Peter High
10-21-2015
Excerpt from the Article:
NES Rentals is in the business of renting aerial lifts and related high-reach equipment to companies in the commercial and industrial construction businesses. The company is a privately held midsize company with revenues of about $400 million, 1,200 employees and 75 branches operating in the Gulf Coast, South, Southeast, Midwest and the Northeast. NES Rentals buys equipment from OEMs and sells its used equipment at auction every four to five years in order to keep its equipment fresh. The company maintains its own equipment at the branches or dispatches mechanics to job sites to attend to breakdowns.
Ananda Rakhit has been CIO at NES Rentals for more than nine years, after having served as an IT leader at ADP and United Airlines, among other companies. In this interview with CIO Insight contributor Peter High, he shares what he’s learned across his long tenure in IT, his focus on predictive analytics and managing a large, virtual workforce.
CIO Insight: What are some examples of strategic imperatives you are driving for the foreseeable future?
Ananda Rakhit: Pricing, equipment allocation, predictive analytics for maintenance and security of our systems are strategic initiatives for NES IT. After two years of painstaking work, the pricing system has now entered a continuous improvement phase.
The ecosystem for price setting at NES is extensive. A sophisticated analytical engine based on statistical and operations research techniques is at the heart of the system. Past company data including seasonality and industry forecasts are combined with latest data gathered from the field. Utilization data is collected daily by scanning bar codes on each piece of equipment at delivery and pickup. Price sensitivity data is gathered daily via inputs from field sales via a smartphone app. A forecasting model and an optimization model generate prices by branch and by equipment category which are delivered to sales via desktops and iPads. Every morning a newly calculated set of rates based on the latest information shows up in a pricing app to guide sales.
Equipment allocation is another strategic problem we are working on to go beyond spreadsheet analysis. In a nutshell, this problem entails buying the right mix of equipment, allocating them to the branches where it will deliver most to the bottom line, moving them from one branch to another as demand patterns change and identifying the equipment to send to auction. While some modules, such as demand forecasts are common, the mathematical model is more complex than pricing. A decision support system can allow the user to carry out repeated runs by changing input parameters.
We are in the early stages of predictive analytics for maintenance. We are looking at data on mean time between failures of various parts and exploring how we can gather engine performance data via GPS devices. We hope to build a statistical model that could enable our mechanics to make proactive repairs at customer sites before the equipment breaks and customers have to call us. Customer service benefits are obvious but, in addition, managing repairs on a systematic basis instead on-demand basis, would save maintenance dollars.
We have put a large focus to ensure our systems are secure from external threats as well as we have adequate disaster recovery solutions in place. We have upgraded our backup and data retention systems and procedures and are now working towards rapidly recovering our business-critical systems from failure to minimize business impact. We are also developing clear incident response procedures and shielding our systems from potential attacks and breaches which include implementation of secure coding practices
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