Machine and device sensors aren't new, but devices with their own IP addresses and 24/7 feedback are.
Whether you call it the "Internet of Things" (IoT) or machine-to-machine (M2M), this new industrial revolution offers an unprecedented amount of objective data on your products and how they are used.
The question is to how to harness and analyze the data to improve design, production and warranty operations.
Without analytics, it's just a pile of numbers and letters.
The IoT possibilities in manufacturing are endless.
GE has publicly predicted $1 trillion in opportunity annually by improving how assets are used and how operations and maintenance are performed within industrial industries.
The key is to manage the data streaming from connected devices.
As we've learned with Big Data, the application of statistical methods and analytics will be essential to make sense of the data and unlock insights that can spur action.
While IoT has gotten a lot of press regarding consumer wearables (such as FitBits) and smart homes, the more immediate impact will be in the product quality space.
Six IoT Quality Improvements
Possible uses in the product quality and warranty space can be done today by applying analytics to sensor data include:
1. Staving off performance degradation: What if a refrigerator sensor could detect energy consumption above the model rating and then do a little detective work. Is the temperature setting too low? Is the door being opened more times a day than the industry standard? If that's the case, the manufacturer can send the owner tips to reduce the energy bill. Or maybe the sensor shows temperature and usage are normal, but the compressor is cycling too frequently. A remote software update to reduce compressor cycles and a note to the customer could solve the problem.
2. Improving warranty costs and service contract profitability: Imagine knowing the health of every product, not just ones with a warranty claim against them. Identifying issues in small subsets of the product population is a key to improving quality for issues as diverse as overheating trucks that operate in the desert or cell phones that fail when used outdoors in freezing temperatures. Connected device data allows the detection of pervasive issues more quickly and accurately so issues are contained proactively and customer dissatisfaction is prevented.
3. Ending scrap and rework: Applying analytics to the data as it streams off production equipment sensors would allow manufacturers to sense and predict output variation. In a networked manufacturing environment (aka M2M), the machine can communicate its output variation to downstream equipment, which automatically makes adjustments to ensure the final product is within specifications.
4. Reinventing the service contract: Let's be honest: Maintenance schedules continue to be engineering best guesses, largely based on just-in-case preventative philosophy. While rudimentary analysis has allowed companies to classify some uses as "harsh conditions" requiring more frequent maintenance, organizations don't have precise knowledge of when a piece of equipment might fail, let alone when routine service visit would help avert the failure. Sensors help determine the right time for service. By properly analyzing that information, companies can design service contracts that make money for the organization and save money for the customer.
5. Out-innovating the competition: Product managers and designers have relied on sample data through interviews, surveys, focus groups and teardowns. Imagine knowing the exact operating environment and usage profile by model, by geography, by climate – by whatever criterion is relevant to a product. With sensors embedded in everything that leaves the factory, it will be easier to identify new segments of users based on how/when/where and under what conditions they use the product. This will certainly lead to new segments and new or repackaged features to create more value for customers – increasing customer loyalty and revenues.
6. Developing new business opportunities: Could a connected car that knows a harried soccer mom's schedule, weather and traffic conditions and her favorite eating spots, save her time by optimizing errands, and suggesting a healthy family meal? Could an HVAC unit help sense that no one is in a room and adjust the temperature? Sensors can provide new services as a competitive differentiator or as new revenue streams. How does a product's usage relate to the user's lifestyle? Now how can that insight become value-added service for customers that would drive deeper loyalty and retention?
It's not hyperbole to suggest that connected devices will reshape manufacturing. But sensor data doesn't do any good without an analytics platform to help make sense of the data. This platform needs robust data management capabilities, querying options that a business user can manage, and (where appropriate) live event streaming that does the analysis as the data is collected avoiding the need to store and organize it.
As with traditional data sources, IoT data, by itself, isn't valuable. The ability to take away insights exposed through the data then act on those insights provides value. Analytics is the key to unlocking the data treasure chest. The ability to identify hidden patterns, predict future events, forecast usage and costs, and derive insights makes analytics priceless.
Mike Hitmar is a Manufacturing Industry Strategist at SAS.
