How Real-Time Analytics Will Power Mass Personalization


Manufacturers know it’s coming, if not here already. Mass personalization, of everything from clothing to motorcycles and autos, will bring manufacturers closer to their customers than ever before. For many companies that will be a good thing, likely strengthening brand loyalty and, according to a study by Deloitte, extracting a willing 20-percent price premium from one in every five customers.

For many manufacturers, however, this will require a seismic shift in business perspective. Customers have historically been at the far end of the production-to-delivery chain. Thanks to big data and real-time analytics, they’re now getting closer to the shop floor. This means that all processes in between will have to be optimized to take in and act on the new data loads coming in from customers.

In other words, as General Electric CEO Jeff Immelt said in 2014, “If you went to bed last night as an industrial company, you’re going to wake up this morning as a software and analytics company.”

Going Granular: Big Data And Real-Time Analytics

As with businesses in all industries, most manufacturers today are using some form of analytics to improve their operations. Big data analytics bring a unique, singular benefit to business: they let managers and executives see every part of the operation in minute detail. Machine logs, network components, RFID chips and other devices and applications send data to the analytics engine. From there, the data can be sliced, diced and viewed in whatever form necessary.

A typical factory floor example involves predictive analytics. Robots and other machines send operational data on behavior and other metrics to an analytics engine. The analytics filter out false data and other noise, then consult historical or other contextual data about the machine and its processes to look for anomalies that could cause future breakdowns.

The analytics might consult repair history, parts recalls, service manuals and other data – shop floor temperature and humidity, for example – to predict the likelihood of failure. More sophisticated scenarios include the use of digital twins, virtual prototypes that march in lockstep with the physical machine, in order to predict possible failures. Big-data analytics in use today also monitor parts-inventory databases and can create work orders for proactive repairs to head off possible failures.

These systems, in wide use today, represent just the tip of the analytics iceberg, however. Still in the future is the smart-manufacturing ideal, where production processes are linked, via operational analytics, to every other aspect of the business. Customer orders and marketing requests flow into business processes, and then to manufacturing, inventory control and shipping. The closer these processes can get to real-time, the better the competitive advantage for the manufacturer.

Even today, many manufacturers are moving closer to that goal with build-to-order production models. According to build-to-order consultant David Anderson, this model combines concurrent engineering and flexible production techniques, as well as supply chain simplification, which may include simplifying product families to take advantage of parts commonalities. But, as Anderson says, while these steps may appear costly at first glance, over time they can actually drive down total costs.

Deloitte tends to agree, saying “Businesses are postponing production until the latest point possible to allow individual customization. Postponing production in this way can help reduce inventory levels and ultimately increase plant efficiency.”

Shrinking Time

As real-time analytics become more embedded in manufacturing, supply chain and operations systems, order-to-production-to-delivery cycles will continue to shrink, thanks to:

  • Breaking down silos – Real-time analytics will help the manufacturer respond more quickly to customer orders and order changes by layering a company-wide visibility plane over the operations ecosystem. This plane can track orders in real time as they traverse the various processes and databases, from bill-of-materials to inventory and shipping, on their way to and from production.
  • Boosting supply chain agility – Extending real-time visibility to supply chain operations will help manufacturers catch inventory problems faster; real-time analytics can then help make on-the-fly corrections in response to problems.
  • Keeping production quality high – Real-time analysis for overall equipment effectiveness, or OEE, and related quality parameters in operations and logistics systems becomes critical as order-to-delivery processes are compressed for fast delivery of mass-customized products. Here again, a real-time visibility layer will be essential for monitoring equipment and process quality, and for supplying manager dashboards with relevant, real-time data.

Reaching The Customer

“As society becomes more affluent, the demand for personalized products and services will continue to increase as manufacturers seek to satisfy consumers,” says Deloitte.

“Deloitte research shows that the demand for personalization is there: in some categories more than 50 percent of consumers expressed interest in purchasing personalized products or services….”

The ultimate arbiter for mass personalization is, of course, the consumer, whether an individual or a business. And this is where real-time analytics shine most brightly.

Real time analytics can uncover customer wants, needs and behaviors, and then link them to the products being manufactured. As products become more instrumented, the products themselves can report back the behaviors of consumers, and from there it’s a small step to blending the information received into new or customized product offerings.

In fact, there’s no place the analytics visibility plane – and its related analytical abilities – can’t go. What’s needed is an end-to-end analytics infrastructure. Once in place, it will be able to use its intelligence capabilities to discern and serve the needs of customers who, according to Deloitte and others, are waiting anxiously for well-targeted new products.

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