Today digital transformation is a well-known concept. The growing connectivity of people, machines and even businesses has changed the demands of the markets. In order to keep up and stay competitive, business have to adjust to these demands by digitizing their processes and business models
Many businesses have already begun to transition themselves towards digital transformation as they realize that it is not something to be left for tomorrow. With this series we want to illustrate, on industry specific use cases, how businesses can re-imagine their business models, processes, products, and services, for the purpose of realizing the benefits of the digital transformation.
In the last blog we covered the first consumer products use case "Real-Time Supply Chain Visibility" and this time we focus on another use case in the consumer products industry "Service Enhanced Products".
Use Case Business Intentions
Let us have a look at the second digitization use case within the consumer product industry: Service Enhanced Products
For companies in this field it is necessary to enable appliance manufacturers to sell not only products but also services. To achieve this, the company needs 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 with analytical insights, which would give customers the ability to monitor their machine's consumption patterns and improving their operations via optimized schedules for replenishment tasks. Another example are predictive maintenance services, which help customers to reduced machine down-times and lowering their maintenance costs. Remote ordering services, give customers the ability to verify stock and offering re-stocking via an application from anywhere. Also appliance manufacturer can leverage data insight from the appliances to switch to on-demand production and inventory management.
In this use case the technology enabler include the Internet of Things (IoT), Big Data, Analytics and Cloud. Sensors are required for transmitting data on appliance usage patterns, their health and performance. Big Data and analyticsfor analyzing the data through predictive analytical models. And in the cloud the data synchronization and collaboration between product manufacturers and product operators will work ideally.
Coffee machine producer customer example
To make this use case more relatable, we will take a closer look at the "from preventive to predictive maintenance strategy" with their before-and-after situation.
This company's goal was it to realize benefits like cost reduction, customer satisfaction, revenue increase, and quality increase. To do this they leveraged all the technology enabler mentioned before.
Before Digital Transformation
The company is an international manufacturer of fully automatic coffee machines for commercial applications such as restaurants, military mess halls, cruise ships and other. In the past, the company had a preventive instead of predictive maintenance strategy. Service technicians recorded usage or equipment deterioration by manual inspections in order to repair or replace worn-out coffee machine parts before they cause system failures. These service costs spent by the company have a huge impact on the company's profitability.
The company wanted to move towards 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 as well as more precise failure predictions. An additional goal was to help its customers to reduced machine downtimes and improve their operations via optimized schedules for replenishment tasks.
After Digital Transformation
With an adequate Cloud Platform (PaaS), the company can now monitor sensor data from the coffee machines. This has enabled the asset manufacturer to provide its customers with new and higher margin service offerings which was not possible before. The coffee machine operator can view consumption patterns and the health of the equipment. The system can predict failures long before they actually happen and can apply mitigation measures by creating a maintenance notification directly integrated with the company info point. Also the company can plan better for the spare parts needed: through machine learning algorithms, the system identifies relevant spare parts which will improve the first-visit-fix rate and prevent further visits by exchanging parts which would have failed in near future.
The benefits for company were numerous. Starting with an improved service scheduling and execution, which made it possible to gain better insights into product improvements. An improved service profitability with lower service costs and new revenue streams. Increased customer satisfaction and retention, which lead to higher service contract renewal rates. Several benefits for the coffee machine operators, which results in the higher customer satisfaction and higher overall equipment effectiveness (asset availability, performance & quality). And last but not least, improved maintenance efficiency, through faster reaction to alarms and failures, lower mean time to repair. Lower maintenance costs is just one of the benefits the company set out to achieve with its move towards a digital business. All of the goals were realized, making the move a great success and the company equipped for the future.
This is only one example of many more to come that don't leave a doubt that the digital transformation is the next step for businesses to stay competitive and take the right path towards the future. Stay tuned for even more digitization use cases with hands-on business examples.
So stay tuned for more examples and follow me via @SDenecken