Design thinking: strategy for digital transformation

As new technology advancements are available on the market, firms tend to adopt them to gain a competitive advantage. Industrial history is dotted with minor or breakthrough technological innovations that encouraged companies to change, like the invention of the steam engine or the development of the microchip or internet. The same is happening nowadays. As technology advancements posed the basis for the ubiquitous spread of blockchain, virtual and augmented reality, cloud computing and, above all, a vast kind of artificial intelligence applications, these innovations are creating a broad spectrum of opportunities for companies, pushing them to restructure their operating models to gain a new level of efficiency or to enable new ways to create and capture value.

Design thinking: strategy for digital

transformation

Luca Vendraminelli, Laura Macchion, Anna Nosella and Andrea Vinelli

1 The problem of governing digital transformation processes

As new technology advancements are available on the market, firms tend to adopt them to gain a competitive advantage Industrial history is dotted with minor or breakthrough technological innovations that encouraged companies to change, like the invention of the steam engine or the development of the microchip or internet The same is happening nowadays As technology advancements posed the basis for the ubiquitous spread of blockchain, virtual and augmented reality, cloud computing and, above all, a vast kind of artificial intelligence applications, these innovations are creating a broad spectrum of opportunities for companies, pushing them to restructure their operating models to gain a new level of efficiency or to enable new ways to create and capture value

Although some firms are born digital without the need to transform, for a large part of the incumbents adopting digital technologies means embarking on a digital transformation, that means steering the process that goes from the exploration of digital opportunities to the reduction

of this complexity to a final set of projects to be designed and executed These design choices are likely to determine operating model reconfigurations and can enable new business models The design of digital transformations is a wicked problem for managers for its complexity and uncertainty (Iansiti and Lakhani, 2020) First, managing a digital transformation process of an operating model is complex as it involves diverse stakeholders with differences in values and priorities, unique problems to deal with and resistances to change A digital transformation process is often transversal to the traditional organizational structures, and this makes it knotty to synthesize stakeholders’ differences in a common operative strategy Thus, incomprehension and tension arise for differences in culture and backgrounds and the difficulty to solve disputes leveraging a hierarchical authority often requires political negotiations, trade-offs and watered-down compromises Second, digital transformation processes have uncertain outcomes Digital technologies can be combined in many ways, determining esthetic changes, but also in-depth and complex redesigns of firms’ operations Thus, learning by trial and error is very problematic because every digital transformation process is unique, and the rapidly changing dynamics of technological and social evolution prevent companies to stick to long-range plans

Given this backdrop of complexity and uncertainty, the purpose of this paper is to explore the use of design thinking to plan and execute a digital transformation strategy, building on the ideas published in this journal byFraser (2007),Holloway (2009)and Golsby-Smith (2007) Design theories fit the crafting of strategies for their natural flexibility, which enables to dynamically tackle the stakeholder’s misalignment proceeding by small incremental iterations instead of drawing long-term plans Management research for a long time has focused on exploring and theorizing best practices to solve complex problems with uncertain outcomes

In this respect, design theories provide precious assets to solve them, recommending a different mindset from analytical thinking, and a set of tools to be used in practice

Luca Vendraminelli,

Laura Macchion,

Anna Nosella and

Andrea Vinelli are all based

at the Department of

Industrial Engineering,

University of Padova,

Padova, Italy.

The research is funded by the

Fondazione Cassa di

Risparmio di Padova e Rovigo.

The remainder of the manuscript is organized as follows We begin by reviewing the

concept of design as a management theory Then, we present the case of a large firm in the

fashion sector that adopted them to design and execute its digital transformation strategy

We conclude the paper by framing the use of design in the development and execution of

digital transformation

2 Design as a management theory

At the heart of any innovation process lies a fundamental practice: the way people create ideas

and solve problems This ‘decision making’ side of innovation is what scholars and practitioners

refer to as ‘design’ ( Verganti et al., 2020 ).

