How to Prepare for Servitization Success
Data collection within manufacturing operations is critical to operational improvements. Be it detecting bottlenecks in production, making critical process changes, to reducing and controlling labor and material costs, there is an inherent ROI to your current Manufacturing Execution System (MES) software.
What if you could further leverage this data collection as a revenue stream for your product-based business? Servitization, defined as “outcome as a service”, is now becoming the next competitive advantage for manufacturers trying to gain market share and improve margins.
In today's blog, we will dive into the importance of servitization and how your data collection systems can be leveraged as a new source of revenue within your current operations. And we will outline the steps OEMs (Original Equipment Manufacturers) can take to identify, implement and scale this new revenue generating model.
Servitization in Manufacturing
Big data often refers to the huge volume of analytical data that can not be manually stored and/or processed using a traditional approach within an allotted amount of time. Being able to source and utilize a massive amount of data can be extremely beneficial for industries, and there are now readily accessible solutions to accomplish this. For example, DataCard is a solution designed to store and process information in a distributed data processing environment, in real-time from any and all assembly and test systems in your factories, along with any supplier reports or data feeds. This yield management solution analyzes trends and root causes in real-time at high speed and with low overhead, with directly accessible web based dashboard. Results from intelligent data analysis solutions like this are a critical and effective tool for improving quality in production and logistics.
The implementation of Big Data has affected manufacturing in several different ways:
Process improvement leads to increased yields, lower costs, higher margins, and more efficient manufacturing.
Machine Learning compares the impacts of various production factors, delivering insights instantly that would take engineers hour, days, and weeks to arrive at
Data-driven workflow changes in supply chain management lead to faster delivery times with less error.
Now that you understand the value of data in manufacturing, let's discuss how you can shift from providing just products to incorporating a data services-based business model.
Building Your Organization for Servitization
As we head into a data-driven industrial revolution, the need for initial investment becomes greater as products grow in complexity, with increasing reliability and quality requirements. This drives the need for a servitization-based business model, where the customer compensates the OEM for the capture and reporting of data, both internally and externally, showing they are delivering on product quality and reliability in order to manage end-market risks.
According to recent studies, industrial, medical and life science OEMs are showing 20 percent of their revenue coming from services, and this continues to grow. Close to 70 percent of manufacturing executives globally reported some form of servitization.1
The adoption of this model continues to trend upward. To position your current operations for success with this, you will need to focus on these key factors:
- Business model
- Organizational structure
- Development process
- Customer management
- Risk management
The best strategy to ensure each of these points are addressed is to architect a servitization business model that enhances your current process. We've used the PESTELS method as a high-level roadmap to review organizational priorities that not only apply to servitization but can be used for other business tactics as well.
Market size will determine the value of the service offering and its marketability. Standard service plans will not suffice for fragmented markets. Individual consumers who are still manually entering data into spreadsheets will need a different solution compared to large corporate users. The Lifetime Value (LTV) of a customer can help determine associated labor costs and the risks/reward.
Capitalizing on servitization requires an in-depth understanding of the targeted market segment, individual customer needs, and associated costs.
For example, a software company may offer 'Real-time reports on fiber product data', whereby an OEM using this software can in turn sell the ability to report on this data as a service, to their end customer.
The software company has several solutions ranging from basic reporting with a service contract and warranty to outcome-based engagements and value-added services such as software training for employees.
As the markets mature, outcome-based models will evolve and become the norm, pushing for service quality beyond basic needs.
In today's world, we can remotely monitor equipment in the field for usage and performance using the Internet of Things (IoT); however, the need to track product usage often leads to payment delays or repayment delays. Using a pay-per-service model requires service engineers to visit the customer's location to track product usage physically.
In the servitization era, the need to include secure and close communication is imperative to convey usage details separated from customer-provided infrastructure.
In summary, the OEM needs to implement their own infrastructure to capture data, without direct involvement from the customer. This creates a seamless delivery of the service.
Product performance data using IoT technology only analyzes a segment of the data; you need to pull raw data from equipment operations to identify insights and take the correct measures. As mentioned, this can be extremely lucrative; however, who owns the data and how accessible it is will require a detailed contractual agreement.
After the OEM product is purchased and delivered, the customer may feel they have ownership of the data. The service agreement may outline data access and ownership rights; however, this can still derail the whole servitization initiative. The OEM has a responsibility to prove their value proposition and can expect initial hesitation from the customer.
To overcome this challenge, OEMs should come to a mutual agreement that is attractive to customers. OEMs outlining the asymmetry between their product performance knowledge and end-users who normally do not have access to these insights. Once you can leverage this, you are equipped to draft an economically attractive, legally binding, but mutually beneficial contract.
Corporations with a product-centric mindset need to adapt their culture to fit into the servitization model to achieve success. This requires systematic thinking that goes further than the product perspective. OEM responsibility does not simply end after product sale but continues throughout those products' life cycle. OEMs need to reposition their organization to migrate from functions to product specific teams. These teams should function as independent units.
Servitization is the right step in decreasing your company's carbon footprint, and organizations should look to reduce carbon and energy footprints according to government policies.
Servitization takes a life cycle approach by extending existing assets which make it easier to measure and make progress in achieving sustainability targets.
As a practical path into the forward-thinking future, regulatory bodies will incentivize and accelerate it. This impacts corporate strategy in the power, industrial, and automotive industries. While embarking on the servitization journey, keep environment-based challenges in mind, with contingency plans to stay current with policies and regulations.
OEMs need to consider political challenges that interrupt business operations and proactively mitigate risk for geographic dependencies. Automation and cost-effective transportation is changing the priorities to determine factory locations. For example, the decision to set up a factory in Thailand instead of the United States should be based on expertise availability and market proximity, not labor costs.
Servitization is in High Demand
The foundation and driver for servitization is connectivity. According to a Cisco forecast, there will be 30 billion IoT endpoints in manufacturing by 2020 and 500 billion across all industries by 2030.1 The demand for servitization business models is increasing as the benefits are highly visible. Servitization success can be obtained by using a holistic scope. Take each of the above challenges mentioned and utilize them as stepping stones toward a path of successful servitization.