In 2017, a strategic analyst with the Ontario Ministry of Energy, was asked to evaluate the potential for decommissioning the Ontario Power Generation's Pickering Nuclear Generating Station (Pickering) in Pickering, Ontario, at the scheduled end of the plant’s operations in 2024, instead of waiting until 2054. Decommissioning Pickering would require $5.264 billion in 2016 terms. Storing the spent fuel would cost another $4.3 billion, for a total of $9.564 billion. Ontario Power Generation had set aside $2.75 billion for decommissioning Pickering and for long-term storage of its spent fuel. To bridge the gap between what it had contributed and what was required, Ontario Power Generation was investing in a decommissioning fund and assuming that its investment would increase in real terms by 3.25 per cent a year. The strategic analyst had to examine what it would cost to decommission Pickering in 2024 versus what it would cost in 2054.
In 2017, a strategic analyst with the Ontario Ministry of Energy, was asked to evaluate the potential for decommissioning the Ontario Power Generation's Pickering Nuclear Generating Station (Pickering) in Pickering, Ontario, at the scheduled end of the plant's operations in 2024, instead of waiting until 2054. Decommissioning Pickering would require $5.264 billion in 2016 terms. Storing the spent fuel would cost another $4.3 billion, for a total of $9.564 billion. Ontario Power Generation had set aside $2.75 billion for decommissioning Pickering and for long-term storage of its spent fuel. To bridge the gap between what it had contributed and what was required, Ontario Power Generation was investing in a decommissioning fund and assuming that its investment would increase in real terms by 3.25 per cent a year. The strategic analyst had to examine what it would cost to decommission Pickering in 2024 versus what it would cost in 2054.
In April 2016, the chairman of the partnership charged with building the $1 billion Highway 407 East Extension Phase 2 in Ontario was reviewing an e-mail from the procurement team that recommended allocation of more than $45 million in contracts for the purchase of granular base material for the highway construction. He knew that the procurement team had worked for several weeks to negotiate and analyze the supplier's quotes, but after reviewing the recommendation, the chairman wondered whether the team's recommendation captured the optimal supplier mix and the most cost-effective selection of suppliers. It was certainly a good solution, but was it the optimal solution? Even with an experienced team, this was a difficult optimization problem to solve.
In April 2016, the chairman of the partnership charged with building the $1 billion Highway 407 East Extension Phase 2 in Ontario was reviewing an e-mail from the procurement team that recommended allocation of more than $45 million in contracts for the purchase of granular base material for the highway construction. He knew that the procurement team had worked for several weeks to negotiate and analyze the supplier’s quotes, but after reviewing the recommendation, the chairman wondered whether the team’s recommendation captured the optimal supplier mix and the most cost-effective selection of suppliers. It was certainly a good solution, but was it the optimal solution? Even with an experienced team, this was a difficult optimization problem to solve.
In October 2013, the Royal Bank of Canada (RBC), Canada’s largest bank, hired a new head of Enterprise Fraud Strategy, a department tasked with protecting RBC’s global customers from fraud. The department head’s immediate priority was to prevent fraudulent transactions by RBC’s own customers—a phenomenon called first-party fraud—by implementing a bourgeoning technology called social network analysis (SNA). The technology used predictive analytics and big data to forecast the occurrence of first-party fraud. The head of Enterprise Fraud Strategy had three primary questions: First, how should SNA be used to bring down the ratio of fraud alerts to actual fraud at RBC? Second, how should the cost of maintaining SNA protocols be reduced? Finally, how should the issues around systemic performance of SNA be resolved?
In October 2013, the Royal Bank of Canada (RBC), Canada's largest bank, hired a new head of Enterprise Fraud Strategy, a department tasked with protecting RBC's global customers from fraud. The department head's immediate priority was to prevent fraudulent transactions by RBC's own customers-a phenomenon called first-party fraud-by implementing a bourgeoning technology called social network analysis (SNA). The technology used predictive analytics and big data to forecast the occurrence of first-party fraud. The head of Enterprise Fraud Strategy had three primary questions: First, how should SNA be used to bring down the ratio of fraud alerts to actual fraud at RBC? Second, how should the cost of maintaining SNA protocols be reduced? Finally, how should the issues around systemic performance of SNA be resolved?
The U.S.-based executive vice-president (EVP) of Glenmark Generics Inc., the subsidiary of an Indian generic manufacturer, is weighing his options on whether or not to proceed with what is known as a Launch @ Risk in the U.S. market. Defined as a “risk taken by a generic company when it puts a product on the market before resolving outstanding patent lawsuits against it,” Launch @ Risk is a widely acknowledged route to gain an entry into the world's largest and most profitable pharmaceutical market. The focal product is the generic version of a hypertension drug whose patent is set to expire in 2015. Glenmark Generics has just secured approval from the U.S. Food and Drugs Administration (FDA) to launch a low-cost generic version of the patented drug but the FDA approval is being contested in a court of law by the patent holder. While awaiting the court’s ruling, the EVP must evaluate his company's options and decide whether to proceed, in the interim, with the product launch.
The head of Data Marketing Analytics and Mobile for Intel Asia-Pacific was reviewing the proposed media plan for the Catch & Win 2.0 campaign. The media purchase needed to be finalized quickly in order to be included in the current quarter’s budget, but he could not help feeling that the proposed spend across the markets and advertising types could be used more effectively. He thought that the key was to use the company’s own experience and data regarding social media engagement within their markets rather than to rely on the generalized industry metrics provided by the contracted media agency, and now he must improve the proposed media plan.
Sinofert Holdings Limited, the largest comprehensive fertilizer enterprise in China, is trying to improve the profitability of its urea business. The company has invested a great deal of time and money but still reported losses in 2007 and 2009 and only a small profit in 2008. Sinofert both manufactures urea and purchases it from external suppliers, as well as distributing it to the provinces. Manufacturing costs, transportation costs, market prices, demand forecasts and manufacturing constraints are all known. An optimal distribution plan using linear programming can be compared to the plan derived by Sinofert management. Substantial profitability increases are shown to be possible, although the optimization reveals some issues with contract constraints. If the company is to make its urea business profitable, it needs a fresh look and a change in the way of doing business. The company's chief analytics officer has been asked to look at the urea business and to provide recommendations to increase profitability.