Visualisation through prototyping in DX
Prototyping has an important place in digital transformation (DX) as a process to rapidly realise and visualise ideas and concepts. Below are some specific benefits and methods of ‘visualisation’ through prototyping in DX.
Advantages of prototyping in DX:
1. rapid validation: prototyping allows the feasibility of an idea or concept to be verified at an early stage, thereby reducing risk and improving the quality of the final product or service
2. building consensus among stakeholders: visual prototypes enable different stakeholders to have a common understanding, which facilitates coordination and consensus building.
3. improving the user experience: through usability testing, user feedback can be obtained at an early stage, enabling improvements based on actual needs.
4. encouraging innovation: prototyping encourages innovation within the team as new ideas are fleshed out, and models that can be touched stimulate creativity.
5. reducing costs: early failures and problems can be identified at an early stage, thus reducing costs and time in later development phases.
Specific prototyping methods include
- Wireframes: simple drawings showing the layout and functionality of a digital product, useful in the early stages of UI/UX design.
- Interactive prototypes: these are prototypes that users can actually interact with, giving them a realistic experience of the user experience.
- Minimum Viable Product (MVP): a product with minimal functionality can be developed and brought to market to gain early user feedback.
- Simulation: use simulation in a virtual environment to visualise system or process behaviour and identify problems.
- Storyboards: visual representation of user experiences and scenarios to deepen understanding of processes. SF prototyping, which has attracted a lot of attention in recent years, is a type of this technique. Reference information WIRED Sci-Fi Prototyping Institute.
These methods are selected depending on the costs and constraints of validating the DX.
Points to bear in mind when prototyping
In carrying out these prototypes, the key to success is to pay attention to the following points.
1. Clarity of purpose: Prototypes are a means to solve a problem, and it is important to clarify what exactly is being solved. For this purpose, it is necessary to analyse the problem in depth, as described in ‘Algorithmic Thinking, Problem Partitioning and Problem Solving’, and to clarify what the outcome of the prototype should be by using various methods, such as those described in ‘Systems Thinking Approach and SDGs’ and ‘About KPI, KGI and OKR (1) Methods to clarify the problem’. Goal setting is important to clarify what kind of value should be created by the prototype deliverables using various methods as described in (1) Methods for clarifying the problem.
2. Appropriate scope setting: In order to perform efficient validation, it is necessary to minimise the number of functions and avoid pursuing too many functions in the early stages. Keeping the scope small also allows for early testing and improvement.
3. team collaboration: it is important to involve not only the IT department, but also the business department, so that the issues and needs of the field are accurately reflected. In addition, it is also important to consider having members with diverse skills, such as UX designers and data scientists, as well as technical experts.
4. emphasis on the user perspective: listen to users before prototyping to ensure that it is tailored to their needs, conduct usability tests and make improvements from the perspective of the people who will actually use it.
5. technology selection: select technologies that can withstand future expansion and scale, and ensure they can be integrated with existing systems and infrastructure. It is also important to choose the right technology to solve the problem, rather than jumping on the bandwagon.
6. rapid iteration: it is important to be aware of the PDCA cycle of planning, execution, evaluation and improvement in a short period of time, and to maintain motivation by achieving results in a short period of time rather than planning on a large scale.
7. building a culture of tolerance for failure: rototypes are a place of trial and error, where failure is assumed. It is important to challenge without fear of the consequences, and to create a system whereby the knowledge gained from failures and challenges can be used in the next cycle.
8. managing costs and time: although prototyping is an experimental phase, it is important to always be aware of cost-effectiveness and should proceed with clear deadlines, not unlimited time.
9. security and data privacy considerations: consider security from the development phase to prevent rework later on, and comply with legislation such as GDPR and privacy laws when dealing with user data.
10. sharing results and involving stakeholders: share the status of the prototype regularly with stakeholders and coordinate quickly with them when changes in direction or new challenges are identified.
Specific application examples
Specific applications of prototyping in DX are described below.
1. manufacturing industry: building a smart factory
Objective: to improve production efficiency and quality in factories.
Prototype:
– Deployment of IoT devices: sensors are attached to machinery and equipment to monitor operating conditions in real time.
– Data dashboard: data obtained from equipment is visualised and used for abnormality detection and maintenance planning.
Outcome:
– Maintenance downtime reduced by 20%.
– Production line operating efficiency increased by 15%.
2. retail: personalised customer experience
Objective: to improve customer experience and repeat business.
Prototype:
– AI recommendation engine: testing a mechanism to analyse customer purchase data and make individually optimised product recommendations.
– In-store beacons: deliver nearby product information and coupons in real-time as customers move around the shop.
Outcome:
– Sales increased by 10% in the initial phase.
– Purchase rate of recommended products increased by 25%.
3. financial sector: expansion of digital services
Objective: to increase user convenience and prevent customer churn.
Prototype:
– Introduction of a chatbot: automated handling of customer enquiries, reducing the burden on customer support.
– Improved mobile app: tested new user interface and evaluated usability.
Outcome:
– Average reduction of 30% in the time taken to respond to enquiries.
– App usage increased by 20%.
4. logistics: improve delivery efficiency
Objective: to reduce delivery costs and save time.
Prototype:
– Algorithm for optimum delivery routes: Test of a system that uses GPS data to suggest the shortest possible delivery route.
– Drone delivery demonstration: automation of package delivery in specific areas.
Outcome:
– Delivery costs reduced by 15%.
– Delivery times in urban areas reduced by 25%.
5. healthcare: promotion of telemedicine
Objective: to improve access to healthcare and increase efficiency of medical treatment.
Prototype:
– Telemedicine platform: a system was developed to enable doctors and patients to treat each other via video calls.
– AI diagnostic support system: provides diagnostic assistance based on patient symptoms and test results.
Outcomes:
– Number of medical appointments increased and waiting times reduced by 50% on average.
– Diagnostic accuracy improved and misdiagnosis rate reduced by 10%.
6. education sector: development of digital teaching materials
Objective: to improve the efficiency of education and the learning experience of students.
Prototype:
– Interactive teaching materials: trial introduction of teaching materials that enable students to learn science and history content in a hands-on way using AR and VR.
– Learning data analysis: Developed a system that uses AI to analyse students’ progress data and identify areas of weakness.
Outcome:
– Student understanding of learning improved by 15%.
– Teachers’ lesson preparation time reduced by 20%.
7. automotive industry: development of smart cars
Objective: to improve safety and the driver experience.
Prototypes:
– ADAS (Advanced Driver Assistance Systems): introducing programmes to test lane keeping and automatic braking.
– In-car interfaces: prototypes tested to control in-car functions with voice and gesture controls.
Outcome:
– Crash rate reduced by 30% in the test phase.
– User satisfaction significantly improved.
8. entertainment industry: providing VR experiences
Objective: to provide new customer experiences and expand the fan base.
Prototypes:
– VR live concerts: music concerts are held in a virtual space, allowing fans in remote locations to participate.
– Interactive film experience: piloting a system that allows viewers to choose how the story unfolds.
Outcome:
– Number of participants in the event doubled compared to normal.
– Satisfaction rate after watching the VR film was over 90%.
In addition to the above, ‘Artificial intelligence technology as a DX case study’ describes various other examples of the application of AI technology. See also.
reference book
Reference books related to digital transformation (DX) and prototyping are discussed.
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