Lower CostsWhile some companies are adopting analytics to increase profits, other companies are relying on analytics to reduce costs in various ways. This is an area where UPS is a leader in the effective use of predictive analytics. The company uses analytics to monitor all of its 60,000 vehicles so it can see when to perform regular maintenance services on each vehicle. Where previously the company replaced parts on its vehicles every few years to reduce potential delays caused by a breakdown, the company is now able to determine which individual parts need to be replaced and when. Since UPS implemented this analytics program, it has saved millions of dollars (Mayer-Schönberger & Cukier, 2013). Fraud detection is another area where predictive analytics is making a difference. The benefit of being able to better detect fraudulent transactions is that it helps companies realize significant cost savings. Siegel (2013) noted two different companies that have advanced fraud detection through predictive analytics. The first company, Citizens Bank, was able to reduce fraud losses by 20% by implementing a fraud prediction model that evaluates each check to predict how likely it is to be fraudulent. The second company, PayPal, reportedly reduced the rate of fraudulent transactions from 20% at launch to less than 1% by improving fraud detection methods by applying predictive analytics. Likewise, companies are cutting costs by using data analytics to transform their supply chain models. In the 1990s, Walmart revolutionized the use of analytics by transforming its entire supply chain model and inventory systems. The company recorded each product in its Retail Link data...... middle of paper......Korte, D., Ariyachandra, T., & Frolick, M. (2013). Business intelligence in the hotel sector. International Journal of Innovation, Management and Technology, 4(4). Bradley, P., & Kaplan, J. (2010). Turning hospital data into dollars. Healthcare Financial Management, 64(2), 64-68.Kiron, D., Prentice, P.K., & Ferguson, R.B. (2014). Raising the bar with analytics. MIT Sloan Management Review, 55(2), 29-33.Davenport, T., & Harris, J. G. (2009). The forecast lovers' manual. MIT Sloan Management Review, 50(2), 32-34.