Digital Transformation according to NRI
Digital Transformation according to NRI
DX1.0 involving back-end systems is designed to streamline existing operations, and aims to optimize and enhance the efficiency of R&D, production, logistics and many other operations. It includes introducing Robotic Process Automation (RPA) to boost the efficiency of office work, using preventive maintenance to improve utilization of production facilities, and tapping accumulated past R&D data to predict new R&D results. DX1.0 involving front-end systems concerns the overall flow of operations and management. One major change brought on by digitalization is that a company’s connection with its customers does not end when a product is sold, but instead extends to how the product is used after the sale. If companies can grasp real-time usage by their customers, they will be able to understand new needs more dynamically and make new proposals to customers scientifically. This will require transformation and optimization of the back-end framework as a whole. Customer Experience (CX) and the customer journey are important because such framework optimization begins with them.
DX2.0 refers to the position of a platformer that goes beyond the corporate framework. To stay connected directly with customers and achieve their true objectives, it is not necessarily enough for companies to provide their own products and services; they may need to bundle various things and provide them. This may mean bundling two or more products and services of their own, or pursue bundling that goes beyond one company.
Digital Transformation PMO
The Chief Digital Officer (CDO) and the digital innovation strategy unit are, in a sense, a digital consulting unit for internal operations. However, resources such as personnel capable of handling digital transformation are limited. Therefore, it is important to identify challenges in day-to-day operations as issues that may be addressed with digital technology and to categorize them by theme. It is necessary to prioritize these issues by such criteria as the expected magnitude of the managerial impact of resolving them and the technical difficulty of a solution. The thinking behind DX1.0 and DX2.0 will serve as the guidelines. With respect to these themes, it is necessary to immediately plan and implement PoC for solutions, and to build a track record of small-scale successes. Doing so will require companies to: establish KPI for PoC, measure the conditions before and after implementation, and use analytics to develop a learning model using PoC data as training data. Moreover, various resources for PoC implementation will have to be procured instantly as well. In the end, it this reform will need to be established in day-to-day operations. With a new business, it will be necessary to find a party that will take over. We support this series of assistance measures as digital transformation PMO.
Digital Transformation Diagnosis Framework
Its customers are always at the center of a company’s overall digital transformation. What is customer’s true objective of using your products and services? When do customers use them, and what effects do they experience? Mapping the customer journey does not mean simply tracking customer actions in chronological order. Following this journey and going beyond a company’s frame to provide the right service at the right time in an optimal way: that’s digital transformation. In light of these, design a process, and build its value as a solution. The organization must be also designed to be data-driven and capable of flexible cooperation. To this end, it is desirable to have data itself democratized in the organization and accessible by anyone. Few people are able to understand such digital technology, are well-versed in data analytics and moreover bring about cooperation between cooperation. So it will be necessary to have a producer who can organize and coordinate various personnel specialized in different aspects. This is a framework that diagnoses these elements using a survey.
Robotic Process Automation (RPA) Deployment Support
RPA automates work that humans perform mainly with computers, and is one of the digital tools that are rapidly becoming more recognized and used. RPA covers a wider range of automation than so-called macro VBA, and its deployment requires a shorter time period and less cost than an IT system. Moreover, it offers tools that people without programming experience can use to develop a work robot, and thus RPA is drawing attention as a new means of end-user computing (EUC).
A byproduct of such convenient and versatile EUC is that so-called “runaway robots” that are not controlled by an organization make it difficult to understand the status of operations and even pose operation control risks. In some cases, companies may deploy RPA but stop using it because expected results were not produced.
Most of the time, these issues are attributable not to RPA as a tool but rather to the procedures, structure, or awareness of the organization that has taken it on. We at NRI think that in order to maintain RPA’s value in day-to-day operations, it is important for people in those operations to autonomously take a square look at RPA, and we help clients with selecting applicable operations, building a structure for development and maintenance, and creating skills.
Artificial Intelligence (AI) Deployment Support
The AI boom is heating up, and it seems that we see announcements and reports on advanced AI technologies, new products, and test deployment cases almost every day. But many companies have yet to determine how they should use AI, or even what the purpose of deployment is. Even if they reach the stage of drawing a future vision by studying progressive cases, they may face a long road to success or, because AI is not closely linked to more urgent managerial issues, they may have a hard time building a structure for examining or securing a budget for multiple years, ultimately suspending efforts to consider concrete steps.
Given the high potential of AI, it's critical to hammer out a future vision from a broad perspective. At the same time, we at NRI think it is also important to consider a more realistic—and more practical—use of AI. In fact, there are many areas in which AI can be useful in solving existing issues using existing data. Internal data is filled with business decisions made in the past and other experiences of an organization, and if AI learns pattern recognition, it can be used to enhance the efficiency and accuracy of processes and judgments going forward.
To achieve these results quickly and discover things AI can do are also important in picturing and making concrete steps for further AI use. We at NRI call such thinking on practical AI use “Realistic AI”, and we support efforts to enhance the digital technology-related inventive and implementation capacities of both senior management and those engaged in day-to-day operations.
The work of responding to inquiries from internal and external users requires many people in different departments beyond the contact center and the internal help desk to invest time. Many of these inquiries are similar to those asked in the past. To reduce repeats of past Q&As to the extent possible, many companies compile and publish a list of FAQs. However, in many cases, the FAQs do not address users’ questions and users end up calling or emailing to ask their questions.
Chatbot is increasingly used as a tool that takes from human agents the job of responding to such inquiries. Since releasing TRUE TELLER in 2001, we at NRI have built a track record of technology development and deployment for such fields as text mining, natural language processing and voice recognition, and since 2016, we have offered TRAINA as an AI with strength in language. In this service, we provide “Smart Knowledge”, which features a powerful chatbot capability, supporting our clients in their business of responding to inquiries via phone calls, email, social media, chat and other points of user contact.
In flexibly promoting digital transformation, tie-ups with startups will be key. Startups in general are equipped with superior technical seeds and business seeds, and pursue business in a limited domain. From the perspective of large corporations, the superior technical seeds and business seeds of startups offer clues to materializing their business ideas that have been narrowed down, and startups may serve as partners offering killer functions. Specific ways of partnering with startups include accelerator programs and matching with startups through networking with venture capital investors, etc. In particular, matching with startups through networking with VC investors is a highly strategic method. VC investors are the players who know startups the best. The most surefire way is to provide VC firms with sufficient information on your targeted business ideas and have them refer you to the most appropriate startup. Networking may take different forms depending on each VC firm, such as a requirement of a limited partnership investment into VC funds or a matching service provided under a service agreement.
Developed by Google and GV (Google Ventures), Sprint is a revolutionary development process used for developing various products. With a five-day structure from Monday to Friday, you set a challenge that will be the target, brainstorm solutions, create a prototype of one of the ideas, and conduct a user test. This approach organizes the philosophy of design thinking as a method. We at NRI also use this method in developing new products and services and planning business improvement ideas. When implementing this process, internal experts in various fields and external experts with whom we have partnerships also take part so we can support brainstorming from different viewpoints.