Design– as the art of rethinking the existent and changing it into a preferred one (Simon, 1982)–

is a pivotal task that managers exploit every day in their routines, in the arrangement of policies,

strategies and processes With the term “design,” we refer to a theory of management that

draws inspiration from the way designers are used to tackle their challenges, applying their

mindset and practices in a business setting either to generate new meanings (Verganti, 2017) or

to solve problems (Brown, 2009) The concept of “Managing as Designing” was inspired by the

fact that managers cope habitually with a class of dilemmas complex and uncertain, resembling

the ones designers are wont to solve (Boland and Collopy, 2004)

Yet the research in this field tends to be normative, and the descriptions of design mindset

and practice vary from author to author in their details Hence, in the following paragraphs,

we summarize the main pillars of design theory that we considered as the referential theory

for this paper, by introducing the mindset that drives designers’ decision-making and the

structure and tools belonging to the design practice

The mindset that informs the work of designers is based on human-centeredness,

abductive reasoning and learning and iterations First, when tackling a challenge in their

work, designers are human-centered, as they base every decision on a deep awareness of

the users’ profiles and habits Second, designers embrace abductive reasoning This

means that in the path to solving a problem, designers start from a random point in the

solution space, and they proceed through adjacent opportunities by making hypotheses

and testing them, converging this way on a path to follow The adoption of abductive

reasoning implicates that:

䊏 the activity of design is largely committed to a learning process; and

䊏 the design activity is extensively based on iterations, as the learning process is

achieved by cycling through making a hypothesis and testing them

Looking instead at the practice of design, the architectures of the most important design

processes[1] unfold in three phases: problem framing, ideation, and development and

release (Fraser, 2007) In each of these phases, designers are extremely mindful of the

objectives to achieve and of the toolkit they have at their disposal to tackle different

situations

2.1 Problem framing

The design process begins with the designer focusing on the problem-to-be-solved, to

define the ideation space This is done iteratively, starting from a data collection (e.g

exploratory interviews) useful to refine the questions asked and then iterating the data

collection to focus on specific aspects of the problem Interviews and field observations are

made to stimulate the learning process to acquire the right vocabulary, master the network

of relationships around the problem, empathize with the users and understand their

priorities, using, for example, mapping tools such as the customer journey map or

job-to-be-done analysis framework

2.2 Ideation Acquired a sufficiently clear overview of the problem, designers begin to think about a possible solution, ideating the first conceptualization As the design process unfolds, often carried out in teams, problem-solvers extensively rely on brainstorming to discover, select and refine ideas The structure of brainstorming typically iterates a divergence–convergence generative approach to produce or search for existent ideas

at first (i.e divergence) to be filtered in a second stage with an analytical-driven approach (i.e convergence) In opposition to the traditional belief that working in teams

is the best way to stimulate creativity, recent contribution has shown the benefits of adopting within the design process also individual moments of reflection to stimulate criticism, or pair discussions (Verganti, 2017)

2.3 Development and release The ideated concept is furthermore refined and tested before its release, and its development is done by developing a prototype to make it tangible A prototype is a representation of a concept that allows designers to interact with their ideas It can be crafted with physical materials or developed in the form of imagery, using “storyboarding, user scenarios, metaphors, experience journeys and business concept illustrations” (Liedtka, 2015) Displaying visually their ideas with physical prototypes or imagery stimulates the learning process, anticipating the discovery of problems and speeding up the concept development Once a prototype attains a satisfactory result, the development process is finalized with a test on a sample of users, to probe its efficacy in a real context of application and understand if the problem identified has been successfully resolved Finally, the solution is released

3 The study This qualitative research aimed at exploring how to apply design theories to govern digital transformation processes Due to the exploratory nature of this aim, the case study methodology was adopted as it allows for a deeper level of observations The case study methodology is appropriate when the research is exploratory and the phenomenon under investigation is still poorly studied, as it offers the opportunity to achieve in-depth results through direct experience We conducted an in-depth case study by selecting one of the leaders in the eyewear sector (renamed as EYEWEAR), producing sunglasses, optical frames and sports eyewear as a contract provider for part of the most important fashion brands in the world, counting thousands of employees in its operations, distributed across a global supply chain The choice of a single case study enabled a thorough examination of a company that adopted design theories to develop and execute its digital transformation process Centering the paper around a single case offered the opportunity for a complete in-depth analysis of how the design process was used, resulting in both high transparency and comprehensibility Specifically, this case reconstructs the three months process that led to the definition of the list of digital projects to be implemented in the year 2020–2021, to exploit the potential of digital technologies in the operations of six production facilities and three distribution centers The eyewear company was selected based on our professional network: thanks to past collaborations, we were already aware of the digital policies in place, helping us to get access to data more easily Indeed, the case analysis is based on the collection of primary data by direct observation The digital transformation of EYEWEAR was assigned to a focal team responsible for carrying out the process, which consisted of eight people: Chief of Product Engineering, Chief Operating Officer, Chief Supply Chain Officer, Head of Logistics, Head of Controlling, Head of Product Engineering, Head of Supply Demand Planning and Head of Customer Demand Planning We participated in meetings, workshops and we constantly monitored their activity, but always as an external

