Design thinking: strategy for digital transformation

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Design thinking: strategy for digitaltransformation

Luca Vendraminelli, Laura Macchion, Anna NosellaandAndrea Vinelli

1. The problem of governing digital transformation processes

As new technology advancements are available on the market, firms tend to adopt them togain a competitive advantage. Industrial history is dotted with minor or breakthroughtechnological innovations that encouraged companies to change, like the invention of thesteam engine or the development of the microchip or internet. The same is happeningnowadays. As technology advancements posed the basis for the ubiquitous spread ofblockchain, virtual and augmented reality, cloud computing and, above all, a vast kind ofartificial intelligence applications, these innovations are creating a broad spectrum ofopportunities for companies, pushing them to restructure their operating models to gain anew 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 theincumbents adopting digital technologies means embarking on a digital transformation, thatmeans steering the process that goes from the exploration of digital opportunities to the reductionof this complexity to a final set of projects to be designed and executed. These design choicesare 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 anduncertainty (Iansiti and Lakhani, 2020). First, managing a digital transformation process of anoperating model is complex as it involves diverse stakeholders with differences in values andpriorities, unique problems to deal with and resistances to change. A digital transformationprocess is often transversal to the traditional organizational structures, and this makes it knotty tosynthesize stakeholders’ differences in a common operative strategy. Thus, incomprehensionand tension arise for differences in culture and backgrounds and the difficulty to solve disputesleveraging a hierarchical authority often requires political negotiations, trade-offs and watered-down compromises. Second, digital transformation processes have uncertain outcomes. Digitaltechnologies can be combined in many ways, determining esthetic changes, but also in-depthand complex redesigns of firms’ operations. Thus, learning by trial and error is very problematicbecause every digital transformation process is unique, and the rapidly changing dynamics oftechnological 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 theuse of design thinking to plan and execute a digital transformation strategy, building on theideas 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 todynamically tackle the stakeholder’s misalignment proceeding by small incremental iterationsinstead of drawing long-term plans. Management research for a long time has focused onexploring and theorizing best practices to solve complex problems with uncertain outcomes.In this respect, design theories provide precious assets to solve them, recommending adifferent 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 basedat the Department ofIndustrial Engineering,University of Padova,Padova, Italy.

The research is funded by theFondazione Cassa diRisparmio di Padova e Rovigo.

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The remainder of the manuscript is organized as follows. We begin by reviewing theconcept of design as a management theory. Then, we present the case of a large firm in thefashion 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 ofdigital transformation.

2. Design as a management theory

At the heart of any innovation process lies a fundamental practice: the way people create ideasand solve problems. This ‘decision making’ side of innovation is what scholars and practitionersrefer 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 thatdraws inspiration from the way designers are used to tackle their challenges, applying theirmindset and practices in a business setting either to generate new meanings (Verganti, 2017) orto solve problems (Brown, 2009). The concept of “Managing as Designing” was inspired by thefact that managers cope habitually with a class of dilemmas complex and uncertain, resemblingthe 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 mindsetand 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 theoryfor this paper, by introducing the mindset that drives designers’ decision-making and thestructure 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 theirwork, designers are human-centered, as they base every decision on a deep awareness ofthe users’ profiles and habits. Second, designers embrace abductive reasoning. Thismeans that in the path to solving a problem, designers start from a random point in thesolution space, and they proceed through adjacent opportunities by making hypothesesand testing them, converging this way on a path to follow. The adoption of abductivereasoning 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 isachieved by cycling through making a hypothesis and testing them.

Looking instead at the practice of design, the architectures of the most important designprocesses[1] unfold in three phases: problem framing, ideation, and development andrelease (Fraser, 2007). In each of these phases, designers are extremely mindful of theobjectives to achieve and of the toolkit they have at their disposal to tackle differentsituations.

2.1 Problem framing

The design process begins with the designer focusing on the problem-to-be-solved, todefine 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 datacollection to focus on specific aspects of the problem. Interviews and field observations aremade to stimulate the learning process to acquire the right vocabulary, master the networkof relationships around the problem, empathize with the users and understand theirpriorities, using, for example, mapping tools such as the customer journey map or job-to-be-done analysis framework.

