Manufacturers: You Made It; Now Talk To It

Businesses in many, if not all, industries face a conundrum in putting the Internet of Things, and the insight it can deliver, to work on their behalf. There’s so much that’s potentially beneficial to company prospects that it’s difficult to focus on how exactly to step forward into the still-forming world of IoT.

Manufacturing is a prime example. Many manufacturers deal with large and complex value chains of suppliers and customers, as well as multifaceted production and operational structures. The potential for gaining insight from IoT analysis is everywhere and anywhere at any given time. It’s up to the manufacturer to determine where, when and how best to make the necessary capital investment.

If you’re like most, you’re already using machine sensors to help with service diagnostics in production machinery and field equipment. You’re also looking at how sensors, implanted in your products, may be able to boost your long-term competitive advantage, and could even give you an entirely new, services-based revenue stream.

Clearly, there’s no one-size-fits-all path available here. But it’s helpful to begin with a clear understanding of what’s doable with today’s IoT, and in particular how real-time analytics can be used to turn the coming deluge of raw data into the analyses – and actions – that are best for you.

Looking At Things In Clouds
As far-reaching as the definition and potential of IoT analytics may be, it’s good to begin with the fundamentals. IoT analytics are – and will continue to be – at their best when they are:

Clouds seem to be everywhere these days, and for good reason: cloud computing obviates the need for capital investment in servers, disk arrays and network equipment that have plagued industry for the past 30 or so years. Today’s cloud technologies support the ultra-high performance needed for big-data analysis. Just as important, they can scale up as the need arises, then down as an analysis project concludes. The same thing would be cost-prohibitive with on-premise computers.

The best IoT analytics infrastructures can take in massive amounts of real-time data, then process it with internal models and databases, and deliver instantaneous analysis. Real-time analytics aren’t required all the time, but when they are, as for shutting down a malfunctioning machine, they can save hundreds of thousands of dollars that would be lost to a stalled production line.

Predictive and prescriptive
The best IoT analytics also employ machine-learning models and automated workflows that can be used to predict events – such as when a piece of equipment in the field may malfunction – in time for pre-emptive replacement or repair. Some forward-thinking manufacturers are now building this capability into their own products and giving customers self-service portals for assessing equipment performance, and heading off downtime, as a value-added feature. Prescriptive analytics take this concept a step further by automatically triggering repair workflows without requiring any human intervention.

Turning Products Into Services
For many manufacturers, cloud-based IoT analytics are most popular for bringing proactive diagnostic and repair capabilities to the shop floor, where they can often deliver measurable ROI. But their value doesn’t stop there. Machine and production analysis can also be used to inform MES, or manufacturing execution systems, as well as the ERP systems used for business planning.

But the next step, tightening the bond with customers, is where the concept of transforming the business is becoming a reality for a growing number of manufacturers. An example is the manufacturer that uses IoT information on the customer’s product usage to improve design or production processes.

In a real-time example, some manufacturers can take a step further by selling platform-as-a-service, or PAAS, capabilities to their customers. Such a move has the potential to replace a one-time product sale with an ongoing stream of revenue.

Examples are plentiful for both discrete and process manufacturers.

A discrete manufacturer can implant the product, say an industrial mold-making machine, with sensor intelligence that communicates performance status back to the manufacturer. Using real-time IoT analytics, the manufacturer can then make performance adjustments, issue parts replacements or carry out other proactive tasks to keep the product running without taking up customer time.

At the other end of the discrete spectrum would be an autonomous vehicle, which will be leased to the customer – or customers – as a sharable service. Maintenance is now the responsibility of the maker and not the customer.

A process manufacturer, a paint supplier, for instance, could supply a steady stream of paint to a customer. This application would use IoT analytics to monitor the customer’s production inventory and real-time needs, and then adjust volume or other attributes as the need arises.

Making the platform-as-a-service a reality is an IoT analytics infrastructure that can take in IoT data, then process it in real time with in-house resources. These include databases that contain historical and other contextual information as well as machine-learning and other specialized models, plus the ultra-high-speed engines to perform the analyses and to trigger process workflows to carry out the prescribed actions.

Building-Block Applications
Also required will be analytic applications that can make use of the proprietary knowledge and experience of manufacturing engineers. This points to a development environment that supports a visual, building-block approach to design, and creates software modules that can be reused for similar types of analytic applications.

If you’re still wondering whether the platform-as-a-service model might fit your own business, you’re probably a bit behind the curve. Two years ago Bain and Company said “Profit pools will shift as digitalization shapes how products are purchased, used, serviced and repaired….Instead of selling a piece of equipment, companies may sell by usage or performance levels.”

Bain went on to say that this will “Recast the sources of competitive advantage, creating openings for new entrants.”

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