Connected Service Life-Cycle Management – A data-driven approach to service operations
The immense potential of aftermarket services for the manufacturing industry is a no-brainer. As per industry standards. Aftermarket services comprise 25-30% of the revenues, with a profitability of up to 55% Service parts management is around 15-20% of the revenue, with profitability of up to 50% Service contracts are high margin businesses with a potential to earn anywhere between 30 to 50% According to a recent Deloitte study, the role of aftermarket services in driving customer lifetime value (CLTV) and sustainable profits has become more profound post-COVID-19. With supply chains being disrupted, the service level expectations of customers, especially for complex products, manufacturing, construction machinery, and transport vehicles, have risen manifold. Customers are willing to pay a premium for uninterrupted services and longer-term contracts that can predict support or replacement proactively before their equipment becomes inoperative. It is a new win-win for both OEMs and customers. Deciphering the aftermarket SLM ecosystem of a manufacturer The case for aftermarket services sounds promising, but does it manifest? Does the transition to SLM translate into tangible business gains? What do OEMs need to realize the true potential of their aftermarket services? Currently, a manufacturer’s aftermarket SLM tech stack can have one or many of these components, independent of each other. Service Parts Management: Covers the spectrum of aftermarket parts sales, from direct customer sales, dealer sales, and service centers to custom programs. Warranty Management: End-to-end management of product warranty processes involving product registration, claims processing, contract management, service plans, returns control, and warranty analytics. Field Service Management (FSM): Provide resources to support products in operation at the customer’s point of use. Capabilities span asset management, mobile workforce management, customer portals, service request management, and contract management to ensure the right resources are delivered at the right time. Service Knowledge Management: Manage, collect, and report on every aspect of customer interactions, including online portals, call center operations, training programs, and product health monitoring. Service Network Management: Plan, manage, and expand service operations through organic capabilities to transform service strategies across MRO operations, component repair & exchange, product modifications, and service delivery. Technical Information Management: Technical information storage about design, bill of materials (BOM), reliability data, parts information, configuration data, maintenance data, and production data to lay the foundation for the life-cycle and performance management of a product. On a standalone basis, these systems are certainly helping manufacturers transform their processes. Still, this siloed approach is incapable of value creation as it tends to ignore the complementarities and interdependencies across the ecosystem – OEMs, suppliers, dealers, customers, and service centres. Not only that, but the multiple system approach also leads to a growth slump, as it cripples OEMs’ ability to see complete and accurate data and deploy that data to build a seamless experience for their customers and gain a competitive advantage. As modern enterprises focus heavily on keeping track of their customers’ needs and aim for proactive service delivery to meet their satisfaction levels and drive customer lifetime value over the life-cycle, the need to implement connected SLM has become more pronounced than ever. From SLM to connected SLM – A case for manufacturing Using AI and analytics to create a 360-degree view of the service life-cycle processes for manufacturers, their channel partners, and customers Let’s look at an industrial equipment manufacturer that faced challenges across its service supply chain. The manufacturer wanted to eliminate inefficiencies and ensure maximum service parts availability across its global operations. This required evolution from a location-based inventory model to a centralized inventory management model, which could predict parts requirement, intelligently analyze parts availability, and automatically allocate resources per customer demand. The journey began with designing, building, and implementing SLM solutions to serve use-cases built around industry-specific challenges. The next step was integrating SLM with existing ERP and SAP systems and using analytics and AI to leverage real-time orders and feed them to SLM systems to ensure optimal inventories. This helped the manufacturer drive inventory turnover by 18-20%, increase parts availability by 3-5%, and save inventory costs by millions. Manufacturers must explore the integration of artificial intelligence (AI), the internet of things (IoT), and analytics tools across processes. IoT devices, or connected devices, help automate data collection from operational equipment to gauge product performance and uptime and diagnose problems. AI and analytics deliver capabilities to derive insights across system uptimes, inventory, service needs, and other functional areas. Unlocking the value of the Convergent SLM Strategy A connected SLM strategy can help build end-to-end interconnected systems that drive optimization across all manufacturing operations. A transition from a pure-play SLM strategy to a connected SLM one enables manufacturers to collect data from field assets, warranty systems, parts management systems, and FSM. This data can be utilized to implement service updates, manage complex technical information, and drive a seamless service experience for end customers. Some other benefits include: Streamlined workflows: Connected SLM solutions can enable organizations to streamline workflows with smart connected products, reduce downtimes, reduce service response times, enhance first-time fix rates, optimize price and parts availability, and reduce costs. Building new service models: Connected SLM solutions can also deliver insights into how products are performing at the customer’s point of use, which can be leveraged to build new service models. Personalizing communication: The connected SLM solutions enhance communication channels by ensuring detailed information is available and curated as per stakeholder needs to perform reactive and proactive service activities. Implementing a feedback loop across the digital thread enables manufacturers to leverage data that serves as input to increase product serviceability and reliability. Manufacturers must explore new revenue streams from real-time engagements with end customers. Smart devices and connected SLM systems will provide capabilities for manufacturers to deliver value-added services, reduce service and parts costs, and adopt a data-driven approach to decision-making.
