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Pedram Danesh-Mand

Asia-Pacific Technical Director – Risk Management

Aquenta Consulting (a Jacobs Company) 

With a successful record of executive positions with high profile companies and as an industry innovation award winner, Pedram has contributed to a number of major projects across Australia and overseas.

As Asia Pacific Technical Director - Risk Management with Aquenta Consulting (a Jacobs Company), Pedram leads and inspires teams; setting a benchmark for delivery of Integrated Project Controls (Scope, Time, Cost, Risk) and effective Risk and Contingency Management approaches to support clients making informed decisions.

Prior to this, Pedram was Head of Planning & Risk with UGL Engineering in a wide range of projects across Water, Power Generation, Power Systems, Coal, LNG/Oil & Gas.

Pedram is an innovation award winner from Australia’s Roads and Maritime Services (RMS) authority and CPB Contractors (previously known as Leighton Contractors) for his exceptional knowledge and practical application of statistical analysis and the Primavera system in analysing project risks and schedules.

As well as being the NSW President of Risk Engineering Society (RES) since 2013, Pedram has been a committee member of a number of international risk and project controls conferences including deputy-chair of RISK 2016 Conference in Sydney, Australia.

Pedram is highly skilled in Project Risk/Contingency Management, Project Planning/Scheduling, Project Controls, Monte Carlo Simulation, Schedule/Cost Risk Analysis, Earned Value (EV), and Enterprise Risk Management (ERM). Pedram is also a seasonal lecturer and tutor on advanced risk and contingency management (postgraduate level) at University of Technology, Sydney.

During his career path, Pedram has successfully helped high level Government Agencies, Law Firms and top contractors including:

Transport for NSW (TfNSW), Roads & Maritime Services (RMS), Transport Main Road (TMR), UGL, Origin Energy, BHP, Leighton Contractors, John Holland, Thiess, and Silcar Communications while working on many major projects and programs including:

$11b Sydney Metro City & Southwest

$11b WestConnex

$11b APLNG Upstream

$5b Brisbane Underground & Bus

$900m Inpex Ichthys LNG CCPP Project

$100m Solomon Power Station, FMG;

RGP5, delay claim;

Gold Coast Rapid Rail, Due Diligence RLB;

Revesby Turnback Project, Thiess vs TIDC;

Australia’s National Broadband Network (NBN) in TAS, NSW, SA;

North West Growth Centre;

Sydney Southern Freight Line;

Upper Hunter Valley Alliance;

$500m Ballina Bypass;

$200m North West T-Way;

$800m Port Botany.


Effective Project Risk and Contingency Management for Major Projects - Case Study

These days, development and delivery of most major projects need a unique combination of proactive risk management skills, experience and approached to identify, assess and manage the most optimum contingency allowances.

The use of Quantitative Cost and Schedule Risk Analysis (QRA) for estimating appropriate levels of cost and schedule contingency allowances on major projects is common in many organisations. Many companies offer QRA services as part of their cost-estimating and scheduling arsenal, with varying degrees of effectiveness in relation to QRA preparation, workshop techniques and the subsequent reporting of QRA outcomes.

When undertaken effectively, the QRA process is a useful part of project risk management. In many cases, however, there is no clear link between the assumptions made during the QRA process and QRA outcomes, nor between the Base Estimate, Base Schedule and the remedial actions to be taken to address threats and opportunities identified during the QRA process.

Pedram’s presentation will outline an explanation of the basics, recommended approach to achieving effective QRA outcomes and common tools and techniques as well as few recent case studies across Australia. The discussion will address the importance of effective pre-QRA briefings, the importance of basis-of-estimate and basis-of-schedule documents, and the importance of documenting the assumptions and reasoning behind the quality of input data, the key elements of the model and its outcomes.

 

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