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2.2 Ideation

Acquired a sufficiently clear overview of the problem, designers begin to think about apossible solution, ideating the first conceptualization. As the design process unfolds,often carried out in teams, problem-solvers extensively rely on brainstorming todiscover, select and refine ideas. The structure of brainstorming typically iterates adivergence–convergence generative approach to produce or search for existent ideasat first (i.e. divergence) to be filtered in a second stage with an analytical-drivenapproach (i.e. convergence). In opposition to the traditional belief that working in teamsis the best way to stimulate creativity, recent contribution has shown the benefits ofadopting within the design process also individual moments of reflection to stimulatecriticism, or pair discussions (Verganti, 2017).

2.3 Development and release

The ideated concept is furthermore refined and tested before its release, and itsdevelopment is done by developing a prototype to make it tangible. A prototype is arepresentation of a concept that allows designers to interact with their ideas. It can becrafted 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 imagerystimulates the learning process, anticipating the discovery of problems and speeding upthe concept development. Once a prototype attains a satisfactory result, the developmentprocess is finalized with a test on a sample of users, to probe its efficacy in a real context ofapplication 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 digitaltransformation processes. Due to the exploratory nature of this aim, the case studymethodology was adopted as it allows for a deeper level of observations. The case studymethodology is appropriate when the research is exploratory and the phenomenon underinvestigation is still poorly studied, as it offers the opportunity to achieve in-depth resultsthrough direct experience. We conducted an in-depth case study by selecting one of theleaders in the eyewear sector (renamed as EYEWEAR), producing sunglasses, opticalframes and sports eyewear as a contract provider for part of the most important fashionbrands in the world, counting thousands of employees in its operations, distributed across aglobal supply chain. The choice of a single case study enabled a thorough examination of acompany that adopted design theories to develop and execute its digital transformationprocess. Centering the paper around a single case offered the opportunity for a completein-depth analysis of how the design process was used, resulting in both high transparencyand comprehensibility. Specifically, this case reconstructs the three months process thatled to the definition of the list of digital projects to be implemented in the year 2020–2021, toexploit the potential of digital technologies in the operations of six production facilities andthree distribution centers. The eyewear company was selected based on our professionalnetwork: thanks to past collaborations, we were already aware of the digital policies inplace, helping us to get access to data more easily. Indeed, the case analysis is based onthe collection of primary data by direct observation. The digital transformation of EYEWEARwas assigned to a focal team responsible for carrying out the process, which consisted ofeight people: Chief of Product Engineering, Chief Operating Officer, Chief Supply ChainOfficer, Head of Logistics, Head of Controlling, Head of Product Engineering, Head ofSupply Demand Planning and Head of Customer Demand Planning. We participated inmeetings, workshops and we constantly monitored their activity, but always as an external

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entity, being careful in not being intrusive, ensuring that the decision-making process wasessentially their responsibility.

4. A design-driven framework for digital transformation processes

The EYEWEAR’s digital transformation process involved exploring the ample spectrum ofdigital opportunities to select part of them and design their adoption in operations andsupply chain. The execution of the selected digital opportunities ferried EYEWEAR’soperating model from its initial configuration A to its final configuration B (Figure 1). Toexamine the use of design theories in managing digital transformation processes, in thissection, 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: thecorporate strategy, to make sure to plan digital transformation aligned to it; a map of theactual operating model of the firm; and the list of digital projects they were alreadyimplementing.

To trace these inputs, they scheduled additional interviews with the CEO, the Chief ofInnovation, the Chief of Marketing and two external suppliers. A large part of the interviewswas also dedicated to the collection of explicit requests from the diverse stakeholders andproblems that they were facing in their routine. With this data on hand, they meet up in asecond brainstorming meeting, where they diverged by adding their reflections on thesetopics, and they converged on a list of needs that future digital projects were supposed tofix (seeTable 1).

