Shanghai FDA Keeps the Food Supply Safe with Risk Analysis

In This Issue
What’s New
» Shanghai FDA Keeps the Food
   Supply Safe with Risk Analysis

» “Risk Analysis and Oil
   Production Curves” in
   Oil & Gas Monitor

Case Study
» Civil engineering consultancy
   uses @RISK for infrastructure
   PPP projects

@RISK Modeling Tips
» Hydroelectric Power
   Example Model

Upcoming Webcasts

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2013 Palisade Risk Conferences
Announcing 2014 Risk Conference dates in Europe, the Middle East, and Africa. Join us for a full day of real-world case studies presented by industry experts, and practical software training presented by Palisade trainers and consultants.
» Copenhagen: 31 March
» Amsterdam: 3 April
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  29-31 de janeiro
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  19-21 de março
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Munich: 4-6 March
London: 4-6 March
Dubai: 6-7 April
Johannesburg: 15-17 April

Latin America
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Houston: 14-16 January
Ottawa: 11-13 February
Atlanta: 18-20 February
Washington DC: 4-6 March


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Shanghai FDA Keeps the Food
Supply Safe with Risk Analysis

The Shanghai Food and Drug Administration monitors the safety of the production, circulation and consumption of food, drugs, health supplements and cosmetics for Shanghai, a city with over 20 million residents. Since 2007, the Shanghai FDA has been using Palisade’s @RISK software for risk and decision analysis to monitor contaminants and assess food safety risk in food service. Tian Mingsheng, Inspector and Chief Physician of the Shanghai FDA, explained in a recent case study how @RISK has benefited Shanghai’s food supply.

@RISK has helped the Shanghai FDA carry out exposure assessments of chemical and biological contaminates, as well as analyzing surveys of data on residents’ expenditure on various foods. Overall, the software “has played an indispensable role in the efficient development of food safety monitoring at the Administration, helping guarantee the safety of the food Shanghai residents consume,” writes Mighsheng.

Mingsheng says, “with @RISK, the Shanghai FDA has been able to eliminate its old-fashioned, backward risk assessment and evaluation methods, and find patterns even at the micro level, finding, for example, patterns and risks in chemical and biological contamination, with important implications for maintaining food safety for Shanghai residents.”

 » Case Study: @RISK Helps Shanghai FDA
    Guarantee Public Food Safety

 » Case study in Chinese:


“Risk Analysis and Oil Production
Curves” in Oil & Gas Monitor

“Risk Analysis and Oil Production 
Curves” in Oil & Gas Monitor

Rafael Hartke’s recent article in Oil & Gas Monitor focuses on the production curve risk of a single oil well by exploring how risk factors affect the production curve, and some common mistakes one should avoid when modeling production curves of oil projects. The article demonstrates how the use of deterministic models to forecast oil production rates can be very misleading in face of uncertainty. The correct approach to follow when modeling oil production curves in the face of uncertainty is to use a probabilistic method such as Monte Carlo simulation, which allows determining all possible production curves based on the uncertain production parameters. The curves obtained via simulation, which include expected production curve, percentiles and the cumulative production, are in fact calculated from all possible production curves obtained from the combination of the uncertain inputs modeled as probability distributions.

 » Read “Risk Analysis and Oil Production Curves” in Oil & Gas Monitor

Case Study
Civil engineering consultancy uses @RISK to minimize financial
risk in infrastructure PPP projects

Recent practice in Private Public Partnership (PPP) transport projects has seen the participating governments and public agencies gradually moving from demand-based contracts to availability-based ones. These latter agreements see the public partner bear the financial implications of actual demand being either over or under that forecast, while risks associated with construction and service availability are transferred to the private partner fulfilling the contract.

Civil engineering consultancy Solvĕre has developed a methodology to enable the partners involved in PPP infrastructure projects to minimize their financial risks by accounting for each element of the project that can affect its financial status and therefore profitability. To do this Solvĕre uses @RISK to estimate the performance and to forecast the potential deductions in the payment mechanism for each project. (How much and at what intervals the government pays the contractor is determined by the ‘payment mechanism’. This relates to the quality of the service provided by the private partner whose revenue is therefore dependent on its performance score and the incentive or penalty rules of the contract).

The key objective is to quantify, for various levels of probability, the economic impact of the performance criteria not being met. Solvĕre’s @RISK model takes into account the contract specifications and the resources committed by the operator to complete the project and undertake maintenance of the infrastructure, combining them in order to evaluate the expected level of performance on this base scenario.

Simulation of Payment Deductions for P90

Solvĕre believes that the highly-complex nature of PPP contracts, coupled with payment mechanisms being subject to a significant degree of uncertainty, requires in-depth analysis in terms of probability and risk. Using @RISK, it has developed a way to do this, thereby enabling informed decisions regarding the feasibility of the project and a proper risk allocation between the partners.

 » Read more about Solvĕre's use of @RISK

Product Spotlight
Hydroelectric Power Example Model
This model illustrates one possible simulation of hydroelectric power generation for a 120-month horizon. There are three sources of uncertainty: monthly desired power (as a percentage of the maximum possible output), monthly rainfall, and monthly evaporation. This model is based on Roy L. Nersesian's book Energy Risk Modeling. To view the model, you must have @RISK 6.0 Standard or higher. Download a trial version here.

 » Download the example model
    (Minimum edition: @RISK 6.0 Standard)


Exploring Oil and Gas Applications of @RISK and the DecisionTools Suite: Examples from Petrobras and Others
Presented by: Rafael Hartke, Palisade
   19 December - 11am EST (Eastern Standard Time)
  » Register Now

Using @RISK in Evaluating Full (late stage) Compound Development in the Pharmaceutical Industry
Presented by: Venkat Raman, VR Advisors LLC
   30 January - 11am EST (Eastern Standard Time)
  » Register Now

» See complete Webcast schedule


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Palisade is the world’s leading provider of risk and decision analysis software and solutions. Founded in 1984, its products @RISK and the DecisionTools Suite are used by over 95% of the Fortune 100, in nearly every industry around the world. Palisade is headquartered in New York State, and has offices in London, Sydney, Rio de Janeiro, and Tokyo.


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