entity, being careful in not being intrusive, ensuring that the decision-making process was

essentially their responsibility

4 A design-driven framework for digital transformation processes

The EYEWEAR’s digital transformation process involved exploring the ample spectrum of

digital opportunities to select part of them and design their adoption in operations and

supply chain The execution of the selected digital opportunities ferried EYEWEAR’s

operating model from its initial configuration A to its final configuration B (Figure 1) To

examine the use of design theories in managing digital transformation processes, in this

section, we will explore this change of state, framed as a three-stroke process based on

Fraser (2007)

4.1 Problem framing: representing reality

The first activity for the focal team was a one-day brainstorming on three topics: the

corporate strategy, to make sure to plan digital transformation aligned to it; a map of the

actual operating model of the firm; and the list of digital projects they were already

implementing

To trace these inputs, they scheduled additional interviews with the CEO, the Chief of

Innovation, the Chief of Marketing and two external suppliers A large part of the interviews

was also dedicated to the collection of explicit requests from the diverse stakeholders and

problems that they were facing in their routine With this data on hand, they meet up in a

second brainstorming meeting, where they diverged by adding their reflections on these

topics, and they converged on a list of needs that future digital projects were supposed to

fix (seeTable 1)

4.2 Ideation: design a digital transformation strategy

In the second phase, the team started from their representation of reality to ideate the

conceptualization of the digital transformation strategy Within the adopted perspective, the

Figure 1 Mechanics of a digital transformation process

Table 1 Problem framing – example of needs identified

䊏 No data sources to monitor production status

䊏 No data from e-commerce

䊏 No data from suppliers

䊏 Data are stocked into locally saved excel spreadsheets not accessible to the organization

䊏 3D early prototypes are not used in the product engineering

䊏 We lose a lot of data for lack of sensing technologies

Data stock:

䊏 We don’t have a central data repository

䊏 We don’t have standards to manage data

Data accessibility

䊏 No visibility on relevant supply chain reports

䊏 We don’t have data and insights in real-time to make decisions

䊏 No visibility on supplier operations– suppliers have a lead time of 14 weeks (MTO)

Policy problems

䊏 For strategic projects, we need to open a ticket with the IT function and this requires time

䊏 We don’t have standard data governance (i.e who is responsible for what)

䊏 There is no global track on digital projects

䊏 The return management is handled manually or with locally saved excel spreadsheets

䊏 There is no software for SKU tracking after they have been shipped

䊏 We don’t have a platform to run A/B testing

䊏 We need to run analytics to predict the value target for purchasing

䊏 We need a corporate platform to make data analysis

䊏 There are no predictions of future problems

䊏 Operators miss the big picture

䊏 “People have no time for added value analysis”

䊏 People are stuck with solving an ordinary problem rather than focusing on innovation

word “strategy” recalls the Logical Incrementalism Theory of strategy-making, whereby

executives’ role consists of pinpointing a direction for the organization to be followed,

allowing tangible plans to emerge in a later stage (Quinn, 1978) Indeed, when talking about

digital changes in the configuration of firms, in this paper we refer to strategies designed to

explain actions or guide decision-making processes (Mintzberg, 1978)

In the EYEWEAR case, the ideation of a digital transformation strategy had the purpose to

coordinate people toward approaching the vast panorama of opportunities offered by

digital technologies, consistently with what was relevant for corporate success The digital

transformation strategy consisted of two parts in practice Initially, the team outlined a vision

of the configuration “B” that the company wanted to reach, which consisted of a description

of how EYEWEAR imagined leveraging digital technologies to transform its operating model

and enable new ways to compete in the market Then, they operationalized this vision in a

roadmap of key strategic goals with a yearly horizon to describe how the company was

planning to execute its digital transformation (seeTable 2)

4.3 Development and release: turn digital transformation strategy into digital

projects

The third phase was dedicated to turning the digital transformation strategy into a portfolio

of projects to be executed Hence, for each key strategic goal, the focal team created one

Table 2 Digital transformation strategy of EYEWEAR

Infrastructure

development We want

to build an operational

platform and develop

the IT infrastructure to

enable future digital

investments.

