Digital Transformation In Service-Enhanced Consumer Products

Sven Denecken

Digital transformation is a well-known concept these days. The increasing connectivity of people, machines, and businesses has changed the demands of the markets. In order to keep up and stay competitive, business need to digitize their processes and business models.

Many businesses have already begun to transition toward digital transformation, realizing that it is not something that can wait until tomorrow. In this series we will illustrate, using industry-specific use cases, how businesses can re-imagine their business models, processes, products, and services, to realize the benefits of the digital transformation.

In the last blog, we covered the first consumer products use case: real-time supply chain visibility. This time we’ll focus on another use case in the consumer products industry: service-enhanced products.

Use case business intentions

Companies in this field enable appliance manufacturers to sell both products and services. To do this, companies need to equip their products with sensors and collect and analyze product performance data through predictive analytical models. Appliance manufacturers can monetize the analyzed data and create more value to the customers (appliance operators) by providing them with new services.

For instance, analytical insights enable customers to monitor their machines’ consumption patterns and improve operations by optimizing replenishment schedules. Another example is predictive maintenance services, which help customers reduce machine down times and lower maintenance costs. Remote ordering services give customers the ability to verify stock and offer restocking from anywhere via applications. Appliance manufacturers can also leverage data insight from appliances and switch to on-demand production and inventory management.

In this use case, the technology enablers include the Internet of Things (IoT), Big Data, analytics, and cloud. Sensors are required to transmit data on appliance usage patterns, their health,and performance. Big Data and analytics analyze data through predictive analytical models. And in the cloud, data synchronization enables collaboration between product manufacturers and product operators.

Customer example: coffee machine producer

To make this use case more relatable, consider the following before-and-after scenario that illustrates the shift from a preventive to a predictive maintenance strategy. The company is an international manufacturer of fully automatic coffee machines for commercial applications such as restaurants, military mess halls, and cruise ships. Its goal was to reduce costs and increase customer satisfaction, revenues, and quality. To do this, it leveraged all the technology enablers listed above.

Before digital transformation

In the past, the company used a preventive rather than a predictive maintenance strategy. Service technicians recorded usage or equipment deterioration via manual inspections in order to repair or replace worn-out coffee machine parts before they could cause system failures. These service costs had a huge impact on the company’s profitability.

The company wanted to move toward a predictive maintenance strategy, which takes into account device and sensor data from the coffee machines in order to come up with optimized maintenance and service schedules and more precise failure predictions. An additional goal was to help customers reduce machine downtimes and improve operations via optimized replenishment schedules.

After digital transformation

With a cloud platform (PaaS), the company can now monitor sensor data directly from the coffee machines. This enables the company to provide its customers with new, higher-margin service offerings. For example, coffee machine operators can view consumption patterns and the health of the equipment. The system can predict failures long before they happen and mitigate them by creating maintenance notifications that are directly integrated with the company. The company can also plan more efficiently: Machine learning algorithms enable the system to identify relevant spare parts, which improve the first-visit-fix rate and prevent further visits by replacing parts before they fail.

Promising results

The benefits for company were numerous: Improved service scheduling and execution helped it gain better insights into product enhancements. Improved service profitability decreased service costs and created new revenue streams. Increased customer satisfaction and retention led to higher service contract renewal rates. Coffee machine operators benefited as well, which resulted in higher customer satisfaction and higher overall equipment effectiveness (asset availability, performance, and quality). And last but not least, faster reaction to alarms and failures improved maintenance efficiency and decreased mean time to repair.

Lower maintenance costs was just one of the benefits the company set out to achieve through digital transformation. In the end, it realized all of its goals, making the move a great success and preparing the company for future success.

This is just one example that shows how digital transformation helps businesses stay competitive and take the right path toward the future. Stay tuned for more digitization use cases with real-world business examples.

Stay tuned for more examples of digital transformation in business, and follow me via @SDenecken.

Sven Denecken

About Sven Denecken

Sven Denecken is Senior Vice President, Product Management and Co-Innovation of SAP S/4HANA, at SAP. His experience working with customers and partners for decades and networking with the SAP field organization and industry analysts allows him to bring client issues and challenges directly into the solution development process, ensuring that next-generation software solutions address customer requirements to focus on business outcome and help customers gain competitive advantage. Connect with Sven on Twitter @SDenecken or e-mail at