4.2 Ideation: design a digital transformation strategy

In the second phase, the team started from their representation of reality to ideate theconceptualization of the digital transformation strategy. Within the adopted perspective, the

Figure 1 Mechanics of a digital transformation process

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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

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word “strategy” recalls the Logical Incrementalism Theory of strategy-making, wherebyexecutives’ 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 aboutdigital changes in the configuration of firms, in this paper we refer to strategies designed toexplain actions or guide decision-making processes (Mintzberg, 1978).

In the EYEWEAR case, the ideation of a digital transformation strategy had the purpose tocoordinate people toward approaching the vast panorama of opportunities offered bydigital technologies, consistently with what was relevant for corporate success. The digitaltransformation strategy consisted of two parts in practice. Initially, the team outlined a visionof the configuration “B” that the company wanted to reach, which consisted of a descriptionof how EYEWEAR imagined leveraging digital technologies to transform its operating modeland enable new ways to compete in the market. Then, they operationalized this vision in aroadmap of key strategic goals with a yearly horizon to describe how the company wasplanning to execute its digital transformation (seeTable 2).

4.3 Development and release: turn digital transformation strategy into digitalprojects

The third phase was dedicated to turning the digital transformation strategy into a portfolioof projects to be executed. Hence, for each key strategic goal, the focal team created one

Table 2 Digital transformation strategy of EYEWEAR

Infrastructuredevelopment. We wantto build an operationalplatform and developthe IT infrastructure toenable future digitalinvestments.

Paperless datacollectionBuild a “SupplyPlatform” to manage theorder from the upstreamof the supply chainAutonomous sensing ofthe within-plants datasources

Complete the migration to theCloud space

Build a unique central datarepository for all the corporatefunctions

Real-time sync with Asia PacificTeam (e-procurement)

Full visibility on the supply chaindata for all the organizationDevelopment of the centralplatform to create a uniquestandard to make analyses andshare them within the company

Algorithms andsoftware development.We want to developnew models andapplications tosubstitute humans inoperations andautomate decision-making.

Build a uniqueforecasting model thatintegrates customerand supply-demanddata

Develop a real-timequality control in theoperations processesAutomatic financialreports

Real-time product tracking inthe operations

Real-time product tracking inthe supply chain

The order release process anda large part of the planningprocess will be automaticDevelop a central platform forfuture app developmentsUse of 3D prototypes toanticipate product engineeringDevelop a tool for projectselection– project management

Insights produced by analyticswill be shared through the sameplatform

Processes will be people-less,as new autonomous problem-solving loops will be created.And people will focus only onvalue-added activities and onimproving the system

The operation platform will allowpeople to make A/B testingSocial media will become a toolfor demand forecastingPeople improvement. AI

developments willrequire training peopleto become processengineers rather thanexecutors.

Hire a data scienceteam

Assessment of thedigital skills ofemployees

Create a manifesto forthe digital

transformation to sharethe defined strategyA tool to manage theworkforce

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

The investment decision considered the fit with the Digital Transformation Strategy definedand 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, possessedhigh 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 anoperational platform and develop the IT infrastructureto enable future digital investments.

Paperless datacollection

The aim of the project was the digitalization of all thedata sources within the plant to create paperlessoperations. To this purpose, the team worked on twolevels of analysis: (1) paper-collected data and (2) not-tracked data. They conducted a deep investigation tomap the flow of activities, highlighting where data wasproduced and how they were tracked. They collectedall the documents employed and for each of them, theydesigned a specific solution that oftentimes requiredthe introduction of a specific workstation (i.e. acomputer connected to the corporate network), toprovide workers with the possibility to insert datamanually. They furthermore designed the introductionof a set of sensors (e.g. RFID) to automatize the datacollection of data that were not tracked in theoperations yet.

Algorithms and software development. We want todevelop new models and applications to substitutehumans in operations and automate decision-making.

Develop a real-timequality control in theoperations processes

The quality control project was organized into twolayers. On the one hand, the team proposed a set oftechnologies to automatize the collection of qualitydata, creating synergies with the “Paperless DataCollection” team. For example, they automatize thescratches detection and the size/shape measurementby working with partners specialists in the optometricfield. On the other hand, they designed a set ofalgorithms to mine insights from the data collected,prototyping a cockpit to enable all the organizations toaccess 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 requiretraining people to become process engineers ratherthan executors.