Paperless data collection Build a “Supply Platform” to manage the order from the upstream

of the supply chain Autonomous sensing of the within-plants data sources

Complete the migration to the Cloud space

Build a unique central data repository for all the corporate functions

Real-time sync with Asia Pacific Team (e-procurement)

Full visibility on the supply chain data for all the organization Development of the central platform to create a unique standard to make analyses and share them within the company

Algorithms and

software development.

We want to develop

new models and

applications to

substitute humans in

operations and

automate

decision-making.

Build a unique forecasting model that integrates customer and supply-demand data

Develop a real-time quality control in the operations processes Automatic financial reports

Real-time product tracking in the operations

Real-time product tracking in the supply chain

The order release process and

a large part of the planning process will be automatic Develop a central platform for future app developments Use of 3D prototypes to anticipate product engineering Develop a tool for project selection – project management

Insights produced by analytics will be shared through the same platform

Processes will be people-less,

as new autonomous problem-solving loops will be created And people will focus only on value-added activities and on improving the system The operation platform will allow people to make A/B testing Social media will become a tool for demand forecasting People improvement AI

developments will

require training people

to become process

engineers rather than

executors.

Hire a data science team

Assessment of the digital skills of employees Create a manifesto for the digital

transformation to share the defined strategy

A tool to manage the workforce

additional group of employees to run a design sprint (for details about the process, we suggest referring toMagistretti et al., 2020) The assigned challenge was to design one digital solution for each strategic goal (Table 3) At the end of the design sprints, the focal team collected a portfolio of the business case that has been reviewed and evaluated to select where to allocate the digital transformation budget

The investment decision considered the fit with the Digital Transformation Strategy defined and the resources required by each proposed project Based on these two dimensions, digital projects were quantitatively classified into four types: quick wins, elephants, collaterals and boulders (seeFigure 2)

Quick wins were projects demanding light processes redesign that, however, possessed high strategic importance Examples were the introduction of a set of sensors in a

Table 3 Examples of digital projects developed

Referential vision

Key strategic goal

Infrastructure development We want to build an

operational platform and develop the IT infrastructure

to enable future digital investments.

Paperless data collection

The aim of the project was the digitalization of all the data sources within the plant to create paperless operations To this purpose, the team worked on two levels of analysis: (1) paper-collected data and (2) not-tracked data They conducted a deep investigation to map the flow of activities, highlighting where data was produced and how they were tracked They collected all the documents employed and for each of them, they designed a specific solution that oftentimes required the introduction of a specific workstation (i.e a computer connected to the corporate network), to provide workers with the possibility to insert data manually They furthermore designed the introduction

of a set of sensors (e.g RFID) to automatize the data collection of data that were not tracked in the operations yet.

Algorithms and software development We want to

develop new models and applications to substitute

humans in operations and automate decision-making.

Develop a real-time quality control in the operations processes

The quality control project was organized into two layers On the one hand, the team proposed a set of technologies to automatize the collection of quality data, creating synergies with the “Paperless Data Collection” team For example, they automatize the scratches detection and the size/shape measurement

by working with partners specialists in the optometric field On the other hand, they designed a set of algorithms to mine insights from the data collected, prototyping a cockpit to enable all the organizations to access the data Following the human-centric principle, the cockpit was designed based on a study of users’ needs and behaviors.

People improvement AI developments will require

training people to become process engineers rather

than executors.