Are Mortgage Lenders Saving Big by Adopting Intelligent Automation and AI?
In 2020, when the pandemic hit the world, it started a wave of rapid digital changes that spread across the globe. In 2021, these changes were put into place. It took a lot of money for businesses around the world to change so that they could work from home, be more socially isolated, and do business in a way that may never be the same again. In 2022, it’s clear that those changes will stay. The technology that is easy for people to use is getting a lot of attention again. Trends are likely to become the norm in the future. AI in Fintech market size is expected to reach $17 billion by 2027, and it’s no surprise that AI and ML (machine learning), and Intelligent automation will be at the heart of this. The only question is, how do fintech companies use these tools to make digital transformation happen and make it work for them? Fannie Mae’s quarterly Mortgage Lender Sentiment Survey® conducted a research among senior mortgage executives in August 2021 to better understand lenders’ views on AI/ML technology and to see how interested they were in different AI/ML applications. The study revealed the following key findings: Most lenders (63%) say they know about AI/ML technology, but only about a quarter (27%) have used or tried AI tools for their mortgage business. Lenders expect to use some AI tools in two years. Lenders who already use AI/ML technology say they mostly use it to make their operations more efficient or improve the customer/borrower experience. People use it to apply for a loan, get a loan, and get it approved. The biggest problems for lenders who haven’t used AI or ML technology are integration issues, high costs, and not having a proven track record of success. AI/ML applications that help businesses run more efficiently are the most appealing to lenders. Lenders found the concept of “Anomaly Detection Automation” to be the most appealing. “Borrower default risk assessment” came in a close second, though. There are solutions, but they are task-oriented rather than holistic. In terms of customer-facing solutions, 75% of organizations say AI supports or drives one. This high figure is reached by combining distinct procedures. Next to loan applications, AI is used for documentation, marketing, and closing. Overall, 83% have at least one AI-powered back-office solution. The top three most reported sub-processes are loan servicing, title search/registration, and underwriting. Mortgage lenders are saving big by automating their manual, time-consuming cumbersome legacy systems and process; thereby increasing cost efficiency and productivity. How AI, ML, and Intelligent Automation Technologies are Game Changers in the Fintech Industry? Cost Reduction and Scalability to Support Growth Given the changing market, more lenders are turning to digital financing. AI and ML deliver a significant gain compared to utilizing only normal statistical models. This invention is at the forefront of sustaining transparency and performance. In response to changes in data and outliers, AI/ML models require less manual intervention, enhancing overall efficiency. By understanding mortgage application information more precisely and quickly, AI and automation can replace optical character recognition (OCR). AI can also read text from emails, documents, and other sources. An AI-powered support automation technology optimizes loan processing by enhancing customer satisfaction and communication between lenders and borrowers. Save Time and Reduce Errors AI eliminates human errors and uses machine learning to improve accuracy. This is huge for the mortgage business. Errors in human data entry have a high cost. AI can handle mortgage papers fast without getting tired or bored, leading to calculation or judgment errors. Enhance Customer Experience (CX) AI-powered chatbots can quickly answer borrowers’ questions and guide them through the loan application process. Mortgage lenders can use AI to quickly gather information from borrowers (for example, their credit scores or student loans). Mortgage businesses start the mortgage procedure and offer superior goods for those consumers. Based on their income and credit history, a company can predict which customers are at higher risk for defaulting, enabling them to offer different types of better loans for those individuals. Improve Efficiency through Intelligent Automation Machine learning, data analytics, neural networks, and other AI-based technologies can greatly improve financial technology. AI is becoming crucial in lending. It is bringing new efficiency and value to Fintech. For example, AI can write expense reports faster and with minor inaccuracies than a human. Also, AI may power technologies that help human workers track and automate operations, including compliance, data input, fraud, and security, while also learning from and verifying events for anomalies. Deliver Great Customer Service Consistently Customer service is one of the most notable areas where AI has benefited Fintech. Artificial intelligence has advanced to where chatbots, virtual assistants, and other AI interfaces can consistently engage with customers. Answering basic questions can significantly reduce front office and helpline expenditures. Wrapping up: COVID-19, as a whole, is proving to be an effective catalyst, with the ability to inspire industry leaders to reinvent their digital strategy. AI adoption is growing: more businesses are catching up, familiarizing themselves with innovative tools, and starting to explore new capabilities. This is a good time to start assessing the impact of AI, ML, and intelligent automation on their mortgage business. What next? Tavant can help mortgage lenders diversify how they do business and effectively unlock savings with next-gen digital technologies. To gain more insights, reach out to us at [email protected] or visit here.
