Global
Continuous Innovation is an important component in the identification and validation of new business models. In my last article, The Exponential Value of IoT Solutions, I wrote about the strategic opportunities available by using insights gleaned from IoT data. The key to maximizing the value of those opportunities is to combine Continuous Innovation principles with the development of those opportunities to drive new business models.
One of the most important concepts that Eric Ries teaches in his book Lean Startup is the Build-Measure-Learn feedback loop. It is indeed a critical tool for companies to use in determining when to fine tune the business model and when to pivot. In addition, it is a great mechanism for enterprise companies to test and evaluate new ideas. Enterprises use many mechanisms to identify new ideas. From employees to customers to consultants to crowdsourcing, new ideas for driving the business abound. The key to maximizing the value of these ideas is to quickly test the hypothesis and determine whether it has merit. This is where the Build-Measure-Learn feedback loop is helpful. Creating MVPs to test the hypothesis and having an analytical foundation to evaluate the results identifies worthy business model candidates and, just as important, narrows the field by discarding those that are not worthy.
IoT creates yet another funnel of innovative ideas. Sensor data applied to everything from machines in the Smart Factory to products distributed from manufacturers to data from the entire customer ecosystem can be distilled and analyzed by employees to generate ideas for creating new business models.
The operational aspects of IoT center on reducing cost by increasing efficiencies in production and maintenance of machines. Predictive maintenance, condition-based maintenance and Industry 4.0 production efficiencies are the key drivers of these operational initiatives. The sensors used in these endeavors generate data that is orders of magnitude larger on both a macro and micro level than just a few years ago. This plethora of data can be distilled into useful information to drive new business models. For example, data used in the condition-based maintenance of power turbines is used to create new models of energy efficiency for the facilities in which they are used. Sensor data from the devices used for industrial cleaning supplies is combined with geolocation data to create new models of cleanliness for employees and customers.
IoT data creates an almost never-ending source of innovation potential. The difficulty is in determining which ideas to move forward with and which to back-burner or discard. The merging of Continuous Innovation principles with the stream of innovative ideas generated from sensor data is the key to solving this problem. At Ness we work with customers to set up Innovation Labs to enable this process. Cross functional teams are assembled, and Innovation Labs are created to quickly develop MVPs to test hypotheses generated from the wealth of information ingested from Smart Machines. Analytics frameworks are created to objectively analyze and evaluate ideas based on criteria created to drive the goals of the organization. The front end of the funnel is idea generation based on the sensor data. These are prioritized into a backlog of hypotheses that are then tested by the Innovation Lab with the creation of MVPs and measured for their results.
The data generated by connected devices creates enormous innovation potential for organizations. Building out Innovation Labs that employ Continuous Innovation methods is the most efficient means of identifying those ideas that will most quickly and effectively drive revenue for your business.
