue to explore ILM opportunities with other land users.
  • Forestry, Data and Analytics
  • Large datasets
  • If you can see it, we likely measure it and store the data in our systems
  • Stratification used to overcome data storage and computing limitations
  • Stratification used to reduce computation load
  • Development of robust forest models
  • what-if analysis
  • Management plans

Innovations

  • LiDAR
  • Hyperspectral imagery
  • Computer generated classification
  • Tree-level
  • Wet-Areas Mapping

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Ecosystem Services and Conservation Offsets-Carol Bettac

The benefits that people obtain from the environment (Millennium Ecosystem Assessment, 2005)

Alberta’s History

  • Dramatic change in its landscapes
  • Energy, forestry, agriculture and urban development
  • Competition for land  and resources between sectors
  • Environmental challenges – limits are being approached; reputation
  • Water quality, habitat loss and fragmentation of the landscape; greenhouse gases
  • Social license to operate
  • Biodiversity, water, and green house gases new drivers of innovation and competitiveness

Alberta’s Policy

  • Land Use Framework 2008
  • Enables development and use of conservation and stewardship tools, including market based instruments
  • Conservation offsets, transfer of development credits
  • Water for Life 2008
  • Guides water allocation and management
  • Provincial Energy Strategy 2008
  • Global energy leader, recognized s a responsible world class energy supplier

Two Projects

  • Alberta’s Bio Resource Information Management System
    • Silvacomand Green Analytics
  • Ecosystem Services Assessment
    • Alberta Biodiversity Monitoring Institute
  • Collaborators:
    • Alberta Innovates
    • Alberta Livestock and Meat Agency
    • Alberta Biodiversity Monitoring Institute
    • Silvacomand Green Analytics
    • MiistakisInstitute
    • Government of Alberta

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Leveraging Alberta’s Oil & Gas Advantage to Support the Green Economy-Mike Kennedy

Outline

  • Alberta’s Oil & Gas Resource
  • Examine the current and future footprint
  • Strategies to leverage this footprint
  • BRIMS as a key Big Data resource for the province

Green Analytics Role

  • Leading the development of data and information systems
  • Identify key strategies and opportunities for governments and business to play a role in the green economy
  • Analyzing opportunities for socio-economic and environmental benefits
  • Communicating opportunities, benefits and challenges to a broad audience Green Analytics Role

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Analytics in the Oil and Gas Industry by Dean Wallace

Fallacies Arising from the Simplest Form of Analysis – The Infamous “Average”

An argument can be made on the same basis that the tree frog, on average, is black. However, this analysis is about as relevant as the knowledge drawn from overly-simplistic mathematical calculations that often are carried out on large data sets.

 

Analytics as the Solution – The Production Intelligence Suite of Analytics Solutions

  • Objective was to understand the effect of interactions on end-of-line measures (e.g. recovery) for the purpose of optimizing the process
  • It was necessary to consider a probabilistic solution in addition to deterministic solutions
  • Required a solution that was not biased by the individual doing the modeling
  • Association Discover*E was an appropriate tool for this application
  • Data-driven rule generation provides an unbiased perspective
  • Rule structure results in transparency so people can assimilate knowledge developed in the analytics process
  • Transparency also results in identification of correlation of supposedly independent variables
  • Allows for simultaneous analysis of quantitative and descriptive variables
  • Possibly a precursor to a control system without human intervention

 

Closing Comments

  • We have found that a statistically-based analytics approach has been able to unlock knowledge about the oil sand extraction process that was not possible using conventional statistical techniques and/or deterministic modeling
  • Elimination of bias during the process and transparency of the results are critical
  • Analytics has led in some cases and has collaborated in others with subject-matter expertise
  • The nature of the process being modelled determines if analytics can provide value
  • Frequency of the data must be much greater than the frequency of actions
  • The greater the influence of interactions on the process, the greater the value of or necessity for an analytics solution

 

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