How Digital Technologies are changing the way we work
How Digital Technologies are changing the way we work:
Intelligent processes create a virtuous cycle of constant improvement fed by continuous feedback. An intelligent process is studded with sensors that monitor every move and feed those observations into sophisticated models that allow people and software to make real-time adjustments and decisions. Digital technologies make it possible to identify opportunities for adaptation, analyze the trade-offs and then adapt faster and more efficiently. By introducing the ability to continuously sense internal operations and external market conditions and to analyze variations quickly, digital capabilities allow intelligent processes to identify opportunities for improvement. And once an opportunity for improvement is found, other digital technologies, such as intelligent tools, advanced collaboration technologies and adaptive robotics, execute changes (even relatively complex ones) quickly. Self-evolution
Intelligent processes make it possible to take advantage of fluctuations in the price of raw materials or spikes in the demand for specific products or services—and then respond in real time or, at a minimum, at a fraction of what it took even adaptable processes to do. By combining the ability to detect and analyze quickly with the ability to respond just as fast, intelligent processes are able to adapt and self-evolve. Significantly, intelligent
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ultimately, from the edge back to the core. Rapid iteration
Fast and flexible. No, that’s not the title of a popular Hollywood action movie franchise. Instead, it’s at the core of an increasingly popular work-design option. What we call rapid iteration is a fail-fast, experiment-driven approach that requires managers to rethink many tasks that, in the past, have followed a more predictable pattern. Large retailers have been among the leaders in this practice, often testing prices and adjusting them rapidly to take account of changing market conditions. Today, some are using their vast amounts of stored data to make and fine-tune offers in real time, catering to “the nonstop customer.” In product development, auto companies and aircraft manufacturers have enthusiastically embraced the use of “nondestructive testing”—for example, computer-based simulations of crashes and other stress conditions— to replace the extraordinarily expensive and labor-intensive practice of building physical prototypes and destroying them to get data. But iterative automobile design has also evolved rapidly in recent years. The focus is no longer solely on using simulated prototypes to predict a vehicle’s durability. Increasingly, carmakers are competing on their engineers’ ability to customize software components that are not only functional but also mirror rapidly changing consumer tastes driven by their experience with smartphones and tablet computers. To this point, crowdsourcing initiatives are quickly changing the tempo and flow of the work done by design engineers. Take, for example, Audi’s Virtual Lab. This online network, which automatically evaluated R&D prototypes based on crowdsourced responses from customers, was used to co-create Audi’s software-based infotainment system. Customers were able to design their ideal in-car multimedia system based on how much money they were willing to spend, creating a simulated purchasing decision that mimicked what happens at a car dealership. Ideal products
This is where rapid iteration came into play: Audi’s automated system intelligently adapted to consumer responses, using rapid data analysis (machine learning) to continually refine the questions it asked customers based on their demographic profiles, on their real-time responses and on existing virtual prototypes (developed by Audi’s R&D team). The system then employed a “closest match analysis” to the prototypes already developed by Audi engineers. Ultimately, the system helped the engineers identify and distinguish between “must-have” and “nice-to-have” features based on customer demand, which then improved the next round of simulated prototyping. The carmaker’s iterative engagement with customers paid off: Audi recently won awards for its infotainment systems, including being named Connected Car of the Year in 2012 and 2013. Audi’s product development teams continue to explore new ways to involve customers in early-stage product development, including refining hardware prototypes. At a Milan furniture show in 2012, the company used thermal-imaging technology to collect data from nearly 1,500 people who tested out its R18 Ultra Chair.
The data and customer feedback were fed into a proprietary algorithm and used to guide further iterations of the Ultra Chair. The net effect on design engineering is more data from a wider array of sources knitted together in faster and richer design cycles. Carmakers aren’t the only ones that see the value of rapid iteration. Pharmaceutical companies have turned to “combinatorial chemistry”—an iterative process of drug discovery that quickly synthesizes compounds and then tests and refines the drugs based on customer data. Pfizer, for instance, is investing heavily in combinatorial chemistry as part of its effort to develop medicines for people with neurological disorders, such as Alzheimer’s and epilepsy. Retail banks, public-sector service agencies and educational institutions are all experimenting with rapid iteration in an effort to better align themselves with customer needs, drive costs out of their development processes and gather valuable data that can accelerate their own evolution.