IDC Manufacturing Insights conducted a survey earlier this year that asked 300 decision-makers of product lifecycle management (PLM) and service lifecycle management (SLM) at manufacturing companies to assess the business drivers, application areas, and investment trends in product innovation and service innovation. The survey data focuses on the manufacturing industry in the USA and Canada, and respondent types included business, executive, and IT roles. The published survey results are available for subscribers here, IDC's Product and Service Innovation Survey Results. What follows are some observations about the service-specific results and a glimpse into how 3^rd Platform technologies and Innovation Accelerators are influencing service innovation.

The adoption of 3^rd Platform technologies for service innovation is now pervasive across manufacturing industries, and this year's survey highlighted how service organizations are leveraging mobile, social, analytics, and cloud for improved service delivery. Cloud adoption is particularly noteworthy, because it reflects the broader trend to move customer-related processes to an agile environment that can more easily support engagement among customers, partners, and field personnel. Below is a chart that highlights not only the adoption level for cloud in the service lifecycle, but also the top elements that manufacturers are adopting in SaaS (Software-as-a-Service) models.

IDC Manufacturing Insights sees manufacturers adopting these processes in one platform, such as Field Service Management, to encompass elements like service planning, contract management, and warranty management. The results are a unified customer experience and improved efficiency for the service organization.

Moving onto the discussion of Innovation Accelerators, Service organizations have high expectations of how advanced technologies will support the service lifecycle and greater customer engagement. Among the list of new technologies, respondents identified Artificial Intelligence (AI) and machine learning as increasing in importance most over the next twelve months. Service organizations are applying AI and machine learning to a variety of areas, from field service technician schedule optimization to fraud detection of warranty claims. The top use cases for AI and machine learning in service according to respondents are "better service planning and execution" as well as "enabling products that are in use to self-heal or suggest preventative maintenance." In many cases, these efforts are just getting off the ground, and most are in proof-of-concept stages. But it is clear that service managers understand the incredible potential that AI and machine learning can provide to the service lifecycle in the coming years.

In a discussion about advanced technologies for service innovation, it would be remiss to not mention IoT, as the technology continues to be a driving force in service transformation. In the survey, IoT was one of the highest priority technology areas for service innovation in the next year.  Today, 85% of manufacturers surveyed have at least 1% of their manufactured products connected to enable service and product performance information transmitted over a wireless network, and they intend to expand that connected base in the coming three years.  IoT is vital to the proactive service operational approach many manufacturers are moving toward. Compared with our survey two years ago, there is a noticeable shift toward proactive and prescriptive service approaches, both of which require a connected product to succeed. We expect the trend to continue, and advanced technologies like cloud, AI and machine learning, and IoT will form the foundation of this service transformation.