INDUSTRIAL Manufacturing

By leveraging actual asset usage data available in industrial manufacturers' loT Clouds, Bolt Data Connect's native integration with Salesforce service management solutions enabled them to offer usage-based preventative maintenance plans. A common use case amongst industrial manufacturers is to maintain precision assets that must be recalibrated within a defined duty cycle to ensure quality. While time-based preventative maintenance has been the normal approach for service, it had shortcomings as assets used more than expected required service ahead of the time-based plan. Similarly, assets used less than expected received unnecessary service. Transitioning to usage-based preventative maintenance creates a high-value play for industrial manufacturers; leveraging real-time data to coordinate service exactly when it was needed.

Building on machine data collected over time, Bolt Data Connect combines customer account type and status, asset entitlements, service history, and other relevant information necessary to run predictive analytics and prescribe the exact service responses as governed by service processes and contracts. Internally, industrial manufacturers have an equally high focus on maintaining their own manufacturing equipment. With highly reliable data points created by AI and IoT solutions, they can track robot performance over time and use statistical analysis like Six Sigma tools to improve overall equipment reliability and operations within production tolerances.

Manufacturers are constantly working to balance inventory levels, especially for high-value parts. AI and IoT solutions monitor usage and influence demand planning for appropriate reordering of mission-critical parts. By predicting the volume and velocity of future maintenance events, and by evaluating the parts consumed delivering past similar services, service teams can improve parts forecasts, reducing the risk of stockouts and the cost of inventory. Bolt Data Connect's native integration with Salesforce service management solutions closed the gap between service forecasting and parts ordering. Industrial manufacturers can automate parts replenishments and reduced inventory levels without stockouts.

Industrial manufacturers have a complex selling channel that included direct sales, regional distributors, and reseller agreements. Bolt Data Connect enables industrial manufacturers to offer Monitoring as a Service (MaaS) to their partners as a premium service to differentiate them in the market. And because industrial manufacturers received little to no information from their distributors about the end customer, Bolt Data Connect provided critical usage and performance data to improve product performance and inform future selling campaigns.

When service technicians are required to be on-site, access to both real-time and historic asset operating data helps them quickly and accurately diagnose and resolve issues. Industrial manufacturers provide their field service technicians with the Bolt Data Connect mobile app so they can see real-time and historic performance data as part of their triage process. With these new insights, service teams quickly identify root causes, spend less time finding issues, and improve their rate of fixing issues the first time. The COVID-19 pandemic has amplified the need to remove barriers to accessing service locations. With Bolt Data Connect's ability to remotely access and control devices from the Bolt Data Connect app, industrial manufacturers enable their call center agents and service technicians to triage and deliver select services remotely. This helps avoid unnecessary truck rolls and site/access denials risks. While reducing service response time and costs, industrial manufacturers also improve business continuity (service revenue) and worker safety by delivering service remotely. Industrial manufacturers commonly reported improvements to their Mean Time to Repair (MTTR) and First Time Fix (FTF) metrics. They also report a reduction in “Fault Not Found" service events based on improved analysis of big-data noise.