With over half of all products expected to be IoT-connected by 2026, many Original Equipment Manufacturers (OEMs) are making significant strides in advancing their IoT strategies through software and service innovation, according to the "IoT Commercialisation & Business Model Adoption Report 2024", released by IoT Analytics. Successful OEMs are benefiting from key insights into customer equipment usage behaviour, leading to an enhanced customer experience and improved operational efficiencies.
Knud Lasse Lueth, CEO at IoT Analytics, said, "Our 2024 IoT Commercialisation & Business Model Adoption Report reveals pivotal insights into what differentiates successful IoT implementations among OEMs. A standout finding is the projection that over 50% of products sold by OEMs will be IoT-connected by 2026. The report also highlights the significance of leveraging customer equipment usage data as a cornerstone for innovation, enabling OEMs to offer tailored solutions that significantly enhance customer experiences and operational efficiencies."
Less successful implementations often result from an inability to leverage behavioural data or to adapt corporate business models to the requirements of an increasingly domesticated IoT market. Ten years after global management thinkers Michael Porter and Jim Heppelman posited that connected IoT products would fundamentally alter traditional industry structures and businesses, progressive companies have heeded the call, investing in digital technology and service upgrade: BMW boasts over 20 million connected vehicles worldwide.
You may not always hear of the small Italian kitchen manufacturer UNOX, yet this 1,200 employee-strong company started its IoT journey in 2015 and has since connected more than 30,000 ovens, opening up new streams of revenue. However, other companies see this merely as generating new value and, instead, focus on the potential improvements in customer service and operational efficiency that IoT-driven data analytics can enable.
While successful IoT adoptions remain conditional on striking the right balance between software, service, and equipment, the potential benefits are extensive. Service sales, for example, are expected to reach $28 billion by 2026 at US machinery giant Caterpillar. This goal is underpinned by the smart, IoT-driven equipment trucks, excavators, and wheel loaders crucially needed in the sector. Hence, the potential IoT commercial sales extend far beyond the direct equipment sale.
One potential challenge that IoT solutions face is the 41-month average from project kick-off time to connecting products to the market. However, integration through outsourcing tech stacks can speed this process and help companies better utilise their resources in developing their products. The survey revealed that Microsoft, Cisco, and AWS were three of the most often mentioned vendors that work behind the scenes to aid IoT solution implementation.
Leveraging IoT to enhance operational efficiency has become an essential factor for the success of OEMs. One of the researched companies, Germany's Trumpf, provides an innovative workflow optimisation tool for sheet metal processors, allowing its customers to improve each processing step. More optimised businesses see quicker returns on their investments, with a 40% gap between successful and less successful companies.
However, the report also highlighted the key issues facing customers in adopting new IoT-based services and software. Top of the list were IT and data security concerns following recent high-profile security breaches. Other significant roadblocks included difficulties integrating products into legacy systems and budget constraints impacting IoT adoption rates.
Despite these challenges, the IoT market shows no sign of slowing, offering significant opportunities for those prepared to innovate and invest in understanding their customers' needs and behaviours. The path to successful IoT commercialisation remains complex, requiring adaptive business models, an eye for the customer experience, and a deep appreciation for the potential of data analytics.