Three Industrial Manufacturing Trends for 2021:
Get to Know Your Install Base
As we look ahead to 2021, we see more and more OEMs investing in installed base visibility. Simply put, if you want to create value in the products you sell, you need to know where these products are, what state they are in, and how they are being used. This becomes more pertinent when intermediaries are selling the product on your behalf. Without adequate visibility, your service delivery will be working blind and your revenue streams will be unpredictable.
Achieving installed base visibility might seem like a simple step. How could you not know what has been sold? Surprisingly, many manufacturers struggle with this either due to poor record-keeping, limited systems reach, or the extensive use of third-parties who further complicate the data collection exercise. Putting the time and resources into capturing a clearer picture of the installed base requires an executive understanding of the value of this exercise. More so, it requires an organization that believes and recognizes that its margin is driven by the aftermarket business and not just the original sale of the product. This is vital. Once this is understood, then it becomes easier to justify the investment.
Data Capture and Preparation is Key for AI Value
Knowing your installed base has far-reaching consequences. It also greatly improves the odds of a positive return on digital investments in mobility, augmented reality, the Internet of Things, or Artificial Intelligence.
Let’s take AI for example. The number of use cases for AI continues to grow, and so does the overall interest in these point-to-point solutions to solve specific problems. We see manufacturers apply learning algorithms to their fault and resolution data to isolate the real service issue and determine the most effective resolution path. Organizations using this type of focused AI solution have greatly improved their first-time fix rates and reduce unnecessary parts shipments. In some areas, these solutions have streamlined the quoting process of add-on work and generated incremental revenue streams.
While specific use cases can be quite impactful, OEMs recognize the strategic value of AI to their organizations. They also recognize that the real value of AI is tied to the quality of data that is currently available in their service organizations. To drive better AI-supported projections and recommendations, organizations will need to continue to improve their data capture and organization, particularly around information on the asset – failure causes, service actions required, part history and more. The development of a will make it easier to record and analyze data for improved efficiency and better performance.
Consider Alternate Data Collection Models
There is a misconception that true visibility into the installed base requires an investment in sensors, real-time condition monitoring, and all the complexities of the Internet of Things. While these do streamline the process and create real-time visibility for improved predictive modelling, there are basic installed base models that can be built within the framework of service and maintenance visits.
For instance, several organizations are relying on their customers to record their assets via self-service portals or applications. Think about it as product registration, but on an industrial scale. More organizations are relying on the digital tools handed to their field service agents to capture pertinent asset information at the point-of-service. If these tools have freed up certain capacity in the calendars of the field technicians, then this additional time can be used to track the assets and installed base on a customer site. In some instances, technicians can capture competitive assets to feed displacement campaigns when the time is right.
There are multiple channels that can be relied on to build a better-installed base. Whatever the channel or combination of channels, it is essential that service organizations begin the capture their installed base and its condition prior to realizing the true value of investments in AI and other digital advancements. This might sound like taking a step back, but it’s actually more like building a data-driven foundation on which these solutions can thrive in order to compete in future.
Fluent.ai x BSH: Voice Automating the Assembly Line
Fluent.ai has deployed its voice recognition solutions in one of BSH’s German factories. BSH leads the market in producing connected appliances—its brands include Bosch, Siemens, Gaggenau, NEFF, and Thermador, and with this new partnership, the company intends to cut transition time in its assembly lines.
According to BSH, voice automation will yield 75-100% efficiency gains—but it’s the collaboration between the two companies that stands out. ‘After considering 11 companies for this partnership, we chose Fluent.ai because of their key competitive differentiators’, explained Ion Hauer, Venture Partner at BSH Startup Kitchen.
What Sets Fluent.ai Apart?
After seven years of research, the company developed a wide range of artificial intelligence (AI) software products to help original equipment manufacturers (OEM) expand their services. Three key aspects stood out to BSH, which operates across the world and in unique factory environments.
- Robust noise controls. The system can operate even in loud conditions.
- Low latency. The AI understands commands quickly and accurately.
- Multilingual support. BSH can expand the automation to any of its 50+ country operations.
How Voice Automation Works
Instead of pressing buttons, BSH factory workers will now be able to speak into a headset fitted with Fluent.ai’s voice recognition technology. After uttering a WakeWord, workers can use a command to start assembly line movement. As the technology is hands-free, workers benefit from less physical strain, which will both reduce employee fatigue and boost line production.
‘Implementing Fluent’s technology has already improved efficiencies within our factory, with initial implementation of the solution cutting down the transition time from four seconds to one and a half”, said Markus Maier, Project Lead at the BSH factory. ‘In the long run, the production time savings will be invaluable’.
Future Global Adoption
In the coming years, BSH and Fluent.ai will continue to push for artificial intelligence on factory lines, pursuing efficiency, ergonomics, and a healthy work environment. ‘We started with Fluent.ai on one factory assembly line, moved to three, and [are now] considering rolling the technology out worldwide’, said Maier.
Said Probal Lala, Fluent.ai’s CEO: ‘We are thrilled to be working with BSH, a company at the forefront of innovation. Seeing your solution out in the real world is incredibly rewarding, and we look forward to continuing and growing our collaboration’.