Traffic on the railway nearly doubled between 1970 and 2006, but its timetable had not changed, leading to commuting problems...
Restructuring increased the percentage of trains arriving within three minutes of schedule from 84.8 percent in 2006 to 87 percent in 2007, while the number of passengers increased 2 percent in the first six months of 2007.
Profit, meanwhile, rose $60 million and the changes made additional capacity available...
It is obvious that OR applications in businesses can make a company very powerful, but it takes the OR talents who can talk business to do it. To quote Brenda Dietrich, an IBM fellow at IBM’s Watson Research Center:
There’s a gap between the math professionals and the nonmath executives in
many companies. The companies who have people who can walk into a business meeting and tell executives how to use OR tools are the ones who’ve got the edge. Deployment is no longer done just by the math people; analytics has become much more usable by a broader set of people within an organization.
Click here to view the video.
Click here to see the full article.
Warning to studio readers — two marketing professors at the Wharton School could very well put you out of a job.
Actually, Z. John Zhang and Jehoshua Eliashberg (plus a bevy of co-authors) claim that their goal is merely to augment your special talents, not replace them. But the paper they published in Management Science magazine in June called 'From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts' establishes a statistical model for analyzing screenplays and predicting whether a resulting movie will be successful at the box office. Which, if accurate, would render your silly personal judgments obsolete.
Greenlighting, or putting a screenplay into active production, relies mostly on the subjective intuition of readers and executives (plus a studio calculus derived from the budget and the past record of the film's genre and potential cast). It's a system that can produce, shall we say, spotty results. Zhang and Eliashberg hope to take some of the guesswork out of it. Their model combines textual analysis (paragraph construction, frequency and distribution of words, etc.) with structural analysis (a clear premise, a surprise ending, and the like) using 22 yes-or-no queries that are posed and then cross-referenced.
forecasting “experts” don’t do much better than novices — or, for that matter, guessing — when it comes to predicting the future.I've been taking a forecasting class, and find the topic rather fascinating. However, as in the course of my study of different forecasting methods, it seems that in a lot of cases the best forecast is the naive forecast - which is simply guessing using current values.
In the case of a woman who does not feel that motherhood will be a significant barrier to her pursuit of a particular milestone, the model suggests attempting to conceive a first child at a younger age. Specifically, the model can calculate for any specific situation the level of anticipated negative career impact at which an individual woman may wish to postpone having a child.The example of a 20-year-old college student illustrates the situation when a woman claims she does not want to have a child until she reaches a certain age, say 35 years old. The model suggests that, especially in cases where both family life and career are important to the woman, having a child much earlier may be a better long-term solution than waiting until she is more established in her career.
For more information on the model, please visit their website:
Posts
