As some companies are adopting analytics to increase profits, other companies are drawing on analytics to cut costs in a variety of ways. This is an area where shipping company, UPS, is leading in the effective employment of predictive analytics. The company makes use of analytics to monitor all of its 60,000 vehicles so it can verify when to perform regular maintenance services on each vehicle. Where the company previously changed parts on its vehicles every few years to reduce the potential for delays caused by a breakdown, the company is now able to determine which individual parts need to replaced and when. Since UPS implemented this analytics program, it has saved millions ...view middle of the document...
Pharmaceutical distribution and healthcare technology company, McKesson, is another leader in the adoption of analytics to drive supply chain operations. The company developed a supply chain model to manage its in-transit inventory, which provides s real-time view of the cost-to-serve by product line, transportation costs and carbon footprint. This enables McKesson to evaluate the impacts of various changes to its operations and has resulted in a savings of over $100 million in operating capital (Kiron, Shockley, Kruschwitz, Finch, & Haydock, 2011).
Beyond the use of big data in increasing profits and cutting costs, companies are differentiating themselves from their competitors by improving business efficiencies. Results from a study conducted by MIT Sloan School of Management found that productivity levels were up to 6% higher at companies utilizing data for decision making compared to those that didn't (Mayer-Schönberger & Cukier, 2013). These productivity improvements can be attributed to new tools specially engineered to process and analyze data at unparalleled speeds. Mayer-Schönberger and Cukier (2013) recap how the credit card company, Visa, was able to utilize one of these tools to decrease the time required to process two years’ worth of customer transactions, which amounted to about 73 billion different transactions. The company successfully decreased processing time from a full month to only 13 minutes.
Some companies have had success improving human resources related processes through the employment of analytics. In 2011, HP, another company leading in the use of analytics, developed a predictive model to establish which of its 330,000 employees might be a flight risk. The model was developed through the evaluation of two years of employee data, including salaries, raises, job ratings, job rotations and attrition. An interesting trend that emerged during this analysis was the fact that within one of its most productive sales compensation teams, promotions actually increased an employee’s flight risk, especially those that came without a salary increase. This actionable insight enabled HP to utilize retention programs to improve attrition rates of key employees within that division, from over 20% in some regions to 15% with a continued downward trend (Siegel, 2013).
Additionally, companies are effectively utilizing analytics to transform billing and claim review processes. According to Mayer-Schönberger and Cukier (2013), the company ZestFinance is a good example of how today’s data analytics can be transformative to a business. The company assists lenders in determining if they should provide small, short-term loans to people with poor credit. In contrast to traditional credit ratings that are based on a small amount of strong signals, such as late payments, ZestFinance uses a much larger volume of weaker variables to determine suitability. As of 2012, the company claims a loan default rate that is a “third less...