5 Questions that can help Maximize Your Customer Experience
In 2020, when the pandemic hit the world, it started a wave of rapid digital changes that spread across the globe. In 2021, these changes were put into place. It took a lot of money for businesses around the world to change so that they could work from home, be more socially isolated, and do business in a way that may never be the same again. In 2022, it’s clear that those changes will stay. The technology that is easy for people to use is getting a lot of attention again. Trends are likely to become the norm in the future. AI in Fintech market size is expected to reach $17 billion by 2027, and it’s no surprise that AI and ML (machine learning), and Intelligent automation will be at the heart of this. The only question is, how do fintech companies use these tools to make digital transformation happen and make it work for them? Fannie Mae’s quarterly Mortgage Lender Sentiment Survey® conducted a research among senior mortgage executives in August 2021 to better understand lenders’ views on AI/ML technology and to see how interested they were in different AI/ML applications. The study revealed the following key findings: Most lenders (63%) say they know about AI/ML technology, but only about a quarter (27%) have used or tried AI tools for their mortgage business. Lenders expect to use some AI tools in two years. Lenders who already use AI/ML technology say they mostly use it to make their operations more efficient or improve the customer/borrower experience. People use it to apply for a loan, get a loan, and get it approved. The biggest problems for lenders who haven’t used AI or ML technology are integration issues, high costs, and not having a proven track record of success. AI/ML applications that help businesses run more efficiently are the most appealing to lenders. Lenders found the concept of “Anomaly Detection Automation” to be the most appealing. “Borrower default risk assessment” came in a close second, though. There are solutions, but they are task-oriented rather than holistic. In terms of customer-facing solutions, 75% of organizations say AI supports or drives one. This high figure is reached by combining distinct procedures. Next to loan applications, AI is used for documentation, marketing, and closing. Overall, 83% have at least one AI-powered back-office solution. The top three most reported sub-processes are loan servicing, title search/registration, and underwriting. Mortgage lenders are saving big by automating their manual, time-consuming cumbersome legacy systems and process; thereby increasing cost efficiency and productivity. How AI, ML, and Intelligent Automation Technologies are Game Changers in the Fintech Industry? Cost Reduction and Scalability to Support Growth Given the changing market, more lenders are turning to digital financing. AI and ML deliver a significant gain compared to utilizing only normal statistical models. This invention is at the forefront of sustaining transparency and performance. In response to changes in data and outliers, AI/ML models require less manual intervention, enhancing overall efficiency. By understanding mortgage application information more precisely and quickly, AI and automation can replace optical character recognition (OCR). AI can also read text from emails, documents, and other sources. An AI-powered support automation technology optimizes loan processing by enhancing customer satisfaction and communication between lenders and borrowers. Save Time and Reduce Errors AI eliminates human errors and uses machine learning to improve accuracy. This is huge for the mortgage business. Errors in human data entry have a high cost. AI can handle mortgage papers fast without getting tired or bored, leading to calculation or judgment errors. Enhance Customer Experience (CX) AI-powered chatbots can quickly answer borrowers’ questions and guide them through the loan application process. Mortgage lenders can use AI to quickly gather information from borrowers (for example, their credit scores or student loans). Mortgage businesses start the mortgage procedure and offer superior goods for those consumers. Based on their income and credit history, a company can predict which customers are at higher risk for defaulting, enabling them to offer different types of better loans for those individuals. Improve Efficiency through Intelligent Automation Machine learning, data analytics, neural networks, and other AI-based technologies can greatly improve financial technology. AI is becoming crucial in lending. It is bringing new efficiency and value to Fintech. For example, AI can write expense reports faster and with minor inaccuracies than a human. Also, AI may power technologies that help human workers track and automate operations, including compliance, data input, fraud, and security, while also learning from and verifying events for anomalies. Deliver Great Customer Service Consistently Customer service is one of the most notable areas where AI has benefited Fintech. Artificial intelligence has advanced to where chatbots, virtual assistants, and other AI interfaces can consistently engage with customers. Answering basic questions can significantly reduce front office and helpline expenditures. Wrapping up: COVID-19, as a whole, is proving to be an effective catalyst, with the ability to inspire industry leaders to reinvent their digital strategy. AI adoption is growing: more businesses are catching up, familiarizing themselves with innovative tools, and starting to explore new capabilities. This is a good time to start assessing the impact of AI, ML, and intelligent automation on their mortgage business. What next? Tavant can help mortgage lenders diversify how they do business and effectively unlock savings with next-gen digital technologies. To gain more insights, reach out to us at [email protected] or visit here.
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