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Why are unions having a tough time in this country? One reason is that companies are getting leverage. A recent study found that companies with a higher debt load were less likely to experience a strike during contract negotiations, particularly at companies with large unions, worse financial prospects, or underfunded pension plans. Some companies seem to anticipate this and load up on debt before contract negotiations, whereas companies that didn’t do this and experienced a strike subsequently add a ton of debt, particularly if the union won the strike. The debt gives the company a bargaining advantage by limiting how much earnings can be shared with workers vis-à-vis lenders, and often takes the form of stock buybacks, to avoid bringing money into the company.

Myers, B. & Saretto, A., “Does Capital Structure Affect the Behavior of Nonfinancial Stakeholders? An Empirical Investigation into Leverage and Union Strikes,” Management Science

Lack of motivation may also play a large role in the reason why 68.8 percent of Americans are overweight or obese. 

So are we all screwed? Or is there a way to keep motivation consistently flowing?

The key may be a process called “temptation bundling,” according to a study in Management Science. 

The process pairs two activities—one you should do, but avoid; and one you enjoy, but isn’t necessarily productive, explains lead study author Katherine Milkman, associate professor of Operations, Information, and Decisions at The Wharton School.

My view of the world is shaped by where I stand, but from this spot the future of analytics for 2016 looks pretty exciting! Analytics has never been more needed or interesting.

Analytics continues to bring dramatic change to the healthcare industry in the United States and other countries, offering advances and challenges for the year ahead. Following are 10 trends to chart in 2016.

David Simchi-Levi, MIT Professor, INFORMS Fellow and Former Editor-in-Chief of Operations Research (INFORMS Journal) 

The ability to understand a combination of historical behavior, market conditions and future needs drives decision making. New analytic capabilities that combine machine learning and optimization can take into account historical characteristics and competitor behavior to determine future demand that will allow optimization for the best results - such as profit, market share or revenue.

Examples of decisions where this approach can be used is assortment, pricing, sourcing strategies for new products, predictive maintenance using process sensors.