A design tool to manage the workforce

The team developed a Workforce Management System

to optimize workforce scheduling The algorithms take into account the abilities and limitations of the workforce The software pivots on a central data set fed with the skill matrix data (i.e data per each worker, describing what capabilities they have and which jobs they are trained to perform) All decisions are tracked and shared within the organization in real-time improving cooperation and increasing knowledge throughout it The system uses ML algorithms to learn how to best allocate people within the production lines and departments.

production line, or the shift from desktop computers to tablets Elephants were instead

projects requiring long-term and complex processes that also required high budget

allocations They were infrastructural projects, such as the migration to a cloud system, the

full redesign of a production line or the automatization of the entire accounting system from

order to invoice Finally, collateral and boulders projects required the same resources,

respectively, of quick wins and elephants, but offered a lower strategic fit, which

determined their exclusion from the investment portfolio Indeed, EYEWEAR’s investment

strategy was to search for an equilibrium between elephants and quick wins The

surrounding idea was to balance short-term results to support a digital culture to get

traction, and leverage long-term investments to impact the enterprise architecture

The exploitation of the digital transformation process allowed the focal team to learn better

the mechanics of their company and empathize with the technological opportunities that the

market offered Consequently, when the investment decision was made, they cycled back

to the ideation phase to review the definition of the problem and strategy to identify new

strategic goals to be turned into new projects

5 Reconnecting to design theory

We framed the EYEWEAR’s digital transformation in a three-stroke process (Figure 1) By

cycling through this design-driven process, the company redefined its capabilities, through a

new configuration of its operating model The framework begins from the definition of the

problem that a digital transformation strategy was required to answer to The identification of

the needs was done using ethnographical tools and visualization, following the design practice

Based on this representation of reality, constantly working at an abstract level, the focal team

envisioned the company’s future state, breaking it down into a roadmap of short-term goals to

Figure 2 A classification tool for digital projects

be achieved This move is very close to the theory of meaning in design (Verganti, 2017), as the definition of a digital transformation strategy was a leadership act that aimed to create a shared meaning for the role of technologies in the organization In EYEWEAR, the meaning was purposely created to align employees’ efforts with what was relevant for corporate success The consequent step was the attempt to turn the digital transformation strategy into a set of projects to be executed Reconnecting to the theory of design, this evidence suggests that prototyping a digital transformation strategy by turning it into projects to be executed allows designers to better learn its feasibility and utility, continuously moving between problem framing and ideation This paper furthermore contributes by proposing a classification of digital projects

in quick wins, elephants, collaterals and boulders The classification of the project portfolio allowed the focal team to make rational investment decisions among the designed projects Finally, the iterations made by the team suggest that when the future configuration B is achieved, the company can use the experience acquired to criticize its problem framing and digital transformation strategy, iterating the design process to target a configuration C, then D and so on (Figure 3) Accordingly, a design-driven digital transformation becomes

an incremental learning process

At a more general level, the evidence that design practice fits digital transformations probes the usefulness of design outside the new product or services development sphere (Dell’Era et al.,

2020) Design thinking helps to navigate the complexity and uncertainty in digital transformation processes where analytical thinking fails, providing mindset, processes and tools

6 Conclusions This paper expands our awareness of the pivotal role of the design theory and practice in managing, supporting and realizing digital transformations This study is a research attempt

at the crossroad between the fields of design, strategy and technology management and groundwork for further field or lab experiments to examine the benefits for managers to adopt design-driven methodologies

Figure 3 Iterations of digital transformation processes

Keywords:

Digital transformation,

Design thinking,

Technology management,

Strategy,

Innovation,

Design,

Change management

1 IDEO, Continuum, Stanford Design School, Rotman Business School and Darden Business School.

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About the authors

Luca Vendraminelli is a Post-Doc Research Fellow at the University of Padova and visiting

fellow at LISH, the Laboratory for Innovation Science at Harvard University His research

activity revolves around the design of digital transformation processes, the effect of AI

adoption on firms’ productivity, jobs characteristics and human behaviors His work has

appeared in scientific journals such as the Journal of Product Innovation Management

Luca Vendraminelli is the corresponding author and can be contacted at: luca

vendraminelli@unipd.it

Laura Macchion is an Assistant Professor at the Department of Management Engineering of

the University of Padua, where she teaches Quality and Operations Management and

Circular Economy Her competencies are focused on Supply Chain Management and

Operations Management Her research deals with the impact of sustainability on the

management of complex and international supply networks and with the possibilities

offered by new technologies to product and process personalization, assessing their

implications for supply chain configurations Laura Macchion has also teaching experience

in Executive and Master Programs in Business Schools and is actively involved in national

and international research projects