As OEMs shift to service-driven subscription-based models, AI-driven automation plays an even more critical role in the ability to compete.
by Thomas Nilsson, Chief Architect, Syncron
Our technological landscape is in constant flux, and manufacturers are under immense pressure to gain a competitive edge. These days, consumers expect a tailored, customizable experience, and many businesses are struggling to keep up.
To stay relevant, manufacturers are tasked with achieving differentiation in the market and meeting the ever-evolving needs of their customers. To succeed, they must leverage artificial intelligence (AI) and machine learning (ML) technology to improve operational efficiency and productivity and facilitate responsive, data-driven analysis.
Recognizing this shift, many OEMs have started automating aspects of their aftermarket services. According to Syncron’s newly released State of Aftermarket Parts Pricing survey report, 92% of manufacturing executives have started implementing automated systems for parts pricing processes but only a fifth are seeing anticipated benefits of time savings and data-driven decision-making. Instead, they report a continued reliance on gut feelings and fine-tuning to achieve expected results.
According to a recent report from Service Council, organizations must fully integrate digitization like AI/ML to transform their service delivery models for proactive, outcome-based servitization.
Embracing Equipment-as-a-Service: A Win-Win Solution for OEMs and Customers
Equipment-as-a-Service (EaaS) is a business model that sells outcomes and services as a product. With EaaS, manufacturers deliver subscription contracts that come with support services. Instead of buying a product, customers rent or lease it until they’re ready to return the equipment to the manufacturer.
EaaS offers a win-win solution for manufacturers and customers alike. The shift to a service-driven business model allows for more predictable revenue streams, improved sustainability and efficiency, and a better overall customer experience.
Additionally, EaaS enables OEMs to own their assets while taking the hassle of maintenance and repair away from the customer. In a service-based model, manufacturers are responsible for all aftermarket services, including repairs and replacements.
Challenges With the EaaS Model
Consumers are embracing EaaS and expect service-based solutions from manufacturers, as seen in durable goods, e-commerce, gaming, video and music streaming, cloud-based applications, and more. However, there are some challenges to adopting EaaS. For one, consumers demand a high level of customization, and their needs are constantly changing. As a result, OEMs must leverage digital solutions to guarantee a high-quality customer experience.
Another challenge is capturing, maintaining, and utilizing all relevant data for cross-functional processes. Doing so requires a top-end, scalable processing platform that can handle large quantities of data and computations.
Moreover, EaaS relies on predictive analytics for pricing models and optimization. With an undefined scope of service, service-based models can result in unprofitable margins if the right technology isn’t in place to secure productivity, revenue, and profit.
Leveraging Predictive Technology to Implement EaaS
To implement an EaaS model, manufacturers need to automate their processes for contract pricing. With EaaS, manufacturers are selling a fixed contract that includes all service, parts, and maintenance during the lifecycle of the contract period.
The challenge OEMs face today is developing a profitable contract, which requires the ability to predict underlying costs. OEMs will need to find the data sources relevant to their problem and apply statistical algorithms and ML to identify the price point that optimizes their return on investment.
Historical methods manufacturers have used to manage aftermarket processes are no longer effective. According to Syncron’s new State of Aftermarket Inventory Management report, 70% of supply chain execs surveyed said they are still using employee-built systems, spreadsheets, or nothing at all for inventory management. As a result, they incur unnecessary costs due to labor shortages, transportation, and delays.
With automation, OEMs can set value-based prices. If you’re using an Excel spreadsheet, for example, there’s no way you can manually go in and adjust each price. As a result, you’ll set a fixed, cost-based price. OEMs need automation and database processing around this to apply rules, algorithms, and ML. Of the millions of parts, there is an optimal price point. Finding it can only be done with automation.
An automated, data-driven platform ensures proactive contract pricing, enabling OEMs to see the full benefits of the EaaS model. A contact pricing tool is a turnkey solution for manufacturers that reduces contract risk and secures profit and margins.
Implications for Global Manufacturing and the Future of EaaS
Service-based models are valuable to global manufacturers in particular. With pricing strategies that depend on taxes, regional fees, and other costs, these OEMs need a responsive, automated solution that leverages historical data for predictive analysis.
EaaS enables manufacturers to future-proof their businesses with a highly tailored customer experience. AI/ML platforms such as Syncron’s Contract Price help OEMs evolve by offering a solution for predictive, data-driven pricing of service contracts.
About the Author:
Thomas Nilsson is the Chief Architect of Syncron, the largest privately-owned global leader in intelligent SaaS solutions dedicated to end-to-end aftermarket service lifecycle management (SLM). For more than a decade, Thomas Nilsson has worked with product research & development with large scale computing and AI/Machine learning for some of the world’s leading brands. He’s held roles as CTO and CPO in which we lead SaaS companies in start-up and transformation phases to become more innovative leveraging latest cloud and AI/ML technologies. In his free time, besides spending time with family and being an avid dog fan, Thomas enjoys being an advisor and mentor for technology start-up companies.