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Designed by service - how service data enables products to perform better, last longer

Sumair Dutta Profile picture for user sdutta June 25, 2024
Summary:
Reducing failure rates and improving customer uptime are driving product design changes, putting field service data at the heart of modernization. Sumair Dutta of ServiceMax dives into the research findings.

Digital Data View © kentoh - Canva.com
(© kentoh - Canva.com)

It doesn’t take a business genius to realize that poor product performance, or in some cases, product failure, can lead to customer dissatisfaction and a loss of sales. According to MIT, nearly 30,000 products are introduced every year with 95% of them failing. Although the majority of these will be consumer products, the principles that undermine product success remain the same. Some organizations still lack customer empathy and understanding. Likewise, some product developers and designers don’t have access to the sort of product intelligence required to have a real insight into what works and doesn’t work for customers.

This ability to build a picture of customer needs and existing product performance and capabilities exists. It is derived from data that links real-world insight from field service teams with CRM, supply chain, and financial data. A growing number of organizations have realized the benefits of connecting all their data to inform strategy and drive R&D. Research has found that service data can increase customer asset uptime and improve cross-functional collaboration – if used correctly. It can also be a source of untapped customer intelligence.

In the report IDC FutureScape: Worldwide Manufacturing Product and Service Innovation 2024 Predictions, the analyst firm cites its top 10 predictions for 2024 on product and service innovation. One of the predictions noted by authors, John Snow, Aly Pinder, Jan Burian, Mukesh Dialani, Stephanie Krishnan, and Ko Shikita, is “to foster innovation, by early 2025, 55% of G2000 service firms will incorporate real-time service data into cross-functional data streams to amplify the value of service outcomes.”

The IDC report's authors expanded upon this prediction, stating: 

As manufacturers and service organizations reckon with delivering more value to customers to grow the business, the ability to have service data inform decisions made across the organization will be a differentiator.

Rethinking innovation with service data and generative AI

With the arrival of generative AI (gen AI), organizations can not only accelerate research into new product development but also deliver greater accuracy in data insights. In short, gen AI can identify insights from rich service data (and do so faster) than perhaps a human, may or may not find. It means that as a tool for innovation, gen AI has huge potential.

In another prediction from the same IDC report, it was suggested that:

By 2028, the demand for product innovation will drive 50% of large manufacturers to evaluate engineering archives using generative AI, to uncover new opportunities for old innovations.

Bringing old products and designs to life with new insights is an interesting use of gen AI and an illustration of its capacity to deliver something extra. Given the growing demands for product makers to reduce carbon footprints and improve the overall efficiency and recyclability of products, this could prove invaluable. This suggestion is in alignment with another prediction from the same IDC report,

By early 2027, 70% of OEMs will rely on generative design technologies to develop high-quality, low-cost, sustainable products that are optimized for manufacturing and supply chain disruptions.

It's an interesting prediction that aligns with the shift towards servitization. This concept works well when customers are more interested in relationships based on outcomes rather than the products themselves. Asset data delivered through sensors and IoT networks becomes the central source of accessing product and customer intelligence. How customers are using products and the sort of outcomes they are demanding should feed back to design teams. It’s a re-prioritization, where live data capture, monitoring, and AI-driven analytics become core to customer relationships and business success.

Connecting this data, achieved by closing data loops within organizations, can lead to greater collaboration with everyone feeding off the same data insights. It’s designing and building products intelligently, based on real-world, live data of how products are performing and being used. This is where teamwork is fundamental to the future success of any organization.

As Deloitte suggests in its report Next Generation Customer Service: The Future of Field Service

To transform to a next generation Field Service, your company needs a 360-degree view of your customer and assets. 

Of course, this can be applied to the broader business. Unifying organizations, bringing departments together, and collaborating regardless of location demands centralized and easily accessible service data.

Manufacturers seeking to be best positioned for a future where technology advances continue to disrupt how production and service processes are performed must not only embrace the investment of these new technologies – but also be prepared to pivot and change course several times over the next few years, given the volume of investment and pace of innovation that is sure to follow!

Data is central to making this happen and service data is the context that will make it work.

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