ISS announces increased revenue for first half of 2016

Image Sensing Systems (ISS) has announced revenue of US$7.9 million for the first half of 2016, a four per cent increase from revenue of US $7.6 million in the first half of 2015. Product sales increased to US$3.9 million in the first half of 2016, a 31 per cent increase from $3.0 million in the first half of 2015. The first six-months of revenue for 2016 included Autoscope video product sales and royalties of US$621,000 and US$4.0 million, respectively, and RTMS radar product sales of US$3.3 million. Pr
August 8, 2016 Read time: 2 mins
6626 Image Sensing Systems (ISS) has announced revenue of US$7.9 million for the first half of 2016, a four per cent increase from revenue of US $7.6 million in the first half of 2015. Product sales increased to US$3.9 million in the first half of 2016, a 31 per cent increase from $3.0 million in the first half of 2015.

The first six-months of revenue for 2016 included 6575 Autoscope video product sales and royalties of US$621,000 and US$4.0 million, respectively, and RTMS radar product sales of US$3.3 million. Product sales gross margin for the first six months of 2016 was 52 per cent, consistent with the same period in 2015.

The company’s second quarter 2016 sales increased approximately six per cent from the prior year period, while second quarter net income from continuing operations improved to US$1.2 million, an 82 per cent increase from the same period in 2015. Cash balance ended the quarter at US$964,000, down from US$1.4 million at the end of first quarter.

“We are in the preliminary stage of a business transformation and new technology innovation.  We continue to drive down operational costs and identify opportunities to operate within a leaner, more agile corporate structure focused on technology and engineering opportunities essential to our growth.  We are also evaluating alternatives to further enhance our credit and liquidity position,” said Chad Stelzig, ISS interim CEO.
Detection, Monitoring & Machine Vision Classification & Data Collection

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