Syncron announced that Norgren, part of IMI plc, a global leader in designing and manufacturing motion and fluid control technologies, has selected Syncron Price to automate and streamline pricing operations, elevate the customer experience and improve efficiency and profitability. "We needed an automated tool to maximize visibility, increase efficiency and create consistency across the entire work product range," said Gianluca Fogu, EMEA pricing manager, Norgren. "Since our business involves product configuration, we needed a streamlined solution for full visibility across our entire portfolio and increased operational efficiency." Syncron Price is an intelligent parts pricing platform that transforms pricing strategies with a fit-for-purpose solution. Leveraging artificial intelligence (AI) and machine learning (ML), it identifies unseen pricing opportunities to maximize sales volumes, revenue and service profitability from aftermarket business.

Syncron, the largest privately-owned global provider of cloud-based after-market service solutions, announced the general availability of Syncron Price Version 20.4, which delivers several new capabilities to further automate and accelerate after-market pricing functions. The new features include usability enhancements and more sophisticated controls, to enable more optimized pricing to be done in less time and with better outcomes. "As the global economy begins to recover in the post-pandemic era, manufactures must provide even more sophisticated techniques to drive smarter pricing decisions," said Erik Lindholm, Vice President of Product Management at Syncron. "Price must be driven by increasingly sophisticated machine learning, algorithms, and comprehensive analytics that can automatically pinpoint sources of revenue and margin changes using real-time data. Today's companies leverage our technologies to transform their pricing strategies into competitive advantages to maintain relevance and viability in an ever-changing, increasingly sophisticated market."

When a new product is introduced, there is no historical data to predict when certain service parts will need replacement or repair. Missteps in part availability and customer service can be costly in competitive industries. When a manufacturer is introducing a new product, machine learning can use algorithms and analytics to track and determine the launch's success, incorporating data from sales, social media chatter and web traffic, among other sources. This helps manufacturers, particularly in their after-sales service organizations, know when and where replacement parts need to be ordered and stocked, oftentimes eliminating excess inventories and overhead costs. Plus, when parts are stocked appropriately, customers are guaranteed a great service experience.