I believe we are ready to test drive new type of Design Thinking powered by LEAN Prototyping consisting of Visualization, Business Analytics, Crowd Sourcing, Gamification, and KickStarter type vendor relationships. In past blogs, I have communicated that the current incarnation of Design Thinking needs to be restructured in order to be of value as a repeatable design approach. Here I will layout how this new approach will work. First, let’s look at the problem statement in front of us: How will designers apply their specific contributions to the world of IOT?
Good design will be defined in the following attributes:
Design goals will include:
• Achieve hardware standardization through software-based customization
• Enable personalization
• Incorporate the ability to support ongoing product upgrades
• Enable predictive, enhanced, or remote service.
Design approaches to be deployed:
• Design for usability includes design for “upgradability”
• Design for simplicity includes design for saving time
• Design for quality includes design for communication
• Design for product innovation includes design for discovery
• Design for manufacturability includes design for creating insight
Designers will be required to stretch their expertise in:
• Systems engineering
• Agile software development is essential to integrate a product’s hardware, electronics, software, operating system, and connectivity components. The importance of understanding clock speed mechanics and how to effectively plan an executed in the software and product development, release and upgrade cycles will redefine the types of challenges and constraints to be faced.
Believe or not, the answers to a new Design Paradigm can be found in approaches and lessons learned in the manufacturing of automobiles. After all, the production of cars are great representative models of technology, software, vast ecosystems of suppliers all working together to produce a highly complex product that is easy and intuitive to use and is cyclically upgraded with new features. Most importantly, people desire to own one or several with heavy emotional overtones.
Think of the world of the auto manufacturer who is the designer, assembler, and marketer of a complex product of high design quotient. There are thousands of parts specified and bid out to many suppliers, each of whom generally work independently from the other suppliers in providing parts manufactured and tested to specifications. These parts when completed are sold back to the auto manufacturer for assembly. The automotive manufacturer then acts as the integrator at the assembly stage of the whole car is prototyped as a whole assembled product and another layer of testing is performed. It is at this point in time when faults in the accumulated design bubble up and there is a need for new specs for adjustments and tweaks by the assembler or new specs are sent back to the original parts creators to be refined, which are then sent back to the assembler to be reassembled and made ready to be put into the market which adds cost and time. It is then sold in the marketplace to be used by real users, in real world conditions and naturally new bugs surface that must be addressed or ignored. How the auto manufacturer decides to deal with these issues impacts cost, brand reputation, customer loyalty and future sales cycles.
Now let’s apply this to the concept of upgrading Design Thinking. Let’s call it Design Thinking 2.0.
Design Thinking as it is currently defined, consisting of three components:
1. Defining the Problem Statement or Mystery, often commonly referred to as asking “Wicked Questions.”
2. Formulating Heuristic Models of possible solutions, identifying patterns that might be good solutions to the “Wicked Questions.”
3. Taking the “Winning Heuristic Models” and industrializing them into Algorithmic Formulas that are doable cost effective solutions. The trick is accurately selecting the “Winning Models” and engineering them to be flexible in nature to change with the needs of the user and the intricacies of the supply value chain.
It is my strong opinion that Design approaches will be collaboration between “integrators, assemblers, and subsystem partners, using of LEAN Prototyping (Visualization), Crowd Sourcing, Gamification, and Business Analytics to align hardware and software product design lifecycles through the use of a “Product Cloud,” a zone within the Cloud for data collection and upgrade implementations to products and services. This approach will be a necessity for designing and delivering large, systems within systems type solutions. The use of next generation visualization tools and techniques (LEAN Prototyping) along with Real-time data analytics to reduce initial errors and auto correct designs in the cloud will be the way of the future for efficient product and services development and releases. The Crowd Sourcing and Gamification elements will encourage end user participation, a key element to designing products and services that users will want and adopt.
So let’s define Design Thinking 2.0 as follows:
1. Use Crowd Sourcing and Business Analytics for defining and validating what are the right “Wicked Questions” to ask.
2. For “Heuristic Models” development the first round can be combination of human invention supplemented with and then later automatically enhanced in Richness of Experience, LEAN and Simplification of the Complexity of spec via Crowd Sourcing, Gamification, Innovation, and Business Information Analytics. Infinite variables addressing SMAC can be simulated and tested, all regulated by time, cost, and quality variables (the “Magic Triangle Riddle” might finally be solved!)
3. Taking the “Winning Heuristic Models” and industrializing them into algorithmic formulas that are doable, cost effective solutions. Algorithms are super charged through Business Intelligence in the product cycle described above directly into build. DevOps modeling and LEAN converge to implement system and program operations through the “Product Cloud Integration Layer” to take cost out, optimize the product to what the crowd and individual wants, while streamlining assemblers, integrators with sub-component designers and suppliers to make it all work.
Now let’s apply the Design 2.0 Methodology to our automotive analogy above. We start using LEAN Prototyping in the Cloud to act as the integrator, assembler, and tester of complex IOT type programs requiring the participation of large ecosystems of partners, suppliers and competitors to deliver viable solutions that address “systems within systems” complexity. The designer could execute his craft using intelligence gathered through Business Intelligence Data inside a Product Cloud Layer for both initial design to build, and then for initial release and subsequent refinement cycles. The owner of the program visualizes out the desired end state of the entire product. During this initial design, specs, feedback loops and design tests could be managed in a “Smart Product Cloud” merging analytical data with the human designer to optimize process. Tools such as Crowd Sourcing and Gamification could be deployed to drive innovation and understanding of user wants and needs. Through a KickStarter type of model, multiple sub-system supplier designers could provide their visualization components, working more efficiently through this type of spec system with an eye towards efficient innovation, integration and ultimately LEAN Production. The assembler could use the Smart Product Cloud to re-visualize to solution in whole using a LEAN Cycle / Agile Cadence, only much more efficiently through Business Intelligence control again and low cost distribution of knowledge through the Cloud and provide specs back to the sub-system suppliers for tweaking any original part more simply conveyed with greater precision. In some cases it may be possible for the Product Cloud to make the adjustment itself. It would also be possible to 3-D print some of these prototypes. A first “Public Release” could be a detailed visualization of a Minimally Viable Product to a set of users where the Product Cloud is used to collect, analyze, store and distribute functional data and social data through feedback based upon user and ecosystem partner interaction through A/B testing and Crowd Sourcing with the visualization. Next, the design spec is optimized and built as a real product or service. As real world feedback continues with users, product upgrades are continuously captured within the Product Cloud and are either executed automatically through the Product Cloud, or sent back with great detail and precision for fine tuning to be re-coded for the exact area of experience. These upgrades are then put back into field through Product Cloud if software related or through the manufacturing process if they are hardware related.
It is through this design methodology that systems themselves become self-aware and part of design cycle. All Product Cloud data gets feed into next generation releases as new hardware and software combinations. Imagine how well this could work across the assembler, DevOps, and ecosystem governance work streams. It all makes for a strong case for visualizing how Design Thinking 2.0 could work.