A design tool to managethe workforce

The team developed a Workforce Management Systemto optimize workforce scheduling. The algorithms takeinto account the abilities and limitations of the

workforce. The software pivots on a central data set fedwith the skill matrix data (i.e. data per each worker,describing what capabilities they have and which jobsthey are trained to perform). All decisions are trackedand shared within the organization in real-timeimproving cooperation and increasing knowledgethroughout it. The system uses ML algorithms to learnhow to best allocate people within the production linesand departments.

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production line, or the shift from desktop computers to tablets. Elephants were insteadprojects requiring long-term and complex processes that also required high budgetallocations. They were infrastructural projects, such as the migration to a cloud system, thefull redesign of a production line or the automatization of the entire accounting system fromorder to invoice. Finally, collateral and boulders projects required the same resources,respectively, of quick wins and elephants, but offered a lower strategic fit, whichdetermined their exclusion from the investment portfolio. Indeed, EYEWEAR’s investmentstrategy was to search for an equilibrium between elephants and quick wins. Thesurrounding idea was to balance short-term results to support a digital culture to gettraction, and leverage long-term investments to impact the enterprise architecture.

The exploitation of the digital transformation process allowed the focal team to learn betterthe mechanics of their company and empathize with the technological opportunities that themarket offered. Consequently, when the investment decision was made, they cycled backto the ideation phase to review the definition of the problem and strategy to identify newstrategic 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). Bycycling through this design-driven process, the company redefined its capabilities, through anew configuration of its operating model. The framework begins from the definition of theproblem that a digital transformation strategy was required to answer to. The identification ofthe 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 teamenvisioned the company’s future state, breaking it down into a roadmap of short-term goals to

Figure 2 A classiﬁcation tool for digital projects

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be achieved. This move is very close to the theory of meaning in design (Verganti, 2017), as thedefinition of a digital transformation strategy was a leadership act that aimed to create a sharedmeaning for the role of technologies in the organization. In EYEWEAR, the meaning waspurposely 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 ofprojects to be executed. Reconnecting to the theory of design, this evidence suggests thatprototyping a digital transformation strategy by turning it into projects to be executed allowsdesigners to better learn its feasibility and utility, continuously moving between problem framingand ideation. This paper furthermore contributes by proposing a classification of digital projectsin quick wins, elephants, collaterals and boulders. The classification of the project portfolioallowed 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 isachieved, the company can use the experience acquired to criticize its problem framingand 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 becomesan incremental learning process.

At a more general level, the evidence that design practice fits digital transformations probes theusefulness 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 transformationprocesses 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 inmanaging, supporting and realizing digital transformations. This study is a research attemptat the crossroad between the fields of design, strategy and technology management andgroundwork for further field or lab experiments to examine the benefits for managers toadopt design-driven methodologies.

Figure 3 Iterations of digital transformation processes

Digital transformation,Design thinking,

Technology management,Strategy,

Change management

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About the authors

Luca Vendraminelli is a Post-Doc Research Fellow at the University of Padova and visitingfellow at LISH, the Laboratory for Innovation Science at Harvard University. His researchactivity revolves around the design of digital transformation processes, the effect of AIadoption on firms’ productivity, jobs characteristics and human behaviors. His work hasappeared in scientific journals such as the Journal of Product Innovation Management.Luca Vendraminelli is the corresponding author and can be contacted at:

Laura Macchion is an Assistant Professor at the Department of Management Engineering ofthe University of Padua, where she teaches Quality and Operations Management andCircular Economy. Her competencies are focused on Supply Chain Management andOperations Management. Her research deals with the impact of sustainability on themanagement of complex and international supply networks and with the possibilitiesoffered by new technologies to product and process personalization, assessing theirimplications for supply chain configurations. Laura Macchion has also teaching experiencein Executive and Master Programs in Business Schools and is actively involved in nationaland international research projects.

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